From afe98ee3570e918de02917976eba3b00b0432ac2 Mon Sep 17 00:00:00 2001 From: Caroline Eastwood Date: Wed, 5 Nov 2025 11:01:57 +0000 Subject: [PATCH 1/9] modified the graph_v2 script to process the folder and tested it with 5 datasets. --- .gitignore | 1 + ...4-9970-44d5e39ce068_cxg_dataset_unique.tsv | 46 +++ ...3-82ad-40fc4458a5db_cxg_dataset_unique.tsv | 46 +++ ...c-8891-dbd1226d6b27_cxg_dataset_unique.tsv | 11 + ...6-afe8-5414cab7739d_cxg_dataset_unique.tsv | 203 ++++++++++ ...d-a487-510435377e55_cxg_dataset_unique.tsv | 20 + ...8-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv | 17 + ...a-9894-683896122708_cxg_dataset_unique.tsv | 14 + ...d-8277-a872c93f5b59_cxg_dataset_unique.tsv | 31 ++ ...1-8b0e-23b557558a4c_cxg_dataset_unique.tsv | 16 + ...b-bc89-ebb2df513dde_cxg_dataset_unique.tsv | 15 + ...8-af70-d631f5eea188_cxg_dataset_unique.tsv | 12 + ...6-8d50-4c2619eb0f46_cxg_dataset_unique.tsv | 18 + ...1-bfc9-202b74d0b60f_cxg_dataset_unique.tsv | 11 + 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https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +absorptive enterocyte of epithelium of small intestine CL:1000334 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +tuft intestinal tuft cell CL:0019032 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +absorptive enterocyte of epithelium of large intestine CL:0002071 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +goblet colon goblet cell CL:0009039 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +BEST4+ BEST4+ enterocyte CL:4030026 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +goblet small intestine goblet cell CL:1000495 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +EEC enteroendocrine cell of colon CL:0009042 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +tuft tuft cell of colon CL:0009041 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +ISC intestinal crypt stem cell of colon CL:0009043 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +ISC intestinal crypt stem cell of small intestine CL:0009017 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +BEST4+ epithelial cell of small intestine CL:0002254 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +TA transit amplifying cell of small intestine CL:0009012 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +TA transit amplifying cell of colon CL:0009011 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +secretory_prog progenitor cell CL:0011026 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +EEC enteroendocrine cell of small intestine CL:0009006 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +paneth paneth cell of epithelium of small intestine CL:1000343 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +FAE microfold cell of epithelium of small intestine CL:1000353 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +SI_6-? enterocyte of epithelium of small intestine CL:1000334 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +C_earlyCC enterocyte of epithelium of large intestine CL:0002071 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +C_lateCC enterocyte of epithelium of large intestine CL:0002071 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +SI_secretory small intestine goblet cell CL:1000495 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad +SI_secretory paneth cell of epithelium of small intestine CL:1000343 https://doi.org/10.1016/j.jcmgh.2022.02.007 https://datasets.cellxgene.cziscience.com/45a7d3bd-dc1a-4565-8881-25f8975247a6.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique.tsv new file mode 100644 index 0000000..e0461b0 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique.tsv @@ -0,0 +1,46 @@ +author_cell_type CL_label CL_ID reference dataset_version +cvLSEC endothelial cell of pericentral hepatic sinusoid CL:0019022 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +LAM-like macrophage CL:0000235 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +lrNK hepatic pit cell CL:2000054 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +C-Hepato2 centrilobular region hepatocyte CL:0019029 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Arterial endothelial cell of artery CL:1000413 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +I-Hepato midzonal region hepatocyte CL:0019028 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD8T CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +P-Hepato periportal region hepatocyte CL:0019026 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +ppLSEC endothelial cell of periportal hepatic sinusoid CL:0019021 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Kupffer Kupffer cell CL:0000091 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +ActMac macrophage CL:0000235 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD4T CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD3T-lrNK hepatic pit cell CL:2000054 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +C-Hepato centrilobular region hepatocyte CL:0019029 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MHCII macrophage CL:0000235 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD8T-cNK natural killer cell CL:0000623 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Stellate hepatic stellate cell CL:0000632 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Kupffer--LSEC-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Tcell T cell CL:0000084 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Neutrophil neutrophil CL:0000775 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Chol intrahepatic cholangiocyte CL:0002538 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cNK natural killer cell CL:0000623 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cDC conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Hepato hepatocyte CL:0000182 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +P-Hepato2 periportal region hepatocyte CL:0019026 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cvLSEC--T-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cvEndo vein endothelial cell CL:0002543 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Monocyte monocyte CL:0000576 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Prolif unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +AntiB plasma cell CL:0000786 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +NKT--Mac-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CholMucus intrahepatic cholangiocyte CL:0002538 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MatB mature B cell CL:0000785 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Fibroblast fibroblast CL:0000057 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Hepato--Mac unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +pDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Mac--Fibro-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +RBC erythrocyte CL:0000232 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MAST mast cell CL:0000097 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MatB--CD4T-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Mac--B-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MatB--RBC unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD4T--RBC-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cNK--RBC-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +NKT natural killer cell CL:0000623 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/07760522-707a-4a1c-8891-dbd1226d6b27_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/07760522-707a-4a1c-8891-dbd1226d6b27_cxg_dataset_unique.tsv new file mode 100644 index 0000000..f8db089 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/07760522-707a-4a1c-8891-dbd1226d6b27_cxg_dataset_unique.tsv @@ -0,0 +1,11 @@ +author_cell_type CL_label CL_ID reference dataset_version +Astro_GBP2_SPOCD1 astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Astro_GJB6_OXTR astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Astro_CYP4F12 astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Astro_GLYATL2 astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Astro_GUCY1A2 astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Astro_SERPINA3 astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Astro_SIDT1 astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Astro_VIM_TNFSRF12A astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Astro_VIM_LHX2 astrocyte CL:0000127 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad +Ependyma_ZBBX ependymal cell CL:0000065 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/16c3e76a-ddc2-4329-9395-fb29d1e1c9fa.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique.tsv new file mode 100644 index 0000000..9c85f88 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique.tsv @@ -0,0 +1,203 @@ +author_cell_type CL_label CL_ID reference dataset_version +epithelial cells epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +stroma cells kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney loop of Henle thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells lymphocyte CL:0000542 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +endothelial cells endothelial cell CL:0000115 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells kidney interstitial alternatively activated macrophage CL:1000695 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney loop of Henle thin descending limb epithelial cell CL:1001111 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells neutrophil CL:0000775 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells podocyte CL:0000653 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney loop of Henle thin ascending limb epithelial cell CL:1001107 https://doi.org/10.1038/s41586-023-05769-3 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https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +dIMCD kidney collecting duct principal cell CL:1001431 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +IMCD kidney collecting duct principal cell CL:1001431 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +dEC endothelial cell CL:0000115 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +dM-FIB kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +cycDCT kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +dPOD podocyte CL:0000653 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +pDC plasmacytoid dendritic cell, human CL:0001058 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +dDTL3 kidney loop of Henle thin descending limb epithelial cell CL:1001111 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +dDCT kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +cycMYOF kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +cycNKC/T lymphocyte CL:0000542 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +CCD-IC-A kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +OMCD-IC-A kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +CCD-PC kidney collecting duct principal cell CL:1001431 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +OMCD-PC kidney collecting duct principal cell CL:1001431 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +dOMCD-PC kidney collecting duct principal cell CL:1001431 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +CNT-PC kidney connecting tubule epithelial cell CL:1000768 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +DCT1 kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +DCT2 kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +CNT-IC-A kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique.tsv new file mode 100644 index 0000000..51ec412 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique.tsv @@ -0,0 +1,20 @@ +author_cell_type CL_label CL_ID reference dataset_version +Inhibitory_4 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Oligodendrocytes oligodendrocyte CL:0000128 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Inhibitory_2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_4 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Inhibitory_1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Astrocytes astrocyte CL:0000127 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_3 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_5 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Microglia microglial cell CL:0000129 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +OPCs oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Endo/Pericytes endothelial cell CL:0000115 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_8 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Inhibitory_3 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_9 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_6 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_10 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_7 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv new file mode 100644 index 0000000..56c7e73 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv @@ -0,0 +1,17 @@ +author_cell_type CL_label CL_ID reference dataset_version +Fibroblasts ADAMDEC1 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Endothelial cells CD36 endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Myofibroblasts HHIP NPNT myofibroblast cell CL:0000186 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Fibroblasts SMOC2 PTGIS fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Endothelial cells DARC endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Fibroblasts NPY SLITRK6 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Myofibroblasts GREM1 GREM2 myofibroblast cell CL:0000186 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Endothelial cells CA4 CD36 endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Glial cells glial cell CL:0000125 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Fibroblasts SFRP2 SLPI fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Endothelial cells LTC4S SEMA3G endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Pericytes HIGD1B STEAP4 pericyte CL:0000669 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Activated fibroblasts CCL19 ADAMADEC1 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Lymphatics lymphocyte CL:0000542 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Fibroblasts KCNN3 LY6H fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Pericytes RERGL NTRK2 pericyte CL:0000669 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique.tsv new file mode 100644 index 0000000..106999d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique.tsv @@ -0,0 +1,14 @@ +author_cell_type CL_label CL_ID reference dataset_version +T cells T cell CL:0000084 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Diff. Keratinocytes keratinocyte CL:0000312 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Macrophages+DC macrophage CL:0000235 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +EpSC and undiff. progenitors stem cell of epidermis CL:1000428 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Secretory-reticular fibroblasts skin fibroblast CL:0002620 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Pericytes pericyte CL:0000669 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Pro-inflammatory fibroblasts skin fibroblast CL:0002620 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Secretory-papillary fibroblasts skin fibroblast CL:0002620 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Mesenchymal fibroblasts skin fibroblast CL:0002620 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Vascular EC endothelial cell of vascular tree CL:0002139 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Melanocytes melanocyte CL:0000148 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Lymphatic EC endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Erythrocytes erythrocyte CL:0000232 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique.tsv new file mode 100644 index 0000000..99be474 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique.tsv @@ -0,0 +1,31 @@ +author_cell_type CL_label CL_ID reference dataset_version +Hepato-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Kupffer Kupffer cell CL:0000091 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Stellate-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +P-Hepato periportal region hepatocyte CL:0019026 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +C-Hepato centrilobular region hepatocyte CL:0019029 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +cvLSEC endothelial cell of pericentral hepatic sinusoid CL:0019022 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Prolif-Mac macrophage CL:0000235 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +C-Hepato2 centrilobular region hepatocyte CL:0019029 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +I-Hepato midzonal region hepatocyte CL:0019028 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Chol intrahepatic cholangiocyte CL:0002538 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +ppLSEC endothelial cell of periportal hepatic sinusoid CL:0019021 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Monocyte monocyte CL:0000576 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Kupffer-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +P-Hepato2 periportal region hepatocyte CL:0019026 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +cvEndo vein endothelial cell CL:0002543 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Stellate hepatic stellate cell CL:0000632 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Prolif unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +CD4T CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +cvLSEC-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +aStellate hepatic stellate cell CL:0000632 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Chol-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +cvLSEC--Mac unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Tcell-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +VSMC vascular associated smooth muscle cell CL:0000359 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +lrNK hepatic pit cell CL:2000054 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Chol--Kupffer-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +AntiB plasma cell CL:0000786 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Fibroblast fibroblast CL:0000057 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Chol--Stellate-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +CholMucus intrahepatic cholangiocyte CL:0002538 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/19e46756-9100-4e01-8b0e-23b557558a4c_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/19e46756-9100-4e01-8b0e-23b557558a4c_cxg_dataset_unique.tsv new file mode 100644 index 0000000..1f69a10 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/19e46756-9100-4e01-8b0e-23b557558a4c_cxg_dataset_unique.tsv @@ -0,0 +1,16 @@ +author_cell_type CL_label CL_ID reference dataset_version +Naive CD4+ T cells CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Non-classical monocytes CD16-positive, CD56-dim natural killer cell, human CL:0000939 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Plasmacytoid Dendritic cells dendritic cell CL:0000451 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Classical Monocytes CD14-positive monocyte CL:0001054 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +CD8+ NKT-like cells CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Naive B cells B cell CL:0000236 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Natural killer cells natural killer cell CL:0000623 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Naive CD8+ T cells CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Erythroid-like and erythroid precursor cells erythroid lineage cell CL:0000764 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +ISG expressing immune cells CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Effector CD4+ T cells CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Myeloid Dendritic cells dendritic cell CL:0000451 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Effector CD8+ T cells CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Memory CD4+ T cells CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad +Platelets platelet CL:0000233 https://doi.org/10.1182/bloodadvances.2023011445 https://datasets.cellxgene.cziscience.com/0d245eaa-4c23-4f0b-8ebb-703ec7d87c61.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique.tsv new file mode 100644 index 0000000..7e5aa4a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique.tsv @@ -0,0 +1,15 @@ +author_cell_type CL_label CL_ID reference dataset_version +naive B cell naive B cell CL:0000788 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +memory B cell memory B cell CL:0000787 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +gamma-delta T cell gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +plasmablast plasmablast CL:0000980 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +regulatory T cell regulatory T cell CL:0000815 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +CD4-positive, alpha-beta memory T cell CD4-positive, alpha-beta memory T cell CL:0000897 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +CD8-positive, alpha-beta memory T cell CD8-positive, alpha-beta memory T cell CL:0000909 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +naive CD8+ T cell naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +naive CD4+ T cell naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +mucosal invariant T cell (MAIT) mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +TissueResMemT memory T cell CL:0000813 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +double-positive T cell (DPT) double-positive, alpha-beta thymocyte CL:0000809 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +double negative T cell (DNT) double negative thymocyte CL:0002489 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +TCRVbeta13.1pos T cell CL:0000084 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/242c6e7f-9016-4048-af70-d631f5eea188_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/242c6e7f-9016-4048-af70-d631f5eea188_cxg_dataset_unique.tsv new file mode 100644 index 0000000..56afc28 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/242c6e7f-9016-4048-af70-d631f5eea188_cxg_dataset_unique.tsv @@ -0,0 +1,12 @@ +author_cell_type CL_label CL_ID reference dataset_version +MO monocyte CL:0000576 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +T.CD8 CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +T.MAIT mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +B B cell CL:0000236 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +T.CD4 CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +NK natural killer cell CL:0000623 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +DC dendritic cell CL:0000451 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +SC stem cell CL:0000034 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +T.gdT gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +T.Proliferating T cell CL:0000084 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad +NK.Proliferating T cell CL:0000084 https://doi.org/10.1016/j.isci.2023.108572 https://datasets.cellxgene.cziscience.com/6758bff5-e8dd-4633-8d27-9a35f47d796f.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/251b1a7e-d050-4486-8d50-4c2619eb0f46_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/251b1a7e-d050-4486-8d50-4c2619eb0f46_cxg_dataset_unique.tsv new file mode 100644 index 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https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Ex_LAMP5_NTNG2 neuron CL:0000540 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Inh_PAX5_CCBE1 inhibitory interneuron CL:0000498 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Ex_LAMP5_BAIAP3 neuron CL:0000540 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Inh_PRLR_RP11-384J4.2 inhibitory interneuron CL:0000498 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Ex_VWA5B1_CALB1 neuron CL:0000540 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Ex_POSTN neuron CL:0000540 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Ex_OPRD1 neuron CL:0000540 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Inh_PAX5_VCAN inhibitory interneuron CL:0000498 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Inh_SIX3 inhibitory interneuron CL:0000498 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Ex_EBF2_CTC-552D5.1 neuron CL:0000540 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Ex_MYO5B neuron CL:0000540 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad +Inh_INHBA inhibitory interneuron CL:0000498 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/1f8ab9eb-5f95-4d66-84b8-6551e79ba65a.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/2856d06c-0ff9-4e01-bfc9-202b74d0b60f_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/2856d06c-0ff9-4e01-bfc9-202b74d0b60f_cxg_dataset_unique.tsv new file mode 100644 index 0000000..9c4e613 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/2856d06c-0ff9-4e01-bfc9-202b74d0b60f_cxg_dataset_unique.tsv @@ -0,0 +1,11 @@ +author_cell_type CL_label CL_ID reference dataset_version +SOX6_AGTR1 dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +SOX6_DDT dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +CALB1_TRHR dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +SOX6_PART1 dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +CALB1_CALCR dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +CALB1_PPP1R17 dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +CALB1_CRYM_CCDC68 dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +SOX6_GFRA2 dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +CALB1_GEM dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad +CALB1_RBP4 dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/a41c9e65-1abd-428b-aa0a-1d11474bfbe7.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique.tsv new file mode 100644 index 0000000..a8c36fd --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique.tsv @@ -0,0 +1,26 @@ +author_cell_type CL_label CL_ID reference dataset_version +B cell IgA Plasma IgA plasma cell CL:0000987 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +B cell memory memory B cell CL:0000787 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +CD8 T CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +gd T gamma-delta T cell CL:0000798 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Mast mast cell CL:0000097 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +ILC innate lymphoid cell CL:0001065 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Macrophage colon macrophage CL:0009038 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +NK natural killer cell CL:0000623 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Follicular B cell follicular B cell CL:0000843 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +B cell IgG Plasma B cell CL:0000236 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Tcm memory T cell CL:0000813 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +B cell cycling B cell CL:0000236 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Treg regulatory T cell CL:0000815 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +LYVE1 Macrophage macrophage CL:0000235 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Th1 T-helper 1 cell CL:0000545 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Th17 T-helper 17 cell CL:0000899 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +cDC2 conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +cycling gd T gamma-delta T cell CL:0000798 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Monocyte monocyte CL:0000576 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +cDC1 conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Activated CD4 T CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +pDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Tfh T follicular helper cell CL:0002038 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Lymphoid DC dendritic cell CL:0000451 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +cycling DCs dendritic cell CL:0000451 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique.tsv new file mode 100644 index 0000000..a51872c --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique.tsv @@ -0,0 +1,3 @@ +author_cell_type CL_label CL_ID reference dataset_version +H1 retina horizontal cell CL:0000745 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/063b53b4-4593-4815-90db-a531f8ce085b.h5ad +H2 retina horizontal cell CL:0000745 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/063b53b4-4593-4815-90db-a531f8ce085b.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique.tsv new file mode 100644 index 0000000..a85289d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique.tsv @@ -0,0 +1,11 @@ +author_cell_type CL_label CL_ID reference dataset_version +non-classical monocyte non-classical monocyte CL:0000875 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +classical monocyte classical monocyte CL:0000860 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +NK_CD16hi CD16-positive, CD56-dim natural killer cell, human CL:0000939 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +NK_CD56loCD16lo natural killer cell CL:0000623 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +plasmacytoid dendritic cell plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +conventional dendritic cell conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +platelet platelet CL:0000233 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +NK_CD56hiCD16lo CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +granulocyte granulocyte CL:0000094 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +intermediate monocyte intermediate monocyte CL:0002393 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique.tsv new file mode 100644 index 0000000..fc82a89 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique.tsv @@ -0,0 +1,43 @@ +author_cell_type CL_label CL_ID reference dataset_version +Plasma cells plasma cell CL:0000786 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Doublets unknown unknown https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +T cells alpha-beta T cell CL:0000789 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +ILC innate lymphoid cell CL:0001065 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Progenitors progenitor cell CL:0011026 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +B cells B cell CL:0000236 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +CD36+ endothelium endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +MNP mononuclear phagocyte CL:0000113 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Cycling unknown unknown https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Mast cells mast cell CL:0000097 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +TA transit amplifying cell of small intestine CL:0009012 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Enterocytes enterocyte of epithelium proper of ileum CL:1000342 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +SM smooth muscle fiber of ileum CL:1000278 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Goblets ileal goblet cell CL:1000326 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Fibs fibroblast CL:0000057 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +ACKR1+ endothelium endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Pericytes pericyte CL:0000669 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Enteroendocrines enteroendocrine cell of small intestine CL:0009006 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Paneth cells paneth cell of epithelium of small intestine CL:1000343 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Glial cells glial cell CL:0000125 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Lymphatics endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells plasma cell CL:0000786 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells alpha-beta T cell CL:0000789 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells innate lymphoid cell CL:0001065 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells B cell CL:0000236 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Endothelium endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells mononuclear phagocyte CL:0000113 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells mast cell CL:0000097 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Endothelium endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma progenitor cell CL:0011026 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma unknown unknown https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma transit amplifying cell of small intestine CL:0009012 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma enterocyte of epithelium proper of ileum CL:1000342 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma smooth muscle fiber of ileum CL:1000278 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma ileal goblet cell CL:1000326 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma fibroblast CL:0000057 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma pericyte CL:0000669 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma enteroendocrine cell of small intestine CL:0009006 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma paneth cell of epithelium of small intestine CL:1000343 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma glial cell CL:0000125 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique.tsv new file mode 100644 index 0000000..ca11e4a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique.tsv @@ -0,0 +1,15 @@ +author_cell_type CL_label CL_ID reference dataset_version +alpha pancreatic A cell CL:0000171 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +beta_major type B pancreatic cell CL:0000169 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +endothelial endothelial cell CL:0000115 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +delta pancreatic D cell CL:0000173 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +stellates pancreatic stellate cell CL:0002410 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +beta_minor type B pancreatic cell CL:0000169 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +duct_major pancreatic ductal cell CL:0002079 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +immune_stellates unknown unknown https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +acinar_minor_mhcclassII pancreatic acinar cell CL:0002064 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +hybrid unknown unknown https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +acinar pancreatic acinar cell CL:0002064 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +epsilon pancreatic epsilon cell CL:0005019 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +duct_acinar_related pancreatic ductal cell CL:0002079 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +pp PP cell CL:0000696 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique.tsv new file mode 100644 index 0000000..63f0566 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique.tsv @@ -0,0 +1,18 @@ +author_cell_type CL_label CL_ID reference dataset_version +Oligodendrocytes oligodendrocyte CL:0000128 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L3-L5 Intratelencephalic Type 1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Astrocytes astrocyte CL:0000127 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L6 Intratelencephalic - Type 1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +SV2C LAMP5 Interneurons neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L6 Corticothalamic / L6B neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L2-L3 Intratelencephalic neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L3-L5 Intratelencephalic Type 2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L6 Intratelencephalic - Type 2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +OPCs oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L5-L6 Near Projecting neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Somatostatin Interneurons neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Microglia microglial cell CL:0000129 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +VIP Interneurons neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L5 Extratelencephalic neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Endothelial endothelial cell CL:0000115 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Parvalbumin interneurons neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/456e8b9b-f872-488b-871d-94534090a865_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/456e8b9b-f872-488b-871d-94534090a865_cxg_dataset_unique.tsv new file mode 100644 index 0000000..c91d401 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/456e8b9b-f872-488b-871d-94534090a865_cxg_dataset_unique.tsv @@ -0,0 +1,189 @@ +author_cell_type CL_label CL_ID reference dataset_version +RBC erythrocyte CL:0000232 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Class-switched B class switched memory B cell CL:0000972 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +IgG PB IgG plasmablast CL:0000982 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +IgA PB IgA plasmablast CL:0000984 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD14 Monocyte CD14-positive monocyte CL:0001054 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD8m T CD8-positive, alpha-beta memory T cell CL:0000909 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD4m T CD4-positive, alpha-beta memory T cell CL:0000897 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD4n T naive T cell CL:0000898 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B B cell CL:0000236 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Platelet platelet CL:0000233 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD4 T CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +NK natural killer cell CL:0000623 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Neutrophil neutrophil CL:0000775 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD16 Monocyte monocyte CL:0000576 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD8eff T effector CD8-positive, alpha-beta T cell CL:0001050 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +gd T gamma-delta T cell CL:0000798 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +pDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Activated Granulocyte granulocyte CL:0000094 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +SC & Eosinophil granulocyte CL:0000094 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +DC conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B class switched memory B cell CL:0000972 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +PB IgG plasmablast CL:0000982 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +PB IgA plasmablast CL:0000984 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD8 T CD8-positive, alpha-beta memory T cell CL:0000909 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD4 T CD4-positive, alpha-beta memory T cell CL:0000897 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD4 T naive T cell CL:0000898 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Granulocyte neutrophil CL:0000775 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CD8 T effector CD8-positive, alpha-beta T cell CL:0001050 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Granulocyte granulocyte CL:0000094 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Erythroblast erythrocyte CL:0000232 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell class switched memory B cell CL:0000972 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell IgG plasmablast CL:0000982 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell IgA plasmablast CL:0000984 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +NK_cell IgA plasmablast CL:0000984 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Monocyte CD14-positive monocyte CL:0001054 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +T_cells class switched memory B cell CL:0000972 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https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell effector CD8-positive, alpha-beta T cell CL:0001050 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +HSC_-G-CSF plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Platelets class switched memory B cell CL:0000972 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Pre-B_cell_CD34- B cell CL:0000236 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +NK_cell neutrophil CL:0000775 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell CD14-positive monocyte CL:0001054 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CMP platelet CL:0000233 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Platelets erythrocyte CL:0000232 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +T_cells plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +BM & Prog. granulocyte CL:0000094 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Monocyte natural killer cell CL:0000623 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Platelets IgA plasmablast 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cell CL:0000898 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Pre-B_cell_CD34- conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +NK_cell conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +MEP class switched memory B cell CL:0000972 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CMP IgG plasmablast CL:0000982 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +MEP IgG plasmablast CL:0000982 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Neutrophils 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https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Neutrophils erythrocyte CL:0000232 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +CMP erythrocyte CL:0000232 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Pro-B_cell_CD34+ B cell CL:0000236 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Platelets B cell CL:0000236 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Neutrophils B cell CL:0000236 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +HSC_CD34+ granulocyte CL:0000094 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell natural killer cell CL:0000623 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +Neutrophils CD8-positive, alpha-beta memory T cell CL:0000909 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +NK_cell CD14-positive monocyte CL:0001054 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +NK_cell monocyte CL:0000576 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell monocyte CL:0000576 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad +B_cell CD8-positive, alpha-beta memory T cell CL:0000909 https://doi.org/10.1038/s41591-020-0944-y https://datasets.cellxgene.cziscience.com/89c999bd-2ba9-4281-9d22-4261347c5c78.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/489318a0-24c3-4f5c-b105-f084ed0ea026_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/489318a0-24c3-4f5c-b105-f084ed0ea026_cxg_dataset_unique.tsv new file mode 100644 index 0000000..a4b795f --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/489318a0-24c3-4f5c-b105-f084ed0ea026_cxg_dataset_unique.tsv @@ -0,0 +1,6 @@ +author_cell_type CL_label CL_ID reference dataset_version +Equatorial lens epithelial cell CL:0002224 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/307de4ad-3839-43cc-982a-37f032f1764b.h5ad +AnteriorEpi anterior lens cell CL:0002223 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/307de4ad-3839-43cc-982a-37f032f1764b.h5ad +Transitional lens epithelial cell CL:0002224 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/307de4ad-3839-43cc-982a-37f032f1764b.h5ad +EarlyFiber secondary lens fiber CL:0002225 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/307de4ad-3839-43cc-982a-37f032f1764b.h5ad +Fiber lens fiber cell CL:0011004 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/307de4ad-3839-43cc-982a-37f032f1764b.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/4c6f9f26-5470-455b-8933-c408232fbf56_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/4c6f9f26-5470-455b-8933-c408232fbf56_cxg_dataset_unique.tsv new file mode 100644 index 0000000..edbadc3 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/4c6f9f26-5470-455b-8933-c408232fbf56_cxg_dataset_unique.tsv @@ -0,0 +1,6 @@ +author_cell_type CL_label CL_ID reference dataset_version +Macro macrophage CL:0000235 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/7d7ad07a-17c3-4300-a7eb-9da10dfd61e0.h5ad +Tumor abnormal cell CL:0001061 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/7d7ad07a-17c3-4300-a7eb-9da10dfd61e0.h5ad +Endo endothelial cell CL:0000115 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/7d7ad07a-17c3-4300-a7eb-9da10dfd61e0.h5ad +vSMC vascular associated smooth muscle cell CL:0000359 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/7d7ad07a-17c3-4300-a7eb-9da10dfd61e0.h5ad +Tcell T cell CL:0000084 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/7d7ad07a-17c3-4300-a7eb-9da10dfd61e0.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/4dd1cd23-fc4d-4fd1-9709-602540f3ca6f_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/4dd1cd23-fc4d-4fd1-9709-602540f3ca6f_cxg_dataset_unique.tsv new file mode 100644 index 0000000..78ef811 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/4dd1cd23-fc4d-4fd1-9709-602540f3ca6f_cxg_dataset_unique.tsv @@ -0,0 +1,6 @@ +author_cell_type CL_label CL_ID reference dataset_version +OPC_CACNG4 oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/f25a8375-1db5-49a0-9c85-b72dbe5e2a92.h5ad +OPC_HOXD3 oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/f25a8375-1db5-49a0-9c85-b72dbe5e2a92.h5ad +OPC_ADM oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/f25a8375-1db5-49a0-9c85-b72dbe5e2a92.h5ad +OPC_KIAA0040 oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/f25a8375-1db5-49a0-9c85-b72dbe5e2a92.h5ad +OPC_MDFI oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/f25a8375-1db5-49a0-9c85-b72dbe5e2a92.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/518d9049-2a76-44f8-8abc-1e2b59ab5ba1_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/518d9049-2a76-44f8-8abc-1e2b59ab5ba1_cxg_dataset_unique.tsv new file mode 100644 index 0000000..733a5fb --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/518d9049-2a76-44f8-8abc-1e2b59ab5ba1_cxg_dataset_unique.tsv @@ -0,0 +1,26 @@ +author_cell_type CL_label CL_ID reference dataset_version +Tregs regulatory T cell CL:0000815 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +T cells CD4 FOSB CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +T cells Naive CD4 naive T cell CL:0000898 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +T cells CD8 CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +NK-like cells ID3 ENTPD1 unknown unknown https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +T cells CD4 IL17A CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +ILCs innate lymphoid cell CL:0001065 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +T cells CD8 KLRG1 CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +NK cells KLRF1 CD3G- natural killer cell CL:0000623 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +T cells OGT T cell CL:0000084 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Mast cells mast cell CL:0000097 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +B cells B cell CL:0000236 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Plasma cells plasma cell CL:0000786 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +B cells AICDA LRMP B cell CL:0000236 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Immune Cycling cells leukocyte CL:0000738 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Macrophages CCL3 CCL4 macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Monocytes CHI3L1 CYP27A1 monocyte CL:0000576 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +DC1 conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +DC2 CD1D plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Monocytes S100A8 S100A9 monocyte CL:0000576 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Mature DCs dendritic cell CL:0000451 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Macrophages LYVE1 macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +DC2 CD1D- plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Macrophages macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad +Macrophages Metallothionein macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/ad192a6c-b24a-4d08-909f-b0873b30f662.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/5829c7ba-697f-418e-8b98-d605b192dc48_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/5829c7ba-697f-418e-8b98-d605b192dc48_cxg_dataset_unique.tsv new file mode 100644 index 0000000..5e13f50 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/5829c7ba-697f-418e-8b98-d605b192dc48_cxg_dataset_unique.tsv @@ -0,0 +1,8 @@ +author_cell_type CL_label CL_ID reference dataset_version +Olig_PLXDC2_SFRP1 oligodendrocyte CL:0000128 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/6c5a3335-30fa-47a0-accc-2126efcbcaef.h5ad +Olig_ENPP6_LUCAT1 oligodendrocyte CL:0000128 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/6c5a3335-30fa-47a0-accc-2126efcbcaef.h5ad +Olig_ENPP6_EMILIN2 oligodendrocyte CL:0000128 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/6c5a3335-30fa-47a0-accc-2126efcbcaef.h5ad +Olig_ENPP6_ACTN2 oligodendrocyte CL:0000128 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/6c5a3335-30fa-47a0-accc-2126efcbcaef.h5ad +Olig_PLXDC2_KCNAB1 oligodendrocyte CL:0000128 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/6c5a3335-30fa-47a0-accc-2126efcbcaef.h5ad +Olig_PLXDC2_KCNK10 oligodendrocyte CL:0000128 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/6c5a3335-30fa-47a0-accc-2126efcbcaef.h5ad +Olig_PLXDC2 oligodendrocyte CL:0000128 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/6c5a3335-30fa-47a0-accc-2126efcbcaef.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique.tsv new file mode 100644 index 0000000..b5bf557 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique.tsv @@ -0,0 +1,70 @@ +author_cell_type CL_label CL_ID reference dataset_version +Plasmablast plasmablast CL:0000980 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +NK natural killer cell CL:0000623 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +B memory memory B cell CL:0000787 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +B naive naive B cell CL:0000788 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +cDC conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +HSPC hematopoietic stem cell CL:0000037 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ T naive naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Platelet platelet CL:0000233 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD8+ Tem effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ CTL CD4-positive, alpha-beta cytotoxic T cell CL:0000934 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Classical Monocyte classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +pDC plasmacytoid dendritic cell, human CL:0001058 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +T/NK proliferative lymphocyte CL:0000542 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Non-classical Monocyte non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +NK CD56bright CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ Tcm central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Treg regulatory T cell CL:0000815 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +RBC erythrocyte CL:0000232 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +B intermediate transitional stage B cell CL:0000818 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ T activated activated CD4-positive, alpha-beta T cell, human CL:0001043 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD8+ T naive naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD8+ T activated activated CD8-positive, alpha-beta T cell, human CL:0001049 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +NK activated natural killer cell CL:0000623 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +MAIT mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Neutrophil neutrophil CL:0000775 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +immature Neutrophil immature neutrophil CL:0000776 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +NK cell natural killer cell CL:0000623 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +B cell memory B cell CL:0000787 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +B cell naive B cell CL:0000788 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ T cell naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD8+ T cell effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ T cell CD4-positive, alpha-beta cytotoxic T cell CL:0000934 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD14+ Monocyte classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Other T lymphocyte CL:0000542 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD16+ Monocyte non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +NK cell CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ T cell central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ T cell regulatory T cell CL:0000815 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +B cell transitional stage B cell CL:0000818 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ T cell activated CD4-positive, alpha-beta T cell, human CL:0001043 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD8+ T cell naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD8+ T cell activated CD8-positive, alpha-beta T cell, human CL:0001049 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD8+ T cell mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Neutrophil immature neutrophil CL:0000776 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_P plasmablast CL:0000980 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK natural killer cell CL:0000623 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_B memory B cell CL:0000787 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_B naive B cell CL:0000788 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Myeloid conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Hematopoietic_SC hematopoietic stem cell CL:0000037 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Hematopoietic_Mega platelet CL:0000233 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK CD4-positive, alpha-beta cytotoxic T cell CL:0000934 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Myeloid classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Myeloid plasmacytoid dendritic cell, human CL:0001058 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK lymphocyte CL:0000542 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Myeloid non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK regulatory T cell CL:0000815 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Hematopoietic_R erythrocyte CL:0000232 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_B transitional stage B cell CL:0000818 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK activated CD4-positive, alpha-beta T cell, human CL:0001043 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK activated CD8-positive, alpha-beta T cell, human CL:0001049 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Lymphoid_T/NK mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Myeloid_G neutrophil CL:0000775 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Myeloid_G immature neutrophil CL:0000776 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/5cdbb2ea-c622-466d-9ead-7884ad8cb99f_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/5cdbb2ea-c622-466d-9ead-7884ad8cb99f_cxg_dataset_unique.tsv new file mode 100644 index 0000000..c0854c6 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/5cdbb2ea-c622-466d-9ead-7884ad8cb99f_cxg_dataset_unique.tsv @@ -0,0 +1,26 @@ +author_cell_type CL_label CL_ID reference dataset_version +Gly1 glycinergic amacrine cell CL:4030028 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba8 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba5 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba3 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba11 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba10 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba17 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba6 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba14 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba4 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba16 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba1 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba9 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba7 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gly8 glycinergic amacrine cell CL:4030028 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gly2 glycinergic amacrine cell CL:4030028 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba2 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba13 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba15 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gly7 glycinergic amacrine cell CL:4030028 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gly6 glycinergic amacrine cell CL:4030028 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gly3 glycinergic amacrine cell CL:4030028 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gly4 glycinergic amacrine cell CL:4030028 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gaba12 GABAergic amacrine cell CL:4030027 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad +Gly5 glycinergic amacrine cell CL:4030028 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/1a68ff8a-3bc1-4221-ba39-ac4152eda38e.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/5ce42b38-d867-487f-9b40-e8bb00b21d0b_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/5ce42b38-d867-487f-9b40-e8bb00b21d0b_cxg_dataset_unique.tsv new file mode 100644 index 0000000..28f021b --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/5ce42b38-d867-487f-9b40-e8bb00b21d0b_cxg_dataset_unique.tsv @@ -0,0 +1,14 @@ +author_cell_type CL_label CL_ID reference dataset_version +Paneth cells paneth cell CL:0000510 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Goblet cells MUC2 TFF1 goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Goblet cells MUC2 TFF1- goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Enterocytes CA1 CA2 CA4- enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Epithelial Cycling cells epithelial cell CL:0000066 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Stem cells OLFM4 LGR5 stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Enteroendocrine cells enteroendocrine cell CL:0000164 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Enterocytes TMIGD1 MEP1A enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Stem cells OLFM4 PCNA stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Stem cells OLFM4 stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Enterocytes BEST4 enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Tuft cells tuft cell CL:0002204 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad +Goblet cells SPINK4 goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/9826fca1-819b-4144-9f18-8a335cdd2347.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/5e717147-0f75-4de1-8bd2-6fda01b8d75f_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/5e717147-0f75-4de1-8bd2-6fda01b8d75f_cxg_dataset_unique.tsv new file mode 100644 index 0000000..4b6ec51 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/5e717147-0f75-4de1-8bd2-6fda01b8d75f_cxg_dataset_unique.tsv @@ -0,0 +1,176 @@ +author_cell_type CL_label CL_ID reference dataset_version +Classical Monocyte classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Non-classical Monocyte non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ Tem effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +B intermediate transitional stage B cell CL:0000818 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +B naive naive B cell CL:0000788 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +gd T gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ T naive naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +cDC conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +T/NK proliferative lymphocyte CL:0000542 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +pDC plasmacytoid dendritic cell, human CL:0001058 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD4+ T naive naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +NK natural killer cell CL:0000623 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD4+ Tcm central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +MAIT mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Neutrophil neutrophil CL:0000775 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Platelet platelet CL:0000233 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +B memory memory B cell CL:0000787 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Treg regulatory T cell CL:0000815 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +NK CD56bright CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +immature Neutrophil immature neutrophil CL:0000776 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Plasmablast plasmablast CL:0000980 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +dn T double negative thymocyte CL:0002489 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD14+ Monocyte classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD16+ Monocyte non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ T cell effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +B cell transitional stage B cell CL:0000818 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +B cell naive B cell CL:0000788 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Other T gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ T cell naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Other T lymphocyte CL:0000542 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD4+ T cell naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +NK cell natural killer cell CL:0000623 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD4+ T cell central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ T cell mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +B cell memory B cell CL:0000787 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD4+ T cell regulatory T cell CL:0000815 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +NK cell CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Neutrophil immature neutrophil CL:0000776 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Other T double negative thymocyte CL:0002489 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +HLA-DR+ CD83+ Monocytes classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +HLA-DR+ CD83+ Monocytes non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad 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https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD4+ T cells_3 CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ T cells_3 double negative thymocyte CL:0002489 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ T cells_1 naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD4+ T cells_3 lymphocyte CL:0000542 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +mDCs non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ T cells_2 mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +CD8+ T cells_2 naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +NK mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +pDCs classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Immature Neutrophils conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Myeloid classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Myeloid non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_B transitional stage B cell CL:0000818 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_B naive B cell CL:0000788 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Myeloid conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK lymphocyte CL:0000542 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Myeloid plasmacytoid dendritic cell, human CL:0001058 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK natural killer cell CL:0000623 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Myeloid_G neutrophil CL:0000775 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Hematopoietic_Mega platelet CL:0000233 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_B memory B cell CL:0000787 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK regulatory T cell CL:0000815 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Myeloid_G immature neutrophil CL:0000776 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_P plasmablast CL:0000980 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad +Lymphoid_T/NK double negative thymocyte CL:0002489 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/826f451b-68ac-4775-bbfa-6816e33f0091.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/6a270451-b4d9-43e0-aa89-e33aac1ac74b_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/6a270451-b4d9-43e0-aa89-e33aac1ac74b_cxg_dataset_unique.tsv new file mode 100644 index 0000000..2082389 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/6a270451-b4d9-43e0-aa89-e33aac1ac74b_cxg_dataset_unique.tsv @@ -0,0 +1,8 @@ +author_cell_type CL_label CL_ID reference dataset_version +T T cell CL:0000084 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/9c123ef4-570c-49c6-ab8f-904ea1f5bd8d.h5ad +PLA plasma cell CL:0000786 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/9c123ef4-570c-49c6-ab8f-904ea1f5bd8d.h5ad +MAS mast cell CL:0000097 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/9c123ef4-570c-49c6-ab8f-904ea1f5bd8d.h5ad +MYE myeloid cell CL:0000763 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/9c123ef4-570c-49c6-ab8f-904ea1f5bd8d.h5ad +FIB fibroblast CL:0000057 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/9c123ef4-570c-49c6-ab8f-904ea1f5bd8d.h5ad +B B cell CL:0000236 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/9c123ef4-570c-49c6-ab8f-904ea1f5bd8d.h5ad +END endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/9c123ef4-570c-49c6-ab8f-904ea1f5bd8d.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/6cf3634d-e911-44ad-bf52-c747a9af3c01_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/6cf3634d-e911-44ad-bf52-c747a9af3c01_cxg_dataset_unique.tsv new file mode 100644 index 0000000..93c7e2e --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/6cf3634d-e911-44ad-bf52-c747a9af3c01_cxg_dataset_unique.tsv @@ -0,0 +1,18 @@ +author_cell_type CL_label CL_ID reference dataset_version +Fibroblasts ADAMDEC1 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Endothelial cells CD36 endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Fibroblasts KCNN3 LY6H fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Lymphatics lymphocyte CL:0000542 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Endothelial cells LTC4S SEMA3G endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Endothelial cells DARC endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Pericytes HIGD1B STEAP4 pericyte CL:0000669 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Glial cells glial cell CL:0000125 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Myofibroblasts HHIP NPNT myofibroblast cell CL:0000186 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Stromal Cycling cells stromal cell CL:0000499 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Inflammatory fibroblasts IL11 CHI3L1 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Fibroblasts NPY SLITRK6 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Fibroblasts SFRP2 SLPI fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Fibroblasts SMOC2 PTGIS fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Pericytes RERGL NTRK2 pericyte CL:0000669 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Activated fibroblasts CCL19 ADAMADEC1 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad +Myofibroblasts GREM1 GREM2 myofibroblast cell CL:0000186 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/5b035773-25c1-4d3f-bb1f-98f2d190a25b.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/716a4acc-919e-4326-9672-ebe06ede84e6_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/716a4acc-919e-4326-9672-ebe06ede84e6_cxg_dataset_unique.tsv new file mode 100644 index 0000000..dd2e998 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/716a4acc-919e-4326-9672-ebe06ede84e6_cxg_dataset_unique.tsv @@ -0,0 +1,269 @@ +author_cell_type CL_label CL_ID reference dataset_version +Lamp5 lamp5 GABAergic cortical interneuron CL:4023011 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +Sncg sncg GABAergic cortical interneuron CL:4023015 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +Vip VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +Lamp5 Lhx6 lamp5 GABAergic cortical interneuron CL:4023011 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +Pax6 caudal ganglionic eminence derived cortical interneuron CL:4023064 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +L5 ET L5 extratelencephalic projecting glutamatergic cortical neuron CL:4023041 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +L5/6 NP near-projecting glutamatergic cortical neuron CL:4023012 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +L6 CT corticothalamic-projecting glutamatergic cortical neuron CL:4023013 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +L6b L6b glutamatergic cortical neuron CL:4023038 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +Astro astrocyte of the cerebral cortex CL:0002605 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +Endo cerebral cortex endothelial cell CL:1001602 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +VLMC vascular leptomeningeal cell CL:4023051 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/686fa217-bac7-49e7-a663-74ff1b9e94c8.h5ad +Micro/PVM microglial 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https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Treg regulatory T cell CL:0000815 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Lymphatic endothelial cell endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +B cell B cell CL:0000236 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Activated B cell B cell CL:0000236 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +pDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Cycling plasma cell plasma cell CL:0000786 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +gd T/NK cell gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +mast cells mast cell CL:0000097 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Paneth cell glial cell CL:0000125 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +BEST4 enterocyte enterocyte CL:0000584 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +activated DC dendritic cell, human CL:0001056 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +S2 fibroblasts fibroblast CL:0000057 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +CD8 T cell CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Macrophage glial cell CL:0000125 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Glial cell glial cell CL:0000125 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Cycling myeloid cells myeloid cell CL:0000763 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad +Tuft intestinal tuft cell CL:0019032 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/5790e30c-cc55-449f-bf80-62f5c12efe40.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/92161459-9103-4379-ae34-73a38eee1d1d_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/92161459-9103-4379-ae34-73a38eee1d1d_cxg_dataset_unique.tsv new file mode 100644 index 0000000..0db5724 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/92161459-9103-4379-ae34-73a38eee1d1d_cxg_dataset_unique.tsv @@ -0,0 +1,3 @@ +author_cell_type CL_label CL_ID reference dataset_version +nonda neuron CL:0000540 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/0f32a263-e4d7-4e50-84c3-ff42bfd6de05.h5ad +da dopaminergic neuron CL:0000700 https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/0f32a263-e4d7-4e50-84c3-ff42bfd6de05.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/94c41723-b2c4-4b59-a49a-64c9b851903e_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/94c41723-b2c4-4b59-a49a-64c9b851903e_cxg_dataset_unique.tsv new file mode 100644 index 0000000..7f986da --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/94c41723-b2c4-4b59-a49a-64c9b851903e_cxg_dataset_unique.tsv @@ -0,0 +1,25 @@ +author_cell_type CL_label CL_ID reference dataset_version +Pyr1 pyramidal neuron CL:0000598 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Olig2 oligodendrocyte CL:0000128 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Olig3 oligodendrocyte CL:0000128 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Micro2 microglial cell CL:0000129 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Micro1 microglial cell CL:0000129 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Olig1 oligodendrocyte CL:0000128 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Astro2 astrocyte CL:0000127 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +OPC1 oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Olig4 oligodendrocyte CL:0000128 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +In1 interneuron CL:0000099 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Den.Gyr1 cerebral cortex neuron CL:0010012 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Astro1 astrocyte CL:0000127 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Endo endothelial cell CL:0000115 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Pyr2 pyramidal neuron CL:0000598 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Micro3 microglial cell CL:0000129 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Olig5 oligodendrocyte CL:0000128 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Den.Gyr3 cerebral cortex neuron CL:0010012 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +In3 interneuron CL:0000099 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +Den.Gyr2 cerebral cortex neuron CL:0010012 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +In2 interneuron CL:0000099 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +OPC2 oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1016/j.neuron.2021.05.003 https://datasets.cellxgene.cziscience.com/703771a1-236f-4eda-9c04-318d882e149b.h5ad +OPC4 oligodendrocyte precursor 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https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +T cells CD8 KLRG1 CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +T cells CD4 IL17A CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +IELs ID3 ENTPD1 intraepithelial lymphocyte CL:0002496 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +T cells CD4 FOSB CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +T cells Naive CD4 naive T cell CL:0000898 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +ILCs innate lymphoid cell CL:0001065 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +NK cells KLRF1 CD3G- natural killer cell CL:0000623 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +T cells OGT T cell CL:0000084 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Mast cells mast cell CL:0000097 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Plasma cells plasma cell CL:0000786 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +B cells B cell CL:0000236 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +B cells AICDA LRMP B cell CL:0000236 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +DC2 CD1D- plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Cycling cells unknown unknown https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Macrophages CCL3 CCL4 macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +DC1 conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +DC2 CD1D plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Mature DCs dendritic cell CL:0000451 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Macrophages LYVE1 macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Macrophages macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Monocytes S100A8 S100A9 monocyte CL:0000576 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Macrophages CXCL9 CXCL10 macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Neutrophils S100A8 S100A9 neutrophil CL:0000775 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Monocytes CHI3L1 CYP27A1 monocyte CL:0000576 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Macrophages PLA2G2D macrophage CL:0000235 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad +Monocytes HBB monocyte CL:0000576 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/6cfb8c33-9cfb-4e16-b868-18aff944e55a.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique.tsv new file mode 100644 index 0000000..240514b --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique.tsv @@ -0,0 +1,8 @@ +author_cell_type CL_label CL_ID reference dataset_version +K_Epi-Basal basal cell CL:0000646 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/9a25a459-dedc-45c0-b3b7-0e4be0a0b2c9.h5ad +K_Epi-Wing corneal epithelial cell CL:0000575 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/9a25a459-dedc-45c0-b3b7-0e4be0a0b2c9.h5ad +K_Fibro fibroblast CL:0000057 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/9a25a459-dedc-45c0-b3b7-0e4be0a0b2c9.h5ad +K_Epi-Superficial corneal epithelial cell CL:0000575 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/9a25a459-dedc-45c0-b3b7-0e4be0a0b2c9.h5ad +K_Endo corneal endothelial cell CL:0000132 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/9a25a459-dedc-45c0-b3b7-0e4be0a0b2c9.h5ad +K_Epi-TA transit amplifying cell CL:0009010 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/9a25a459-dedc-45c0-b3b7-0e4be0a0b2c9.h5ad +Immune leukocyte CL:0000738 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/9a25a459-dedc-45c0-b3b7-0e4be0a0b2c9.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/a5d5c529-8a1f-40b5-bda3-35208970070d_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/a5d5c529-8a1f-40b5-bda3-35208970070d_cxg_dataset_unique.tsv new file mode 100644 index 0000000..6bca12f --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/a5d5c529-8a1f-40b5-bda3-35208970070d_cxg_dataset_unique.tsv @@ -0,0 +1,164 @@ +author_cell_type CL_label CL_ID reference dataset_version +Inh L2-5 VIP TOX2 VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1 LAMP5 GGT8P lamp5 GABAergic cortical interneuron CL:4023011 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1 LAMP5 NDNF lamp5 GABAergic cortical interneuron CL:4023011 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1-3 VIP ZNF322P1 VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L3 VIP CBLN1 VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1-4 LAMP5 DUSP4 lamp5 GABAergic cortical interneuron CL:4023011 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Exc L2-3 LINC00507 RPL9P17 glutamatergic neuron CL:0000679 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1 SST CXCL14 VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1 PAX6 GRIP2 caudal ganglionic eminence derived cortical interneuron CL:4023064 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1-2 VIP PPAPDC1A VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Oligo L4-6 OPALIN oligodendrocyte CL:0000128 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1 PAX6 CA4 caudal ganglionic eminence derived cortical interneuron CL:4023064 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1 ADARB2 ADAM33 VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1-4 VIP CHRNA2 VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Astro L1-6 FGFR3 ETNPPL astrocyte CL:0000127 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L2-6 VIP VIP VIP GABAergic cortical interneuron CL:4023016 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L1-6 LAMP5 CA13 lamp5 GABAergic cortical interneuron CL:4023011 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Exc L5-6 THEMIS GPR21 L2/3-6 intratelencephalic projecting glutamatergic neuron CL:4023040 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Exc L5-6 FEZF2 MYBPHL near-projecting glutamatergic cortical neuron CL:4023012 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Exc L4-5 RORB RPL31P31 glutamatergic neuron CL:0000679 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Exc L4-5 RORB LCN15 glutamatergic neuron CL:0000679 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Inh L4-6 SST MTHFD2P6 sst GABAergic cortical interneuron CL:4023017 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Exc L6 THEMIS LINC00343 glutamatergic neuron CL:0000679 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Exc L6 FEZF2 FAM95C L6 corticothalamic-projecting glutamatergic cortical neuron CL:4023042 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +Exc L4-5 RORB LINC01474 glutamatergic neuron CL:0000679 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/dfba29ea-d368-44c2-bb35-69d3e8f730ce.h5ad +OPC L1-6 MYT1 oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1126/science.adf6812 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a/cellsem_agent/graphs/cxg_annotate/amica_test_data/af8b241a-c72c-4470-b1a4-80e7336c6ab6_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/af8b241a-c72c-4470-b1a4-80e7336c6ab6_cxg_dataset_unique.tsv new file mode 100644 index 0000000..50b6466 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/af8b241a-c72c-4470-b1a4-80e7336c6ab6_cxg_dataset_unique.tsv @@ -0,0 +1,12 @@ +author_cell_type CL_label CL_ID reference dataset_version +T/NK-cell mature NK T cell CL:0000814 https://doi.org/10.1073/pnas.1914143116 https://datasets.cellxgene.cziscience.com/23f26a44-264a-45c0-aecd-1047bf684357.h5ad +Macrophage macrophage CL:0000235 https://doi.org/10.1073/pnas.1914143116 https://datasets.cellxgene.cziscience.com/23f26a44-264a-45c0-aecd-1047bf684357.h5ad +Melanocyte melanocyte CL:0000148 https://doi.org/10.1073/pnas.1914143116 https://datasets.cellxgene.cziscience.com/23f26a44-264a-45c0-aecd-1047bf684357.h5ad +Schwann2 Schwann cell CL:0002573 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https://datasets.cellxgene.cziscience.com/23f26a44-264a-45c0-aecd-1047bf684357.h5ad +Schwann1 Schwann cell CL:0002573 https://doi.org/10.1073/pnas.1914143116 https://datasets.cellxgene.cziscience.com/23f26a44-264a-45c0-aecd-1047bf684357.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/b46237d1-19c6-4af2-9335-9854634bad16_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/b46237d1-19c6-4af2-9335-9854634bad16_cxg_dataset_unique.tsv new file mode 100644 index 0000000..c3aaffd --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/b46237d1-19c6-4af2-9335-9854634bad16_cxg_dataset_unique.tsv @@ -0,0 +1,87 @@ +author_cell_type CL_label CL_ID reference dataset_version +mesoderm 1 mesodermal cell CL:0000222 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +WNT4 FLC fibroblast CL:0000057 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +immune cells hematopoietic cell CL:0000988 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +SMC enteric smooth muscle cell CL:0002504 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Erythroblasts erythroblast CL:0000765 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +mesoderm 2 mesodermal cell CL:0000222 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Neural crest cells neural crest cell CL:0011012 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Enteric neurons enteric neuron CL:0007011 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https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Serosa/mesothelial cells mesothelial cell CL:0000077 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Pericyte pericyte CL:0000669 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Venous endothelial cell vein endothelial cell CL:0002543 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +FOXL1+ fibroblasts fibroblast CL:0000057 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Arterial endothelial cell endothelial cell of artery CL:1000413 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Lymphatic endothelial 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https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +BEST4+ enterocyte enterocyte CL:0000584 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Enteroendocrine enteroendocrine cell CL:0000164 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Enterocyte enterocyte CL:0000584 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +G2M/S enterocytes enterocyte CL:0000584 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Goblet intestine goblet cell CL:0019031 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +Early enterocyte enterocyte CL:0000584 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +LGR5 stem stem cell CL:0000034 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +NTS+ epithelial cells epithelial cell CL:0000066 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +mesenchymal mesodermal cell CL:0000222 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +mesenchymal fibroblast CL:0000057 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +immune hematopoietic cell CL:0000988 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +mesenchymal enteric smooth 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+mesenchymal pericyte CL:0000669 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +vasculature vein endothelial cell CL:0002543 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +vasculature pericyte CL:0000669 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +vasculature endothelial cell of artery CL:1000413 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +vasculature endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +mesenchymal myofibroblast cell CL:0000186 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +epithelium enterocyte CL:0000584 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +epithelium colon epithelial cell CL:0011108 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +mesenchymal interstitial cell of Cajal CL:0002088 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +vasculature fibroblast CL:0000057 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +epithelium enteroendocrine cell CL:0000164 https://doi.org/10.1016/j.devcel.2020.11.010 https://datasets.cellxgene.cziscience.com/fda4e033-5b51-40bf-b67e-d24c9742bfbc.h5ad +vasculature mesodermal cell CL:0000222 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https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +pre_OPC progenitor cell CL:0011026 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +glioblast_PWM glioblast CL:0000030 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +progenitor_RL_early macroglial cell CL:0000126 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +progenitor_isthmic macroglial cell CL:0000126 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +noradrenergic noradrenergic cell CL:0000459 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +nonparenh_macrophage central nervous system macrophage CL:0000878 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +glut_DN_maturing glutamatergic neuron CL:0000679 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +mural/endoth brain vascular cell CL:4023072 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +erythroid erythroid lineage cell CL:0000764 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +immune leukocyte CL:0000738 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +interneuron_GL interneuron CL:0000099 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +NTZ_neuroblast_3 neuroblast (sensu Vertebrata) CL:0000031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +interneuron_PL interneuron CL:0000099 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +OPC_early oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +NTZ_neuroblast_2 neuroblast (sensu Vertebrata) CL:0000031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +isth_N_diff unknown unknown https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +interneuron_ML2 interneuron CL:0000099 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +GC_diff_2_late cerebellar granule cell CL:0001031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +GC_diff_1_late cerebellar granule cell CL:0001031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +T-cell T cell CL:0000084 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +meningeal meningeal macrophage CL:0000879 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +Purkinje_maturing unknown unknown https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +astro_Bergmann Bergmann glial cell CL:0000644 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +astro_parenh macroglial cell CL:0000126 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +interneuron_ML1 interneuron CL:0000099 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +GC_defined cerebellar granule cell CL:0001031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +COP_early oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +astroblast immature astrocyte CL:0002626 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +oligodendrocyte oligodendrocyte CL:0000128 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +isthmic_neuroblast neuroblast (sensu Vertebrata) CL:0000031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +OPC_late committed oligodendrocyte precursor CL:4023059 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +NTZ_neuroblast_1 neuroblast (sensu Vertebrata) CL:0000031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +progenitor_Nckap5_neg macroglial cell CL:0000126 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +GC cerebellar granule cell CL:0001031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +glutamatergic_uncertain unknown unknown https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +glut_DN glutamatergic neuron CL:0000679 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +astroglia macroglial cell CL:0000126 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +Purkinje Purkinje cell CL:0000121 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +VZ_neuroblast neuroblast (sensu Vertebrata) CL:0000031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +GABA_DN GABAergic neuron CL:0000617 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +isth_N unknown unknown https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +GC cerebellar granule cell precursor CL:0002362 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +interneuron interneuron CL:0000099 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +UBC unipolar brush cell CL:4023161 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +immune microglial cell CL:0000129 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +GC/UBC neuroblast (sensu Vertebrata) CL:0000031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +oligo progenitor cell CL:0011026 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +astroglia glioblast CL:0000030 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +immune central nervous system macrophage CL:0000878 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +NTZ_neuroblast neuroblast (sensu Vertebrata) CL:0000031 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +oligo oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +immune T cell CL:0000084 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +Purkinje unknown unknown https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +astroglia Bergmann glial cell CL:0000644 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +astroglia immature astrocyte CL:0002626 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +oligo oligodendrocyte CL:0000128 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad +oligo committed oligodendrocyte precursor CL:4023059 https://doi.org/10.1038/s41586-023-06884-x https://datasets.cellxgene.cziscience.com/1201b573-417d-4a9d-86f7-c43cc2bc36e3.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/bc7260e0-54cf-4b0b-823d-93f5b850f757_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/bc7260e0-54cf-4b0b-823d-93f5b850f757_cxg_dataset_unique.tsv new file mode 100644 index 0000000..850fda9 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/bc7260e0-54cf-4b0b-823d-93f5b850f757_cxg_dataset_unique.tsv @@ -0,0 +1,5 @@ +author_cell_type CL_label CL_ID reference dataset_version +Muller Mueller cell CL:0000636 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/e49d16f0-187e-4dab-b0c5-e74a798db461.h5ad +MicroGlia microglial cell CL:0000129 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/e49d16f0-187e-4dab-b0c5-e74a798db461.h5ad +Endothelium endothelial cell CL:0000115 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/e49d16f0-187e-4dab-b0c5-e74a798db461.h5ad +Astrocytes astrocyte CL:0000127 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/e49d16f0-187e-4dab-b0c5-e74a798db461.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/bd65a70f-b274-4133-b9dd-0d1431b6af34_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/bd65a70f-b274-4133-b9dd-0d1431b6af34_cxg_dataset_unique.tsv new file mode 100644 index 0000000..88470fe --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/bd65a70f-b274-4133-b9dd-0d1431b6af34_cxg_dataset_unique.tsv @@ -0,0 +1,32 @@ +author_cell_type CL_label CL_ID reference dataset_version +CD8A+ NK-like CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD8A+ Proliferating CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD8A+ Exhausted IEG naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +Conventional NK mature NK T cell CL:0000814 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD8A+ Tissue-resident CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD45- Vascular Endothelium endothelial cell CL:0000115 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +NK HSP+ mature NK T cell CL:0000814 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD4+ Activated IEG activated CD4-positive, alpha-beta T cell, human CL:0001043 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +TAM/TCR (Ambiguos) abnormal cell CL:0001061 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD45- PAX8+ renal epithelium myofibroblast cell CL:0000186 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +TAM HLAhi abnormal cell CL:0001061 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD8A+ Exhausted naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD14+ Monocyte CD14-positive monocyte CL:0001054 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD14+/CD16+ Monocyte CD14-positive, CD16-positive monocyte CL:0002397 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD45- Myofibroblast myofibroblast cell CL:0000186 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD4+ Naive naive T cell CL:0000898 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +cDC2 conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +TAM HLAint abnormal cell CL:0001061 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +Ambiguous unknown unknown https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +Mast mast cell CL:0000097 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD4+ Proliferating CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD45- ccRCC CA9+ abnormal cell CL:0001061 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD4+ Effector effector CD4-positive, alpha-beta T cell CL:0001044 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +TAM ISGint abnormal cell CL:0001061 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +B cell B cell CL:0000236 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +CD4+ Treg regulatory T cell CL:0000815 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +TAM ISGhi abnormal cell CL:0001061 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +cDC1 conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +Megakaryocyte megakaryocyte CL:0000556 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +pDC dendritic cell, human CL:0001056 https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad +Ambiguous/Dead unknown unknown https://doi.org/10.1016/j.ccell.2021.03.007 https://datasets.cellxgene.cziscience.com/04e8a75c-86b7-494b-9b88-68a457306cb8.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/be39785b-67cb-4177-be19-a40ee3747e45_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/be39785b-67cb-4177-be19-a40ee3747e45_cxg_dataset_unique.tsv new file mode 100644 index 0000000..21c1d17 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/be39785b-67cb-4177-be19-a40ee3747e45_cxg_dataset_unique.tsv @@ -0,0 +1,13 @@ +author_cell_type CL_label CL_ID reference dataset_version +Tumor abnormal cell CL:0001061 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/23138a44-4709-4760-828c-eeb3e0cf84a8.h5ad +Macro macrophage CL:0000235 https://doi.org/10.1073/pnas.2103240118 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CL:0000115 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Pericytes pericyte CL:0000669 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Pulmonary venous endothelial cells vein endothelial cell CL:0002543 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Capillary endothelial cells capillary endothelial cell CL:0002144 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +CD4+ T cells CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Activated B cells B cell CL:0000236 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Tregs regulatory T cell CL:0000815 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +B cells B cell CL:0000236 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Inflamed endothelial cells endothelial cell CL:0000115 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +CD8+ T cells CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Pathological FB fibroblast CL:0000057 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Systemic venous endothelial cells vein endothelial cell CL:0002543 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Airway epithelial cells lung multiciliated epithelial cell CL:1000271 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Macrophages alveolar macrophage CL:0000583 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Smooth muscle vascular associated smooth muscle cell CL:0000359 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Airway epithelial cells lung goblet cell CL:1000143 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Macrophages macrophage CL:0000235 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Fibroblasts mesothelial fibroblast CL:4023054 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Endothelial cells endothelial cell CL:0000115 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Fibroblasts fibroblast CL:0000057 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Airway epithelial cells club cell CL:0000158 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Other epithelial cells epithelial cell CL:0000066 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Airway epithelial cells brush cell of tracheobronchial tree CL:0002075 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Airway epithelial cells respiratory basal cell CL:0002633 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Fibroblasts alveolar adventitial fibroblast CL:4028006 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Endothelial cells endothelial cell of artery CL:1000413 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Smooth muscle tracheobronchial smooth muscle cell CL:0019019 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Airway epithelial cells mucus secreting cell CL:0000319 https://doi.org/10.1038/s41586-021-03569-1 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https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Myeloid alveolar macrophage CL:0000583 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Epithelial cells pulmonary alveolar type 2 cell CL:0002063 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Fibroblasts vascular associated smooth muscle cell CL:0000359 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Epithelial cells lung goblet cell CL:1000143 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Myeloid macrophage CL:0000235 https://doi.org/10.1038/s41586-021-03569-1 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https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Mast cells dendritic cell CL:0000451 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +T cells CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +T cells regulatory T cell CL:0000815 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +T cells CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +Mast cells monocyte CL:0000576 https://doi.org/10.1038/s41586-021-03569-1 https://datasets.cellxgene.cziscience.com/75c059c8-8fb7-4e6e-a618-a3e01ac42060.h5ad +APC-like macrophage 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https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Fibroblasts II skin fibroblast CL:0002620 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Fibroblasts I skin fibroblast CL:0002620 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Differentiated keratinocytes I keratinocyte CL:0000312 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Endothelial cells endothelial cell CL:0000115 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Pericytes pericyte CL:0000669 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Melanocytes/melanocyte-like melanocyte CL:0000148 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Differentiated keratinocytes II keratinocyte CL:0000312 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Undifferentiated keratinocytes I keratinocyte CL:0000312 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad +Terminally differentiated keratinocytes keratinocyte CL:0000312 https://doi.org/10.1016/j.celrep.2023.111994 https://datasets.cellxgene.cziscience.com/55df92aa-5c10-4e03-a44a-5a9aebe60c65.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/dea1aa78-c0a2-413f-b375-f91cce49e4d0_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/dea1aa78-c0a2-413f-b375-f91cce49e4d0_cxg_dataset_unique.tsv new file mode 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epithelial cell CL:1001106 https://datasets.cellxgene.cziscience.com/1be76d4c-94aa-4c62-9254-f063872ba04f.h5ad +PT kidney loop of Henle thin descending limb epithelial cell CL:1001111 https://datasets.cellxgene.cziscience.com/1be76d4c-94aa-4c62-9254-f063872ba04f.h5ad +T kidney interstitial alternatively activated macrophage CL:1000695 https://datasets.cellxgene.cziscience.com/1be76d4c-94aa-4c62-9254-f063872ba04f.h5ad +PC epithelial cell of proximal tubule CL:0002306 https://datasets.cellxgene.cziscience.com/1be76d4c-94aa-4c62-9254-f063872ba04f.h5ad +TAL kidney connecting tubule epithelial cell CL:1000768 https://datasets.cellxgene.cziscience.com/1be76d4c-94aa-4c62-9254-f063872ba04f.h5ad +TAL kidney collecting duct principal cell CL:1001431 https://datasets.cellxgene.cziscience.com/1be76d4c-94aa-4c62-9254-f063872ba04f.h5ad +EC kidney loop of Henle thin descending limb epithelial cell CL:1001111 https://datasets.cellxgene.cziscience.com/1be76d4c-94aa-4c62-9254-f063872ba04f.h5ad +PC 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glutamatergic cortical neuron CL:4023012 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +excitatory corticothalamic-projecting glutamatergic cortical neuron CL:4023013 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +excitatory L6b glutamatergic cortical neuron CL:4023038 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +non-neuronal astrocyte of the cerebral cortex CL:0002605 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +non-neuronal vascular leptomeningeal cell CL:4023051 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +non-neuronal cerebral cortex endothelial cell CL:1001602 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +non-neuronal microglial cell CL:0000129 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +non-neuronal oligodendrocyte CL:0000128 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +non-neuronal oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +excitatory L2/3-6 intratelencephalic projecting glutamatergic neuron CL:4023040 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +inhibitory chandelier pvalb GABAergic cortical interneuron CL:4023036 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +inhibitory pvalb GABAergic cortical interneuron CL:4023018 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad +inhibitory sst GABAergic cortical interneuron CL:4023017 https://doi.org/10.1126/science.adf6812 https://datasets.cellxgene.cziscience.com/0cc3b103-ee19-4187-832f-ea77285be599.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/e40c6272-af77-4a10-9385-62a398884f27_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/e40c6272-af77-4a10-9385-62a398884f27_cxg_dataset_unique.tsv new file mode 100644 index 0000000..80169dc --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/e40c6272-af77-4a10-9385-62a398884f27_cxg_dataset_unique.tsv @@ -0,0 +1,10 @@ +author_cell_type CL_label CL_ID reference dataset_version +GOB goblet cell CL:0000160 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad +TAC transit amplifying cell CL:0009010 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad +ASC neoplastic cell CL:0001063 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad +TUF tuft cell CL:0002204 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad +ABS gut absorptive cell CL:0000677 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad +STM intestinal crypt stem cell of colon CL:0009043 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad +SSC abnormal cell CL:0001061 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad +EE intestinal enteroendocrine cell CL:1001516 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad +CT intestinal epithelial cell CL:0002563 https://doi.org/10.1016/j.cell.2021.11.031 https://datasets.cellxgene.cziscience.com/da9edb77-7e04-45fb-9384-d0bdc418c49a.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/e6361237-ac4e-4c5d-ad8f-f16aca0c0a8f_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/e6361237-ac4e-4c5d-ad8f-f16aca0c0a8f_cxg_dataset_unique.tsv new file mode 100644 index 0000000..7b84ff2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/e6361237-ac4e-4c5d-ad8f-f16aca0c0a8f_cxg_dataset_unique.tsv @@ -0,0 +1,19 @@ +author_cell_type CL_label CL_ID reference dataset_version +OPCs oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Astrocytes astrocyte CL:0000127 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_4 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Inhibitory_1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_6 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Oligodendrocytes oligodendrocyte CL:0000128 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_3 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Endo/Pericytes endothelial cell CL:0000115 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Microglia microglial cell CL:0000129 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Inhibitory_4 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Inhibitory_2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Inhibitory_3 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_7 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_8 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_5 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad +Excitatory_9 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/d46df52a-3c01-422b-bb6e-55b093c21de1.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/e6dad530-418b-47f9-af6e-472e56a7b314_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/e6dad530-418b-47f9-af6e-472e56a7b314_cxg_dataset_unique.tsv new file mode 100644 index 0000000..3e1464a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/e6dad530-418b-47f9-af6e-472e56a7b314_cxg_dataset_unique.tsv @@ -0,0 +1,28 @@ +author_cell_type CL_label CL_ID reference dataset_version +26.0 retinal cone cell CL:0000573 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +22.0 retinal ganglion cell CL:0000740 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +13.0 Mueller cell CL:0000636 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +20.0 unknown unknown https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +25.0 retina horizontal cell CL:0000745 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +21.0 retinal bipolar neuron CL:0000748 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +1.0 retinal rod cell CL:0000604 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +23.0 amacrine cell CL:0000561 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +6.0 amacrine cell CL:0000561 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +2.0 retinal bipolar neuron CL:0000748 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +27.0 retinal pigment epithelial cell CL:0002586 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +16.0 unknown unknown https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +15.0 astrocyte CL:0000127 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +3.0 amacrine cell CL:0000561 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +9.0 amacrine cell CL:0000561 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +24.0 retinal bipolar neuron CL:0000748 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +8.0 retinal bipolar neuron CL:0000748 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +14.0 retinal bipolar neuron CL:0000748 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +12.0 Mueller cell CL:0000636 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +11.0 retinal bipolar neuron CL:0000748 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +5.0 retinal bipolar neuron CL:0000748 https://doi.org/10.1016/j.celrep.2019.12.082 https://datasets.cellxgene.cziscience.com/9fee53bf-67f8-452d-9a9b-5efd878916e7.h5ad +10.0 retinal bipolar neuron 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a/cellsem_agent/graphs/cxg_annotate/amica_test_data/ed5d841d-6346-47d4-ab2f-7119ad7e3a35_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/ed5d841d-6346-47d4-ab2f-7119ad7e3a35_cxg_dataset_unique.tsv new file mode 100644 index 0000000..865092f --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/ed5d841d-6346-47d4-ab2f-7119ad7e3a35_cxg_dataset_unique.tsv @@ -0,0 +1,103 @@ +author_cell_type CL_label CL_ID reference dataset_version +Mono CD14-positive monocyte CL:0001054 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 T central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 T naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +NK natural killer cell CL:0000623 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 T effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Mono CD14-low, CD16-positive monocyte CL:0002396 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +B B cell CL:0000236 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 T naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 T CD4-positive, alpha-beta 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https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 T effector memory CD4-positive, alpha-beta T cell CL:0000905 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 T memory regulatory T cell CL:0002678 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +DC conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +other hematopoietic stem cell CL:0000037 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +other platelet CL:0000233 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +NK CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 T CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +other erythrocyte CL:0000232 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +DC myeloid dendritic cell, human CL:0001057 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +other innate lymphoid cell CL:0001065 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD14 Mono CD14-positive monocyte CL:0001054 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 TCM central 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https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 CTL CD4-positive, alpha-beta cytotoxic T cell CL:0000934 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +B naive naive B cell CL:0000788 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +MAIT mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +gdT gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 TCM central memory CD8-positive, alpha-beta T cell CL:0000907 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +dnT double negative thymocyte CL:0002489 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +B memory memory B cell CL:0000787 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Doublet unknown unknown https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +pDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 Proliferating CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Treg naive regulatory T cell CL:0002677 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Plasmablast plasmablast CL:0000980 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 TEM effector memory CD4-positive, alpha-beta T cell CL:0000905 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Treg memory regulatory T cell CL:0002678 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +cDC2 conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +NK Proliferating natural killer cell CL:0000623 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +ASDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +HSPC hematopoietic stem cell CL:0000037 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Platelet platelet CL:0000233 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +NK_CD56bright CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 Proliferating CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Eryth erythrocyte CL:0000232 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +cDC1 conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +ASDC myeloid dendritic cell, human CL:0001057 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +ILC innate lymphoid cell CL:0001065 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 TCM_1 central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +NK_2 natural killer cell CL:0000623 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 TEM_1 effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +B intermediate lambda B cell CL:0000236 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +B naive kappa naive B cell CL:0000788 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 TCM_3 central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 TCM_2 central memory CD4-positive, alpha-beta T cell CL:0000904 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 TEM_2 effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +gdT_3 gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +NK_1 natural killer cell CL:0000623 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 TCM_1 central memory CD8-positive, alpha-beta T cell CL:0000907 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +dnT_2 double negative thymocyte CL:0002489 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +B intermediate kappa B cell CL:0000236 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +B memory kappa memory B cell CL:0000787 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 TEM_5 effector memory CD8-positive, alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +gdT_1 gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +B naive lambda naive B cell CL:0000788 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +NK_4 natural killer cell CL:0000623 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 TCM_2 central memory CD8-positive, alpha-beta T cell CL:0000907 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Treg Naive naive regulatory T cell CL:0002677 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Plasma plasmablast CL:0000980 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 TEM_1 effector memory CD4-positive, alpha-beta T cell CL:0000905 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +Treg Memory memory regulatory T cell CL:0002678 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD4 TEM_3 effector memory CD4-positive, alpha-beta T cell CL:0000905 https://doi.org/10.1016/j.cell.2021.04.048 https://datasets.cellxgene.cziscience.com/3f3dec10-a48a-4c3d-a96e-8926da82b67e.h5ad +CD8 TCM_3 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astrocyte CL:4033015 https://doi.org/10.1093/hmg/ddab140 https://datasets.cellxgene.cziscience.com/4d7c53aa-892b-4c73-8616-a97a5be4fb2c.h5ad +rod-BC rod bipolar cell CL:0000751 https://doi.org/10.1093/hmg/ddab140 https://datasets.cellxgene.cziscience.com/4d7c53aa-892b-4c73-8616-a97a5be4fb2c.h5ad +cone-off-BC-BC3A cone retinal bipolar cell CL:0000752 https://doi.org/10.1093/hmg/ddab140 https://datasets.cellxgene.cziscience.com/4d7c53aa-892b-4c73-8616-a97a5be4fb2c.h5ad +microglia microglial cell CL:0000129 https://doi.org/10.1093/hmg/ddab140 https://datasets.cellxgene.cziscience.com/4d7c53aa-892b-4c73-8616-a97a5be4fb2c.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique.tsv new file mode 100644 index 0000000..6f96780 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique.tsv @@ -0,0 +1,26 @@ +author_cell_type CL_label CL_ID reference dataset_version +PC renal principal cell CL:0005009 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +CNT kidney collecting duct cell CL:1001225 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +DCT kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +TAL kidney loop of Henle thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Macro macrophage CL:0000235 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +IC-A renal alpha-intercalated cell CL:0005011 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +PT-C epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +IC-B renal beta-intercalated cell CL:0002201 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +AVR vasa recta ascending limb cell CL:1001131 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +PT-B epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Mono monocyte CL:0000576 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +tAL kidney loop of Henle thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +IC-PC columnar/cuboidal epithelial cell CL:0000075 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +vSMC vascular associated smooth muscle cell CL:0000359 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +GC glomerular capillary endothelial cell CL:1001005 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +AEA-DVR vasa recta descending limb cell CL:1001285 https://doi.org/10.1073/pnas.2103240118 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https://doi.org/10.1038/s41593-022-01061-1 https://datasets.cellxgene.cziscience.com/3d20535b-7b7d-496f-b29d-33cff288b190.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/fa8605cf-f27e-44af-ac2a-476bee4410d3_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/fa8605cf-f27e-44af-ac2a-476bee4410d3_cxg_dataset_unique.tsv new file mode 100644 index 0000000..95f47e1 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/fa8605cf-f27e-44af-ac2a-476bee4410d3_cxg_dataset_unique.tsv @@ -0,0 +1,17 @@ +author_cell_type CL_label CL_ID reference dataset_version +CD4 T cell 1 CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +Monocyte 2 monocyte CL:0000576 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +Monocyte 1 monocyte CL:0000576 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +NK cell natural killer cell CL:0000623 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +Monocyte 5 monocyte CL:0000576 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +CD8 T cell 1 CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +Dendritic Cell dendritic cell CL:0000451 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +B cell 1 B cell CL:0000236 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +B cell 2 B cell CL:0000236 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +T cell alpha-beta T cell CL:0000789 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +CD8 T cell 2 CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +Monocyte 3 monocyte CL:0000576 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +Monocyte 4 monocyte CL:0000576 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +pDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +Platelet platelet CL:0000233 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad +Plasmablast plasmablast CL:0000980 https://doi.org/10.1101/2020.11.20.20227355 https://datasets.cellxgene.cziscience.com/e83de69d-1904-4b85-a1ed-aedaff9821b6.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/amica_test_data/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/amica_test_data/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique.tsv new file mode 100644 index 0000000..3e1c383 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/amica_test_data/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique.tsv @@ -0,0 +1,18 @@ +author_cell_type CL_label CL_ID reference dataset_version +Stem cells OLFM4 PCNA stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Enterocytes TMIGD1 MEP1A enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Stem cells OLFM4 stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Goblet cells MUC2 TFF1- goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Stem cells OLFM4 GSTA1 stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Goblet cells MUC2 TFF1 goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Epithelial Cycling cells epithelial cell CL:0000066 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Stem cells OLFM4 LGR5 stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Enterocytes BEST4 enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +L cells type L enteroendocrine cell CL:0002279 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Epithelial cells METTL12 MAFB epithelial cell CL:0000066 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Paneth cells paneth cell CL:0000510 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Enterocytes TMIGD1 MEP1A GSTA1 enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Goblet cells SPINK4 goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Epithelial HBB HBA epithelial cell CL:0000066 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Tuft cells tuft cell CL:0002204 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Enterochromaffin cells type EC enteroendocrine cell CL:0000577 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py b/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py index 93d6363..48bc8da 100644 --- a/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py +++ b/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py @@ -7,7 +7,10 @@ from pydantic_graph import BaseNode, End, Graph, GraphRunContext from cellsem_agent.agents.annotator.annotator_agent import annotator_agent -from cellsem_agent.agents.paper_celltype.paper_celltype_agent import celltype_agent, CellTypeEntry +from cellsem_agent.agents.paper_celltype.paper_celltype_agent import ( + celltype_agent, + CellTypeEntry, +) from cellsem_agent.agents.annotator.annotator_agent import TextAnnotation from cellsem_agent.utils.pubmed_utils import get_doi_text @@ -19,7 +22,7 @@ cxg_annotate_logger.setLevel(logging.INFO) console = logging.StreamHandler() console.setLevel(logging.INFO) -formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') +formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") console.setFormatter(formatter) cxg_annotate_logger.addHandler(console) @@ -28,13 +31,17 @@ ANNOTATIONS_BATCH_SIZE = 5 -IS_TEST_MODE = True +IS_TEST_MODE = False TEST_ANNOTATIONS_COUNT = 4 # Number of annotations to process in test mode CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) RESOURCES_DIR = os.path.join(CURRENT_DIR, "resources") PUBLICATIONS_DIR = os.path.join(RESOURCES_DIR, "publications") EXPANSIONS_DIR = os.path.join(RESOURCES_DIR, "expansions") +INPUT_DIR = os.path.join(RESOURCES_DIR, "input") # Directory containing input TSV files +OUTPUT_DIR = os.path.join( + RESOURCES_DIR, "output" +) # Directory for output folders per dataset @dataclass @@ -51,6 +58,7 @@ class State: annotations: list[dict] article_to_annotations: dict[str, dict] paper_expansion: dict[str, CellTypeEntry] + dataset_names: list[str] # Track which datasets were processed is_test_mode: bool = IS_TEST_MODE @@ -62,19 +70,26 @@ async def run(self, ctx: GraphRunContext[State]) -> End: cxg_annotate_logger.info(f"Total annotations to process: {len(annotations)}") for annotation in annotations: - if 'enrichment' not in annotation: - annotation['enrichment'] = CellTypeEntry( - name=annotation['annotation_text'], + if "enrichment" not in annotation: + annotation["enrichment"] = CellTypeEntry( + name=annotation["annotation_text"], full_name="", paper_synonyms="", - tissue_context="" + tissue_context="", + ) + print( + f"Warning: No enrichment found for annotation '{annotation['annotation_text']}', using blank entry." ) - print(f"Warning: No enrichment found for annotation '{annotation['annotation_text']}', using blank entry.") # delete tissue_context of all enrichments - annotation['enrichment'].tissue_context = "" + annotation["enrichment"].tissue_context = "" # Sort annotations by article_id_doi, then annotation_text - annotations.sort(key=lambda annot: (annot.get('article_id_doi') or "", annot.get('annotation_text') or "")) + annotations.sort( + key=lambda annot: ( + annot.get("article_id_doi") or "", + annot.get("annotation_text") or "", + ) + ) cache_dir = os.path.join(RESOURCES_DIR, "cache") os.makedirs(cache_dir, exist_ok=True) @@ -83,54 +98,99 @@ async def run(self, ctx: GraphRunContext[State]) -> End: all_groundings = [] for i in range(0, len(annotations), batch_size): batch_index = i // batch_size - batch = annotations[i:i + batch_size] - batch_cache_path = os.path.join(cache_dir, f"groundings_batch_{batch_index}.json") + batch = annotations[i : i + batch_size] + batch_cache_path = os.path.join( + cache_dir, f"groundings_batch_{batch_index}.json" + ) if os.path.exists(batch_cache_path): print(f"Loading cached results for batch {batch_index}") with open(batch_cache_path, "r") as f: - batch_groundings = [TextAnnotation(**entry) for entry in json.load(f)] + batch_groundings = [ + TextAnnotation(**entry) for entry in json.load(f) + ] else: - print("Processing batch: ", i // batch_size + 1, " of ", - (len(annotations) + batch_size - 1) // batch_size) - expansions_json = json.dumps([annotation['enrichment'].model_dump() for annotation in batch], indent=2) + print( + "Processing batch: ", + i // batch_size + 1, + " of ", + (len(annotations) + batch_size - 1) // batch_size, + ) + expansions_json = json.dumps( + [annotation["enrichment"].model_dump() for annotation in batch], + indent=2, + ) agent_response = await annotator_agent.run(expansions_json) batch_groundings = agent_response.output.annotations with open(batch_cache_path, "w") as f: - json.dump([entry.model_dump() for entry in batch_groundings], f, indent=2) + json.dump( + [entry.model_dump() for entry in batch_groundings], f, indent=2 + ) all_groundings.extend(batch_groundings) # update batch annotations with grounding results for annotation in batch: # convert enrichment to json to make df mode readable - annotation['enrichment'] = annotation['enrichment'].model_dump() + annotation["enrichment"] = annotation["enrichment"].model_dump() if "grounding_cl_id" not in annotation: - related_groundings = [gr for gr in batch_groundings if - gr.input_name == annotation['annotation_text']] + related_groundings = [ + gr + for gr in batch_groundings + if gr.input_name == annotation["annotation_text"] + ] if related_groundings: valid_grounding = next( - (g for g in related_groundings if "NO MATCH" not in g.cl_id), None) + ( + g + for g in related_groundings + if "NO MATCH" not in g.cl_id + ), + None, + ) if valid_grounding: grounding_to_use = valid_grounding else: grounding_to_use = related_groundings[0] - annotation['grounding_cl_id'] = grounding_to_use.cl_id - annotation['grounding_cl_label'] = grounding_to_use.cl_label + annotation["grounding_cl_id"] = grounding_to_use.cl_id + annotation["grounding_cl_label"] = grounding_to_use.cl_label else: - annotation['grounding_cl_id'] = "" - annotation['grounding_cl_label'] = "" - - - data = [entry.model_dump() for entry in all_groundings] - df = pd.DataFrame(data) - df.to_csv(os.path.join(RESOURCES_DIR, "cell_type_annotations_un_filtered.tsv"), sep='\t', index=False) - - # print annotations that has groundings as tsv (annotation_text, cl_id, grounding_cl_id, grounding_cl_label, article_id_doi) - df = pd.DataFrame(annotations) - df_filtered = df[df['grounding_cl_id'].notna()] - df_filtered['result'] = df_filtered['cl_id'].eq(df_filtered['grounding_cl_id']).map( - {True: 'TRUE', False: 'FALSE'}) - df_filtered.to_csv(os.path.join(RESOURCES_DIR, "groundings.tsv"), sep='\t', index=False) + annotation["grounding_cl_id"] = "" + annotation["grounding_cl_label"] = "" + + # Create output directory + os.makedirs(OUTPUT_DIR, exist_ok=True) + + # Save per-dataset results in separate folders + for dataset_name in ctx.state.dataset_names: + dataset_output_dir = os.path.join(OUTPUT_DIR, dataset_name) + os.makedirs(dataset_output_dir, exist_ok=True) + + dataset_annotations = [ + ann for ann in annotations if ann.get("dataset_name") == dataset_name + ] + if dataset_annotations: + # Save all annotations for this dataset + df_dataset_all = pd.DataFrame(dataset_annotations) + all_annotations_file = os.path.join( + dataset_output_dir, "cell_type_annotations_un_filtered.tsv" + ) + df_dataset_all.to_csv(all_annotations_file, sep="\t", index=False) + + # Save filtered groundings + df_dataset_filtered = df_dataset_all[ + df_dataset_all["grounding_cl_id"].notna() + ] + if not df_dataset_filtered.empty: + df_dataset_filtered["result"] = ( + df_dataset_filtered["cl_id"] + .eq(df_dataset_filtered["grounding_cl_id"]) + .map({True: "TRUE", False: "FALSE"}) + ) + groundings_file = os.path.join(dataset_output_dir, "groundings.tsv") + df_dataset_filtered.to_csv(groundings_file, sep="\t", index=False) + cxg_annotate_logger.info( + f"Saved results for dataset: {dataset_name} to {dataset_output_dir}" + ) return End("Report generated and saved to individual dataset folders.") @@ -143,7 +203,9 @@ async def run(self, ctx: GraphRunContext[State]) -> GetGroundings: if not os.path.exists(EXPANSIONS_DIR): os.makedirs(EXPANSIONS_DIR) article_to_annotations = ctx.state.article_to_annotations - articles = sorted(str(a) if a is not None else "" for a in set(article_to_annotations.keys())) + articles = sorted( + str(a) if a is not None else "" for a in set(article_to_annotations.keys()) + ) index = 1 for article_pmc in articles: print(f"Processing article: {article_pmc} - {index}/{len(articles)}") @@ -151,16 +213,24 @@ async def run(self, ctx: GraphRunContext[State]) -> GetGroundings: # get all annotations for this article article_annotations = article_to_annotations[article_pmc] - for batch_index in range(0, len(article_annotations), ANNOTATIONS_BATCH_SIZE): - batch = article_annotations[batch_index:batch_index + ANNOTATIONS_BATCH_SIZE] - dataset_cache = os.path.join(EXPANSIONS_DIR, - f"{normalise_file_name(article_pmc)}_batch_{batch_index // ANNOTATIONS_BATCH_SIZE}.json") - cc_labels = [{"cc.label": ann['annotation_text']} for ann in batch] + for batch_index in range( + 0, len(article_annotations), ANNOTATIONS_BATCH_SIZE + ): + batch = article_annotations[ + batch_index : batch_index + ANNOTATIONS_BATCH_SIZE + ] + dataset_cache = os.path.join( + EXPANSIONS_DIR, + f"{normalise_file_name(article_pmc)}_batch_{batch_index // ANNOTATIONS_BATCH_SIZE}.json", + ) + cc_labels = [{"cc.label": ann["annotation_text"]} for ann in batch] if not os.path.exists(dataset_cache): - full_text_path = os.path.join(PUBLICATIONS_DIR, f"{normalise_file_name(article_pmc)}.txt") + full_text_path = os.path.join( + PUBLICATIONS_DIR, f"{normalise_file_name(article_pmc)}.txt" + ) if os.path.exists(full_text_path): - with open(full_text_path, 'r', encoding='utf-8') as f: + with open(full_text_path, "r", encoding="utf-8") as f: paper_full_text = f.read() prompt_instructions = f""" @@ -201,48 +271,60 @@ async def run(self, ctx: GraphRunContext[State]) -> GetGroundings: for entry in agent_response.output.cell_type_annotations: print( - f"Name: {entry.name}, Full Name: {entry.full_name}, Synonyms: {entry.paper_synonyms}, Tissue Context: {entry.tissue_context}") + f"Name: {entry.name}, Full Name: {entry.full_name}, Synonyms: {entry.paper_synonyms}, Tissue Context: {entry.tissue_context}" + ) # add entry to the related article_annotations for ann in article_annotations: - if ann['annotation_text'] == entry.name: - ann['enrichment'] = entry + if ann["annotation_text"] == entry.name: + ann["enrichment"] = entry break # ctx.state.paper_expansion[article_pmc] = agent_response.output.cell_type_annotations expansions = agent_response.output.cell_type_annotations print(f"Saving results to cache for article: {article_pmc}") - with open(dataset_cache, 'w') as cache_file: + with open(dataset_cache, "w") as cache_file: json.dump( [entry.model_dump() for entry in expansions], - cache_file, indent=2) + cache_file, + indent=2, + ) else: - print(f"Error: Full text file not found for article for name expansion: {article_pmc}") + print( + f"Error: Full text file not found for article for name expansion: {article_pmc}" + ) else: print(f"Using cached data for article: {article_pmc}") - with open(dataset_cache, 'r') as cache_file: + with open(dataset_cache, "r") as cache_file: cached_data = json.load(cache_file) for cached_entry in cached_data: for ann in article_annotations: - if ann['annotation_text'] == cached_entry["name"]: - ann['enrichment'] = CellTypeEntry(**cached_entry) - print("Using cached enrichment data for annotation:", ann['annotation_text']) + if ann["annotation_text"] == cached_entry["name"]: + ann["enrichment"] = CellTypeEntry(**cached_entry) + print( + "Using cached enrichment data for annotation:", + ann["annotation_text"], + ) break # ctx.state.paper_expansion[article_pmc] = [CellTypeEntry(**entry) for entry in cached_data] return GetGroundings() + @dataclass class PrepareData(BaseNode[State, None, str]): async def run(self, ctx: GraphRunContext[State]) -> GetFullNames: print("Running PrepareData node") - annotations, article_to_annotations = load_cxg_annotations() + annotations, article_to_annotations, dataset_names = load_cxg_annotations() if ctx.state.is_test_mode: # only process a few annotations in test mode annotations = list(annotations)[:TEST_ANNOTATIONS_COUNT] # filter article_to_annotations to only include those in annotations - article_to_annotations = {k: v for k, v in article_to_annotations.items() if k in - {ann['article_id_doi'] for ann in annotations}} + article_to_annotations = { + k: v + for k, v in article_to_annotations.items() + if k in {ann["article_id_doi"] for ann in annotations} + } unique_dois = set(article_to_annotations.keys()) print(f"Unique DOISs to download: {len(unique_dois)}") @@ -252,28 +334,72 @@ async def run(self, ctx: GraphRunContext[State]) -> GetFullNames: ctx.state.articles = articles ctx.state.annotations = annotations ctx.state.article_to_annotations = article_to_annotations + ctx.state.dataset_names = dataset_names return GetFullNames() + def load_cxg_annotations(): - tsv_path = os.path.join(os.getcwd(),"resources", "ac8619d0-4fff-4296-913a-819d1e361ba0_cxg_dataset_unique.tsv") - df = pd.read_csv(tsv_path, sep='\t') + """ + Load annotations from all TSV files in the INPUT_DIR. + Returns: (annotations, article_to_annotations, dataset_names) + """ + if not os.path.exists(INPUT_DIR): + # Fallback to old hardcoded path for backward compatibility + cxg_annotate_logger.warning( + f"Input directory not found: {INPUT_DIR}. Using legacy single file." + ) + tsv_path = os.path.join( + os.getcwd(), + "resources", + "ac8619d0-4fff-4296-913a-819d1e361ba0_cxg_dataset_unique.tsv", + ) + if not os.path.exists(tsv_path): + raise FileNotFoundError( + f"Neither input directory nor legacy file found. Please create {INPUT_DIR} with TSV files." + ) + tsv_files = [tsv_path] + else: + # Find all TSV files in INPUT_DIR + tsv_files = [ + os.path.join(INPUT_DIR, f) + for f in os.listdir(INPUT_DIR) + if f.endswith(".tsv") or f.endswith(".TSV") + ] + + if not tsv_files: + raise FileNotFoundError(f"No TSV files found in {INPUT_DIR}") + + cxg_annotate_logger.info(f"Found {len(tsv_files)} TSV file(s) to process") annotations = [] article_to_annotations = {} + dataset_names = [] + + for tsv_path in tsv_files: + dataset_name = os.path.splitext(os.path.basename(tsv_path))[0] + dataset_names.append(dataset_name) + cxg_annotate_logger.info(f"Loading dataset: {dataset_name}") - for _, row in df.iterrows(): - paper_doi = str(row['reference']).replace("https://doi.org/", "DOI:") - annotation = { - 'annotation_text': row['author_cell_type'], - 'cl_id': row['CL_ID'], - 'cl_label': row['CL_label'], - 'article_id_doi': paper_doi - } - annotations.append(annotation) - article_to_annotations.setdefault(paper_doi, []).append(annotation) + df = pd.read_csv(tsv_path, sep="\t") + + for _, row in df.iterrows(): + paper_doi = str(row["reference"]).replace("https://doi.org/", "DOI:") + annotation = { + "annotation_text": row["author_cell_type"], + "cl_id": row["CL_ID"], + "cl_label": row["CL_label"], + "article_id_doi": paper_doi, + "dataset_name": dataset_name, # Track which dataset this came from + } + annotations.append(annotation) + article_to_annotations.setdefault(paper_doi, []).append(annotation) + + cxg_annotate_logger.info( + f"Loaded {len(annotations)} total annotations from {len(dataset_names)} dataset(s)" + ) + return annotations, article_to_annotations, dataset_names - return annotations, article_to_annotations def download_publication_texts(dois, publications_dir=PUBLICATIONS_DIR): """ @@ -288,7 +414,9 @@ def download_publication_texts(dois, publications_dir=PUBLICATIONS_DIR): articles = set() for doi in dois: if doi: - file_path = os.path.join(publications_dir, f"{normalise_file_name(doi)}.txt") + file_path = os.path.join( + publications_dir, f"{normalise_file_name(doi)}.txt" + ) if os.path.exists(file_path): articles.add(doi) continue @@ -301,11 +429,13 @@ def download_publication_texts(dois, publications_dir=PUBLICATIONS_DIR): print(f"Error: No full-text found for ID {doi}") return articles + def normalise_file_name(doi: str) -> str: return doi.replace("/", "_").replace(":", "_").replace(".", "_") + async def main(): - state = State(set(), list(), dict(), dict(), is_test_mode=IS_TEST_MODE) + state = State(set(), list(), dict(), dict(), list(), is_test_mode=IS_TEST_MODE) validation_graph = Graph(nodes=(PrepareData, GetFullNames, GetGroundings)) result = await validation_graph.run(PrepareData(), state=state) print(result.output) @@ -315,4 +445,4 @@ async def main(): if __name__ == "__main__": load_dotenv() print(os.environ.get("OPENAI_API_KEY")) - asyncio.run(main()) \ No newline at end of file + asyncio.run(main()) diff --git a/cellsem_agent/graphs/cxg_annotate/resources/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..2188dfb --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,9 @@ +input_name text cl_id cl_label +CD36+ endothelial cells CD36+ endothelial cell CL:0000115 endothelial cell +Endothelial cells CD36 Endothelial cell CD36 CL:0000115 endothelial cell +ADAMDEC1+ fibroblasts ADAMDEC1+ fibroblast CL:0000057 fibroblast +Fibroblasts ADAMDEC1 Fibroblast ADAMDEC1 CL:0000057 fibroblast +SMOC2+ PTGIS+ fibroblasts SMOC2+ PTGIS+ fibroblast CL:0000057 fibroblast +Fibroblasts SMOC2 PTGIS Fibroblast SMOC2 PTGIS CL:0000057 fibroblast +HHIP+ NPNT+ myofibroblasts HHIP+ NPNT+ myofibroblast CL:0000186 myofibroblast cell +Myofibroblasts HHIP NPNT Myofibroblast HHIP NPNT CL:0000186 myofibroblast cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_0.json new file mode 100644 index 0000000..5fa4db0 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "Oligodendrocytes", + "full_name": "Oligodendrocytes", + "paper_synonyms": "", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "L3-L5 Intratelencephalic Type 1", + "full_name": "L3-L5 intratelencephalic type 1", + "paper_synonyms": "", + "tissue_context": "frontal cortex" + }, + { + "name": "Astrocytes", + "full_name": "Astrocytes", + "paper_synonyms": "", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "L6 Intratelencephalic - Type 1", + "full_name": "L6 intratelencephalic type 1", + "paper_synonyms": "", + "tissue_context": "frontal cortex" + }, + { + "name": "SV2C LAMP5 Interneurons", + "full_name": "SV2C LAMP5 Interneurons", + "paper_synonyms": "", + "tissue_context": "frontal cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_1.json new file mode 100644 index 0000000..7cae50b --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "L6 Corticothalamic / L6B", + "full_name": "L6 corticothalamic / L6B", + "paper_synonyms": "L6 corticothalamic; L6B", + "tissue_context": "frontal cortex" + }, + { + "name": "L2-L3 Intratelencephalic", + "full_name": "L2-L3 intratelencephalic", + "paper_synonyms": null, + "tissue_context": "frontal cortex" + }, + { + "name": "L3-L5 Intratelencephalic Type 2", + "full_name": "L3-L5 intratelencephalic type 2", + "paper_synonyms": null, + "tissue_context": "frontal cortex" + }, + { + "name": "L6 Intratelencephalic - Type 2", + "full_name": "L6 intratelencephalic type 2", + "paper_synonyms": null, + "tissue_context": "frontal cortex" + }, + { + "name": "OPCs", + "full_name": "oligodendrocyte progenitor cells", + "paper_synonyms": "oligodendrocyte progenitor cells", + "tissue_context": "frontal cortex; occipital cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_2.json new file mode 100644 index 0000000..4e2443e --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "L5-L6 Near Projecting", + "full_name": "L5-L6 near projecting", + "paper_synonyms": null, + "tissue_context": "frontal cortex" + }, + { + "name": "Somatostatin Interneurons", + "full_name": "Somatostatin Interneurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex" + }, + { + "name": "Microglia", + "full_name": "Microglia", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "VIP Interneurons", + "full_name": "VIP Interneurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex" + }, + { + "name": "L5 Extratelencephalic", + "full_name": "L5 Extratelencephalic", + "paper_synonyms": null, + "tissue_context": "frontal cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_3.json new file mode 100644 index 0000000..67b6cbc --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "Endothelial", + "full_name": "endothelial cells", + "paper_synonyms": "", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Parvalbumin interneurons", + "full_name": "parvalbumin interneurons", + "paper_synonyms": "", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Inhibitory_4", + "full_name": "inhibitory neurons", + "paper_synonyms": "", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Oligodendrocytes", + "full_name": "oligodendrocytes", + "paper_synonyms": "", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_2", + "full_name": "excitatory neurons", + "paper_synonyms": "", + "tissue_context": "frontal cortex; occipital cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_4.json new file mode 100644 index 0000000..54c0573 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": "Inhibitory_2", + "full_name": "inhibitory neurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_4", + "full_name": "excitatory neurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_1", + "full_name": "excitatory neurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Inhibitory_1", + "full_name": "inhibitory neurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Astrocytes", + "full_name": "astrocytes", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_5.json new file mode 100644 index 0000000..45a716a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_5.json @@ -0,0 +1,32 @@ +[ + { + "name": "Excitatory_3", + "full_name": "excitatory neuron 3", + "paper_synonyms": "excitatory neurons", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_5", + "full_name": "excitatory neuron 5", + "paper_synonyms": "excitatory neurons", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Microglia", + "full_name": "microglia", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "OPCs", + "full_name": "oligodendrocyte progenitor cells", + "paper_synonyms": "oligodendrocyte progenitor cells; OPCs", + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Endo/Pericytes", + "full_name": "endothelial cells", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_6.json new file mode 100644 index 0000000..1b87d7a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_6.json @@ -0,0 +1,32 @@ +[ + { + "name": "Excitatory_8", + "full_name": "excitatory neuron 8", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Inhibitory_3", + "full_name": "inhibitory neuron 3", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_9", + "full_name": "excitatory neuron 9", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_6", + "full_name": "excitatory neuron 6", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_10", + "full_name": "excitatory neuron 10", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_7.json new file mode 100644 index 0000000..3d2cc42 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_7.json @@ -0,0 +1,8 @@ +[ + { + "name": "Excitatory_7", + "full_name": "excitatory neurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_0.json new file mode 100644 index 0000000..5ff2f37 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "Fibroblasts ADAMDEC1", + "full_name": "ADAMDEC1+ fibroblasts", + "paper_synonyms": "ADAMDEC+ fibroblasts", + "tissue_context": "terminal ileum (TI); colon (CO)" + }, + { + "name": "Endothelial cells CD36", + "full_name": "CD36+ endothelial cells", + "paper_synonyms": null, + "tissue_context": "terminal ileum (TI); colon (CO)" + }, + { + "name": "Myofibroblasts HHIP NPNT", + "full_name": "HHIP+ NPNT+ myofibroblasts", + "paper_synonyms": null, + "tissue_context": "terminal ileum (TI); colon (CO)" + }, + { + "name": "Fibroblasts SMOC2 PTGIS", + "full_name": "SMOC2+ PTGIS+ fibroblasts", + "paper_synonyms": "SMOC2+ PTGIS+ fibroblasts", + "tissue_context": "terminal ileum (TI); colon (CO)" + }, + { + "name": "Endothelial cells DARC", + "full_name": "DARC+ endothelial cells", + "paper_synonyms": "ACKR1", + "tissue_context": "terminal ileum (TI); colon (CO)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_1.json new file mode 100644 index 0000000..ad43237 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "Fibroblasts NPY SLITRK6", + "full_name": "Fibroblasts", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon" + }, + { + "name": "Myofibroblasts GREM1 GREM2", + "full_name": "GREM1+ GREM2+ myofibroblasts", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon" + }, + { + "name": "Endothelial cells CA4 CD36", + "full_name": "Endothelial cells CA4+ CD36+", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon" + }, + { + "name": "Glial cells", + "full_name": "Glial cells", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon" + }, + { + "name": "Fibroblasts SFRP2 SLPI", + "full_name": "Fibroblasts", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_2.json new file mode 100644 index 0000000..d0c8490 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "Endothelial cells LTC4S SEMA3G", + "full_name": "Endothelial cells", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon; lamina propria" + }, + { + "name": "Pericytes HIGD1B STEAP4", + "full_name": "Pericytes HIGD1B+ STEAP4+", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon; lamina propria" + }, + { + "name": "Activated fibroblasts CCL19 ADAMADEC1", + "full_name": "ADAMDEC+ Fibroblast clusters", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon; lamina propria" + }, + { + "name": "Lymphatics", + "full_name": "Lymphatics", + "paper_synonyms": "lymphatic endothelial cells", + "tissue_context": "terminal ileum; colon; lamina propria" + }, + { + "name": "Fibroblasts KCNN3 LY6H", + "full_name": "Fibroblasts", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon; lamina propria" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_3.json new file mode 100644 index 0000000..5d30844 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_3.json @@ -0,0 +1,8 @@ +[ + { + "name": "Pericytes RERGL NTRK2", + "full_name": "Pericytes", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon; lamina propria" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_0.json new file mode 100644 index 0000000..7278454 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "human kidney; cortex; medulla; nephron segments; interstitium" + }, + { + "name": "stroma cells", + "full_name": "stromal cells", + "paper_synonyms": "STR; aStr", + "tissue_context": "human kidney; cortex; medulla; interstitium" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "human kidney; cortex; medulla; nephron segments; interstitium" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "IMM; leukocytes", + "tissue_context": "human kidney; cortex; medulla" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "human kidney; cortex; medulla; nephron segments; interstitium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_1.json new file mode 100644 index 0000000..5c2362f --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "endothelial cells", + "full_name": "endothelial cells", + "paper_synonyms": "EC", + "tissue_context": "human kidney; renal corpuscle; glomerular capillaries (EC-GC); afferent/efferent arterioles (EC-AEA); endothelial cells of the lymphatics (EC-LYM); vasa recta (EC-AVR, EC-DVR)" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes; IMM", + "tissue_context": "human kidney; cortex; medulla; interstitium" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "", + "tissue_context": "human kidney; nephron; cortex; medulla" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes; IMM", + "tissue_context": "human kidney; cortex; medulla; interstitium" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "", + "tissue_context": "human kidney; nephron; cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_10.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_10.json new file mode 100644 index 0000000..1744ee1 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_10.json @@ -0,0 +1,32 @@ +[ + { + "name": "Proximal Tubule Epithelial Cell Segment 1 / Segment 2", + "full_name": "Proximal tubule epithelial cell, segments 1 and 2", + "paper_synonyms": "PT-S1; PT-S2; PT-S1/PT-S2; PT", + "tissue_context": "proximal tubule (PT); cortex" + }, + { + "name": "Proximal Tubule Epithelial Cell Segment 3", + "full_name": "Proximal tubule epithelial cell, segment 3", + "paper_synonyms": "PT-S3; PT", + "tissue_context": "proximal tubule (PT)" + }, + { + "name": "Cortical Thick Ascending Limb Cell", + "full_name": "Cortical thick ascending limb cell", + "paper_synonyms": "C-TAL; cortical TAL", + "tissue_context": "cortex; cortical thick ascending limb (C-TAL)" + }, + { + "name": "Outer Medullary Collecting Duct Principal Cell", + "full_name": "Outer medullary collecting duct principal cell", + "paper_synonyms": "OMCD; principal cells (PC); medullary principal cell (M-PC)", + "tissue_context": "outer medulla; outer medullary collecting duct (OMCD)" + }, + { + "name": "Fibroblast", + "full_name": "Fibroblast", + "paper_synonyms": "FIB", + "tissue_context": "interstitium; stroma" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_11.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_11.json new file mode 100644 index 0000000..6a2dcfd --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_11.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Outer Medullary Collecting Duct Principal Cell", + "full_name": "Degenerative outer medullary collecting duct principal cell", + "paper_synonyms": "degenerative medullary principal cells; dM-PCs", + "tissue_context": "outer medulla; collecting duct" + }, + { + "name": "T Cell", + "full_name": "T cell", + "paper_synonyms": "T; CD3+ cells", + "tissue_context": "cortex; medulla" + }, + { + "name": "Plasma Cell", + "full_name": "Plasma cell", + "paper_synonyms": "PL", + "tissue_context": "human kidney" + }, + { + "name": "Connecting Tubule Principal Cell", + "full_name": "Connecting tubule principal cell", + "paper_synonyms": "CNT-PC", + "tissue_context": "connecting tubule; cortex" + }, + { + "name": "Distal Convoluted Tubule Cell Type 1", + "full_name": "Distal convoluted tubule cell type 1", + "paper_synonyms": "DCT1", + "tissue_context": "distal convoluted tubule; cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_12.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_12.json new file mode 100644 index 0000000..f064292 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_12.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Fibroblast", + "full_name": "degenerative fibroblast", + "paper_synonyms": "FIB", + "tissue_context": "cortex; interstitium" + }, + { + "name": "Degenerative Cortical Thick Ascending Limb Cell", + "full_name": "degenerative cortical thick ascending limb cell", + "paper_synonyms": "C-TAL; TAL", + "tissue_context": "cortex; cortical thick ascending limb (C-TAL)" + }, + { + "name": "Vascular Smooth Muscle Cell", + "full_name": "vascular smooth muscle cell", + "paper_synonyms": "VSMC; VSM/P", + "tissue_context": "afferent/efferent arterioles" + }, + { + "name": "Schwann Cell / Neural", + "full_name": "Schwann/neuronal cell", + "paper_synonyms": "SCI/NEU", + "tissue_context": "kidney" + }, + { + "name": "Descending Thin Limb Cell Type 3", + "full_name": "descending thin limb cell type 3", + "paper_synonyms": "DTL3; DTL", + "tissue_context": "medulla; descending thin limb (DTL)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_13.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_13.json new file mode 100644 index 0000000..f9306a3 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_13.json @@ -0,0 +1,32 @@ +[ + { + "name": "Glomerular Capillary Endothelial Cell", + "full_name": "glomerular capillaries", + "paper_synonyms": "EC-GC", + "tissue_context": "renal corpuscle" + }, + { + "name": "Vascular Smooth Muscle Cell / Pericyte", + "full_name": "vascular smooth muscle cell or pericyte", + "paper_synonyms": "VSM/P; VSMC", + "tissue_context": "afferent/efferent arterioles" + }, + { + "name": "Cycling Mononuclear Phagocyte", + "full_name": "cycling mononuclear phagocyte", + "paper_synonyms": "cycMNP", + "tissue_context": "human kidney" + }, + { + "name": "Myofibroblast", + "full_name": "myofibroblast", + "paper_synonyms": "MyoF", + "tissue_context": "interstitium" + }, + { + "name": "Degenerative Peritubular Capilary Endothelial Cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "vasculature" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_14.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_14.json new file mode 100644 index 0000000..2082db6 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_14.json @@ -0,0 +1,32 @@ +[ + { + "name": "Non-classical Monocyte", + "full_name": "Non-classical Monocyte", + "paper_synonyms": "ncMON", + "tissue_context": "human kidney" + }, + { + "name": "Cycling Endothelial Cell", + "full_name": "Cycling Endothelial Cell", + "paper_synonyms": null, + "tissue_context": "vasculature; human kidney" + }, + { + "name": "Classical Dendritic Cell", + "full_name": "Classical Dendritic Cell", + "paper_synonyms": "cDC", + "tissue_context": "human kidney" + }, + { + "name": "Lymphatic Endothelial Cell", + "full_name": "endothelial cells of the lymphatics", + "paper_synonyms": "EC-LYM", + "tissue_context": "lymphatics; human kidney" + }, + { + "name": "Distal Convoluted Tubule Cell Type 2", + "full_name": "Distal Convoluted Tubule Cell Type 2", + "paper_synonyms": "DCT2", + "tissue_context": "distal convoluted tubule; human kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_15.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_15.json new file mode 100644 index 0000000..ac26006 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_15.json @@ -0,0 +1,32 @@ +[ + { + "name": "Mesangial Cell", + "full_name": "mesangial cell", + "paper_synonyms": null, + "tissue_context": "renal corpuscle" + }, + { + "name": "Intercalated Cell Type B", + "full_name": "intercalated cell", + "paper_synonyms": "IC", + "tissue_context": "connecting tubules; collecting duct" + }, + { + "name": "Connecting Tubule Cell", + "full_name": "connecting tubule cell", + "paper_synonyms": "CNT", + "tissue_context": "connecting tubules" + }, + { + "name": "Mast Cell", + "full_name": "mast cell", + "paper_synonyms": "MAST", + "tissue_context": "cortex; medulla" + }, + { + "name": "Degenerative Vascular Smooth Muscle Cell", + "full_name": "degenerative vascular smooth muscle cell", + "paper_synonyms": "VSMC; VSM/P", + "tissue_context": "afferent/efferent arterioles" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_16.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_16.json new file mode 100644 index 0000000..5cbeb62 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_16.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Ascending Thin Limb Cell", + "full_name": "degenerative ascending thin limb (ATL) cell", + "paper_synonyms": "ATL", + "tissue_context": "ascending thin limbs (ATL) of the inner medulla" + }, + { + "name": "Renin-positive Juxtaglomerular Granular Cell", + "full_name": "juxtaglomerular renin-producing granular (REN) cell", + "paper_synonyms": "renin-producing granular (REN) cells; REN; juxtaglomerular renin-producing granular cells (REN)", + "tissue_context": "juxtaglomerular apparatus; afferent/efferent arterioles (EC-AEA); renal corpuscle" + }, + { + "name": "B Cell", + "full_name": "B cell", + "paper_synonyms": "B", + "tissue_context": "" + }, + { + "name": "Degenerative Cortical Intercalated Cell Type A", + "full_name": "degenerative cortical intercalated cell type A", + "paper_synonyms": "IC; intercalated cells", + "tissue_context": "cortex" + }, + { + "name": "Degenerative Connecting Tubule Cell", + "full_name": "degenerative connecting tubule (CNT) cell", + "paper_synonyms": "CNT", + "tissue_context": "connecting tubule; cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_17.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_17.json new file mode 100644 index 0000000..79ce036 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_17.json @@ -0,0 +1,32 @@ +[ + { + "name": "Adaptive / Maladaptive / Repairing Fibroblast", + "full_name": "adaptive (successful or maladaptive repair) fibroblast", + "paper_synonyms": "aFIB; aStr", + "tissue_context": "interstitium; region of fibrosis within the cortex of a CKD biopsy" + }, + { + "name": "Parietal Epithelial Cell", + "full_name": "parietal epithelial cell", + "paper_synonyms": null, + "tissue_context": "renal corpuscle" + }, + { + "name": "Cycling Connecting Tubule Cell", + "full_name": "cycling connecting tubule cell", + "paper_synonyms": "CNT", + "tissue_context": "connecting tubule; cortical distal nephron" + }, + { + "name": "Degenerative Inner Medullary Collecting Duct Cell", + "full_name": "degenerative inner medullary collecting duct cell", + "paper_synonyms": "IMCD", + "tissue_context": "inner medullary collecting duct" + }, + { + "name": "Inner Medullary Collecting Duct Cell", + "full_name": "inner medullary collecting duct cell", + "paper_synonyms": "IMCD", + "tissue_context": "inner medullary collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_18.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_18.json new file mode 100644 index 0000000..4efe6b9 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_18.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Endothelial Cell", + "full_name": "degenerative endothelial cell", + "paper_synonyms": "EC", + "tissue_context": "vasculature; afferent/efferent arterioles; glomerular capillaries; vasa recta; lymphatics" + }, + { + "name": "Degenerative Medullary Fibroblast", + "full_name": "degenerative medullary fibroblast", + "paper_synonyms": "FIB", + "tissue_context": "medulla; interstitium" + }, + { + "name": "Connecting Tubule Intercalated Cell Type A", + "full_name": "connecting tubule intercalated cell", + "paper_synonyms": "CNT-IC; IC", + "tissue_context": "connecting tubules (CNT)" + }, + { + "name": "Cycling Distal Convoluted Tubule Cell", + "full_name": "cycling distal convoluted tubule cell", + "paper_synonyms": "DCT; Cyc", + "tissue_context": "distal convoluted tubule (DCT)" + }, + { + "name": "Degenerative Podocyte", + "full_name": "degenerative podocyte", + "paper_synonyms": "POD", + "tissue_context": "renal corpuscle; glomeruli" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_19.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_19.json new file mode 100644 index 0000000..03b56eb --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_19.json @@ -0,0 +1,32 @@ +[ + { + "name": "Plasmacytoid Dendritic Cell", + "full_name": "Plasmacytoid dendritic cell", + "paper_synonyms": "pDC", + "tissue_context": "human kidney" + }, + { + "name": "Degenerative Descending Thin Limb Cell Type 3", + "full_name": "Degenerative descending thin limb cell type 3", + "paper_synonyms": "DTL3; DTL", + "tissue_context": "medulla" + }, + { + "name": "Degenerative Distal Convoluted Tubule Cell", + "full_name": "Degenerative distal convoluted tubule cell", + "paper_synonyms": "DCT", + "tissue_context": "distal convoluted tubule; cortex" + }, + { + "name": "Cycling Myofibroblast", + "full_name": "Cycling myofibroblast", + "paper_synonyms": "cycMyoF", + "tissue_context": "stroma; interstitium" + }, + { + "name": "Papillary Tip Epithelial Cell", + "full_name": "Papillary tip epithelial cell", + "paper_synonyms": "PapE", + "tissue_context": "papillary tip; calyx" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_2.json new file mode 100644 index 0000000..b6837c8 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "human kidney; nephron segments; renal tubules; cortex; outer medulla; inner medulla; papillary tip" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "human kidney; cortex; medulla; areas of injury; interstitial fibrosis" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "human kidney; nephron segments; renal tubules; cortex; outer medulla; inner medulla; papillary tip" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "human kidney; cortex; medulla; areas of injury; interstitial fibrosis" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "human kidney; cortex; medulla; areas of injury; interstitial fibrosis" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_20.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_20.json new file mode 100644 index 0000000..264ab2a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_20.json @@ -0,0 +1,32 @@ +[ + { + "name": "Cycling Natural Killer Cell / Natural Killer T Cell", + "full_name": "Cycling natural killer cell / natural killer T cell", + "paper_synonyms": "NKT", + "tissue_context": "areas of injury; renal cortical and medullary structures" + }, + { + "name": "PT", + "full_name": "proximal tubule", + "paper_synonyms": null, + "tissue_context": "cortex" + }, + { + "name": "FIB", + "full_name": "fibroblast", + "paper_synonyms": null, + "tissue_context": "interstitium; cortex" + }, + { + "name": "TAL", + "full_name": "thick ascending limb", + "paper_synonyms": "C-TAL; M-TAL", + "tissue_context": "cortex; medulla; outer medullary stripe" + }, + { + "name": "IMM", + "full_name": "immune cell", + "paper_synonyms": "immune", + "tissue_context": "renal cortical and medullary structures" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_21.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_21.json new file mode 100644 index 0000000..6976d42 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_21.json @@ -0,0 +1,32 @@ +[ + { + "name": "IC", + "full_name": "intercalated cells", + "paper_synonyms": null, + "tissue_context": "connecting tubules; collecting duct" + }, + { + "name": "EC", + "full_name": "endothelial cells", + "paper_synonyms": null, + "tissue_context": "glomerular capillaries; afferent/efferent arterioles; lymphatics; vasa recta" + }, + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla" + }, + { + "name": "DTL", + "full_name": "descending thin limb", + "paper_synonyms": null, + "tissue_context": "loop of Henle; medulla" + }, + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_22.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_22.json new file mode 100644 index 0000000..8586ce1 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_22.json @@ -0,0 +1,32 @@ +[ + { + "name": "POD", + "full_name": "podocyte", + "paper_synonyms": "PODs", + "tissue_context": "renal corpuscle; glomerulus; cortex" + }, + { + "name": "ATL", + "full_name": "ascending thin limb", + "paper_synonyms": null, + "tissue_context": "inner medulla" + }, + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla; areas of injury; region of fibrosis" + }, + { + "name": "PC", + "full_name": "principal cells", + "paper_synonyms": null, + "tissue_context": "collecting duct; connecting tubules; cortex; outer medulla; inner medulla" + }, + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla; areas of injury; region of fibrosis" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_23.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_23.json new file mode 100644 index 0000000..dbd1359 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_23.json @@ -0,0 +1,32 @@ +[ + { + "name": "IMM", + "full_name": "immune cell", + "paper_synonyms": null, + "tissue_context": "cortex; medulla" + }, + { + "name": "CNT", + "full_name": "connecting tubule", + "paper_synonyms": null, + "tissue_context": "cortex; cortical distal nephrons" + }, + { + "name": "DCT", + "full_name": "distal convoluted tubule", + "paper_synonyms": null, + "tissue_context": "cortex" + }, + { + "name": "VSM/P", + "full_name": "vascular smooth muscle cell or pericyte", + "paper_synonyms": "vascular smooth muscle cell; pericyte; VSMC", + "tissue_context": "afferent/efferent arterioles; renal corpuscle" + }, + { + "name": "NEU", + "full_name": "neuronal cell", + "paper_synonyms": "Schwann/neuronal; SCI/NEU", + "tissue_context": "human kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_24.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_24.json new file mode 100644 index 0000000..1b0f75d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_24.json @@ -0,0 +1,32 @@ +[ + { + "name": "IMM", + "full_name": "immune cell", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla; kidney biopsy samples" + }, + { + "name": "IMM", + "full_name": "immune cell", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla; kidney biopsy samples" + }, + { + "name": "IMM", + "full_name": "immune cell", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla; kidney biopsy samples" + }, + { + "name": "IMM", + "full_name": "immune cell", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla; kidney biopsy samples" + }, + { + "name": "PEC", + "full_name": "parietal epithelial cell", + "paper_synonyms": null, + "tissue_context": "renal corpuscle" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_25.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_25.json new file mode 100644 index 0000000..d9130b0 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_25.json @@ -0,0 +1,32 @@ +[ + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": null, + "tissue_context": "renal cortical and medullary structures" + }, + { + "name": "PapE", + "full_name": "papillary tip epithelial cells abutting the calyx", + "paper_synonyms": null, + "tissue_context": "papillary tip; calyx" + }, + { + "name": "dPT", + "full_name": "degenerative proximal tubule cells", + "paper_synonyms": null, + "tissue_context": "proximal tubule (PT)" + }, + { + "name": "aPT", + "full_name": "adaptive proximal tubule cells", + "paper_synonyms": null, + "tissue_context": "proximal tubule (PT)" + }, + { + "name": "M-FIB", + "full_name": "medullary fibroblasts", + "paper_synonyms": null, + "tissue_context": "medulla; interstitium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_26.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_26.json new file mode 100644 index 0000000..ec0df29 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_26.json @@ -0,0 +1,32 @@ +[ + { + "name": "MD", + "full_name": "macula densa cells", + "paper_synonyms": null, + "tissue_context": "renal corpuscle; afferent/efferent arterioles" + }, + { + "name": "NKC/T", + "full_name": "T cells", + "paper_synonyms": "T", + "tissue_context": "areas of tissue damage; fibrosis; around vessels" + }, + { + "name": "tPC-IC", + "full_name": "transitioning principal and intercalated cells", + "paper_synonyms": "principal cells (PC); intercalated cells (IC)", + "tissue_context": "medullary tubules; collecting duct" + }, + { + "name": "EC-DVR", + "full_name": "endothelial cells of the vasa recta", + "paper_synonyms": null, + "tissue_context": "vasa recta; medulla" + }, + { + "name": "M-TAL", + "full_name": "medullary thick ascending limb", + "paper_synonyms": "thick ascending limb (TAL)", + "tissue_context": "inner medulla; outer medullary stripe" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_27.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_27.json new file mode 100644 index 0000000..6d108f3 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_27.json @@ -0,0 +1,32 @@ +[ + { + "name": "C-IC-A", + "full_name": "cortical intercalated cell", + "paper_synonyms": "IC", + "tissue_context": "connecting tubules (CNT); collecting ducts" + }, + { + "name": "dM-TAL", + "full_name": "thick ascending limb", + "paper_synonyms": "TAL", + "tissue_context": "cortex; outer medulla; inner medulla" + }, + { + "name": "EC-AVR", + "full_name": "endothelial cell, vasa recta", + "paper_synonyms": "EC", + "tissue_context": "vasa recta" + }, + { + "name": "MAC-M2", + "full_name": "M2 macrophage", + "paper_synonyms": "M2 macrophages", + "tissue_context": "cortex; medulla" + }, + { + "name": "cycPT", + "full_name": "cycling proximal tubule cell", + "paper_synonyms": "PT; cycling", + "tissue_context": "proximal tubule (PT)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_28.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_28.json new file mode 100644 index 0000000..39e6def --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_28.json @@ -0,0 +1,32 @@ +[ + { + "name": "M-IC-A", + "full_name": "intercalated cells", + "paper_synonyms": "IC", + "tissue_context": "connecting tubules (CNT-IC and CNT-PC); cortical, outer medullary and inner medullary collecting duct subpopulations (CCD, OMCD, IMCD)" + }, + { + "name": "aTAL1", + "full_name": "adaptive thick ascending limb 1", + "paper_synonyms": "aTAL; aEpi", + "tissue_context": "thick ascending limb (TAL); cortical thick ascending limb (C-TAL)" + }, + { + "name": "EC-AEA", + "full_name": "endothelial cells of the afferent/efferent arterioles", + "paper_synonyms": "AEA", + "tissue_context": "afferent/efferent arterioles; renal corpuscle" + }, + { + "name": "DTL2", + "full_name": "descending thin limb 2", + "paper_synonyms": "DTL", + "tissue_context": "descending thin limb (DTL2); medulla" + }, + { + "name": "N", + "full_name": "neutrophils", + "paper_synonyms": "MPO+ cells", + "tissue_context": "cortical or medullary epithelium (N6 and N11)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_29.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_29.json new file mode 100644 index 0000000..7fcf21c --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_29.json @@ -0,0 +1,32 @@ +[ + { + "name": "DTL1", + "full_name": "descending thin limb cell type 1", + "paper_synonyms": null, + "tissue_context": "DTL: AQP1+ cells in the medulla." + }, + { + "name": "MDC", + "full_name": "monocyte-derived cells", + "paper_synonyms": null, + "tissue_context": "monocyte-derived cells (MDCs) localized to a region of fibrosis within the cortex of a CKD biopsy" + }, + { + "name": "C-PC", + "full_name": "cortical principal cell", + "paper_synonyms": null, + "tissue_context": "cortex" + }, + { + "name": "EC-PTC", + "full_name": "endothelial cell", + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "PT-S1/2", + "full_name": "proximal tubule S1/S2", + "paper_synonyms": "PT-S1/PT-S2", + "tissue_context": "proximal tubule (PT)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_3.json new file mode 100644 index 0000000..d873918 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "epithelium; tubular epithelium", + "tissue_context": "nephron; cortex; outer medulla; inner medulla; papillary tip" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "epithelium; tubular epithelium", + "tissue_context": "nephron; cortex; outer medulla; inner medulla; papillary tip" + }, + { + "name": "stroma cells", + "full_name": "stromal cells", + "paper_synonyms": "stroma; STR", + "tissue_context": "interstitium; cortex; medulla" + }, + { + "name": "neural cells", + "full_name": "neural cell types", + "paper_synonyms": "neuronal; Schwann/neuronal; SCI/NEU", + "tissue_context": "human kidney" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes; IMM", + "tissue_context": "cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_30.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_30.json new file mode 100644 index 0000000..8142e01 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_30.json @@ -0,0 +1,32 @@ +[ + { + "name": "PT-S3", + "full_name": "proximal tubule S3", + "paper_synonyms": "proximal tubule (PT)", + "tissue_context": "proximal tubule (PT); cortex" + }, + { + "name": "C-TAL", + "full_name": "cortical thick ascending limb", + "paper_synonyms": "thick ascending limb (TAL)", + "tissue_context": "cortex; cortical thick ascending limb (C-TAL)" + }, + { + "name": "M-PC", + "full_name": "medullary principal cell", + "paper_synonyms": "principal cells (PC)", + "tissue_context": "medulla; Medullary collecting ducts" + }, + { + "name": "dM-PC", + "full_name": "degenerative medullary principal cell", + "paper_synonyms": "dM-PCs", + "tissue_context": "medulla; collecting duct" + }, + { + "name": "T", + "full_name": "T cell", + "paper_synonyms": "CD3+ cells; lymphoid or T cells", + "tissue_context": "cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_31.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_31.json new file mode 100644 index 0000000..804860f --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_31.json @@ -0,0 +1,32 @@ +[ + { + "name": "PL", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human kidney" + }, + { + "name": "dFIB", + "full_name": "degenerative fibroblast", + "paper_synonyms": null, + "tissue_context": "stroma; interstitium; cortex" + }, + { + "name": "dC-TAL", + "full_name": "degenerative cortical thick ascending limb", + "paper_synonyms": "thick ascending limb (TAL); cortical thick ascending limb (C-TAL)", + "tissue_context": "cortical thick ascending limb (C-TAL); cortex" + }, + { + "name": "VSMC", + "full_name": "vascular smooth muscle cell", + "paper_synonyms": "VSM/P; pericyte", + "tissue_context": "afferent/efferent arterioles; renal corpuscle" + }, + { + "name": "SC/NEU", + "full_name": "Schwann/neuronal", + "paper_synonyms": "SCI/NEU", + "tissue_context": "human kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_32.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_32.json new file mode 100644 index 0000000..e1ced84 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_32.json @@ -0,0 +1,32 @@ +[ + { + "name": "DTL3", + "full_name": "descending thin limb 3", + "paper_synonyms": "descending thin limb; DTL", + "tissue_context": "medulla" + }, + { + "name": "EC-GC", + "full_name": "glomerular capillary endothelial cell", + "paper_synonyms": "glomerular capillaries; EC-GC", + "tissue_context": "renal corpuscle; glomeruli" + }, + { + "name": "VSMC/P", + "full_name": "vascular smooth muscle cell or pericyte", + "paper_synonyms": "VSM/P; VSMC; vascular smooth muscle cell; pericyte", + "tissue_context": "afferent/efferent arterioles" + }, + { + "name": "cycMNP", + "full_name": "cycling", + "paper_synonyms": null, + "tissue_context": "cortex; medulla" + }, + { + "name": "MYOF", + "full_name": "myofibroblast", + "paper_synonyms": "MyoF", + "tissue_context": "interstitium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_33.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_33.json new file mode 100644 index 0000000..46b97bb --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_33.json @@ -0,0 +1,32 @@ +[ + { + "name": "dEC-PTC", + "full_name": "endothelial cells", + "paper_synonyms": null, + "tissue_context": "vasculature; human kidney" + }, + { + "name": "ncMON", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human kidney" + }, + { + "name": "cycEC", + "full_name": "cycling endothelial cells", + "paper_synonyms": null, + "tissue_context": "vasculature; human kidney" + }, + { + "name": "cDC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human kidney" + }, + { + "name": "EC-LYM", + "full_name": "endothelial cells of the lymphatics", + "paper_synonyms": null, + "tissue_context": "lymphatics; human kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_34.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_34.json new file mode 100644 index 0000000..4d38755 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_34.json @@ -0,0 +1,32 @@ +[ + { + "name": "MC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "IC-B", + "full_name": "intercalated cells B", + "paper_synonyms": "IC; intercalated cells", + "tissue_context": "connecting tubules (CNT); collecting duct" + }, + { + "name": "MAST", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "dVSMC", + "full_name": "degenerative vascular smooth muscle cell", + "paper_synonyms": "VSMC; vascular smooth muscle cell; VSM/P", + "tissue_context": "afferent/efferent arterioles; renal corpuscle" + }, + { + "name": "dATL", + "full_name": "degenerative ascending thin limb", + "paper_synonyms": "ATL; ascending thin limbs", + "tissue_context": "inner medulla; outer medullary stripe" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_35.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_35.json new file mode 100644 index 0000000..c66ce14 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_35.json @@ -0,0 +1,32 @@ +[ + { + "name": "REN", + "full_name": "juxtaglomerular renin-producing granular cells", + "paper_synonyms": "renin-producing granular cells", + "tissue_context": "juxtaglomerular apparatus; afferent/efferent arterioles; renal corpuscle" + }, + { + "name": "B", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "aTAL2", + "full_name": "adaptive thick ascending limb 2", + "paper_synonyms": "adaptive TAL", + "tissue_context": "C-TAL; cortex; corticomedullary sections" + }, + { + "name": "dC-IC-A", + "full_name": "degenerative cortical intercalated cell", + "paper_synonyms": null, + "tissue_context": "cortex" + }, + { + "name": "dCNT", + "full_name": "degenerative connecting tubule", + "paper_synonyms": null, + "tissue_context": "connecting tubule; cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_36.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_36.json new file mode 100644 index 0000000..5067213 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_36.json @@ -0,0 +1,32 @@ +[ + { + "name": "aFIB", + "full_name": "adaptive fibroblast", + "paper_synonyms": null, + "tissue_context": "interstitium; stroma; cortex" + }, + { + "name": "cycCNT", + "full_name": "cycling connecting tubule", + "paper_synonyms": null, + "tissue_context": "connecting tubule; cortical distal nephron; cortex" + }, + { + "name": "dIMCD", + "full_name": "degenerative inner medullary collecting duct", + "paper_synonyms": null, + "tissue_context": "inner medulla; collecting duct" + }, + { + "name": "IMCD", + "full_name": "inner medullary collecting duct", + "paper_synonyms": null, + "tissue_context": "inner medulla; collecting duct" + }, + { + "name": "dEC", + "full_name": "degenerative endothelial cell", + "paper_synonyms": null, + "tissue_context": "vasculature" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_37.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_37.json new file mode 100644 index 0000000..257ac91 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_37.json @@ -0,0 +1,32 @@ +[ + { + "name": "dM-FIB", + "full_name": "degenerative medullary fibroblast", + "paper_synonyms": "FIB", + "tissue_context": "medulla" + }, + { + "name": "cycDCT", + "full_name": "cycling distal convoluted tubule cell", + "paper_synonyms": "DCT", + "tissue_context": "distal convoluted tubule; cortex" + }, + { + "name": "dPOD", + "full_name": "degenerative podocyte", + "paper_synonyms": "POD", + "tissue_context": "renal corpuscle; glomerulus" + }, + { + "name": "pDC", + "full_name": "plasmacytoid dendritic cell", + "paper_synonyms": "", + "tissue_context": "cortex; medulla" + }, + { + "name": "dDTL3", + "full_name": "degenerative descending thin limb cell type 3", + "paper_synonyms": "DTL3", + "tissue_context": "descending thin limb; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_38.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_38.json new file mode 100644 index 0000000..1f3759a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_38.json @@ -0,0 +1,32 @@ +[ + { + "name": "dDCT", + "full_name": "degenerative distal convoluted tubule cells", + "paper_synonyms": "DCT", + "tissue_context": "distal convoluted tubule; cortex" + }, + { + "name": "cycMYOF", + "full_name": "cycling myofibroblasts", + "paper_synonyms": "MyoF; cycMyoF; myofibroblasts", + "tissue_context": "stroma; interstitium; fibrosis within the cortex" + }, + { + "name": "cycNKC/T", + "full_name": "cycling T cells", + "paper_synonyms": "T; T cells", + "tissue_context": "cortex; medulla; areas of tissue damage; fibrosis" + }, + { + "name": "CCD-IC-A", + "full_name": "cortical collecting duct intercalated cells", + "paper_synonyms": "CCD; C-CD; IC; intercalated cells", + "tissue_context": "cortex; collecting duct" + }, + { + "name": "OMCD-IC-A", + "full_name": "outer medullary collecting duct intercalated cells", + "paper_synonyms": "OMCD; IC; intercalated cells", + "tissue_context": "outer medulla; collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_39.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_39.json new file mode 100644 index 0000000..df6a6fc --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_39.json @@ -0,0 +1,32 @@ +[ + { + "name": "CCD-PC", + "full_name": "cortical collecting duct principal cell", + "paper_synonyms": "PC; principal cells; CCD; cortical collecting duct", + "tissue_context": "cortical, outer medullary and inner medullary collecting duct subpopulations (CCD, OMCD, IMCD)" + }, + { + "name": "OMCD-PC", + "full_name": "outer medullary collecting duct principal cell", + "paper_synonyms": "PC; principal cells; OMCD; outer medullary collecting duct", + "tissue_context": "cortical, outer medullary and inner medullary collecting duct subpopulations (CCD, OMCD, IMCD)" + }, + { + "name": "dOMCD-PC", + "full_name": "degenerative outer medullary collecting duct principal cell", + "paper_synonyms": "PC; principal cells; OMCD; outer medullary collecting duct; degenerative medullary principal cells (dM-PCs)", + "tissue_context": "an area showing intraluminal cellular cast formation, cell sloughing and loss of nuclei that were associated with degenerative CD cells, including degenerative medullary principal cells (dM-PCs)" + }, + { + "name": "CNT-PC", + "full_name": "connecting tubule principal cell", + "paper_synonyms": "PC; principal cells; CNT; connecting tubules", + "tissue_context": "intercalated and principal cells of the connecting tubules (CNT-IC and CNT-PC)" + }, + { + "name": "DCT1", + "full_name": "distal convoluted tubule cell (type 1)", + "paper_synonyms": "DCT; distal convoluted tubule", + "tissue_context": "two types of distal convoluted tubule cells (DCT1, 2)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_4.json new file mode 100644 index 0000000..2d290c0 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "IMM", + "tissue_context": "human kidney; cortex; medulla" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "IMM", + "tissue_context": "human kidney; cortex; medulla" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "IMM", + "tissue_context": "human kidney; cortex; medulla" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "human kidney; cortex; medulla; nephron segments; tubules" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "IMM", + "tissue_context": "human kidney; cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_40.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_40.json new file mode 100644 index 0000000..8ba8d9a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_40.json @@ -0,0 +1,14 @@ +[ + { + "name": "DCT2", + "full_name": "distal convoluted tubule cell 2", + "paper_synonyms": "DCT", + "tissue_context": "distal convoluted tubule" + }, + { + "name": "CNT-IC-A", + "full_name": "connecting tubule intercalated cell", + "paper_synonyms": "CNT-IC; IC; CNT", + "tissue_context": "connecting tubule" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_5.json new file mode 100644 index 0000000..4e6d863 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_5.json @@ -0,0 +1,32 @@ +[ + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "across different regions of the human kidney spanning the cortex to the papillary tip; along the nephron" + }, + { + "name": "Degenerative Proximal Tubule Epithelial Cell", + "full_name": "degenerative proximal tubule epithelial cell", + "paper_synonyms": null, + "tissue_context": "proximal tubule (PT)" + }, + { + "name": "Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell", + "full_name": "adaptive (successful or maladaptive tubular repair) proximal tubule epithelial cell", + "paper_synonyms": "aPT; adaptive epithelial (aEpi)", + "tissue_context": "proximal tubule (PT); cortex" + }, + { + "name": "Medullary Fibroblast", + "full_name": "medullary fibroblast", + "paper_synonyms": "FIB", + "tissue_context": "medulla; outer medulla; inner medulla" + }, + { + "name": "Macula Densa Cell", + "full_name": "macula densa cell", + "paper_synonyms": "MD", + "tissue_context": "juxtaglomerular apparatus cells; afferent/efferent arterioles (EC-AEA); renal corpuscle" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_6.json new file mode 100644 index 0000000..2c449cf --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_6.json @@ -0,0 +1,32 @@ +[ + { + "name": "Natural Killer Cell / Natural Killer T Cell", + "full_name": "Natural Killer Cell / Natural Killer T Cell", + "paper_synonyms": "NKT", + "tissue_context": "cortex; medulla" + }, + { + "name": "Transitional Principal-Intercalated Cell", + "full_name": "transitioning principal and intercalated cells", + "paper_synonyms": "", + "tissue_context": "medulla; collecting duct" + }, + { + "name": "Descending Vasa Recta Endothelial Cell", + "full_name": "endothelial cell of the descending vasa recta", + "paper_synonyms": "EC-DVR", + "tissue_context": "medulla; vasa recta" + }, + { + "name": "Medullary Thick Ascending Limb Cell", + "full_name": "medullary thick ascending limb cell", + "paper_synonyms": "M-TAL; TAL", + "tissue_context": "outer medullary stripe; inner medulla" + }, + { + "name": "Cortical Collecting Duct Intercalated Cell Type A", + "full_name": "cortical collecting duct intercalated cell", + "paper_synonyms": "CCD; IC; C-CD", + "tissue_context": "cortex; collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_7.json new file mode 100644 index 0000000..b3405d8 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_7.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Medullary Thick Ascending Limb Cell", + "full_name": "Degenerative Medullary Thick Ascending Limb Cell", + "paper_synonyms": "M-TAL; TAL", + "tissue_context": "outer medullary stripe; medulla" + }, + { + "name": "Ascending Vasa Recta Endothelial Cell", + "full_name": "Ascending Vasa Recta Endothelial Cell", + "paper_synonyms": "EC-AVR", + "tissue_context": "vasa recta; medulla" + }, + { + "name": "M2 Macrophage", + "full_name": "M2 Macrophage", + "paper_synonyms": "MAC-M2", + "tissue_context": "cortex; fibrotic regions" + }, + { + "name": "Cycling Proximal Tubule Epithelial Cell", + "full_name": "Cycling Proximal Tubule Epithelial Cell", + "paper_synonyms": "PT; Cyc", + "tissue_context": "cortex" + }, + { + "name": "Outer Medullary Collecting Duct Intercalated Cell Type A", + "full_name": "Outer Medullary Collecting Duct Intercalated Cell Type A", + "paper_synonyms": "OMCD; IC", + "tissue_context": "outer medulla; collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_8.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_8.json new file mode 100644 index 0000000..8d31d92 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_8.json @@ -0,0 +1,32 @@ +[ + { + "name": "Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell", + "full_name": "adaptive/maladaptive repairing thick ascending limb epithelial cell", + "paper_synonyms": "aTAL; adaptive TAL; adaptive epithelial (aEpi)", + "tissue_context": "cortical thick ascending limb (C-TAL); medullary thick ascending limb (M-TAL); cortex; medulla" + }, + { + "name": "Afferent / Efferent Arteriole Endothelial Cell", + "full_name": "endothelial cell of the afferent/efferent arterioles", + "paper_synonyms": "EC-AEA", + "tissue_context": "afferent/efferent arterioles; renal corpuscle; glomerular corpuscle; Macula Densa (MD)" + }, + { + "name": "Descending Thin Limb Cell Type 2", + "full_name": "descending thin limb cell type 2", + "paper_synonyms": "DTL2", + "tissue_context": "medulla" + }, + { + "name": "Neutrophil", + "full_name": "neutrophil", + "paper_synonyms": "N; MPO+ (N)", + "tissue_context": "cortex; medulla" + }, + { + "name": "Podocyte", + "full_name": "podocyte", + "paper_synonyms": "PODs", + "tissue_context": "renal corpuscle; glomerulus; cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_9.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_9.json new file mode 100644 index 0000000..3a344df --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_9.json @@ -0,0 +1,32 @@ +[ + { + "name": "Ascending Thin Limb Cell", + "full_name": "ascending thin limb (ATL) cell", + "paper_synonyms": "ATL", + "tissue_context": "inner medulla; outer medullary stripe" + }, + { + "name": "Descending Thin Limb Cell Type 1", + "full_name": "descending thin limb cell type 1 (DTL1)", + "paper_synonyms": "DTL1", + "tissue_context": "medulla" + }, + { + "name": "Monocyte-derived Cell", + "full_name": "monocyte-derived cell", + "paper_synonyms": "MDCs", + "tissue_context": "cortex" + }, + { + "name": "Cortical Collecting Duct Principal Cell", + "full_name": "cortical collecting duct principal cell", + "paper_synonyms": "PC; C-PC", + "tissue_context": "cortex" + }, + { + "name": "Peritubular Capilary Endothelial Cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41598-020-66092-9_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41598-020-66092-9_batch_0.json new file mode 100644 index 0000000..5503965 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41598-020-66092-9_batch_0.json @@ -0,0 +1,14 @@ +[ + { + "name": "H1", + "full_name": "H1 horizontal cell", + "paper_synonyms": null, + "tissue_context": "fovea; peripheral retina; human retina" + }, + { + "name": "H2", + "full_name": "H2 horizontal cell", + "paper_synonyms": null, + "tissue_context": "fovea; peripheral retina; human retina" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/groundings.tsv new file mode 100644 index 0000000..80dcd7c --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/groundings.tsv @@ -0,0 +1,5 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Endothelial cells CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CD36', 'full_name': 'CD36+ endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Fibroblasts ADAMDEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts ADAMDEC1', 'full_name': 'ADAMDEC1+ fibroblasts', 'paper_synonyms': 'ADAMDEC+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Fibroblasts SMOC2 PTGIS CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SMOC2 PTGIS', 'full_name': 'SMOC2+ PTGIS+ fibroblasts', 'paper_synonyms': 'SMOC2+ PTGIS+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Myofibroblasts HHIP NPNT CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts HHIP NPNT', 'full_name': 'HHIP+ NPNT+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/groundings_0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/groundings_0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv new file mode 100644 index 0000000..80dcd7c --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/groundings_0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv @@ -0,0 +1,5 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Endothelial cells CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CD36', 'full_name': 'CD36+ endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Fibroblasts ADAMDEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts ADAMDEC1', 'full_name': 'ADAMDEC1+ fibroblasts', 'paper_synonyms': 'ADAMDEC+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Fibroblasts SMOC2 PTGIS CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SMOC2 PTGIS', 'full_name': 'SMOC2+ PTGIS+ fibroblasts', 'paper_synonyms': 'SMOC2+ PTGIS+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Myofibroblasts HHIP NPNT CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts HHIP NPNT', 'full_name': 'HHIP+ NPNT+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique.tsv new file mode 100644 index 0000000..9c85f88 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique.tsv @@ -0,0 +1,203 @@ +author_cell_type CL_label CL_ID reference dataset_version +epithelial cells epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +stroma cells kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney loop of Henle thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells lymphocyte CL:0000542 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +endothelial cells endothelial cell CL:0000115 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells kidney interstitial alternatively activated macrophage CL:1000695 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney loop of Henle thin descending limb epithelial cell CL:1001111 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells neutrophil CL:0000775 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells podocyte CL:0000653 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney loop of Henle thin ascending limb epithelial cell CL:1001107 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells mononuclear phagocyte CL:0000113 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney collecting duct principal cell CL:1001431 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells T cell CL:0000084 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells plasma cell CL:0000786 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney connecting tubule epithelial cell CL:1000768 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +stroma cells renal interstitial pericyte CL:1001318 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +neural cells neural cell CL:0002319 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells non-classical monocyte CL:0000875 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells mast cell CL:0000097 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells B cell CL:0000236 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells parietal epithelial cell CL:1000452 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +immune cells plasmacytoid dendritic cell, human CL:0001058 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +epithelial cells papillary tips cell CL:1000597 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Degenerative Proximal Tubule Epithelial Cell epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Medullary Fibroblast kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Macula Densa Cell kidney loop of Henle thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1038/s41586-023-05769-3 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thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Outer Medullary Collecting Duct Principal Cell kidney collecting duct principal cell CL:1001431 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Fibroblast kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Degenerative Outer Medullary Collecting Duct Principal Cell kidney collecting duct principal cell CL:1001431 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +T Cell T cell CL:0000084 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Plasma Cell plasma cell 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https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Vascular Smooth Muscle Cell renal interstitial pericyte CL:1001318 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Schwann Cell / Neural neural cell CL:0002319 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Descending Thin Limb Cell Type 3 kidney loop of Henle thin descending limb epithelial cell CL:1001111 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Glomerular Capillary Endothelial Cell endothelial cell CL:0000115 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Vascular Smooth Muscle Cell / Pericyte renal interstitial pericyte CL:1001318 https://doi.org/10.1038/s41586-023-05769-3 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https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Classical Dendritic Cell conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Lymphatic Endothelial Cell endothelial cell CL:0000115 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Distal Convoluted Tubule Cell Type 2 kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Mesangial Cell renal interstitial pericyte CL:1001318 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Intercalated Cell Type B kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 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https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +B Cell B cell CL:0000236 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Degenerative Cortical Intercalated Cell Type A kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Degenerative Connecting Tubule Cell kidney connecting tubule epithelial cell CL:1000768 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Adaptive / Maladaptive / Repairing Fibroblast kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Parietal Epithelial Cell parietal epithelial cell CL:1000452 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kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Connecting Tubule Intercalated Cell Type A kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Cycling Distal Convoluted Tubule Cell kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Degenerative Podocyte podocyte CL:0000653 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Plasmacytoid Dendritic Cell plasmacytoid dendritic cell, human CL:0001058 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Degenerative Descending Thin Limb Cell Type 3 kidney loop of Henle thin descending limb epithelial cell CL:1001111 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Degenerative Distal Convoluted Tubule Cell kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Cycling Myofibroblast kidney interstitial fibroblast CL:1000692 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Papillary Tip Epithelial Cell papillary tips cell CL:1000597 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +Cycling Natural Killer Cell / Natural Killer T Cell lymphocyte CL:0000542 https://doi.org/10.1038/s41586-023-05769-3 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https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad +CNT-IC-A kidney collecting duct intercalated cell CL:1001432 https://doi.org/10.1038/s41586-023-05769-3 https://datasets.cellxgene.cziscience.com/3f2bf1e8-75a6-4fa2-9f79-dd6bb54529a0.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique.tsv new file mode 100644 index 0000000..51ec412 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique.tsv @@ -0,0 +1,20 @@ +author_cell_type CL_label CL_ID reference dataset_version +Inhibitory_4 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Oligodendrocytes oligodendrocyte CL:0000128 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Inhibitory_2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_4 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Inhibitory_1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Astrocytes astrocyte CL:0000127 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_3 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_5 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Microglia microglial cell CL:0000129 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +OPCs oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Endo/Pericytes endothelial cell CL:0000115 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_8 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Inhibitory_3 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_9 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_6 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_10 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad +Excitatory_7 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/b279d4ec-2674-4c92-aad0-3786fa651fad.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv new file mode 100644 index 0000000..56c7e73 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique.tsv @@ -0,0 +1,17 @@ +author_cell_type CL_label CL_ID reference dataset_version +Fibroblasts ADAMDEC1 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Endothelial cells CD36 endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Myofibroblasts HHIP NPNT myofibroblast cell CL:0000186 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Fibroblasts SMOC2 PTGIS fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Endothelial cells DARC endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Fibroblasts NPY SLITRK6 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Myofibroblasts GREM1 GREM2 myofibroblast cell CL:0000186 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Endothelial cells CA4 CD36 endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Glial cells glial cell CL:0000125 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Fibroblasts SFRP2 SLPI fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Endothelial cells LTC4S SEMA3G endothelial cell CL:0000115 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Pericytes HIGD1B STEAP4 pericyte CL:0000669 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Activated fibroblasts CCL19 ADAMADEC1 fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Lymphatics lymphocyte CL:0000542 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Fibroblasts KCNN3 LY6H fibroblast CL:0000057 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad +Pericytes RERGL NTRK2 pericyte CL:0000669 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/1a640ddc-ea3c-4711-ba8e-07084cc40a88.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique.tsv new file mode 100644 index 0000000..a51872c --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique.tsv @@ -0,0 +1,3 @@ +author_cell_type CL_label CL_ID reference dataset_version +H1 retina horizontal cell CL:0000745 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/063b53b4-4593-4815-90db-a531f8ce085b.h5ad +H2 retina horizontal cell CL:0000745 https://doi.org/10.1038/s41598-020-66092-9 https://datasets.cellxgene.cziscience.com/063b53b4-4593-4815-90db-a531f8ce085b.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique.tsv new file mode 100644 index 0000000..63f0566 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique.tsv @@ -0,0 +1,18 @@ +author_cell_type CL_label CL_ID reference dataset_version +Oligodendrocytes oligodendrocyte CL:0000128 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L3-L5 Intratelencephalic Type 1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Astrocytes astrocyte CL:0000127 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L6 Intratelencephalic - Type 1 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +SV2C LAMP5 Interneurons neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L6 Corticothalamic / L6B neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L2-L3 Intratelencephalic neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L3-L5 Intratelencephalic Type 2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L6 Intratelencephalic - Type 2 neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +OPCs oligodendrocyte precursor cell CL:0002453 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L5-L6 Near Projecting neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Somatostatin Interneurons neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Microglia microglial cell CL:0000129 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +VIP Interneurons neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +L5 Extratelencephalic neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Endothelial endothelial cell CL:0000115 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad +Parvalbumin interneurons neuron CL:0000540 https://doi.org/10.1007/s00401-023-02599-5 https://datasets.cellxgene.cziscience.com/8ec7b265-1c95-4c33-9df3-45c850b8b6b5.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..bfa994a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,203 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +ATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ATL', 'full_name': 'ascending thin limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +Adaptive / Maladaptive / Repairing Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Fibroblast', 'full_name': 'adaptive (successful or maladaptive repair) fibroblast', 'paper_synonyms': 'aFIB; aStr', 'tissue_context': ''} CL:0000057 fibroblast +Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell', 'full_name': 'adaptive (successful or maladaptive tubular repair) proximal tubule epithelial cell', 'paper_synonyms': 'aPT; adaptive epithelial (aEpi)', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell', 'full_name': 'adaptive/maladaptive repairing thick ascending limb epithelial cell', 'paper_synonyms': 'aTAL; adaptive TAL; adaptive epithelial (aEpi)', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +Afferent / Efferent Arteriole Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Afferent / Efferent Arteriole Endothelial Cell', 'full_name': 'endothelial cell of the afferent/efferent arterioles', 'paper_synonyms': 'EC-AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole +Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Thin Limb Cell', 'full_name': 'ascending thin limb (ATL) cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +Ascending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Vasa Recta Endothelial Cell', 'full_name': 'Ascending Vasa Recta Endothelial Cell', 'paper_synonyms': 'EC-AVR', 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell +B CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'B', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +B Cell CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'B Cell', 'full_name': 'B cell', 'paper_synonyms': 'B', 'tissue_context': ''} CL:0000236 B cell +C-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-IC-A', 'full_name': 'cortical intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell +C-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-PC', 'full_name': 'cortical principal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell +C-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-TAL', 'full_name': 'cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell +CCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-IC-A', 'full_name': 'cortical collecting duct intercalated cells', 'paper_synonyms': 'CCD; C-CD; IC; intercalated cells', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell +CCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-PC', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; CCD; cortical collecting duct', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell +CNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT', 'full_name': 'connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +CNT-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-IC-A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC; CNT', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell +CNT-PC CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-PC', 'full_name': 'connecting tubule principal cell', 'paper_synonyms': 'PC; principal cells; CNT; connecting tubules', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell +Classical Dendritic Cell CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Classical Dendritic Cell', 'full_name': 'Classical Dendritic Cell', 'paper_synonyms': 'cDC', 'tissue_context': ''} CL:0000990 conventional dendritic cell +Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Cell', 'full_name': 'connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +Connecting Tubule Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Intercalated Cell Type A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell +Connecting Tubule Principal Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Principal Cell', 'full_name': 'Connecting tubule principal cell', 'paper_synonyms': 'CNT-PC', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell +Cortical Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Intercalated Cell Type A', 'full_name': 'cortical collecting duct intercalated cell', 'paper_synonyms': 'CCD; IC; C-CD', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell +Cortical Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Principal Cell', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'PC; C-PC', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell +Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Thick Ascending Limb Cell', 'full_name': 'Cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; cortical TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell +Cycling Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Connecting Tubule Cell', 'full_name': 'cycling connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +Cycling Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Distal Convoluted Tubule Cell', 'full_name': 'cycling distal convoluted tubule cell', 'paper_synonyms': 'DCT; Cyc', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +Cycling Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Endothelial Cell', 'full_name': 'Cycling Endothelial Cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Cycling Mononuclear Phagocyte CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Mononuclear Phagocyte', 'full_name': 'cycling mononuclear phagocyte', 'paper_synonyms': 'cycMNP', 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte +Cycling Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Myofibroblast', 'full_name': 'Cycling myofibroblast', 'paper_synonyms': 'cycMyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell +Cycling Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Cycling natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:4033071 cycling natural killer cell +Cycling Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Proximal Tubule Epithelial Cell', 'full_name': 'Cycling Proximal Tubule Epithelial Cell', 'paper_synonyms': 'PT; Cyc', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +DCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT', 'full_name': 'distal convoluted tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT1', 'full_name': 'distal convoluted tubule cell (type 1)', 'paper_synonyms': 'DCT; distal convoluted tubule', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule +DCT2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT2', 'full_name': 'distal convoluted tubule cell 2', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule +DTL CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL', 'full_name': 'descending thin limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +DTL1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL1', 'full_name': 'descending thin limb cell type 1', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +DTL2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL2', 'full_name': 'descending thin limb 2', 'paper_synonyms': 'DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +DTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL3', 'full_name': 'descending thin limb 3', 'paper_synonyms': 'descending thin limb; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Degenerative Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Ascending Thin Limb Cell', 'full_name': 'degenerative ascending thin limb (ATL) cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +Degenerative Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Connecting Tubule Cell', 'full_name': 'degenerative connecting tubule (CNT) cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +Degenerative Cortical Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Intercalated Cell Type A', 'full_name': 'degenerative cortical intercalated cell type A', 'paper_synonyms': 'IC; intercalated cells', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell +Degenerative Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Thick Ascending Limb Cell', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell +Degenerative Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Descending Thin Limb Cell Type 3', 'full_name': 'Degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Degenerative Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Distal Convoluted Tubule Cell', 'full_name': 'Degenerative distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +Degenerative Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Endothelial Cell', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell +Degenerative Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Fibroblast', 'full_name': 'degenerative fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast +Degenerative Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Inner Medullary Collecting Duct Cell', 'full_name': 'degenerative inner medullary collecting duct cell', 'paper_synonyms': 'IMCD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell +Degenerative Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Fibroblast', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast +Degenerative Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Thick Ascending Limb Cell', 'full_name': 'Degenerative Medullary Thick Ascending Limb Cell', 'paper_synonyms': 'M-TAL; TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell +Degenerative Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'degenerative medullary principal cells; dM-PCs', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell +Degenerative Peritubular Capilary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell +Degenerative Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Podocyte', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'POD', 'tissue_context': ''} CL:0000653 podocyte +Degenerative Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Proximal Tubule Epithelial Cell', 'full_name': 'degenerative proximal tubule epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Degenerative Vascular Smooth Muscle Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Vascular Smooth Muscle Cell', 'full_name': 'degenerative vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell +Descending Thin Limb Cell Type 1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 1', 'full_name': 'descending thin limb cell type 1 (DTL1)', 'paper_synonyms': 'DTL1', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Descending Thin Limb Cell Type 2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 2', 'full_name': 'descending thin limb cell type 2', 'paper_synonyms': 'DTL2', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 3', 'full_name': 'descending thin limb cell type 3', 'paper_synonyms': 'DTL3; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Descending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Vasa Recta Endothelial Cell', 'full_name': 'endothelial cell of the descending vasa recta', 'paper_synonyms': 'EC-DVR', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell +Distal Convoluted Tubule Cell Type 1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 1', 'full_name': 'Distal convoluted tubule cell type 1', 'paper_synonyms': 'DCT1', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule +Distal Convoluted Tubule Cell Type 2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 2', 'full_name': 'Distal Convoluted Tubule Cell Type 2', 'paper_synonyms': 'DCT2', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule +EC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +EC-AEA CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AEA', 'full_name': 'endothelial cells of the afferent/efferent arterioles', 'paper_synonyms': 'AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole +EC-AVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AVR', 'full_name': 'endothelial cell, vasa recta', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell +EC-DVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-DVR', 'full_name': 'endothelial cells of the vasa recta', 'paper_synonyms': None, 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell +EC-GC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-GC', 'full_name': 'glomerular capillary endothelial cell', 'paper_synonyms': 'glomerular capillaries; EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell +EC-LYM CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-LYM', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': None, 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel +EC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-PTC', 'full_name': 'endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'FIB', 'full_name': 'fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': 'Fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast +Glomerular Capillary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Glomerular Capillary Endothelial Cell', 'full_name': 'glomerular capillaries', 'paper_synonyms': 'EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell +IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC', 'full_name': 'intercalated cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005010 renal intercalated cell +IC-B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC-B', 'full_name': 'intercalated cells B', 'paper_synonyms': 'IC; intercalated cells', 'tissue_context': ''} CL:0002201 renal beta-intercalated cell +IMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMCD', 'full_name': 'inner medullary collecting duct', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell +IMM CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Inner Medullary Collecting Duct Cell', 'full_name': 'inner medullary collecting duct cell', 'paper_synonyms': 'IMCD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell +Intercalated Cell Type B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Intercalated Cell Type B', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell +Lymphatic Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Lymphatic Endothelial Cell', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': 'EC-LYM', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel +M-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-FIB', 'full_name': 'medullary fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:4030022 renal medullary fibroblast +M-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-IC-A', 'full_name': 'intercalated cells', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell +M-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-PC', 'full_name': 'medullary principal cell', 'paper_synonyms': 'principal cells (PC)', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell +M-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-TAL', 'full_name': 'medullary thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell +M2 Macrophage CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M2 Macrophage', 'full_name': 'M2 Macrophage', 'paper_synonyms': 'MAC-M2', 'tissue_context': ''} CL:0000890 alternatively activated macrophage +MAC-M2 CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MAC-M2', 'full_name': 'M2 macrophage', 'paper_synonyms': 'M2 macrophages', 'tissue_context': ''} CL:0000890 alternatively activated macrophage +MAST CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MAST', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell +MC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell +MD CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MD', 'full_name': 'macula densa cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000850 macula densa epithelial cell +MDC CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MDC', 'full_name': 'monocyte-derived cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0011031 monocyte-derived dendritic cell +MYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MYOF', 'full_name': 'myofibroblast', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell +Macula Densa Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Macula Densa Cell', 'full_name': 'macula densa cell', 'paper_synonyms': 'MD', 'tissue_context': ''} CL:1000850 macula densa epithelial cell +Mast Cell CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Mast Cell', 'full_name': 'mast cell', 'paper_synonyms': 'MAST', 'tissue_context': ''} CL:0000097 mast cell +Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Fibroblast', 'full_name': 'medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast +Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Thick Ascending Limb Cell', 'full_name': 'medullary thick ascending limb cell', 'paper_synonyms': 'M-TAL; TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell +Mesangial Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Mesangial Cell', 'full_name': 'mesangial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000650 mesangial cell +Monocyte-derived Cell CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Monocyte-derived Cell', 'full_name': 'monocyte-derived cell', 'paper_synonyms': 'MDCs', 'tissue_context': ''} NO MATCH found +Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Myofibroblast', 'full_name': 'myofibroblast', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell +N CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'N', 'full_name': 'neutrophils', 'paper_synonyms': 'MPO+ cells', 'tissue_context': ''} CL:0000775 neutrophil +NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NEU', 'full_name': 'neuronal cell', 'paper_synonyms': 'Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0000540 neuron +NKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NKC/T', 'full_name': 'T cells', 'paper_synonyms': 'T', 'tissue_context': ''} CL:0000084 T cell +Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Natural Killer Cell / Natural Killer T Cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:0000623 natural killer cell +Neutrophil CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'neutrophil', 'paper_synonyms': 'N; MPO+ (N)', 'tissue_context': ''} CL:0000775 neutrophil +Non-classical Monocyte CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Non-classical Monocyte', 'full_name': 'Non-classical Monocyte', 'paper_synonyms': 'ncMON', 'tissue_context': ''} CL:0000875 non-classical monocyte +OMCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-IC-A', 'full_name': 'outer medullary collecting duct intercalated cells', 'paper_synonyms': 'OMCD; IC; intercalated cells', 'tissue_context': ''} CL:1000717 kidney outer medulla collecting duct intercalated cell +OMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-PC', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; OMCD; outer medullary collecting duct', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell +Outer Medullary Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'full_name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'paper_synonyms': 'OMCD; IC', 'tissue_context': ''} CL:4030015 kidney collecting duct alpha-intercalated cell +Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; principal cells (PC); medullary principal cell (M-PC)', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell +PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PC', 'full_name': 'principal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell +PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PEC', 'full_name': 'parietal epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell +PL CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PL', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} +POD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'POD', 'full_name': 'podocyte', 'paper_synonyms': 'PODs', 'tissue_context': ''} +PT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT', 'full_name': 'proximal tubule', 'paper_synonyms': None, 'tissue_context': ''} +PT-S1/2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S1/2', 'full_name': 'proximal tubule S1/S2', 'paper_synonyms': 'PT-S1/PT-S2', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +PT-S3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S3', 'full_name': 'proximal tubule S3', 'paper_synonyms': 'proximal tubule (PT)', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 +PapE CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PapE', 'full_name': 'papillary tip epithelial cells abutting the calyx', 'paper_synonyms': None, 'tissue_context': ''} CL:0000731 urothelial cell +Papillary Tip Epithelial Cell CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Papillary Tip Epithelial Cell', 'full_name': 'Papillary tip epithelial cell', 'paper_synonyms': 'PapE', 'tissue_context': ''} CL:0000731 urothelial cell +Parietal Epithelial Cell CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Parietal Epithelial Cell', 'full_name': 'parietal epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell +Peritubular Capilary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell +Plasma Cell CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasma Cell', 'full_name': 'Plasma cell', 'paper_synonyms': 'PL', 'tissue_context': ''} CL:0000786 plasma cell +Plasmacytoid Dendritic Cell CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasmacytoid Dendritic Cell', 'full_name': 'Plasmacytoid dendritic cell', 'paper_synonyms': 'pDC', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell +Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Podocyte', 'full_name': 'podocyte', 'paper_synonyms': 'PODs', 'tissue_context': ''} CL:0000653 podocyte +Proximal Tubule Epithelial Cell Segment 1 / Segment 2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 1 / Segment 2', 'full_name': 'Proximal tubule epithelial cell, segments 1 and 2', 'paper_synonyms': 'PT-S1; PT-S2; PT-S1/PT-S2; PT', 'tissue_context': ''} CL:1000838 kidney proximal convoluted tubule epithelial cell +Proximal Tubule Epithelial Cell Segment 3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 3', 'full_name': 'Proximal tubule epithelial cell, segment 3', 'paper_synonyms': 'PT-S3; PT', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 +REN CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'REN', 'full_name': 'juxtaglomerular renin-producing granular cells', 'paper_synonyms': 'renin-producing granular cells', 'tissue_context': ''} CL:0000648 kidney granular cell +Renin-positive Juxtaglomerular Granular Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Renin-positive Juxtaglomerular Granular Cell', 'full_name': 'juxtaglomerular renin-producing granular (REN) cell', 'paper_synonyms': 'renin-producing granular (REN) cells; REN; juxtaglomerular renin-producing granular cells (REN)', 'tissue_context': ''} CL:0000648 kidney granular cell +SC/NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'SC/NEU', 'full_name': 'Schwann/neuronal', 'paper_synonyms': 'SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell +Schwann Cell / Neural CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Schwann Cell / Neural', 'full_name': 'Schwann/neuronal cell', 'paper_synonyms': 'SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell +T CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T', 'full_name': 'T cell', 'paper_synonyms': 'CD3+ cells; lymphoid or T cells', 'tissue_context': ''} CL:0000084 T cell +T Cell CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T Cell', 'full_name': 'T cell', 'paper_synonyms': 'T; CD3+ cells', 'tissue_context': ''} CL:0000084 T cell +TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'C-TAL; M-TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +Transitional Principal-Intercalated Cell CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Transitional Principal-Intercalated Cell', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +VSM/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSM/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'vascular smooth muscle cell; pericyte; VSMC', 'tissue_context': ''} NO MATCH found +VSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSM/P; pericyte', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell +VSMC/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC; vascular smooth muscle cell; pericyte', 'tissue_context': ''} CL:0008034 mural cell +Vascular Smooth Muscle Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell +Vascular Smooth Muscle Cell / Pericyte CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell / Pericyte', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC', 'tissue_context': ''} CL:0008034 mural cell +aFIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aFIB', 'full_name': 'adaptive fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +aPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aPT', 'full_name': 'adaptive proximal tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +aTAL1 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL1', 'full_name': 'adaptive thick ascending limb 1', 'paper_synonyms': 'aTAL; aEpi', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +aTAL2 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL2', 'full_name': 'adaptive thick ascending limb 2', 'paper_synonyms': 'adaptive TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +cDC CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000990 conventional dendritic cell +cycCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycCNT', 'full_name': 'cycling connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +cycDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycDCT', 'full_name': 'cycling distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +cycEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycEC', 'full_name': 'cycling endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +cycMNP CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMNP', 'full_name': 'cycling', 'paper_synonyms': None, 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte +cycMYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMYOF', 'full_name': 'cycling myofibroblasts', 'paper_synonyms': 'MyoF; cycMyoF; myofibroblasts', 'tissue_context': ''} CL:0000186 myofibroblast cell +cycNKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycNKC/T', 'full_name': 'cycling T cells', 'paper_synonyms': 'T; T cells', 'tissue_context': ''} CL:4033069 cycling T cell +cycPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycPT', 'full_name': 'cycling proximal tubule cell', 'paper_synonyms': 'PT; cycling', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +dATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dATL', 'full_name': 'degenerative ascending thin limb', 'paper_synonyms': 'ATL; ascending thin limbs', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +dC-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-IC-A', 'full_name': 'degenerative cortical intercalated cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell +dC-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-TAL', 'full_name': 'degenerative cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL); cortical thick ascending limb (C-TAL)', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell +dCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dCNT', 'full_name': 'degenerative connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +dDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDCT', 'full_name': 'degenerative distal convoluted tubule cells', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +dDTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDTL3', 'full_name': 'degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +dEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +dEC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC-PTC', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +dFIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dFIB', 'full_name': 'degenerative fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +dIMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dIMCD', 'full_name': 'degenerative inner medullary collecting duct', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell +dM-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-FIB', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast +dM-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-PC', 'full_name': 'degenerative medullary principal cell', 'paper_synonyms': 'dM-PCs', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell +dM-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +dOMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dOMCD-PC', 'full_name': 'degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; OMCD; outer medullary collecting duct; degenerative medullary principal cells (dM-PCs)', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell +dPOD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPOD', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'POD', 'tissue_context': ''} CL:0000653 podocyte +dPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPT', 'full_name': 'degenerative proximal tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +dVSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dVSMC', 'full_name': 'degenerative vascular smooth muscle cell', 'paper_synonyms': 'VSMC; vascular smooth muscle cell; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell +endothelial cells CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'endothelial cells', 'full_name': 'endothelial cells', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell +epithelial cells CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell +immune cells CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'IMM', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +ncMON CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ncMON', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000875 non-classical monocyte +neural cells CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'neural cells', 'full_name': 'neural cell types', 'paper_synonyms': 'neuronal; Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell +pDC CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'pDC', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell +stroma cells CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': 'stromal cells', 'paper_synonyms': 'stroma; STR', 'tissue_context': ''} CL:0000499 stromal cell +stroma cells CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000499 stromal cell +tPC-IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'tPC-IC', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': 'principal cells (PC); intercalated cells (IC)', 'tissue_context': ''} CL:1001225 kidney collecting duct cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..e2771ac --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,200 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +ATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ATL', 'full_name': 'ascending thin limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +Adaptive / Maladaptive / Repairing Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Fibroblast', 'full_name': 'adaptive (successful or maladaptive repair) fibroblast', 'paper_synonyms': 'aFIB; aStr', 'tissue_context': ''} CL:0000057 fibroblast FALSE +Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell', 'full_name': 'adaptive (successful or maladaptive tubular repair) proximal tubule epithelial cell', 'paper_synonyms': 'aPT; adaptive epithelial (aEpi)', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell', 'full_name': 'adaptive/maladaptive repairing thick ascending limb epithelial cell', 'paper_synonyms': 'aTAL; adaptive TAL; adaptive epithelial (aEpi)', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +Afferent / Efferent Arteriole Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Afferent / Efferent Arteriole Endothelial Cell', 'full_name': 'endothelial cell of the afferent/efferent arterioles', 'paper_synonyms': 'EC-AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole FALSE +Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Thin Limb Cell', 'full_name': 'ascending thin limb (ATL) cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +Ascending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Vasa Recta Endothelial Cell', 'full_name': 'Ascending Vasa Recta Endothelial Cell', 'paper_synonyms': 'EC-AVR', 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell FALSE +B CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'B', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell TRUE +B Cell CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'B Cell', 'full_name': 'B cell', 'paper_synonyms': 'B', 'tissue_context': ''} CL:0000236 B cell TRUE +C-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-IC-A', 'full_name': 'cortical intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE +C-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-PC', 'full_name': 'cortical principal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE +C-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-TAL', 'full_name': 'cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE +CCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-IC-A', 'full_name': 'cortical collecting duct intercalated cells', 'paper_synonyms': 'CCD; C-CD; IC; intercalated cells', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE +CCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-PC', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; CCD; cortical collecting duct', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE +CNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT', 'full_name': 'connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +CNT-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-IC-A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC; CNT', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell FALSE +CNT-PC CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-PC', 'full_name': 'connecting tubule principal cell', 'paper_synonyms': 'PC; principal cells; CNT; connecting tubules', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell FALSE +Classical Dendritic Cell CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Classical Dendritic Cell', 'full_name': 'Classical Dendritic Cell', 'paper_synonyms': 'cDC', 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE +Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Cell', 'full_name': 'connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +Connecting Tubule Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Intercalated Cell Type A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell FALSE +Connecting Tubule Principal Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Principal Cell', 'full_name': 'Connecting tubule principal cell', 'paper_synonyms': 'CNT-PC', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell FALSE +Cortical Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Intercalated Cell Type A', 'full_name': 'cortical collecting duct intercalated cell', 'paper_synonyms': 'CCD; IC; C-CD', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE +Cortical Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Principal Cell', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'PC; C-PC', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE +Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Thick Ascending Limb Cell', 'full_name': 'Cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; cortical TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE +Cycling Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Connecting Tubule Cell', 'full_name': 'cycling connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +Cycling Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Distal Convoluted Tubule Cell', 'full_name': 'cycling distal convoluted tubule cell', 'paper_synonyms': 'DCT; Cyc', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +Cycling Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Endothelial Cell', 'full_name': 'Cycling Endothelial Cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Cycling Mononuclear Phagocyte CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Mononuclear Phagocyte', 'full_name': 'cycling mononuclear phagocyte', 'paper_synonyms': 'cycMNP', 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte FALSE +Cycling Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Myofibroblast', 'full_name': 'Cycling myofibroblast', 'paper_synonyms': 'cycMyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE +Cycling Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Cycling natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:4033071 cycling natural killer cell FALSE +Cycling Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Proximal Tubule Epithelial Cell', 'full_name': 'Cycling Proximal Tubule Epithelial Cell', 'paper_synonyms': 'PT; Cyc', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +DCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT', 'full_name': 'distal convoluted tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT1', 'full_name': 'distal convoluted tubule cell (type 1)', 'paper_synonyms': 'DCT; distal convoluted tubule', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule FALSE +DCT2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT2', 'full_name': 'distal convoluted tubule cell 2', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule FALSE +DTL CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL', 'full_name': 'descending thin limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +DTL1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL1', 'full_name': 'descending thin limb cell type 1', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +DTL2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL2', 'full_name': 'descending thin limb 2', 'paper_synonyms': 'DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +DTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL3', 'full_name': 'descending thin limb 3', 'paper_synonyms': 'descending thin limb; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Degenerative Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Ascending Thin Limb Cell', 'full_name': 'degenerative ascending thin limb (ATL) cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +Degenerative Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Connecting Tubule Cell', 'full_name': 'degenerative connecting tubule (CNT) cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +Degenerative Cortical Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Intercalated Cell Type A', 'full_name': 'degenerative cortical intercalated cell type A', 'paper_synonyms': 'IC; intercalated cells', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell FALSE +Degenerative Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Thick Ascending Limb Cell', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE +Degenerative Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Descending Thin Limb Cell Type 3', 'full_name': 'Degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Degenerative Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Distal Convoluted Tubule Cell', 'full_name': 'Degenerative distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +Degenerative Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Endothelial Cell', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Degenerative Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Fibroblast', 'full_name': 'degenerative fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast FALSE +Degenerative Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Inner Medullary Collecting Duct Cell', 'full_name': 'degenerative inner medullary collecting duct cell', 'paper_synonyms': 'IMCD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE +Degenerative Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Fibroblast', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE +Degenerative Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Thick Ascending Limb Cell', 'full_name': 'Degenerative Medullary Thick Ascending Limb Cell', 'paper_synonyms': 'M-TAL; TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE +Degenerative Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'degenerative medullary principal cells; dM-PCs', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE +Degenerative Peritubular Capilary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell FALSE +Degenerative Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Podocyte', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'POD', 'tissue_context': ''} CL:0000653 podocyte TRUE +Degenerative Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Proximal Tubule Epithelial Cell', 'full_name': 'degenerative proximal tubule epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Degenerative Vascular Smooth Muscle Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Vascular Smooth Muscle Cell', 'full_name': 'degenerative vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE +Descending Thin Limb Cell Type 1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 1', 'full_name': 'descending thin limb cell type 1 (DTL1)', 'paper_synonyms': 'DTL1', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Descending Thin Limb Cell Type 2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 2', 'full_name': 'descending thin limb cell type 2', 'paper_synonyms': 'DTL2', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 3', 'full_name': 'descending thin limb cell type 3', 'paper_synonyms': 'DTL3; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Descending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Vasa Recta Endothelial Cell', 'full_name': 'endothelial cell of the descending vasa recta', 'paper_synonyms': 'EC-DVR', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE +Distal Convoluted Tubule Cell Type 1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 1', 'full_name': 'Distal convoluted tubule cell type 1', 'paper_synonyms': 'DCT1', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule FALSE +Distal Convoluted Tubule Cell Type 2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 2', 'full_name': 'Distal Convoluted Tubule Cell Type 2', 'paper_synonyms': 'DCT2', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule FALSE +EC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +EC-AEA CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AEA', 'full_name': 'endothelial cells of the afferent/efferent arterioles', 'paper_synonyms': 'AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole FALSE +EC-AVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AVR', 'full_name': 'endothelial cell, vasa recta', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE +EC-DVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-DVR', 'full_name': 'endothelial cells of the vasa recta', 'paper_synonyms': None, 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE +EC-GC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-GC', 'full_name': 'glomerular capillary endothelial cell', 'paper_synonyms': 'glomerular capillaries; EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell FALSE +EC-LYM CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-LYM', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': None, 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel FALSE +EC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-PTC', 'full_name': 'endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'FIB', 'full_name': 'fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast FALSE +Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': 'Fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast FALSE +Glomerular Capillary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Glomerular Capillary Endothelial Cell', 'full_name': 'glomerular capillaries', 'paper_synonyms': 'EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell FALSE +IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC', 'full_name': 'intercalated cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE +IC-B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC-B', 'full_name': 'intercalated cells B', 'paper_synonyms': 'IC; intercalated cells', 'tissue_context': ''} CL:0002201 renal beta-intercalated cell FALSE +IMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMCD', 'full_name': 'inner medullary collecting duct', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE +IMM CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Inner Medullary Collecting Duct Cell', 'full_name': 'inner medullary collecting duct cell', 'paper_synonyms': 'IMCD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE +Intercalated Cell Type B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Intercalated Cell Type B', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE +Lymphatic Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Lymphatic Endothelial Cell', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': 'EC-LYM', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel FALSE +M-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-FIB', 'full_name': 'medullary fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE +M-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-IC-A', 'full_name': 'intercalated cells', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE +M-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-PC', 'full_name': 'medullary principal cell', 'paper_synonyms': 'principal cells (PC)', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell TRUE +M-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-TAL', 'full_name': 'medullary thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE +M2 Macrophage CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M2 Macrophage', 'full_name': 'M2 Macrophage', 'paper_synonyms': 'MAC-M2', 'tissue_context': ''} CL:0000890 alternatively activated macrophage FALSE +MAC-M2 CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MAC-M2', 'full_name': 'M2 macrophage', 'paper_synonyms': 'M2 macrophages', 'tissue_context': ''} CL:0000890 alternatively activated macrophage FALSE +MAST CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MAST', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell TRUE +MC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell FALSE +MD CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MD', 'full_name': 'macula densa cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000850 macula densa epithelial cell FALSE +MDC CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MDC', 'full_name': 'monocyte-derived cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0011031 monocyte-derived dendritic cell FALSE +MYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MYOF', 'full_name': 'myofibroblast', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE +Macula Densa Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Macula Densa Cell', 'full_name': 'macula densa cell', 'paper_synonyms': 'MD', 'tissue_context': ''} CL:1000850 macula densa epithelial cell FALSE +Mast Cell CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Mast Cell', 'full_name': 'mast cell', 'paper_synonyms': 'MAST', 'tissue_context': ''} CL:0000097 mast cell TRUE +Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Fibroblast', 'full_name': 'medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE +Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Thick Ascending Limb Cell', 'full_name': 'medullary thick ascending limb cell', 'paper_synonyms': 'M-TAL; TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE +Mesangial Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Mesangial Cell', 'full_name': 'mesangial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000650 mesangial cell FALSE +Monocyte-derived Cell CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Monocyte-derived Cell', 'full_name': 'monocyte-derived cell', 'paper_synonyms': 'MDCs', 'tissue_context': ''} NO MATCH found FALSE +Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Myofibroblast', 'full_name': 'myofibroblast', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE +N CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'N', 'full_name': 'neutrophils', 'paper_synonyms': 'MPO+ cells', 'tissue_context': ''} CL:0000775 neutrophil TRUE +NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NEU', 'full_name': 'neuronal cell', 'paper_synonyms': 'Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0000540 neuron FALSE +NKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NKC/T', 'full_name': 'T cells', 'paper_synonyms': 'T', 'tissue_context': ''} CL:0000084 T cell FALSE +Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Natural Killer Cell / Natural Killer T Cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:0000623 natural killer cell FALSE +Neutrophil CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'neutrophil', 'paper_synonyms': 'N; MPO+ (N)', 'tissue_context': ''} CL:0000775 neutrophil TRUE +Non-classical Monocyte CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Non-classical Monocyte', 'full_name': 'Non-classical Monocyte', 'paper_synonyms': 'ncMON', 'tissue_context': ''} CL:0000875 non-classical monocyte TRUE +OMCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-IC-A', 'full_name': 'outer medullary collecting duct intercalated cells', 'paper_synonyms': 'OMCD; IC; intercalated cells', 'tissue_context': ''} CL:1000717 kidney outer medulla collecting duct intercalated cell FALSE +OMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-PC', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; OMCD; outer medullary collecting duct', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE +Outer Medullary Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'full_name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'paper_synonyms': 'OMCD; IC', 'tissue_context': ''} CL:4030015 kidney collecting duct alpha-intercalated cell FALSE +Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; principal cells (PC); medullary principal cell (M-PC)', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE +PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PC', 'full_name': 'principal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell FALSE +PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PEC', 'full_name': 'parietal epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell TRUE +PT-S1/2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S1/2', 'full_name': 'proximal tubule S1/S2', 'paper_synonyms': 'PT-S1/PT-S2', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +PT-S3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S3', 'full_name': 'proximal tubule S3', 'paper_synonyms': 'proximal tubule (PT)', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 FALSE +PapE CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PapE', 'full_name': 'papillary tip epithelial cells abutting the calyx', 'paper_synonyms': None, 'tissue_context': ''} CL:0000731 urothelial cell FALSE +Papillary Tip Epithelial Cell CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Papillary Tip Epithelial Cell', 'full_name': 'Papillary tip epithelial cell', 'paper_synonyms': 'PapE', 'tissue_context': ''} CL:0000731 urothelial cell FALSE +Parietal Epithelial Cell CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Parietal Epithelial Cell', 'full_name': 'parietal epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell TRUE +Peritubular Capilary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell FALSE +Plasma Cell CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasma Cell', 'full_name': 'Plasma cell', 'paper_synonyms': 'PL', 'tissue_context': ''} CL:0000786 plasma cell TRUE +Plasmacytoid Dendritic Cell CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasmacytoid Dendritic Cell', 'full_name': 'Plasmacytoid dendritic cell', 'paper_synonyms': 'pDC', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell FALSE +Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Podocyte', 'full_name': 'podocyte', 'paper_synonyms': 'PODs', 'tissue_context': ''} CL:0000653 podocyte TRUE +Proximal Tubule Epithelial Cell Segment 1 / Segment 2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 1 / Segment 2', 'full_name': 'Proximal tubule epithelial cell, segments 1 and 2', 'paper_synonyms': 'PT-S1; PT-S2; PT-S1/PT-S2; PT', 'tissue_context': ''} CL:1000838 kidney proximal convoluted tubule epithelial cell FALSE +Proximal Tubule Epithelial Cell Segment 3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 3', 'full_name': 'Proximal tubule epithelial cell, segment 3', 'paper_synonyms': 'PT-S3; PT', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 FALSE +REN CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'REN', 'full_name': 'juxtaglomerular renin-producing granular cells', 'paper_synonyms': 'renin-producing granular cells', 'tissue_context': ''} CL:0000648 kidney granular cell FALSE +Renin-positive Juxtaglomerular Granular Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Renin-positive Juxtaglomerular Granular Cell', 'full_name': 'juxtaglomerular renin-producing granular (REN) cell', 'paper_synonyms': 'renin-producing granular (REN) cells; REN; juxtaglomerular renin-producing granular cells (REN)', 'tissue_context': ''} CL:0000648 kidney granular cell FALSE +SC/NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'SC/NEU', 'full_name': 'Schwann/neuronal', 'paper_synonyms': 'SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell TRUE +Schwann Cell / Neural CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Schwann Cell / Neural', 'full_name': 'Schwann/neuronal cell', 'paper_synonyms': 'SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell TRUE +T CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T', 'full_name': 'T cell', 'paper_synonyms': 'CD3+ cells; lymphoid or T cells', 'tissue_context': ''} CL:0000084 T cell TRUE +T Cell CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T Cell', 'full_name': 'T cell', 'paper_synonyms': 'T; CD3+ cells', 'tissue_context': ''} CL:0000084 T cell TRUE +TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'C-TAL; M-TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +Transitional Principal-Intercalated Cell CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Transitional Principal-Intercalated Cell', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +VSM/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSM/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'vascular smooth muscle cell; pericyte; VSMC', 'tissue_context': ''} NO MATCH found FALSE +VSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSM/P; pericyte', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE +VSMC/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC; vascular smooth muscle cell; pericyte', 'tissue_context': ''} CL:0008034 mural cell FALSE +Vascular Smooth Muscle Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE +Vascular Smooth Muscle Cell / Pericyte CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell / Pericyte', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC', 'tissue_context': ''} CL:0008034 mural cell FALSE +aFIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aFIB', 'full_name': 'adaptive fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast FALSE +aPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aPT', 'full_name': 'adaptive proximal tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +aTAL1 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL1', 'full_name': 'adaptive thick ascending limb 1', 'paper_synonyms': 'aTAL; aEpi', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +aTAL2 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL2', 'full_name': 'adaptive thick ascending limb 2', 'paper_synonyms': 'adaptive TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +cDC CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE +cycCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycCNT', 'full_name': 'cycling connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +cycDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycDCT', 'full_name': 'cycling distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +cycEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycEC', 'full_name': 'cycling endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +cycMNP CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMNP', 'full_name': 'cycling', 'paper_synonyms': None, 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte FALSE +cycMYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMYOF', 'full_name': 'cycling myofibroblasts', 'paper_synonyms': 'MyoF; cycMyoF; myofibroblasts', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE +cycNKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycNKC/T', 'full_name': 'cycling T cells', 'paper_synonyms': 'T; T cells', 'tissue_context': ''} CL:4033069 cycling T cell FALSE +cycPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycPT', 'full_name': 'cycling proximal tubule cell', 'paper_synonyms': 'PT; cycling', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +dATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dATL', 'full_name': 'degenerative ascending thin limb', 'paper_synonyms': 'ATL; ascending thin limbs', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +dC-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-IC-A', 'full_name': 'degenerative cortical intercalated cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE +dC-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-TAL', 'full_name': 'degenerative cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL); cortical thick ascending limb (C-TAL)', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE +dCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dCNT', 'full_name': 'degenerative connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +dDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDCT', 'full_name': 'degenerative distal convoluted tubule cells', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +dDTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDTL3', 'full_name': 'degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +dEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +dEC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC-PTC', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +dFIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dFIB', 'full_name': 'degenerative fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast FALSE +dIMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dIMCD', 'full_name': 'degenerative inner medullary collecting duct', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE +dM-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-FIB', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE +dM-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-PC', 'full_name': 'degenerative medullary principal cell', 'paper_synonyms': 'dM-PCs', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell TRUE +dM-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +dOMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dOMCD-PC', 'full_name': 'degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; OMCD; outer medullary collecting duct; degenerative medullary principal cells (dM-PCs)', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE +dPOD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPOD', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'POD', 'tissue_context': ''} CL:0000653 podocyte TRUE +dPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPT', 'full_name': 'degenerative proximal tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +dVSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dVSMC', 'full_name': 'degenerative vascular smooth muscle cell', 'paper_synonyms': 'VSMC; vascular smooth muscle cell; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE +endothelial cells CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'endothelial cells', 'full_name': 'endothelial cells', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell TRUE +epithelial cells CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE +immune cells CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'IMM', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +ncMON CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ncMON', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000875 non-classical monocyte TRUE +neural cells CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'neural cells', 'full_name': 'neural cell types', 'paper_synonyms': 'neuronal; Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell TRUE +pDC CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'pDC', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell FALSE +stroma cells CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': 'stromal cells', 'paper_synonyms': 'stroma; STR', 'tissue_context': ''} CL:0000499 stromal cell FALSE +stroma cells CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000499 stromal cell FALSE +tPC-IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'tPC-IC', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': 'principal cells (PC); intercalated cells (IC)', 'tissue_context': ''} CL:1001225 kidney collecting duct cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..65cb3d5 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,20 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Astrocytes CL:0000127 astrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Astrocytes', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} +Endo/Pericytes CL:0000115 endothelial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Endo/Pericytes', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} +Excitatory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_1', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_10 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_10', 'full_name': 'excitatory neuron 10', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_2', 'full_name': 'excitatory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_3', 'full_name': 'excitatory neuron 3', 'paper_synonyms': 'excitatory neurons', 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_4', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_5 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_5', 'full_name': 'excitatory neuron 5', 'paper_synonyms': 'excitatory neurons', 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_6 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_6', 'full_name': 'excitatory neuron 6', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_7 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_7', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_8 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_8', 'full_name': 'excitatory neuron 8', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_9 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_9', 'full_name': 'excitatory neuron 9', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Inhibitory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_1', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron +Inhibitory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_2', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron +Inhibitory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_3', 'full_name': 'inhibitory neuron 3', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron +Inhibitory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_4', 'full_name': 'inhibitory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000617 GABAergic neuron +Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Microglia', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000129 microglial cell +OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'OPCs', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell +Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..7255207 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,18 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Excitatory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_1', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_10 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_10', 'full_name': 'excitatory neuron 10', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_2', 'full_name': 'excitatory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_3', 'full_name': 'excitatory neuron 3', 'paper_synonyms': 'excitatory neurons', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_4', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_5 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_5', 'full_name': 'excitatory neuron 5', 'paper_synonyms': 'excitatory neurons', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_6 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_6', 'full_name': 'excitatory neuron 6', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_7 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_7', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_8 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_8', 'full_name': 'excitatory neuron 8', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_9 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_9', 'full_name': 'excitatory neuron 9', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Inhibitory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_1', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE +Inhibitory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_2', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE +Inhibitory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_3', 'full_name': 'inhibitory neuron 3', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE +Inhibitory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_4', 'full_name': 'inhibitory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE +Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Microglia', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000129 microglial cell TRUE +OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'OPCs', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell TRUE +Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..18cf7c6 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,17 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Activated fibroblasts CCL19 ADAMADEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Activated fibroblasts CCL19 ADAMADEC1', 'full_name': 'ADAMDEC+ Fibroblast clusters', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Endothelial cells CA4 CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CA4 CD36', 'full_name': 'Endothelial cells CA4+ CD36+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Endothelial cells CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CD36', 'full_name': 'CD36+ endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Endothelial cells DARC CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells DARC', 'full_name': 'DARC+ endothelial cells', 'paper_synonyms': 'ACKR1', 'tissue_context': ''} CL:0000115 endothelial cell +Endothelial cells LTC4S SEMA3G CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells LTC4S SEMA3G', 'full_name': 'Endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Fibroblasts ADAMDEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts ADAMDEC1', 'full_name': 'ADAMDEC1+ fibroblasts', 'paper_synonyms': 'ADAMDEC+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast +Fibroblasts KCNN3 LY6H CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts KCNN3 LY6H', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Fibroblasts NPY SLITRK6 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts NPY SLITRK6', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Fibroblasts SFRP2 SLPI CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SFRP2 SLPI', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Fibroblasts SMOC2 PTGIS CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SMOC2 PTGIS', 'full_name': 'SMOC2+ PTGIS+ fibroblasts', 'paper_synonyms': 'SMOC2+ PTGIS+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast +Glial cells CL:0000125 glial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Glial cells', 'full_name': 'Glial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000125 glial cell +Lymphatics CL:0000542 lymphocyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Lymphatics', 'full_name': 'Lymphatics', 'paper_synonyms': 'lymphatic endothelial cells', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel +Myofibroblasts GREM1 GREM2 CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts GREM1 GREM2', 'full_name': 'GREM1+ GREM2+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell +Myofibroblasts HHIP NPNT CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts HHIP NPNT', 'full_name': 'HHIP+ NPNT+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell +Pericytes HIGD1B STEAP4 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes HIGD1B STEAP4', 'full_name': 'Pericytes HIGD1B+ STEAP4+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte +Pericytes RERGL NTRK2 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes RERGL NTRK2', 'full_name': 'Pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..5ef9a7f --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,17 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Activated fibroblasts CCL19 ADAMADEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Activated fibroblasts CCL19 ADAMADEC1', 'full_name': 'ADAMDEC+ Fibroblast clusters', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +Endothelial cells CA4 CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CA4 CD36', 'full_name': 'Endothelial cells CA4+ CD36+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Endothelial cells CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CD36', 'full_name': 'CD36+ endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Endothelial cells DARC CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells DARC', 'full_name': 'DARC+ endothelial cells', 'paper_synonyms': 'ACKR1', 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Endothelial cells LTC4S SEMA3G CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells LTC4S SEMA3G', 'full_name': 'Endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Fibroblasts ADAMDEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts ADAMDEC1', 'full_name': 'ADAMDEC1+ fibroblasts', 'paper_synonyms': 'ADAMDEC+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Fibroblasts KCNN3 LY6H CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts KCNN3 LY6H', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +Fibroblasts NPY SLITRK6 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts NPY SLITRK6', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +Fibroblasts SFRP2 SLPI CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SFRP2 SLPI', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +Fibroblasts SMOC2 PTGIS CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SMOC2 PTGIS', 'full_name': 'SMOC2+ PTGIS+ fibroblasts', 'paper_synonyms': 'SMOC2+ PTGIS+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Glial cells CL:0000125 glial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Glial cells', 'full_name': 'Glial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000125 glial cell TRUE +Lymphatics CL:0000542 lymphocyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Lymphatics', 'full_name': 'Lymphatics', 'paper_synonyms': 'lymphatic endothelial cells', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel FALSE +Myofibroblasts GREM1 GREM2 CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts GREM1 GREM2', 'full_name': 'GREM1+ GREM2+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE +Myofibroblasts HHIP NPNT CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts HHIP NPNT', 'full_name': 'HHIP+ NPNT+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE +Pericytes HIGD1B STEAP4 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes HIGD1B STEAP4', 'full_name': 'Pericytes HIGD1B+ STEAP4+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE +Pericytes RERGL NTRK2 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes RERGL NTRK2', 'full_name': 'Pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..e337b26 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,3 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +H1 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H1', 'full_name': 'H1 horizontal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0004217 H1 horizontal cell +H2 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H2', 'full_name': 'H2 horizontal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0004218 H2 horizontal cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..c887a9e --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,3 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +H1 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H1', 'full_name': 'H1 horizontal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0004217 H1 horizontal cell FALSE +H2 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H2', 'full_name': 'H2 horizontal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0004218 H2 horizontal cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..3c4800c --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,18 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Astrocytes CL:0000127 astrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Astrocytes', 'full_name': 'astrocytes', 'paper_synonyms': None, 'tissue_context': ''} +Endothelial CL:0000115 endothelial cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Endothelial', 'full_name': 'endothelial cells', 'paper_synonyms': '', 'tissue_context': ''} +L2-L3 Intratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L2-L3 Intratelencephalic', 'full_name': 'L2-L3 intratelencephalic', 'paper_synonyms': None, 'tissue_context': ''} CL:4030059 L2/3 intratelencephalic projecting glutamatergic neuron +L3-L5 Intratelencephalic Type 1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 1', 'full_name': 'L3-L5 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron +L3-L5 Intratelencephalic Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 2', 'full_name': 'L3-L5 intratelencephalic type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron +L5 Extratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5 Extratelencephalic', 'full_name': 'L5 Extratelencephalic', 'paper_synonyms': None, 'tissue_context': ''} CL:4023041 L5 extratelencephalic projecting glutamatergic cortical neuron +L5-L6 Near Projecting CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5-L6 Near Projecting', 'full_name': 'L5-L6 near projecting', 'paper_synonyms': None, 'tissue_context': ''} CL:4030067 L5/6 near-projecting glutamatergic neuron +L6 Corticothalamic / L6B CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Corticothalamic / L6B', 'full_name': 'L6 corticothalamic / L6B', 'paper_synonyms': 'L6 corticothalamic; L6B', 'tissue_context': ''} CL:4023042 L6 corticothalamic-projecting glutamatergic cortical neuron +L6 Intratelencephalic - Type 1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 1', 'full_name': 'L6 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron +L6 Intratelencephalic - Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 2', 'full_name': 'L6 intratelencephalic type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron +Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Microglia', 'full_name': 'microglia', 'paper_synonyms': None, 'tissue_context': ''} CL:0000129 microglial cell +OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'OPCs', 'full_name': 'oligodendrocyte progenitor cells', 'paper_synonyms': 'oligodendrocyte progenitor cells; OPCs', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell +Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': 'oligodendrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte +Parvalbumin interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Parvalbumin interneurons', 'full_name': 'parvalbumin interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023018 pvalb GABAergic interneuron +SV2C LAMP5 Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'SV2C LAMP5 Interneurons', 'full_name': 'SV2C LAMP5 Interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023011 lamp5 GABAergic interneuron +Somatostatin Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Somatostatin Interneurons', 'full_name': 'Somatostatin Interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023017 sst GABAergic interneuron +VIP Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'VIP Interneurons', 'full_name': 'VIP Interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023016 VIP GABAergic interneuron diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..bc4b120 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,16 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +L2-L3 Intratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L2-L3 Intratelencephalic', 'full_name': 'L2-L3 intratelencephalic', 'paper_synonyms': None, 'tissue_context': ''} CL:4030059 L2/3 intratelencephalic projecting glutamatergic neuron FALSE +L3-L5 Intratelencephalic Type 1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 1', 'full_name': 'L3-L5 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron FALSE +L3-L5 Intratelencephalic Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 2', 'full_name': 'L3-L5 intratelencephalic type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron FALSE +L5 Extratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5 Extratelencephalic', 'full_name': 'L5 Extratelencephalic', 'paper_synonyms': None, 'tissue_context': ''} CL:4023041 L5 extratelencephalic projecting glutamatergic cortical neuron FALSE +L5-L6 Near Projecting CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5-L6 Near Projecting', 'full_name': 'L5-L6 near projecting', 'paper_synonyms': None, 'tissue_context': ''} CL:4030067 L5/6 near-projecting glutamatergic neuron FALSE +L6 Corticothalamic / L6B CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Corticothalamic / L6B', 'full_name': 'L6 corticothalamic / L6B', 'paper_synonyms': 'L6 corticothalamic; L6B', 'tissue_context': ''} CL:4023042 L6 corticothalamic-projecting glutamatergic cortical neuron FALSE +L6 Intratelencephalic - Type 1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 1', 'full_name': 'L6 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron FALSE +L6 Intratelencephalic - Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 2', 'full_name': 'L6 intratelencephalic type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron FALSE +Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Microglia', 'full_name': 'microglia', 'paper_synonyms': None, 'tissue_context': ''} CL:0000129 microglial cell TRUE +OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'OPCs', 'full_name': 'oligodendrocyte progenitor cells', 'paper_synonyms': 'oligodendrocyte progenitor cells; OPCs', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell TRUE +Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': 'oligodendrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte TRUE +Parvalbumin interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Parvalbumin interneurons', 'full_name': 'parvalbumin interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023018 pvalb GABAergic interneuron FALSE +SV2C LAMP5 Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'SV2C LAMP5 Interneurons', 'full_name': 'SV2C LAMP5 Interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023011 lamp5 GABAergic interneuron FALSE +Somatostatin Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Somatostatin Interneurons', 'full_name': 'Somatostatin Interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023017 sst GABAergic interneuron FALSE +VIP Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'VIP Interneurons', 'full_name': 'VIP Interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023016 VIP GABAergic interneuron FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1007_s00401-023-02599-5.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1007_s00401-023-02599-5.txt new file mode 100644 index 0000000..cbcf236 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1007_s00401-023-02599-5.txt @@ -0,0 +1,146 @@ +Cryptic exon detection and transcriptomic changes revealed in single-nuclei RNA sequencing of C9ORF72 patients spanning the ALS-FTD spectrum +The C9ORF72-linked diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are characterized by the nuclear depletion and cytoplasmic accumulation of TAR DNA-binding protein 43 (TDP-43). Recent studies have shown that the loss of TDP-43 function leads to the inclusion of cryptic exons (CE) in several RNA transcript targets of TDP-43. Here, we show for the first time the detection of CEs in a single-nuclei RNA sequencing (snRNA-seq) dataset obtained from frontal and occipital cortices of C9ORF72 patients that phenotypically span the ALS-FTD disease spectrum. We assessed each cellular cluster for detection of recently described TDP-43-induced CEs. Transcripts containing CEs in the genes STMN2 and KALRN were detected in the frontal cortex of all C9ORF72 disease groups with the highest frequency in excitatory neurons in the C9ORF72-FTD group. Within the excitatory neurons, the cluster with the highest proportion of cells containing a CE had transcriptomic similarities to von Economo neurons, which are known to be vulnerable to TDP-43 pathology and selectively lost in C9ORF72-FTD. Differential gene expression and pathway analysis of CE-containing neurons revealed multiple dysregulated metabolic processes. Our findings reveal novel insights into the transcriptomic changes of neurons vulnerable to TDP-43 pathology. +Supplementary Information +The online version contains supplementary material available at 10.1007/s00401-023-02599-5. +Introduction +Nuclear depletion and cytoplasmic aggregation of the TAR DNA-binding protein 43 (TDP-43) is a pathological hallmark of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) patients caused by a hexanucleotide repeat expansion mutation in the C9ORF72 gene (C9-ALS and C9-FTD). It is also the primary pathology seen in sporadic ALS, most familial forms of ALS (with the exception of FUS and SOD1 mutations), and 50% of sporadic and familial FTD cases, and has also been described in other neurodegenerative diseases, such as Alzheimer's disease. While in ALS with TDP-43 inclusions (ALS-TDP), TDP-43 pathology is primarily observed within neurons and glia in the spinal cord, motor cortex and brainstem motor nuclei, the distribution and morphology of TDP-43 inclusions in FTD with TDP-43 inclusions (FTD-TDP), particularly in C9ORF72 mutation carriers, is more heterogenous. It is currently unknown if there are cell types within the cortex that are more susceptible to TDP-43 mislocalization. +TDP-43 is a DNA/RNA binding protein that performs a variety of functions associated with RNA metabolism including transcription, splicing, transport, and stabilization. A recently described phenotype governed by the mislocalization of TDP-43 in ALS-TDP and FTD-TDP is a failure of TDP-43 to repress the inclusion of cryptic exons (CEs) in several RNA transcript targets of TDP-43. The inclusion of CEs within these transcripts can introduce a frameshift or premature stop codons, destabilize the mRNA, resulting in aberrant transcripts being targeted for nonsense-mediated decay (NMD), or code for aberrant peptides that might have deleterious effects if produced. +Two well-characterized CEs that occur as a result of the loss-of-function of TDP-43 in ALS-TDP and FTD-TDP are in the genes STMN2 and UNC13A. For STMN2, loss-of-function of TDP-43 results in the inclusion of a CE, exon 2a, between exon 1 and exon 2. This introduces a premature stop codon and polyadenylation signal into the STMN2 mRNA transcript. A similar phenomenon occurs in UNC13A transcripts as a CE gets included in UNC13A mRNA between exon 20 and exon 21 upon loss-of-function of TDP-43. This cryptic splicing event results in a decrease in UNC13A mRNA and protein, likely because the mRNA is degraded by the process of NMD. The CEs in both STMN2 and UNC13A transcripts were detected by bulk RNA sequencing in postmortem tissue from patients with ALS-TDP and FTD-TDP, and their detection is considered a marker for TDP-43 pathology. In addition to STMN2 and UNC13A, several other genes have demonstrated alternative splicing due to a loss-of-function of TDP-43, including several genes involved in synaptic function, such as KALRN, RAPGEF6 and SYT7. +Despite being able to detect TDP-43-induced CEs in patient post-mortem tissue, thus far it has not been possible to determine which specific cell types these CEs originate from. Understanding the cellular origin of these CEs will provide a better insight into the cell types that are vulnerable to TDP-43 pathology. This is of particular interest in FTD-TDP where the cell types of the frontal cortex that are impacted by TDP-43 pathology are less well-defined than the cells of the spinal cord, motor cortex and brainstem motor nuclei that are known to be susceptible to TDP-43 pathology in ALS-TDP. +In this study, we performed single-nuclei RNA sequencing (snRNA-seq) on the frontal cortex of C9ORF72 mutation carriers that spanned the ALS-FTD phenotypic spectrum (C9-ALS, C9-ALS/FTD, C9-FTD) to identify individual cells that contained CEs in known splicing targets of TDP-43. CEs in the transcripts of STMN2 and KALRN were detected in multiple neuronal subtypes, as well as non-neuronal cell types, in all C9ORF72 disease groups in a manner reflective of the expected level of TDP-43 pathological burden in the frontal cortex for each group (C9-FTD > C9-ALS/FTD > C9-ALS). Furthermore, we determined that L5 extra telencephalic neurons were susceptible to TDP-43 pathology by quantifying the number of cells containing CEs. When we analyzed the transcriptome of CE-containing cells, genes in the NMD pathway were dysregulated, a known cellular pathway involved in the clearance of some CE-affected mRNAs. This data provides a novel perspective on the cell type and number of cells with detectable TDP-43-induced CEs in C9ORF72 patients and provides insight into the transcriptional changes in these cells. +Methods +Postmortem tissue samples +Frozen, post-mortem frontal and occipital cortex tissue samples from C9ORF72 patients and control donors without neurological disorders were obtained from the Target ALS Human Postmortem Tissue Core, the Queen Square Brain Bank for Neurological Disorders and the Banner Sun Health Research Institute Brain and Body Donation Program. Individuals were properly consented to participation at each biorepository. Disease groups were determined based on the pathological diagnosis of disease provided by the brain banks (ALS only, FTD only, ALS-FTD and Control). Demographic information on the tissues used in this study is provided in Supplementary Table 1. +Isolation and purification of nuclei +Frozen frontal cortex tissue (50 mg) was dounce homogenized in 1 mL of Nuclei Lysis buffer [Nuclei EZ Lysis Buffer (Sigma-Aldrich, St. Louis, MO, USA), 1 x cOmplete Protease Inhibitor Cocktail (Sigma-Aldrich, St. Louis, MO, USA), and RNasin Plus (Promega)] 10-15 times using pestle A "loose" followed by pestle B "tight'' 10-15 times (DWK Life Sciences, Millville, NJ, USA). Homogenate was passed through a 70 microm 1.5 mL mini strainer (PluriSelect, El Cajon, CA, USA) and centrifuged at 500 rcf for 5 min at 4 C. Nuclei pellet was resuspended in an additional 1 mL of Nuclei Lysis buffer and incubated for 5 min followed by centrifugation at 500 rcf for 5 min at 4 C. 500 microl of 1 x wash buffer (1 x PBS, 2% BSA, 0.2 U/microl RNasin Plus) is added to the nuclei pellet and incubated for 5 min to allow adequate buffer exchange followed by centrifugation at 500 rcf for 5 min at 4 C, repeated once more and resuspend in 500 microl of 1 x wash buffer. Resuspended nuclei were incubated in 1-2 drops of NucBlue Live ReadyProbes Reagent (ThermoFisher Scientific) and immediately sorted using the DAPI channel on the Sony SH800S (Sony Biotechnology, San Jose, CA, USA) with a 100 microm chip. +10x Genomics snRNA-seq and sequencing +Nuclei were sorted for 15,000 events directly into 10 x 3 v3 RT Reagent Master Mix and immediately processed with the 10x Genomics Chromium Next GEM Single Cell 3 v3 kit (10x Genomics, Pleasanton, CA). To minimize batch effects, each 10x chip contained samples from all disease and control groups. Samples were loaded, cDNA amplified, and library constructed following the manufacturer's protocol. Library quality control (QC) was based on Agilent TapeStation 4200 HS D1000 screentapes (Agilent Technologies, Waldbronn, Germany). Multiplexed library pool was based on Agilent TapeStation 4200 HS D1000 and Kapa Library Quantification Kit for Illumina platforms (Kapa BioSystems, Boston, MA) and sequenced at shallow depths on Illumina's iSeq 100 v2 flow cell for 28 x 8x91 cycles for estimated reads per cell. After demultiplexing, libraries were rebalanced based on reads per cell. Normalized pool QC was based on Agilent TapeStation 4200 HS D1000 and Kapa Library Quantification Kit for Illumina platforms and high depth sequenced on Illumina's NovaSeq 6000 S4 v1.5 flow cell for 28 x 8 x 91 cycles. +SnRNA-Seq quality control filtering and normalization +BCL files from NovaSeq S4 flowcells were processed using Cell Ranger v. 5.0.1 (10x Genomics) using cell ranger mkfastq to make fastq files for each sample. Each sample was then processed with cell ranger count using the human reference database provided by 10x Genomics (gex-GRCh38-2020-A) using the -include-introns option, as recommended for single-nuclei data. All resulting filtered counts matrices (filtered_feature_bc_matrix files) for all samples were loaded into scanpy and combined into a single gene expression matrix using concatenate function. Resulting data object was then filtered to remove any nuclei with > 5% ribosomal genes, > 5% mitochondrial genes, > 0.1% hemoglobin genes and less than 200 total genes. The highly expressed and variable lncRNA MALAT1 was removed from the data along with all ribosomal (RPL & RPS), mitochondrial (MT-) and hemoglobin (HB-) genes. Genes not expressed in at least 3 nuclei were also removed. Doublets were then identified and removed using scrublets. The resulting, quality filtered, data matrix was normalized using the computeSumFactors function from the scran R package. This package performs a scaling normalization of single-cell RNA-seq data by deconvolving size factors from cell clusters. Following size factor normalization, the counts data were logarithmized using the log1p function in scanpy and the resulting log-transformed gene counts data was stored as a data object for downstream analysis. +Batch correction and cluster generation +To facilitate accurate cluster determination, batch correction was performed using scanorama between samples (batch_key = 'sample') on the top 4000 most highly variable genes, as determined using highly_variable_genes(flavor = 'cell_ranger') with the integrate_scanpy() function. Principal component analysis (PCA) and nearest neighbor calculations were performed using batch-corrected data (n_neighbors = 50, n_pcs = 50, use_rep = "Scanorama"), followed by uniform manifold approximation and projection (UMAP) generation. Graph clustering was performed using the Leiden algorithm in scanpy with a cluster resolution of 0.3. Samples from some subjects were prepared more than once to balance sample loading on the 10x Genomics chips, to provide equal numbers of males, females or diseases and controls with each run. In this case, samples from the same subject were combined by subject ID (Supplementary Table 1, online resource). +Cluster cell type annotation +Leiden clusters were annotated using established marker genes from cortex samples by analyzing dot plots and UMAPs depicting the expression levels of marker genes found in each Leiden cluster. The following marker gene list was used: AQP4 and GFAP (astrocytes), EPAS1 and CLDN5 (endothelial cells), RBFOX3 (neurons), SATB2, SLC17A7 and NRGN (excitatory neurons), GAD1 and GAD2 (inhibitory neurons), ADAM28 and APBB1IP (microglia), RNF220 and ST18 (oligodendrocytes), PDGFRA and SMOC1 (oligodendrocyte progenitor cells; OPCs). This resulted in one astrocyte cluster, one endothelial cell cluster, eight excitatory neuron clusters, four inhibitory neuron clusters, one microglia cluster, one oligodendrocyte cluster and one OPC cluster. In the frontal cortex, we were able to further define the excitatory and inhibitory neuron clusters using marker genes. The following marker gene list was used: SNAP25 (neuron marker), SATB2 (excitatory neurons), CUX2, PCDH8 and CCDC88C (L2-L3 intratelencephalic), RORB, TWIST2 and ALDH1A1 (L3-L5 intratelencephalic type 1), PKD2L1 and ABCC12 (L3-L5 intratelencephalic type 2), HTR2C, ADAMTS12 and NPSR1 (L5-L6 near projecting), POU3F1, ABCB11 and SLC66A1L (L5 extratelencephalic), OPRK1 (L6 intratelencephalic type 1), SMYD1 and SNTB1 (L6 intratelencephalic type 2), SYT6, CTGF, and GALR1 (L6 corticothalamic / L6B), GAD1 (inhibitory neurons), SST, HGF and LHX6 (somatostatin interneurons), PVALB and CALN1 (parvalbumin interneurons), VIP and NR2F2-AS1 (VIP interneurons), SV2C and LAMP5 (SVC2 LAMP5 interneurons). Differences in cell type abundance between disease groups were calculated using stat_compare_means() in R using the Wilcoxon test method. +Cryptic exon junction analysis +Junctions were identified as described in Brown et al.. Briefly, fastq files were aligned to the human genome (GRCh38) using STAR with ENCODE standard alignment parameters. Resulting BAM files were then analyzed with regtools junctions extract (options: -a 6 -m 30 -M 500000) to generate junction files, which include novel junctions, for all samples. Leafcutter's leafcutter_cluster_regtools.py (options: -m 10 -p 0.0001) was then used to generate a summary counts matrix of all junctions found in the dataset. For single-nuclei data, each sample was aligned to the human genome using cellranger counts (described above) and each sample's BAM files were split into multiple cell-type specific BAM files using each cell's 10x gem barcode and the cluster annotation for that barcode, as determined above. Further, nuclei-level BAMs were generated by parsing each sample's BAM file and splitting by 10x barcodes to generate one BAM file per barcode. Each per-sample cell-type and nuclei-specific BAM file was then processed independently through regtools (using the above parameters) and all resulting junction files were collapsed using leafcutter (using the above parameters) to generate a summary counts matrix of all junctions found in all individual nuclei. A list of 66 alternatively spliced genes due to TDP-43 mislocalization was recently published. Using this list of 66 genes and the NeuN(+) TDP-43(+) and TDP-43(-) nuclear sequencing data from Lui et al., we identified genomic coordinates and trained our detection pipeline for CEs using the methods outlined above starting with regtools. We selected junctions that were found to have an average of > 10 counts in TDP-43 negative samples, and a fold change > 20 when compared to matched TDP-43 positive samples in the data. We then tested these genomic coordinates in the single nuclei data, and a junction was considered detected in the 10x Genomics data if it was found at an average expression level > 0.2 in C9ORF72 samples with a fold change > 10 when compared to matched controls. CE junctions were defined by their overlap between those found in our single-nuclei data and those in our re-analysis of the data from Lui et al.. This resulted in two, well-detected CE junctions (STMN2 chr8:79,611,214-79,616,822 and KALRN chr3:124,701,255-124,702,038). +Cryptic exon differential gene expression and pathway analysis +For identified STMN2 or KALRN CEs, we subset the single-nuclei expression data to only C9-FTD samples with a detected CE, and only to excitatory neuron clusters with a detected CE. From this subset of nuclei we used normalized, log-transformed genes count matrices to perform differential expression within each cluster between cells with and cells without an STMN2 or KALRN CE using MAST with the covariate formula: ~ CE_YN + sex + number_of_genes_in_the_cell. Multiple corrections testing (FDR) using the Hurdle Model was implemented to give the adjusted p-values reported. Differentially expressed genes lists from each cluster were subset to genes with a p-value < 0.05 and abs(log2FC > 0.1) and analyzed using the Gene Ontology Biological Process (GOBP) pathway database in clusterProfiler. The pathways with the lowest q-value were subjected to Gene Set Enrichment Analysis (GSEA), performed using the fgsea package in R using the same gene lists as the above clusterProfiler analysis. Barcode plots from the GSEA analysis were plotted using the plotEnrichment() function in fgsea. +Deeply sequenced subject:C9-FTD 4 +To explore the effect of sequencing depth on CE detection a single subject was re-sequenced to a depth of 4,183,288,336 reads (252,629 reads per cell). The single subject was chosen for the high number of CEs detected and the inclusion of CEs in L5 extra telencephalic neurons. This subject had 13,079 cells across two libraries that were pooled equimolarly and sequenced on one lane of an S4 flowcell using the XP 4-Lane workflow using 101 x 12 x x12 x 101 cycles. Cellranger counts were used as above to align this sample to the human genome and generate a counts table for each 10x cell barcode in the sample. Regtools were run as described above to find KALRN and STMN2 CE junctions on the annotated cell clusters. Nuclei barcodes containing a KALRN or STMN2 CE were flagged in scanpy and highlighted on a UMAP. Differential expression analysis was then performed between nuclei that did or did not contain a KALRN or STMN2 CE in the L2/L3 intratelencephalic neuron cluster with the covariate formula: ~ CE_YN + number_of_genes_in_the_cell. Pathway analysis using the GOBP database was then performed on this differentially expressed gene list using clusterProfiler. GSEA was performed using fsea on the pathway with the lowest qvalue or on a significant pathway of interest. +Coverage analysis +Excitatory neurons (902) from subject C9-FTD 4 were analyzed for gene coverage in both regular and deeply sequenced BAM files. BAM files were generated by filtering the BAMs generated by cell ranger count from C9-FTD 4 to the same cells in both datasets. Coverage was determined for BAMs using bedtools coverage with the -d option to output coverage at each base. Coverage files were filtered to all exons in the ensembl primary (RefSeq; 34,791,404) isoform of each gene. Filtered coverage files were then parsed to determine the coverage at each base pair along each gene - going away from the 3 end of the gene as measured from the terminal base in the final exon in the gene. Distance from the 3 end was measured as the total exon distance, meaning distance was calculated using the full lengths of all exons in the primary transcript. Coverage at each base location was then averaged across all genes with any coverage in the data set. The coverage per gene was then divided by the number of included cells (902) to give the average coverage per gene per cell that is reported. Average coverage per gene per cell is plotted against the distance away from the 3 end of the gene. +IGV analysis +BAM files from our single-nuclei data were loaded into IGV to further investigate the presence of CE junctions. For sashimi plots, the junction tracks were filtered to only junctions with > 10 counts to focus on well-supposed junctions. This allows visual identification of the canonical exon-exon and novel exon-cryptic exon junctions in these datasets. To visually compare coverage depth within a gene region between samples, coverage (pile-up) track y-axis was scaled based on the relative number of genome-mapped reads in the sample. +Data availability +The snRNA seq raw data are available at Synapse (syn45351388; 10.7303/syn45351388). Interactive data visualization and exploration of gene expression using CELLxGENE; https://cellxgene.cziscience.com/collections/aee9c366-f2fb-470b-8937-577d5d87d3fc. +Results +Detection of STMN2 and KALRN cryptic exons in single-nuclei sequencing data from subjects with C9-ALS, C9-ALS-FTD, and C9-FTD. a Schematic diagram illustrating the workflow of nuclei isolation, single nuclei sequencing and analysis from the frontal and occipital cortices of subjects with C9-ALS (n = 10), C9-ALS-FTD (n = 6), and C9-FTD (n = 9), and aged-matched controls (n = 12). b UMAP depicting the 270,731 single nuclei sequenced from frontal cortex tissue separating into 17 distinct clusters. c Cell-type annotation performed based on the expression of previously described marker genes for each cell type in the frontal cortex. The size of the dot represents fraction of cells in which the marker gene was detected, and the color represents the average expression level in the cluster. d An IGV plot of the full-length STMN2 gene. The top track shows data from the combined excitatory neuron clusters from control subjects (n = 12). The bottom track shows data from the combined excitatory neuron clusters from subjects with C9-FTD (n = 9). e Box plot of the average STMN2 CE junctions detected per subject in each group. There is a significant increase in the detection of the STMN2 CE between control subjects and C9-ALS/FTD (p = 0.00048; Wilcoxon test) and C9-FTD (p = 0.00079; Wilcoxon test) in the frontal cortex. There was also a significant increase in STMN2 CE detection in the occipital cortex between control and C9-FTD subjects (p = 0.044, Wilcoxon test). f, Stacked bar plot displaying the number of subjects in which an STMN2 CE was detected in the frontal cortex (left) and occipital cortex (right). g An IGV plot of the region of the KALRN gene containing the CE. The top track shows data from the combined excitatory neuron clusters from control subjects (n = 12). The bottom track shows data from the combined excitatory neuron clusters from patients with C9-FTD (n = 9). h Box plot of the average KARLN CE junctions detected per subject in each group. There is a significant increase in the detection of the KALRN CE in C9-FTD (p = 0.00042; Wilcoxon test) in the frontal cortex. i, Stacked bar plot displaying the number of individuals in which a KALRN CE was detected in the frontal cortex (left) and occipital cortex (right) +Demographics of post-mortem samples used in this study + Number of subjects Sex Age of onset (years +- SD) Age at death (years +- SD) Disease duration (years +- SD) Control 12 6 F: 6 M n/a 77.08 +- 11.96 n/a C9-ALS 10 5 F: 5 M 53.22 +- 8.47 57.10 +- 8.46 2.59 +- 1.73 C9-ALS/FTD 6 3 F: 3 M 65.17 +- 7.49 67.17 +- 6.31 1.89 +- 0.98 C9-FTD 9 4 F: 5 M 58.56 +- 4.75 66.22 +- 5.05 7.67 +- 2.06 +C9-ALS and C9-FTD exist on a disease spectrum sharing clinical and neuropathological similarities including progressive loss of neurons and TDP-43 pathology. To elucidate transcriptional differences, including TDP-43-associated CE inclusion, at a cell-specific level, we performed snRNA-seq on the frontal (disease-affected) and occipital (a less affected brain region) cortices of 12 neurologically normal controls and 25 C9ORF72 patients whose clinical diagnosis spanned the ALS-FTD spectrum (C9-ALS n = 10, C9-ALS/FTD n = 6, C9-FTD n = 9) (Fig. 1a and Table 1; Supplementary Table 1, online resource). Following snRNA-seq and quality-control filtering, a total of 270,731 single nuclei were sampled from the frontal cortex, with a median of 3,509 genes and 9,218 transcripts detected per nucleus, and a total of 191,494 single nuclei were sampled from the occipital cortex with a median of 3,371 genes and 8,759 transcripts detected per nucleus. Visualization of the single-nuclei transcriptomes in uniform manifold approximation and projection (UMAP) space revealed unbiased separation of nuclei in the frontal cortex into 17 distinct clusters (Fig. 1b), with nuclei from both sexes and all disease types distributed across all clusters (Supplementary Fig. 1 a-d, online resource). Each cluster of nuclei in the frontal cortex was annotated on the basis of the expression of known cell-type-enriched markers for layer-specific and/or subtype-specific cortical neurons, in addition to non-neuronal cells (oligodendrocytes, oligodendrocyte progenitor cells (OPCs), astrocytes, microglia and endothelial cells) (Fig. 1b, c). The major neuronal and non-neuronal cell types were also identified in the occipital cortex by marker genes (Supplementary Fig. 1e, f, online resource). The number of cells varied within the tissue isolated; therefore, we provided the number of cells identified in each cell type across disease states, displayed in Supplementary Table 2 (online resource). +The identification of CE-containing cells could help identify cell populations vulnerable to TDP-43 pathology, thus we next sought to determine whether known CEs in transcriptional targets of TDP-43 could be detected in single cells within our snRNA-seq dataset. STMN2 is currently the best-studied example of a gene in which TDP-43 dysfunction results in the inclusion of a CE between exon 1 and exon 2 in STMN2 transcripts. In our snRNA-seq dataset, we had read coverage of the STMN2 gene in excitatory neurons across the full length of the STMN2 transcript from the 5 to the 3 end of the gene, including the CE junction between exons 1 and 2 (Fig. 1d). As detection of the annotated or CE junction required the presence of a sequencing read crossing the junction, read coverage in this region enabled detection of the annotated exon 1 to exon 2 junction, as well as the CE junction (Fig. 1d). Figure 1d illustrates the detection of the annotated exon 1 to exon 2 junction in excitatory neurons in control subjects, and the addition of the CE junction between these exons in excitatory neurons isolated from C9-FTD subjects. Quantification of the number of STMN2 CE junctions detected in the excitatory neurons in each of the disease groups showed that there was a significant increase in STMN2 CEs in the C9-FTD group compared to controls in both the frontal cortex (p = 0.00079; Wilcoxon test) and the occipital cortex (p = 0.044; Wilcoxon test), as well as a significant increase in the C9-ALS/FTD group compared to controls in the frontal cortex (p = 0.00048; Wilcoxon test) (Fig. 1e). In total, the STMN2 CE junction was detected in the frontal cortex of 7 of 9 subjects in the C9-FTD group, 6 of 6 subjects in the C9-ALS/FTD group, and 4 of 10 subjects in the C9-ALS group (Fig. 1f). The STMN2 CE junction was also detected in 1 of 12 control subjects in the frontal cortex (Fig. 1f). Interestingly, STMN2 CE junctions were also detected in the occipital cortex in all three of the C9ORF72 disease groups, although in fewer subjects than in the frontal cortex (Fig. 1f). To our knowledge, this is the first-time single cells containing TDP-43-associated CEs have been detected and described in human postmortem brain tissue in a snRNA-seq dataset. Furthermore, given that the presence of CEs in known TDP-43 target transcripts is a potential indication of TDP-43 dysfunction, this is also the first transcriptomic description of single cells with TDP-43 dysfunction. +We next sought to detect CEs in other TDP-43 transcript targets. KALRN was recently demonstrated to include a CE in the absence of TDP-43. Using the bulk RNA sequencing dataset from Liu et al. (2019), which described the transcriptome of TDP-43 positive and negative nuclei from FTD-ALS postmortem brain with TDP-43 pathology, we confirmed the presence of a CE junction in the KALRN gene between exons 56 and 57, near the 3 end of the gene (Supplementary Fig. 2b, online resource). This CE junction could also be detected in excitatory neurons in our snRNA-seq dataset. Figure 1g illustrates the detection of the canonical exon 56 to exon 57 junction in excitatory neurons in control subjects, and the addition of the CE junction between these exons in excitatory neurons derived from C9-FTD subjects (Fig. 1g). Similar to the STMN2 CE, the number of KALRN CE junctions detected was significantly higher in the frontal cortex of C9-FTD subjects compared to controls (p = 0.00042; Wilcoxon test) (Fig. 1h). In total, the KALRN CE junction was detected in the frontal cortex of 7 of 9 subjects in the C9-FTD group, 1 of 6 subjects in the C9-ALS/FTD group, and 1 of 10 subjects in the C9-ALS group (Fig. 1i). It was also detected in 1 of 12 control subjects in the frontal cortex (Fig. 1i). In the occipital cortex the KALRN CE junction was not detected in any control or C9-ALS/FTD subjects and was only detected in 1 of 9 subjects in the C9-FTD group and 1 of 10 subjects in the C9-ALS group. +Another transcript described to include a CE upon depletion of TDP-43 in cellular models and in FTD-ALS postmortem tissue with TDP-43 pathology is UNC13A. We were unable to detect this CE junction in any nuclei in our snRNA-seq dataset (Supplementary Fig. 2a, online resource). Potential explanations for the lack of detection may include the lower average gene count for UNC13A compared with STMN2 and KALRN in our dataset (Supplementary Fig. 2b, c, online resource), or low read coverage in the region of the UNC13A gene containing the CE (Supplementary Fig. 2a, online resource). Another possibility for not being able to detect the CE in UNC13A is the length of the CE from the 3 end of the gene. The 10x Genomics sample preparation kits used for generating these data have a significant 3 coverage bias because the priming for cDNA synthesis occurs at the poly(A) tail. We investigated how significant this bias was with respect to gene length. Supplementary Fig. 3a (online resource) displays the average read coverage, per cell, across the length of all genes identified. Files were filtered to include all exons, to make the longest possible isoform (using Ensembl), and then used as a reference to determine the coverage at each base pair starting from the 3 end of the gene (where 10x Genomics priming starts), as measured by the end of the final exon in the gene. These data display higher coverage, as expected, at the 3 end of the gene and low coverage towards the 5 end. We then examined the largest list of potential CEs generated by the loss of TDP-43 function; 66 genes described by Ma et al. (2022) to be differentially spliced or to contain CEs in the TDP-43 negative neuronal nuclei obtained from Liu et al. (2019) dataset. Supplementary Table 3 (online resource) provides data on which of these genes we were able to detect a CE in the Liu et al. dataset and their chromosomal coordinates. We then looked for the detection of these CEs in our single nuclei data. For reference, the UNC13A CE is approximately 5627 base pairs (bp) from the 3 end of the gene, using the longest possible isoform. This makes coverage over this region less likely with 10x Genomics RNASeq data. In contrast, the STMN2 CE is approximately 227 bp from a premature polyadenylation site, making it within the peak of the 10x Genomics read depth. We looked at IGV plots for the three genes with the predicted closest CEs to the 3 end; TRAPPC12, MADD, RAP1GAP (Supplementary Fig. 3b, d, f, online resource). There was very little read coverage in the region of the CEs for these genes, but this is possibly due to the low levels of expression of these genes (Supplementary Fig. 3c, e, g, h, online resource) compared with STMN2 and KALRN (Supplementary Fig. 2b, c, online resource). +Detection of CEs for STMN2 and KALRN, displayed as counts for each cell type and across each disease + Total Cells STMN2 CE Counts KALRN CE Counts CE Detected/ Cells in Cluster Control(n = 13) C9-ALS(n = 10) C9-ALS-FTD(n = 6) C9-FTD(n = 9) Control C9-ALS C9-ALS-FTD C9-FTD Control C9-ALS C9-ALS-FTD C9-FTD C9-ALS C9-ALS-FTD C9-FTD L2-L3 Intratelencephalic 11,132 23,300 9,763 11,619 2 12 13 38 0 2 5 38 0.0006 0.0018 0.0065 L3-L5 Intratelencephalic:Type 1 3952 3660 1888 2418 0 0 0 2 0 0 0 2 - - 0.0017 L3-L5 Intratelencephalic:Type 2 4759 4337 2491 2844 0 0 5 22 0 0 2 2 - 0.0028 0.0084 L5-L6 Near Projecting 548 547 302 619 0 0 0 0 0 0 0 0 - - - L5 Extratelencephalic 258 230 163 53 0 0 0 6 0 0 1 2 - 0.0061 0.1509 L6 Intratelencephalic:Type 1 3554 2414 1543 2124 0 2 8 9 1 0 0 1 0.0008 0.0052 0.0047 L6 Intratelencephalic:Type 2 873 671 366 736 0 0 0 0 0 0 0 5 - - 0.0068 L6 Corticothalamic / L6B 2519 1879 944 1447 0 0 0 0 0 0 0 1 - - 0.0007 Somatostatin Interneurons 3611 7505 2875 3436 0 0 3 0 0 0 0 0 - 0.0010 - Parvalbumin interneurons 315 515 237 280 0 0 0 0 0 0 0 0 - - - VIP Interneurons 2222 4750 1879 3103 0 0 0 2 0 0 0 0 - - 0.0006 SV2C LAMP5 Interneurons 1098 1461 663 1748 0 0 0 0 0 0 0 0 - - - Astrocytes 8048 8760 3184 10,565 0 3 0 3 1 0 0 0 0.0003 - 0.0003 Microglia 2328 2834 696 2631 0 0 0 2 0 0 0 0 - - 0.0008 Oligodendrocytes 23,771 26,536 7702 22,291 0 0 2 1 0 0 0 0 - 0.0003 0.0000 Total 68,988 89,399 34,696 65,914 2 17 31 85 2 2 8 51 +Numbers were bolded to highlight the non-zero values in columns 6-13, as this represents cell types where cryptic exons were detected +After identifying CEs in STMN2 and KALRN by looking at the combined excitatory neurons, we then investigated whether a specific cell subtype was more likely to contain these CEs. The STMN2 CE junction was detected in several excitatory and inhibitory neuronal subtypes, and non-neuronal cells across the C9ORF72 disease groups with the highest abundance in L2-L3 intratelencephalic neurons in C9-FTD subjects (Table 2). The KALRN CE junction was almost exclusively detected in excitatory neuron subtypes, with no KALRN CE junctions detected in any inhibitory neuron subtype or non-neuronal cells in C9ORF72 subjects, with the highest abundance in L2-L3 intratelencephalic neurons in C9-FTD subjects (Table 2). The differences in the cell type distribution of STMN2 and KALRN CEs may be reflective of expression levels of the STMN2 and KALRN genes in different cell types. STMN2 gene expression is similar across all neuronal subtypes and lower in glial cells, while KALRN is expressed highest in excitatory neurons, lower in inhibitory neurons and astrocytes, and lower still in microglia and oligodendrocytes (Supplementary Fig. 2e, online resource). To determine which cell type was most vulnerable to CEs in STMN2 and KALRN, we first normalized the number of CE junctions detected to the number of cells in each cluster (last three columns of Table 2). This revealed that there was a disproportionately high number of CE junctions detected in C9-FTD subjects in a very small cell cluster, L5 extratelencephalic neurons. Interestingly, the total cell counts for this neuronal subtype were found to be decreased in subjects with C9-FTD vs control subjects (cell numbers across groups are provided in Table 2; p = 0.043, Wilcoxon test). +STMN2 and KALRN CE detection in the four cell types with the highest number of CEs in C9-FTD. a-d UMAPs displaying the nuclei that contain a CE in KALRN (orange), STMN2 (blue), or both KALRN and STMN2 (green) laid over the non-CE containing nuclei (grey) in the a, L2-L3 intratelencephalic cluster, b the L3-L5 intratelencephalic type 2 neuron cluster, c the L6 intratelencephalic type 1 neuron cluster, and d, the L5 extratelencephalic neuron cluster. e Dot plot displaying the expression levels of Von Economo neurons marker genes taken from Hodge et al. in each excitatory neuron cluster. The size of dot represents fraction of cells in which the marker gene was detected. f Pathway analysis using the differentially expressed genes between cells with detected CEs (STMN2 and KALRN CE-containing cells combined) and cells with no CE detected in the L2-L3 intratelencephalic neuron cluster shown in (a). g-h Gene set enrichment data for the pathways with the lowest q value (0.0071) following clusterProfiler are displayed as barcode plots. g +Oxidative phosphorylation and h +ATP Synthesis Couple Electron Transport, showing an upregulation of gene expression; highest log2fc on the left to lowest on the right +As the presence of CEs in known TDP-43 target transcripts is thought to be an indication of TDP-43 dysfunction, we next investigated transcriptional changes occurring in cells containing the STMN2 and KALRN CEs to provide insight into transcriptional effects of TDP-43 dysfunction. We first assessed whether TARDBP expression levels differed across cell types in control patient samples, and if this accounted for the vulnerability of certain cell populations to TDP-43 pathology and the incorporation of CEs. The mean expression of TARDBP in control nuclei was similar across all neuronal subtypes and large increases or decreases in expression were not associated with the neuronal subtypes that contained higher numbers of CE-containing transcripts (Supplementary Table 4, online resource). We next focused on the four cell clusters that had the highest number of CEs in C9-FTD: L2-L3 intratelencephalic neurons, L3-L5 intratelencephalic Type 2 neurons, L6 intratelencephalic Type 1 neurons and L5 extratelencephalic neurons. For each of these neuronal subtypes we identified every cell that contained a CE junction and visualized them in UMAP space (Fig. 2a-d). The L5 extratelencephalic neurons had the highest number of CEs detected in individuals with C9-FTD, relative to the size of the cell cluster, with 8 CEs detected in 53 neurons (0.1509 CEs detected per cell). Interestingly, we found that these cells express transcriptional marker genes that resemble Von Economo Neurons (VENs) and Fork cells based on a list of common extratelencephalic markers and markers found to be enriched in VENs generated by Hodge et al., who used snRNA-seq to characterize the transcriptome of VENs and Fork cells in Layer 5 of the human frontoinsular cortex (Fig. 2e). The L5 extratelencephalic neurons in our dataset display increased expression of these marker genes indicating that they have transcriptomic similarities to VENs and Fork cells of the frontoinsular cortex (Fig. 2e). +As the L2-L3 intratelencephalic neuron cluster contained the highest number of CE containing cells (n = 58), we performed pathway analysis on differentially expressed genes identified between the CE-containing cells and the non-CE-containing cells (Supplementary Table 4, online resource) of the cluster using clusterProfiler and the Gene Ontology Biological Process pathway database (Fig. 2f). The two pathways with the lowest q-value (q = 0.0071), 'Oxidative phosphorylation' and 'ATP synthesis coupled electron transport', were assessed via Gene Set Enrichment Analysis (Fig. 2g, h). Both pathways display an increased expression in genes related to ATP and energy metabolism in the cells. +Cryptic exon detection for STMN2 and KALRN in a deeply sequenced subject (C9-FTD 4) +Subject: C9-FTD 4 Number of cells sequenced Baseline Sequencing Deep Sequencing STMN2 CE junctions KALRN CE junctions CE detected/ Cells in cluster STMN2 CE junctions KALRN CE junctions CE detected/ Cells in cluster L2-L3 Intratelencephalic 2,830 8 6 0.005 83 53 0.048 L3-L5 Intratelencephalic:Type 1 582 1 0 0.002 10 0 0.017 L3-L5 Intratelencephalic:Type 2 901 13 0 0.014 93 6 0.110 L5-L6 Near Projecting 169 0 0 0.000 0 0 0.000 L5 Extratelencephalic 37 6 0 0.162 24 0 0.649 L6 Intratelencephalic:Type 1 674 4 0 0.006 36 0 0.053 L6 Intratelencephalic:Type 2 178 0 0 0.000 0 0 0.000 L6 Corticothalamic/L6B 315 0 0 0.000 0 0 0.000 Somatostatin Interneurons 876 0 0 0.000 0 0 0.000 Parvalbumin interneurons 94 0 0 0.000 0 0 0.000 VIP Interneurons 498 0 0 0.000 0 0 0.000 SV2C LAMP5 Interneurons 276 0 0 0.000 0 0 0.000 Astrocytes 1644 1 0 0.001 6 0 0.004 Microglia 267 0 0 0.000 0 0 0.000 Oligodendrocytes 3738 0 0 0.000 0 0 0.000 Total 13,079 33 6 0.003 252 59 0.024 +Deep sequencing reveals a larger number of STMN2 and KALRN CE-containing nuclei. a UMAP displaying the 6 excitatory neuron clusters in which STMN2 or KALRN CEs were detected. b Overlay on the UMAP in (a). Medium grey dots depict nuclei in which the annotated junction between exon 1 and exon 2 in STMN2 was detected at the baseline level of sequencing, while additional cells containing an annotated junction identified after deeper sequencing are depicted in black circles. c An overlay of the nuclei in which an STMN2 CE junction was detected in the baseline sequencing (light blue) and additional junctions detected with the deep sequencing (dark blue). d Box plot displaying normalized counts of STMN2 in the excitatory neurons containing an annotated STMN2 junction compared with excitatory neurons containing an STMN2 CE junction in subject C9-FTD 4 (p = 0.000017, t-test). e Overlay on the UMAP in (a). Medium grey dots depict nuclei in which the annotated junction between exon 56 and exon 57 in KALRN was detected at the baseline level of sequencing, while additional cells containing an annotated junction identified after deeper sequencing are depicted in black circles. f An overlay of the nuclei in which a KALRN CE junction was detected in the baseline sequencing (light orange) and additional junctions detected with the deep sequencing (dark orange). g Box plot displaying normalized counts of KALRN in excitatory neurons containing an annotated KALRN junction compared with excitatory neurons containing a KALRN CE junction in subject C9-FTD 4 (p = 0.17, t-test). h Pathway analysis using the differentially expressed genes between cells with detected CEs in deeply sequenced data (STMN2 and KALRN CE-containing cells combined) and cells with no CE detected. i, Barcode plot of gene set enrichment analysis displays upregulation of gene expression in the pathway with the lowest q value (q = 1.24e-04) (f), establishment of protein localization to endoplasmic reticulum. j Gene set enrichment analysis displays upregulation of gene expression in the significant (q = 0.008) pathway nuclear-transcribed mRNA catabolic process; nonsense-mediated decay +The detection of CE junctions in our snRNA-seq data is likely an underestimation due to the sparse nature of the 10x Genomics technology used to sequence the data, as there is less read depth and shorter read lengths than is typically acquired with bulk RNA sequencing. We, therefore, evaluated whether increasing the read depth would increase the number of cells with CE junctions detected and increase the total number of detected junctions per cell. We chose to deeply sequence one subject (C9-FTD 4) in our data because this subject had the largest number of STMN2 CE junctions in the cell cluster with the highest proportion of CEs, the L5 extratelencephalic neurons. Following deeper sequencing, the number of reads in the C9-FTD 4 sample increased from 519,837,269 to a total of 4,183,288,336 reads, and the average number of reads per cell increased from 36,897 to 252,629. The number of CE junctions detected in both STMN2 and KALRN increased by approximately eightfold following deeper sequencing, with the number of STMN2 CE detected increasing from 33 to 252, and KALRN CE detection increasing from 6 to 59 (Table 3). In the L5-extratelencephalic cluster, the number of CEs detected per the number of cells in the cluster increased from 0.16 to 0.65. To visualize the impact of deeper sequencing on the number of cells with a detectable CE junction, we displayed all the excitatory neuron clusters that had a detectable CE in UMAP space, 5202 cells separated into 6 clusters (Fig. 3a). We identified the cells where the annotated exon junction and the CE junctions were detected for both STMN2 (Fig. 3b, c) and KALRN (Fig. 3e, f). For STMN2 prior to deep sequencing, the annotated exon 1 to exon 2 junction was detected in 46 cells (Fig. 3b) and the CE junction was detected in 21 cells (Fig. 3c). Following deep sequencing, these numbers increased to 213 cells containing the annotated exon 1 to exon 2 junction and 58 cells containing the CE junction. Following baseline sequencing, the annotated exon 56 to exon 57 junction in KALRN was detected in 159 cells, which increased to 438 cells after deeper sequencing (Fig. 3e). However, detection of the KALRN CE junction only increased from 5 to 14 cells following deeper sequencing (Fig. 3f). For KALRN, deeper sequencing had less of an impact on the detection of CE junctions, possibly because KALRN is already more highly expressed in these cells (Supplementary Fig. 2d, e, online resource). We also assessed whether the inclusion of a CE, a potential indicator of TDP-43 pathology, affected the expression level of each of these genes. The mean expression of STMN2 was significantly reduced in deeply sequenced cells containing STMN2 transcripts with the inclusion of the CE, compared to deeply sequenced cells containing STMN2 transcripts with the annotated junction (p = 0.000017; t-test) (Fig. 3d, and using all neurons in Supplementary Fig. 4a, online resource). This pattern of reduced STMN2 expression in cells containing CE-containing transcripts was also observed using the Liu et al. (2019) dataset, as TDP-43 negative nuclei (CE-containing nuclei) had a significantly lower expression of STMN2 than TDP-43 positive nuclei (p = 0.0028; t-test) (Supplementary Fig. 4c, online resource). In contrast to STMN2 expression, there was no significant difference in the mean expression of KALRN in cells containing KALRN transcripts with the inclusion of the CE, compared to cells containing KALRN transcripts with the annotated junction (p = 0.17; t-test) (Fig. 3g, and using all neurons in Supplementary Fig. 4b, online resource). This differed from the Liu et al. (2019) dataset, where TDP-43 negative nuclei (CE-containing nuclei) had a significantly higher mean expression of KALRN compared to TDP-43 positive nuclei (p = 0.0088; t-test) (Supplementary Fig. 4d, online resource). +As deeper sequencing increased the number of cells presumed to be affected by TDP-43 dysfunction due to the presence of STMN2 and/or KALRN CE junctions, we again performed differential gene expression analysis on the largest neuronal subgroup (L2-L3 intratelencephalic neurons) between nuclei containing a CE (either STMN2 and/or KALRN) and nuclei not containing a CE for the deeply sequenced subject; C9-FTD 4 (Supplementary Table 5, online resource). Pathway analysis of the DEGs found the most significantly affected biological pathway impacted in CE-containing cells to be 'protein localization to the endoplasmic reticulum' (q = 1.24e-04; Fig. 3h). We also examined the significant 'nonsense mediated decay pathway' (q = 0.008) (Fig. 3h). These two pathways were assessed with Gene Set Enrichment Analysis and barcode plots to indicate the direction of expression of the genes (Fig. 3i, j). Both pathways showed an upregulation of gene expression in CE containing cells. +Discussion +The GGGGCC (G4C2) hexanucleotide repeat expansion in the first intron of the C9ORF72 gene is the most common genetic abnormality associated with ALS and FTD. While TDP-43 pathology is one of the hallmarks of C9ORF72 disease, the molecular and cellular mechanisms underlying cortical neurodegeneration in this disease remain largely understudied, and the cell-type specific impacts of TDP-43 pathology are unknown. Here, we performed snRNA-seq analyses of frontal and occipital cortices from all subgroups of C9ORF72 patients with the goal of providing a comprehensive, cell-type specific transcriptomic profile across the C9ORF72 spectrum. In addition, using this dataset, we show for the first time the detection of CEs in single nuclei for two known splicing targets of TDP-43 and, using this as a hallmark of TDP-43 dysregulation, we assessed gene expression changes in these cells to provide insight into the transcriptome of individual cells with TDP-43 pathology. +Several recent studies indicate that nuclear depletion of TDP-43 leads to dysregulation in pre-mRNA splicing and results in a failure of TDP-43 repressing the inclusion of intronic sequences that become incorporated into mature mRNA as CEs. Detection of CEs in a specific transcriptional target of TDP-43, such as STMN2, is considered a hallmark of TDP-43 dysregulation and a potential way to identify cells with TDP-43 nuclear depletion. Initially, we focused on the detection of a previously described CE in STMN2. We also detected a recently identified CE in KALRN. The STMN2 CE is detected at higher rates in excitatory neurons than KALRN in these data, despite STMN2 being expressed at lower levels. The number of both these CEs is significantly higher in C9ORF72 tissues compared to controls. For STMN2, the number of CEs detected in each C9ORF72 subgroup reflected the expected level of TDP-43 pathological burden in the frontal cortex, with C9-FTD tissues having the highest STMN2 CE counts and C9-ALS tissues having the lowest. In contrast, the KALRN CE was more specific to the C9-FTD tissue. A possible explanation for this could be dose-dependent effects of TDP-43 dysfunction on CE inclusion, as has been suggested for STMN2 and UNC13A. It is possible that STMN2 requires a lower level of TDP-43 dysfunction for the CE to be included, while the inclusion of the CE in KALRN may require higher losses of TDP-43 function, although this hypothesis needs further testing. An alternative hypothesis is that different CE-containing transcripts may be vulnerable in the different phenotypic presentations of the C9ORF72 mutation, with KALRN CEs being more specific to an FTD presentation of disease, although the KALRN CE was recently identified in an analysis of ALS patient postmortem spinal cord, thus further investigation is required to explore this hypothesis. It is also interesting to note that CEs for STMN2 were found in seven occipital cortices having the C9ORF72 repeat expansion, and the KALRN CE was detected in two C9ORF72 cases in the occipital cortex, indicating that this brain region may not be unaffected by TDP-43 dysfunction. A careful examination of TDP-43 pathology and changes in the occipital cortex transcriptome should be performed in future studies. +After identifying CEs in STMN2 and KALRN in the combined excitatory neuron clusters, we investigated whether there were subtypes of excitatory neurons that were more susceptible to CEs in STMN2 and KALRN. Neuronal subtypes of the frontal cortex were annotated (Fig. 1c) using the expression of marker genes previously identified in a snRNA-seq study of the human frontal cortex. Although the cluster identified as L2-L3 intratelencephalic neurons contained the highest number of STMN2 and KALRN CEs (Table 2), the cluster with the greatest proportion of cells with CEs was the L5 extratelencephalic neuronal cluster. Despite being the smallest neuronal cell cluster identified in our dataset, the high proportion of cells containing a CE in this cluster may suggest that this neuronal subtype is vulnerable to TDP-43 dysfunction. In addition, the number of L5 extratelencephalic neurons identified in the frontal cortex of C9-FTD subjects was significantly lower than in controls, further supporting the idea that these cells may be vulnerable in disease. Further investigation into this neuronal subtype revealed that the L5 extratelencephalic excitatory neuron cluster has a similar transcriptional profile to VENs and Fork cells. Marker genes identified by Hodge et al., in a single-nuclei RNA sequencing analysis of layer 5 of the human frontoinsular cortex, described well-characterized VEN markers including, GABRQ, ADRA1A, and LYPD1 that were also enriched in the L5 extratelencephalic neurons. VENs are believed to be restricted to the anterior cingulate and frontoinsular cortex in humans; however, there are reports of VENs being present on the medial surface of the superior frontal gyrus (Brodmann Area 9) and polar region of medial Brodmann Area 10, albeit to a lesser extent than in the anterior cingulate. VENs and fork cells in C9ORF72-FTD cases are more likely to have TDP-43 aggregation and TDP-43 nuclear depletion than neighboring layer 5 neurons. The L5-L6 near projecting neuronal cluster had no detectable STMN2 or KALRN CEs (Table 2), thus it is possible that this cell type may be spared of TDP-43 dysfunction, or CEs in other transcripts not assessed in this study may be more prevalent in this cell type. Further studies are required to understand why some neuronal subtypes are more vulnerable than others to STMN2 or KALRN CEs, and by extension, TDP-43 dysfunction. +With regard to other cell types, a low number of STMN2 or KALRN CE-containing transcripts were detected in interneurons and glial cells. The lower detection in the glial cells is likely related to the higher expression of STMN2 and KALRN in neurons compared to glial cells (Supplementary Fig. 2c, online resource). Furthermore, it should be noted that most studies focused on identifying CEs in TDP-43 transcriptional targets have done so in neurons or neuronal cell-types, which may have resulted in a bias for transcriptional targets that are more specific to neurons. It is, therefore, possible that glial cells have different transcriptional targets of TDP-43 that harbor CEs in gene transcripts more relevant to these cell types. +Identifying specific cells that contained either an STMN2 or KALRN CE allowed us to explore the transcriptomic changes of those cell types in which we detect a CE and are likely affected by TDP-43 pathology. To obtain such a TDP-43 pathology-associated, cell-type specific transcriptomic signature, we focused on the L2-L3 intratelencephalic neuron cluster as this cluster contained the most cells containing CEs. Differential gene expression and subsequent pathway analysis revealed an increased expression in genes related to oxidative phosphorylation, ATP synthesis and energy metabolism in the cells containing either STMN2 or KALRN CEs. This indicates cells containing CE-containing transcripts that are likely associated with TDP-43 pathology have altered energy metabolism demands, which is a common feature linked to several neurodegenerative diseases. This finding also supports a previous study that reported destabilization of RNAs encoding oxidative phosphorylation and ribosome components in patient-derived C9-ALS cell models, in control induced pluripotent cells overexpressing TDP-43, and ALS and FTD postmortem brain and spinal cord. Together these findings implicate abnormalities in the oxidative phosphorylation and ribosomal pathways in ALS and FTD characterized by TDP-43 pathology. +Differential gene expression and pathway analysis performed on L2-L3 intratelencephalic neuron cluster in the deeply sequenced subject also implicated protein localization to the endoplasmic reticulum and NMD as two additional pathways that are upregulated in cells containing STMN2 or KALRN CEs. Given the findings for changes in protein localization in the ER and the changes in mitochondrial function found in Fig. 2f, it is possible that ER-mitochondrial signaling is impaired in CE-containing cells. ER-mitochondrial signaling has been found to be disrupted in many neurodegenerative diseases, including FTD and ALS (reviewed in). One important feature of ER-mitochondrial signaling is to bring the organelles into close proximity with tethering proteins, which have been shown to be disrupted in FTD and Alzheimer's disease. Disruptions in organelle signaling and proximity between the ER and mitochondria can lead to changes in ATP production, and mitochondrial function (Fig. 2f), as well as synaptic damage (Figs. 2f and 3h). This will be important to investigate in future studies. The dysregulation of 'nonsense mediated decay pathway' in CE-containing cells is interesting given that it has been suggested that many transcripts containing CEs are degraded by the process of NMD, as is hypothesized to happen with UNC13A. Furthermore, alternative splicing coupled to NMD (AS-NMD) is an important post-transcriptional mechanism for regulating gene expression and is known to be involved in maintaining homeostatic expression and autoregulation of many RNA-binding proteins. The reason for the upregulation of the NMD pathway is unclear from these data. It is possible that NMD-associated genes are upregulated in response to CE-containing transcripts as a mechanism to clear these transcripts from the cell. Alternatively, impairment of the AS-NMD process may be contributing to the inclusion of the CEs in transcripts, as the expression of many transcripts is regulated by this post-transcriptional mechanism. Given the well-described dysfunction of many RNA-binding proteins in ALS and FTD, including TDP-43, the upregulation of NMD and its roles as both a surveillance mechanism and homeostatic regulator should be explored further in the context of TDP-43 pathology and CE inclusion in disease. +We also observed that the mean expression level of STMN2 was reduced in CE-containing cells compared to cells containing the annotated junction (Fig. 3d and Supplementary Fig. 4a). This is in accordance with previous reports of reduced STMN2 mRNA expression in TDP-43 knock-down models that result in the production of the CE-containing transcript. In contrast, KALRN expression was not significantly altered in KALRN CE-containing cells in our snRNA-seq dataset, but analysis of the Liu et al. (2019) dataset indicated a significant increase in KALRN expression in TDP-43 negative cells. It is interesting that the inclusion of a CE has different consequences for the expression of STMN2 and KALRN, which may point to the CEs having different functional impacts on these two transcripts and should be explored further. +There are limitations to the interpretation of these data that should be addressed. As stated previously, one of the obvious caveats of 10x Genomics data is the strong 3 end bias and sparse transcript coverage in the data (Supplementary Fig. 3, online resource). For example, a CE in the UNC13A gene has been associated with TDP-43 dysfunction. We were unable to detect this CE, or other CEs reported in other genes in our snRNA-seq data set, despite the UNC13A gene being detectable in the frontal cortex of the tissues analyzed (Supplementary Fig. 2a, online resource). Technical issues associated with 10x Genomics single-nuclei sequencing resulted in low read coverage in the region of the UNC13A CE. Low read coverage can be due to low expression of the gene in individual cells (both STMN2, and especially KALRN, had higher gene expression than UNC13A), or the sparse nature of single nuclei data (low read depth within individual cells). The 3 additional genes that we displayed more fully in Supplementary Fig. 3, TRAPPC12, MADD, and RAP1GAP, had CEs predicted to be close to the 3 end, and these 3 genes had very low expression compared to KALRN (Supplementary Fig. 2b and 3h). The 3 end bias also likely impacts the lack of detection of the UNC13A CE, and other reported CEs because these CEs are located further from the 3 end of the gene. It is also important to note that the sequencing of nuclei, rather than cells, may hinder the ability to detect CEs because many of the transcript's sequenced are likely pre-spliced mRNAs and thus neither have an annotated exon junction nor CE. +Another important factor to consider when interpreting this data is the possibility of cells containing undetected CEs. As stated previously, in any of the analyses, we only detect a small number of reads that span the junctions of interest in this study. In the deeply sequenced sample, we detect a read crossing the annotated junctions for STMN2 (exon 1-2, approximately 1805 bp from 3 end) in 213 cells of 5202 (4%) and we detect the annotated KALRN junction between exons 56-57) in 438 cells of 5202 (8%). It is highly likely that the number of cells containing the annotated junctions is much higher, and our data therefore likely underestimates both the number of cells containing annotated junctions and CE junctions. This underestimation may mean that some cells labeled as non-CE-containing nuclei may contain an undetected CE. This caveat is important when considering the differentially expressed genes and pathway analysis performed in this study. +Additional limitations of this study are that we did not perform discovery for CEs in different cell clusters, and therefore, may have potentially missed CEs that are important and unique to each cell type, particularly glial cells. In addition, there are a small number of subjects used in these comparisons and the addition of more subjects in the validation of these findings would be advantageous. Furthermore, the variability of the precise sample location from tissue could have an impact on the number of cell types detected from each subject, and the potential to include rare cell types, such as L5 extratelencephalic cells. +In summary, we show for the first time the presence of CEs in known transcriptional targets of TDP-43 in select, neuronal subtypes and disease-associated brain regions using single-nuclei RNA-sequencing technology. This allowed us to assess the cell-type-specific transcriptomic changes of presumed cells affected by TDP-43 pathology within an endogenous disease context. This is particularly important as limited information on cell-type specific vulnerability to TDP-43 pathology in the frontal cortex of C9ORF72 patients has been available. These data provide a first insight into the disruption of cortical neuronal subtypes caused by the inclusion of CEs as a downstream consequence of pathologic TDP-43 nuclear depletion. Further investigations should consider the use of long-read sequencing technologies in combination with single-nuclei/single-cell separation to provide a more comprehensive quantification and description of CE-containing cells. +Supplementary Information +Below is the link to the electronic supplementary material. +Publisher's Note +Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. +Lauren M. Gittings, Eric B. Alsop have contributed equally. +Rita Sattler, Kendall Van Keuren-Jensen are shared senior authors. +Author contributions +LMG, MS and JA performed wet lab experiments. EBA performed bioinformatics analyses. LMG and EBA performed data interpretation. KVK-J, LMG, TGW and EBA wrote the manuscript with input from all authors. RS and KVK-J conceptualized and provided oversight of the study including experimental design, data interpretation and manuscript preparation. +Funding +Open access funding provided by SCELC, Statewide California Electronic Library Consortium. This project was supported by the U.S. Department of Defense (Grant no. #PR180487, R.S. and K.V.K.-J.). R.S. is supported by the Barrow Neurological Foundation. L.M.G. is supported by the ALS Association Milton Safenowitz Postdoctoral Fellowship Program and the Barrow Neurological Foundation. +Declarations +Conflict of interest +All authors declare no competing interests. +References +Cracking the cryptic code in amyotrophic lateral sclerosis and frontotemporal dementia: Towards therapeutic targets and biomarkers +Mechanism of STMN2 cryptic splice-polyadenylation and its correction for TDP-43 proteinopathies +Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type +TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A +Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer +Ensembl 2022 +Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS +Von Economo neurons and fork cells: a neurochemical signature linked to monoaminergic function +NOVA-dependent regulation of cryptic NMD exons controls synaptic protein levels after seizure +Von Economo neurons are present in the dorsolateral (dysgranular) prefrontal cortex of humans +MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data +The severity of behavioural symptoms in FTD is linked to the loss of GABRQ-expressing VENs and pyramidal neurons +The functional consequences of intron retention: alternative splicing coupled to NMD as a regulator of gene expression +Synaptic dysfunction in ALS and FTD: anatomical and molecular changes provide insights into mechanisms of disease +Gennady Korotkevich VS, Nikolay Budin, Boris Shpak, Maxim N. Artyomov, Alexey Sergushichev (2021) Fast gene set enrichment analysis. 10.1101/060012 +The VAPB-PTPIP51 endoplasmic reticulum-mitochondria tethering proteins are present in neuronal synapses and regulate synaptic activity +Von Economo Neurons in the Human Medial Frontopolar cortex +Von Economo neurons: cellular specialization of human limbic cortices? +Efficient integration of heterogeneous single-cell transcriptomes using Scanorama +ER-mitochondria tethering by PDZD8 regulates Ca(2+) dynamics in mammalian neurons +Conserved cell types with divergent features in human versus mouse cortex +Transcriptomic evidence that von Economo neurons are regionally specialized extratelencephalic-projecting excitatory neurons +Quantitative analysis of cryptic splicing associated with TDP-43 depletion +ALS-implicated protein TDP-43 sustains levels of STMN2, a mediator of motor neuron growth and repair +Disruption of ER-mitochondria signalling in fronto-temporal dementia and related amyotrophic lateral sclerosis +Disruption of endoplasmic reticulum-mitochondria tethering proteins in post-mortem Alzheimer's disease brain +Evidence for the widespread coupling of alternative splicing and nonsense-mediated mRNA decay in humans +Annotation-free quantification of RNA splicing using LeafCutter +TDP-43 repression of nonconserved cryptic exons is compromised in ALS-FTD +Loss of nuclear TDP-43 Is associated with decondensation of LINE retrotransposons +A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor +Molecular and cellular evolution of the primate dorsolateral prefrontal cortex +TDP-43 represses cryptic exon inclusion in the FTD-ALS gene UNC13A +Pathological TDP-43 distinguishes sporadic amyotrophic lateral sclerosis from amyotrophic lateral sclerosis with SOD1 mutations +The neuropathology associated with repeat expansions in the C9ORF72 gene +Subcortical TDP-43 pathology patterns validate cortical FTLD-TDP subtypes and demonstrate unique aspects of C9orf72 mutation cases +Frontotemporal dementia with the C9ORF72 hexanucleotide repeat expansion: clinical, neuroanatomical and neuropathological features +Endoplasmic reticulum-mitochondria signaling in neurons and neurodegenerative diseases +Premature polyadenylation-mediated loss of stathmin-2 is a hallmark of TDP-43-dependent neurodegeneration +TDP-43 pathology in Alzheimer's disease +Neurodegenerative diseases:is metabolic deficiency the root cause? +Auto-regulatory feedback by RNA-binding proteins +Clinical and neuropathologic heterogeneity of c9FTD/ALS associated with hexanucleotide repeat expansion in C9ORF72 +Neurons selectively targeted in frontotemporal dementia reveal early stage TDP-43 pathobiology +Limbic-predominant age-related TDP-43 encephalopathy (LATE): consensus working group report +Frontotemporal lobar degeneration TDP-43-immunoreactive pathological subtypes: clinical and mechanistic significance +Review: neuropathology of non-tau frontotemporal lobar degeneration +Ubiquitinated TDP-43 in frontotemporal lobar degeneration and amyotrophic lateral sclerosis +Ultraconserved elements are associated with homeostatic control of splicing regulators by alternative splicing and nonsense-mediated decay +Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia +Physiological functions and pathobiology of TDP-43 and FUS/TLS proteins +A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD +Distinctive neurons of the anterior cingulate and frontoinsular cortex: a historical perspective +Extensive cryptic splicing upon loss of RBM17 and TDP43 in neurodegeneration models +Abnormal RNA stability in amyotrophic lateral sclerosis +Single-cell genomics identifies cell type-specific molecular changes in autism +SCANPY: large-scale single-cell gene expression data analysis +Scrublet: computational identification of cell doublets in single-cell transcriptomic data +Transcriptomic landscape of von Economo neurons in human anterior cingulate cortex revealed by microdissected-cell RNA sequencing +clusterProfiler: an R package for comparing biological themes among gene clusters +Massively parallel digital transcriptional profiling of single cells +Alternative splicing and nonsense-mediated mRNA decay enforce neural specific gene expression +Integrated transcriptome landscape of ALS identifies genome instability linked to TDP-43 pathology \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_immuni_2023_01_002.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_immuni_2023_01_002.txt new file mode 100644 index 0000000..28ca46d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_immuni_2023_01_002.txt @@ -0,0 +1,205 @@ +The landscape of immune dysregulation in Crohn's Disease revealed through single-cell transcriptomic profiling in the ileum and colon +SUMMARY +Crohn's disease (CD) is a chronic gastrointestinal disease, increasing in prevalence worldwide. CD is multifactorial, involving the complex interplay of genetic, immune, and environmental factors, necessitating a systems-level understanding of its etiology. To characterize cell type-specific transcriptional heterogeneity in active CD, we profiled 720,633 cells from terminal ileum and colon of 71 donors with varying inflammation status. Our integrated datasets revealed organ and compartment-specific responses to acute and chronic inflammation; most immune changes were in cell composition while transcriptional changes dominated among epithelial and stromal cells. These changes correlated with endoscopic inflammation, but small and large intestines exhibited distinct responses, particularly apparent when focusing on IBD risk genes. Finally, we mapped markers of disease-associated myofibroblast activation, and identified CHMP1A, TBX3, and RNF168 as regulators of fibrotic complications. Altogether, our results provide a roadmap for understanding cell type- and organ-specific differences in CD and potential directions for therapeutic development. +Graphical Abstract +eTOC Blurb +Crohn's disease (CD) is a heterogenous condition impacting the ileum and colon in unique ways. Here, Kong et al. define the unique epithelial, stromal, and immune characteristics of CD by generated a single-cell transcriptomic atlas of the ileum and colon, and uncover novel regulators of collagen production in disease-associated fibroblasts. +INTRODUCTION +The inflammatory bowel diseases (IBD), comprising ulcerative colitis (UC) and Crohn's disease (CD), are immune-mediated relapsing-remitting chronic disorders affecting millions of people worldwide. The prevalence of IBD is increasing, and the treatment burden in the United States alone is estimated at $14.6-31.6 billion. CD and UC are both characterized by a dysfunctional and hyperactive immune response resulting in uncontrolled inflammation. In CD, this inflammation and resulting damage affects all layers of the gut, whereas in UC, this is limited to the colonic epithelium. Unlike UC, CD is generally characterized by discontinuous inflammation that can occur in distinct segments of the intestinal tract. Additionally, analyses of colonic mucosal samples of CD and UC have shown marked separations between the two diseases, in particular among T cell subsets. CD primarily affects the ileum and colon, and recent work has suggested that ileal-dominant and colonic CD should be considered separate disease subtypes. This highlights the importance of understanding whether and how the cellular processes underlying colonic and ileal inflammation differ. +Genome-wide association studies and exome sequencing studies define a broad set of risk genes related to epithelial barrier function, microbe sensing and restriction, and adaptive immunity. This is perhaps not surprising given the fact that the proper function of the intestine is also characterized by a complex set of interactions amongst multiple host cell types- including epithelial, stromal and immune cells- and environmental factors- including dietary chemicals and microbes. Understanding the complex cellular networks that characterize health and IBD pathogenesis requires high-resolution system-level measurements such as single-cell RNA sequencing. For example, in UC, both compartment-specific and tissue-wide single-cell analyses illustrate changes in epithelial cell subsets, adaptive immune cells, and stromal compartments. These are associated with disease but also importantly with treatment outcomes. For example, an oncostatin M (OSM) circuit in inflammatory monocytes and fibroblasts is associated with resistance to anti-TNF therapy. These single-cell datasets also help functionalize genetic risk loci by mapping gene expression to specific cell types. In the context of CD, high-resolution studies correlate genetic and cellular modules in the ileum with disease outcomes and altered T cell subset distributions in inflammation. The cellular module highlighted in that study demonstrates an enrichment of cytokine-cytokine receptor and chemokine-chemokine receptor pairs, but also an increase in OSM, suggesting some commonalities between anti-TNF resistance mechanisms in CD and UC. Single-cell technologies have also been used to identify CD-associated expression changes linked with the reactivation of developmental programs in a pediatric cohort, and to map immune cell programs which are adopted by IBD to recruit and retain immune cells in inflammation. +Because CD can occur across both the small and large bowel, which are characterized by distinct cellular networks in health, a cross-organ analysis would be a key resource to understand the mechanistic commonalities and differences between ileal, ileocolonic and colonic CD. To address this, we collected tissue from a total of 71 CD patients and non-IBD donors from inflamed and non-inflamed regions of the terminal ileum and colon, and used single-cell sequencing to elucidate the cell type- and location-specific changes that occur in CD. Our work identified a complex network of changes associated with disease. We observed broad compositional changes across immune and stromal cell subsets, while transcriptional reprogramming was more pronounced across epithelial cells, highlighting the different factors that participate in rewiring during disease. We demonstrated that some of these changes were restricted to either the colon or the ileum suggesting distinct tissue-specific responses. We also combined these transcriptomic analyses with existing genetic datasets to map risk gene expression. Finally, we mapped disease-associated changes in fibroblast gene expression and validated three regulators of fibroblast collagen induction that may represent novel targets for the management of fibrotic complications, thus demonstrating the applicability of the dataset. Altogether, this work offers a comprehensive view of the common cellular and transcriptomic changes associated with Crohn's disease and can also serve as a foundational resource to explore the impact of disease progression and therapeutic strategies. +RESULTS +Terminal ileum and colon biopsies show both region- and disease-associated shifts in cell type composition +We collected data from 136 samples from 46 CD and 25 non-IBD patients at Massachusetts General Hospital. These include 24 samples from 12 non-IBD donors published previously. In the majority of samples, we separated and independently processed the epithelial (E) and lamina propria (L) fractions (for a total of 89 epithelial channels and 100 L channels), while 36 samples were processed without separation, altogether resulting in 720,633 high quality single-cell transcriptomes in 225 channels (Fig. 1A, Table S1, Methods). Samples were obtained from three segments of the GI tract: 289,730 cells from colon (CO), 77,554 cells across the small bowel (SB) and 353,349 cells specifically from terminal ileum (TI). Due to the small number of SB samples, these were combined with TI samples for all analyses. +Because of the segmental nature of inflammation in CD, we specifically aimed to compare inflamed and non-inflamed regions. As part of clinical care, patients were scored using a simple endoscopic score for Crohn's disease (SES-CD), which was summed across all the segments evaluated. When active disease was present (SES-CD >= 3 across the whole intestine), we aimed to collect samples from both visibly inflamed and non-inflamed regions (i.e. regions that have a segmental score of 0). In inactive disease (SES-CD <= 2), we only collected non-inflamed samples (see Methods). +Annotation by cell type markers (Methods, Table S2) first broadly classified cells into three major cell type compartments (numbers listed for colon and TI, respectively): 97,788 and 154,136 epithelial cells, 39,433 and 75,695 stromal cells, and 152,509 and 201,072 immune cells (Fig. 1B). We evaluated standard quality metrics (number of genes and UMI per cell, percentage of mitochondrial reads) within each type of sample processing (Fig. S1A). The number of genes and UMI per cell was generally lower in the non-separated sample, potentially reflecting a lower recovery. The fraction of mitochondrial genes generally showed more limited differences between the different sample fractions, but as expected the percentage of mitochondrial reads was higher in epithelial cells compared to immune or stromal cells, as epithelial cells are prone to death by anoikis during tissue dissociation. To account for these technical factors, we adjusted for layer and the number of genes detected in applicable downstream analyses. +Further detailed clustering and annotation resulted in 65 cell types/states (Fig. 1C, Fig. S1B), which we next used to perform an analysis of Bray-Curtis dissimilarities to identify the main drivers of cell type composition across samples. As expected, the processing of biopsies, either in a single digestion step or as separated epithelial and lamina propria fractions (Methods), was the main driver of cell type composition (Fig. 1D, top; PERMANOVA R2 = 0.32, p < 10-4). Location (colon vs terminal ileum; Fig. 1D, middle) also accounted for a large portion of the variability (R2 = 0.14, p < 10-4). We noted that the previously published control samples did not form an outgroup, and clustered with the rest of the colon samples from this study (Fig. S1C). Separation by disease status (healthy, non-inflamed or inflamed; Fig. 1D, bottom) was visible, with a less obvious role in the first two axes of variation compared to layer (PCoA plots for each layer can be found in Fig. S1D, where these differences are more apparent). These compositional differences were still statistically significant (R2 = 0.055, p < 10-4), prompting us to further evaluate the influence of disease and inflammation status in more detailed analyses. +CD and inflammation broadly restructure immune and stromal compartment composition +We first examined disease-related differences in cell type composition of each sample using Dirichlet regression (Methods, Fig. 1E, Fig. S1E-F). Consistent with their inflamed status, we found that numerous immune cell types were compositionally overrepresented in inflamed samples, after adjusting for layer differences (Methods). This compartment-level observation was replicated in both TI and colon, though individual cell types had different patterns. Overall, we observed greater remodeling of the immune and stromal compartments in both locations compared to epithelial cells, though this was predominantly among T cells in the colon and myeloid cells in TI. Specifically, in TI, we observed 10 of 27 (37%) immune cell groups were altered in disease (Inflamed vs Healthy) compared to 6/16 (38%) stromal cells and 3/17 (18%) epithelial cells. In the colon, 8 of 25 (32%) immune cell groups were altered in disease compared to 6/17 (35%) stromal cells and 0/13 (0%) epithelial cells, though several of these epithelial cell types were significantly reduced in non-inflamed samples. Plasma cells made up the largest fraction of immune cells in all conditions, and were underrepresented in the inflamed colon (Fig. S1F) (consistent with previous observations). However, in TI, we found the opposite pattern, with plasma cells overrepresented in inflamed tissue. Some of these compositional changes in the immune compartment already existed in diseased non-inflamed samples (Fig. 1E, top), including several expanded myeloid cell subsets (DC2 in TI, mast cells in both TI and colon), an increase in some fibroblast subsets in TI, as well as the expansion of lymphatic endothelial cells. On the other hand, the expansion of some subsets such as S100A8 S100A9 monocytes was strongly associated with active inflammation. This is consistent with the presence of either underlying low-grade inflammation or permanent reshaping of cellular compartments even in regions of the intestine that do not appear inflamed by endoscopic examination. +With an opposite trend to plasma cells, several stromal cell subsets were enriched in inflammation in TI and depleted in CO. The abundance of several fibroblast subsets in particular was reduced in CO, including the SMOC2+ PTGIS+ and ADAMDEC+ Fibroblast clusters (Fig. 1E, bottom). A previously-identified IL11-producing inflammation-associated fibroblast was expanded in the inflamed colon, and not detected in TI. As fibroblasts are associated with resistance to anti-TNF therapy, but also participate in the development of fibrotic lesions and strictures present in CD complications, these results suggest that there may be organ-specific processes that require further characterization. We further discuss some of the differentiation processes involved in these distinct fibroblast subsets in more detail below (Fig. 5). Pericytes were an exception to this trend, showing a large compositional enrichment in inflamed colon samples, which was absent in TI. +As expected, in samples where we collected both an epithelial fraction (stripping the epithelial layer with EDTA) and a lamina propria fraction (enzymatically digesting the underlying tissue), the epithelial fraction was mostly comprised of epithelial cells (mean 54% +- 24% sd), while the lamina propria fraction comprised mainly immune and stromal cells (mean 77% +- 19% sd). However, we also noted a significant representation of some immune cells in the epithelial fraction in TI, likely representing intraepithelial lymphocytes (IELs). As these cells can be ontogenically and functionally distinct from lamina propria lymphocytes, we analyzed compositional changes for immune cells across epithelial fractions separately. In particular, we identified a population of ID3+ ENTPD1(CD39)+ IELs, consistent with previous reports that was only detected in epithelial samples (Fig. S2A-B) and was compositionally underrepresented in non-inflamed disease samples (Fig. S2C). This suggested a remodeling of the IELs compartment that can persist in the absence of overt inflammation. We observed an enrichment of plasma cells, which was consistent with the general trend (Fig. 1E), but even more marked in the epithelial fraction as healthy epithelial samples did not contain any intra-epithelial plasma cells (Fig. S2C). Finally, we also noted an overrepresentation of other immune cell types, in particular CD4+ and CD8+ T, which was generally more marked than tissue-level trends where few differences were observed for these cell types. Overall, our results suggest a remodeling of the IEL compartment during disease, both in inflamed and non-inflamed tissue, that is characterized by a reduction in the frequency of bona fide ITGAE(CD103)+ ENTPD1(CD39)+ IELs and an increase in plasma cells and conventional T cells. +Inflammation-related differences in core IBD risk gene expression are site-specific +After analyzing differences in cell type composition, we focused on gene expression across these cell types. IBD has a significant genetic component and multiple GWAS as well as more recent exome studies have identified a large set of risk genes over the years, but the relevant cell types and mechanisms of action of some of these genes has remained relatively unclear. Using Gini coefficients as a measure of unequal expression distributions, we first quantified the cell subset specificity of a core set of IBD risk genes identified from fine mapping GWAS. Most IBD risk genes were highly specific to certain cell types (mean Gini coefficient 0.55, Fig. 2A, Table 1). Expression specificity was largely consistent between TI and colon (Fig. 2A), with some exceptions including CARD9 and IL2RA. CARD9 is a key signaling protein involved in the innate immune system's response to fungi and bacteria and is primarily expressed in the TI by a subset of macrophages, while it is expressed in colonic cells within a subset of dendritic cells. IL2RA, a receptor for interleukin 2, is specifically expressed in Tregs in the colon, and is expressed by a more diverse constellation of cell types in the TI, which includes Tregs but is dominated by PLA2G2D+ macrophages. +Then, we examined how disease and inflammation status impact the expression of risk genes. Core IBD risk gene expression distributions between healthy and diseased samples show gene, cell type, and location differences. Myeloid cells in TI tend to have reduced expression of core IBD risk genes in diseased samples (Fig. 2B, left panels). This reduction is not visible for other cell types. NKX2-3 in particular is expressed in a higher fraction of healthy stromal cells compared to both inflamed and non-inflamed stromal cells (Fig. 2B, left panels). On the other hand, in the colon, numerous IBD risk genes were expressed in a higher fraction of both inflamed and non-inflamed cells compared to healthy cells (Fig. 2B, right panels). Differential expression analysis (Methods) further highlighted PRDM1 as differentially expressed (DE) in several cell types in colon (Fig. 2B, right panels). DE core IBD genes (FDR < 0.05, Fig S3A-B) were further biased towards being up-regulated in the colon (208 up-regulated gene-cell type pairs vs 7 down), while comparatively fewer were observed in TI (1 up-regulated gene-cell type pairs vs 13 down). These results suggest that even in the context of shared risk genes between different subtypes of inflammatory bowel disease (ileal and colonic Crohn's but also for many of these genes ulcerative colitis, which was not included here), the changes in gene expression associated with inflammation are distinct across ileum and colon. +To contextualize these changes between sites, we additionally looked for baseline differences between the two sites among the healthy donors (Fig. S3C-D). We found that, in general, these IBD risk genes have lower expression in colon than in TI, in particular for immune-related cell types (Fig. S3E), showing the opposite trend from the DE results above in inflammation. This is particularly true of EP300, and PRDM1, the latter of which was highlighted above. +In addition to these well-characterized core-IBD genes, exome sequencing approaches recently identified five additional IBD-associated loci: IL10RA, DOK2, CCR7, PTAFR, and PDLIM5. IL10RA, DOK2, CCR7, and PTAFR were primarily expressed in immune cells, while PDLIM5 had more broadly-distributed expression (Fig. S4A). PDLIM5 showed the greatest expression differences in disease, frequently overexpressed in inflamed samples compared to healthy colon samples (Fig. S4B, Table S3). While further investigation of PDLIM5's role in epithelial cells is warranted, these analyses demonstrate the potential of scRNAseq resources to identify relevant cell types for risk genes, and suggests that PDLIM5 coding variants could modulate the epithelial barrier. +Inflammation-associated transcriptional changes are largely site-specific and more pronounced in the colon +Differential expression in inflamed versus healthy tissue was quantified on a per location and per cell type basis (Methods, Fig. 3A-B, Table S3). There was some consistency between the differential expression profiles between the two sites (Fig. 3C), primarily observed in epithelial and stromal cells (Spearman rho = 0.25 and 0.34, respectively; P < 10-307), with immune cells showing the least correlation (rho = 0.21; P = 10-204). These weak though highly significant correlations indicate there is commonality in the genetic programs driving the inflammation signatures in the two sites, though the two sites still behave very differently. We therefore quantified the degree to which different cell types exhibited a more consistent set of DEGs in the two locations (Methods), and find that several myeloid cell types, DC2 CD1D-, Macrophages, and Mature DCs, exhibit the most consistent inflammatory signal (Fig. 3D). Consistent genes in this group highlight existing IBD-associated genes including STAT1, LSP1, and HIF1A(Table S4). Our results also replicate and extend a previous finding that inflammation-related expression differences are highly correlated with the differences between non-inflamed vs healthy already present in individuals with IBD, which we observe in both sites (Fig. 3E). +Differential expression was more pronounced in the colon compared to TI, in terms of the total numbers of differentially expressed genes (DEGs) detected (Fig. 3A-B). The transcriptional response to inflammation in the colon is therefore more marked than in TI. These differences were particularly strong among epithelial cell types, where some cell types in the colon showed thousands of DEGs, in particular among some enterocyte groups and goblet cells. This level of DEGs is roughly an order of magnitude larger than what we observed in immune cells. This is in stark contrast to the earlier observations at the compositional level, where epithelial cell types showed the least differences in inflammation (Fig. 1E). Some of these expression differences are already visible in non-inflamed tissue (Fig. S4C-D), particularly in a subset of goblet cells (with 1994 common DEGs, a 50% overlap), showing that these cells may already be primed for the inflammatory response in CD patients. +Pathways enriched in the epithelial compartment included antigen processing and presentation and cell adhesion molecules, as well as many disease-related pathways, which were broadly perturbed across numerous cell types (Fig. 3F; discussed in more detail in the following section). In the colon, numerous metabolic pathways were significantly down-regulated, largely due to down-regulation of the ketogenesis pathway (Fig. S4E), a common component of these enriched pathways. This suggests reduced potential for ketogenesis during inflammation. Treatment of IBD using ketogenic diets has been tested, with mixed results. Ketogenesis is regulated in part by the PPAR signaling pathway, which also shows a consistent pattern of altered expression (Fig. 3F; Fig. S4E), in particular PPARG, suggesting that this is the key regulatory factor of ketogenesis in CD. +In contrast to DEGs in the epithelial compartment, we found that the majority of DEGs in the stromal cell types were consistently down-regulated across cell types in TI, while colon DEGs were more balanced (Fig. 3A-B). Despite this broad downward trend in TI DEGs, several pathways were positively enriched in this location, including oxidative phosphorylation and NOD-like receptor signaling pathways across numerous cell types. Other positively enriched pathways in TI included numerous disease-related pathways which were significantly enriched in particular in three cell types: HHIP+ NPNT+ Myofibroblasts, Glial cells, and Lymphatics (Table S5). These trends were not observed in the colon. +Despite expectations that cell types from the immune compartment would exhibit a greater inflammation-related DE signature, the immune compartment showed the least differential expression in inflammation (Fig. 3A-B), which is reflected in a reduced number of significantly enriched pathways (Fig. 3F). This effect was not explained by a difference in power linked to cell count differences, as the immune cells accounted for a plurality of cells in both TI and colon (46.7% and 52.6% respectively). Instead, this is consistent with the notion that compositional changes, for example caused by the infiltration of activated immune cells (Fig. 1E), are the main drivers of immune differences. Meanwhile, transcriptional changes in epithelial and to a lesser extent stromal cells appear to account for the majority of the response in that compartment. This effect is most pronounced in TI, where stromal and epithelial cell types both exhibited similar magnitude differences, while immune cells showed an order of magnitude fewer DEGs and have a similar bias towards down-regulation, also seen in stromal TI cells. +MHC class II genes drive distinct inflammatory signals in TI and the colon +Pathway enrichment analysis highlighted several immune- and disease-related pathways (Fig. 3F). These were largely driven by a core set of HLA genes common to all of these gene sets (Fig. S5A), primarily from MHC class II. Differential expression profiles of these genes revealed similar differential expression patterns in both TI and colon, with several notable exceptions. First, HLA genes as a group tended to be downregulated in inflammation in immune cells in the colon, particularly in dendritic cells, and this expression pattern was not observed in TI (Fig. S5A). Further, HLA-DRB5 was broadly overexpressed in inflammation in numerous epithelial cell types in TI yet it was mostly absent in the colon. +Mucin and claudin expression changes highlight a site-specific rewiring of barrier functions +Cell surface mucins contribute to the protective mucosal barrier between the intestinal epithelium and the lumen. While this barrier protects against bacterial invasion, it also modulates inflammatory signals. Ectopic mucin expression may therefore contribute to an exaggerated immune response. We found that MUC1, a cell surface mucin typically expressed in the stomach, was upregulated in non-inflamed TI samples in CD (Fig. S5B, right), and was further increased during inflammation across epithelial cell types in both TI and colon (Fig. S5B, left). In the colon specifically, we additionally observed broad up-regulation of several other mucins: MUC2, MUC4, MUC5B, and MUC12. These are more typical of colonic mucins with the exception of MUC5B, which is a salivary mucin. +We also observed differential expression for other constituents of the mucosal layer. In particular, TFF1, a trefoil peptide which stabilizes the mucosa, followed a pattern consistent with MUC1 and was strongly up-regulated in the inflamed colon and weakly up-regulated in TI (Fig. S5B, left), with almost no changes in non-inflamed samples (Fig. S5B, right). +Claudins, on the other hand, serving as backbone of tight junctions, are involved in the establishment of barrier properties and help to maintain the specificity of tight junction permeability. Increased permeability and remodeling of tight junctions has been seen in CD patients. Altered expression of claudin 2 and occludin has also been observed prior to CD onset. The overall expression patterns of detected Claudin family genes were consistent between TI and colon (Fig. S5C), but there were a few claudins that expressed differently between the sites (Fig. S5D). In particular, claudin 2 and 15, two pore-forming claudins, showed higher expression among many epithelial cell types in TI compared to colon, suggesting increased paracellular permeability in the TI epithelium. On the other hand, claudins 3,4 and 5, which are sealing or barrier-forming claudins, showed higher expression in several colon stromal cell types, indicating greater barrier function in colon. We then focused on the impact of disease on the expression of this family. Across sites, claudins overall showed consistent expression changes, but changes in the colon were more pronounced than in the TI. Among pore-forming claudins, claudin-2 was strongly upregulated, whereas claudin-15, which forms Na+ channels, was downregulated. Claudins 3, 4, 7 and 23 were broadly downregulated, and a number of these differences occurred in stem/cycling cells, indicating a potential interaction with epithelial proliferation or crypt biology. These disease-associated expression changes in claudins are broadly consistent with previous measurements. +CD leads to metabolic changes in enteroendocrine cells +Enteroendocrine cells (EECs) sense microbial metabolites, and thus are key players in the initiation of the intestinal immune response. Because of their rarity, EECs are hard to profile in single-cell studies, and studies have relied on ex vivo culture and enrichment of these cells. Thus, their response to intestinal perturbations remains poorly characterized. Leveraging the size of our dataset, we focused on the TI epithelial compartment and detected 670 high-quality enteroendocrine cells (EEC), exhibiting high expression in markers CHGA and CHGB (Methods, Fig. 4A-C). These further clustered into 8 EEC subsets (Fig. 4A) based on established marker genes. No donor or disease group was dominant in one of these subtypes, showing that this heterogeneity is not a donor-specific artifact (Fig. 4B). The two most common EEC subsets were both enterochromaffin (EC) cells, expressing TPH1 and REG4. N-cells and progenitors were the next largest EEC subsets, followed by several rarer EEC cell types: L-cells, D-cells, I-cells and K-cells. +Given the limited number of EECs, only the two largest clusters, EC THP1+CES+ and EC REG4+NPW+, were used for differential expression analysis. In EC THP1+CES+ cells, DEGs suggested endoplasmic reticulum (ER) stress in CD, with UBA5, NCK1, SERINC3, CREB3L1, PDIA3, and TMEM33 showing altered expression in non-inflamed or inflamed tissue (FDR < 0.05; see Table S3). This was coupled with an overall increase in several respiratory genes, pointing to increased energy consumption by these EC cells. DEGs also included ATIC (FDR 0.001) and MTHFD1 (FDR 0.021), two genes involved in purine metabolism, which were both up-regulated in inflamed and non-inflamed samples (Table S3). Previous studies have found altered purine signaling in CD, which may therefore be driven in part by these EC cells. DEGs in EC REG4+NPW+ cells were negatively enriched in oxidative phosphorylation (FDR 6.79 x 10-4) suggesting an inverse relationship between these two EC clusters. +In the colon, we also detected a similar, though smaller population of EECs with 164 cells in total (Fig. S6A-C). EC and progenitors were common subsets between TI and the colon. D/L/N-cells were less distinguishable in the colon, and I/K-cells were not detected. We also detected a unique subset among colon EECs (Fig. S6D) which was annotated as LEFTY1+. Marker genes from this subset were associated with colon homeostasis, tumor suppression, host defense against inflammation, and cytokine activity. +Pseudotime analysis identifies CHMP1A, TBX3, and RNF168 as regulators of collagen expression in myofibroblasts +As noted above, we observed that a population of myofibroblasts in the TI was expanded during ileal inflammation (denoted Myofibroblasts HHIP+ NPNT+; Fig. 1E, top). This myofibroblast population was enriched in genes involved in extracellular matrix deposition, such as COL18A1, and COL23A1 (Fig. 5A), which are implicated in beneficial wound healing responses but also associated with fibrotic strictures observed in CD. This population of myofibroblasts clustered closely with the myofibroblast population denoted GREM1+ GREM2+ (Fig. 5B); however, we observed that GREM1+ GREM2+ myofibroblasts lacked collagen expression (Fig. 5A). To explore the regulatory network that drives collagen expression in CD myofibroblasts, we utilized pseudotime trajectory analysis (Methods) to organize cells starting from collagen-negative GREM1+ GREM2+ myofibroblasts to HHIP+ NPNT+ collagen-positive myofibroblasts (Fig. 5C). +This analysis revealed numerous genes with pseudotime-dependent expression in the transition between these myofibroblast groups (Fig. 5D). We selected a subset of these for follow-up based on gene annotation and expression levels, resulting in a set of 6 transcription factors and 10 other genes (Methods). We used 4 pooled siRNA oligos to knockdown (KD) each of these candidate genes in an arrayed approach in normal human intestinal fibroblasts. We assessed induction of COL4A1, COL4A2, COL5A3, and COL7A1, as well as four HHIP+ NPNT+ marker genes following stimulation with canonical collagen-inducer growth factor TGF-beta. While some gene KDs had collagen-specific effects, we observed that KDs of CHMP1A, TBX3, and RNF168 significantly impaired production of several of these collagen genes and proteins (FDR < 0.05; Fig. 5E-F). In particular, the transcription factor TBX3 was strongly associated with TGF-beta-driven collagen gene expression, and has previously been implicated in driving carcinomas and sarcomas. TBX3 has also been reported in several clusters in a recent fibroblast cell atlas in mice, including a cluster (characterized by Adamdec1) that is specifically associated with colitis in the perturbed-state dataset. Both tumors and fibrotic scars are associated with enhanced deposition of extracellular matrix, thus future efforts may investigate the in vivo role of TBX3 in driving tissue fibrosis in CD. We also observed a similar overall pattern in the colon, though fewer myofibroblast cells were sampled there (Fig. S6E-H). Other genes identified in the pseudotime analysis may therefore be of interest for further follow-up. +Finally, we applied the NicheNet algorithm to find putative ligands responsible for transitioning myofibroblasts between the two states. Since more differential expression was detected in the colon (Methods, Fig. 3B), we focused this analysis there. Ligands responsible for the induction of collagen genes more commonly interacted with GREM1+ GREM2+ myofibroblasts than with HHIP+ NPNT+ (Fig. S6I-J). In addition to the TGF-beta signature, we also noticed activity by a related set of genes in these cells, BMP2/5/7, largely derived from other fibroblasts (Fig. S6I). BMP2/5/7 were all differentially expressed in at least one other fibroblast cell type in diseased samples, indicating that these may play a part in the miscommunication resulting in CD progression. Interestingly, these BMP ligands have been identified as markers of a mesenchymal niche in a previous study of the colonic mesenchyme, and CyTOF analysis based on a subset of markers suggests that these cells may be diminished in disease. +DISCUSSION +In this study we describe the single-cell expression profiles of 720,633 cells from 71 patients, providing the largest single-cell resource to date to study CD. Our dataset covers the epithelial, immune and stromal compartments across multiple locations and multiple disease statuses, therefore allowing us to characterize the cell-type-specific differences along these important dimensions. +One striking difference observed across compartments was the nature of the response to disease and inflammation. The epithelium experienced the greatest changes in expression profiles, including a broad increase in expression of MHC class II genes, as found in Thomas et al. Meanwhile, immune cell differences in gene expression were comparatively smaller (including decreased HLA expression), but their compositional changes were more marked. Stromal cells displayed both transcriptional and compositional changes, perhaps reflecting a joint reprogramming and tissue remodeling. In all three compartments, transcriptional changes in non-inflamed disease samples and inflamed samples were strongly correlated, a phenomenon that has also been reported in the case of ulcerative colitis both for broad cellular networks and more specifically among epithelial cells. This may reflect ongoing disease processes even in the context of endoscopic remission. Specifically in CD, our results extend previous findings showing that cell type composition profiles poorly discriminate between inactive and active CD. Understanding the pathways that are involved in the maintenance of this "inflamed-like" transcriptional network in endoscopically normal tissue might uncover key targets for disease-modifying therapies and ultimately curative approaches. +As CD can occur throughout the intestinal tract, we were also able to directly compare the inflammatory response in the colon and TI, and observed a notably stronger transcriptional response in the colon. Expression differences in TI and colon were correlated, though not strongly so, indicating that the transcriptional programs underlying the inflammatory response are largely different in the two sites. Among pathways specifically enriched in the colon, we found numerous metabolic pathways largely different due to a down-regulation of the ketogenesis sub-pathway, driven by PPARG. Interestingly, ketogenic diets have been trialed with mixed success in treating CD. Our results provide a novel resource to analyze the network associated with the ketogenesis pathway and may offer insights on the individuality of patient responses to ketogenic diets, which may also be driven by personalized factors such as the microbiome, as has been reported with epilepsy. More broadly, this may be combined with recent developments in single-cell proteomics and metabolomics (reviewed by Islam, et al), which provide the opportunity to directly explore the associations between transcriptional programs and metabolic networks. +In addition to the analysis of these large scale, cross-compartment changes, the scale of our study also allowed us to focus on rarer cell subsets. For example, we identified a sizable and transcriptionally distinct subset of immune cells in our epithelial fractions (i.e. cells detached from the tissue with EDTA + DTT disruption of junctions, in the absence of enzymatic digestion). We confirmed that these cells are intraepithelial lymphocytes, consistent with a previously-described ID3+ ENTPD1(CD39)+ IEL group. Importantly, we observed an overall depletion of these cells in diseased samples, reminiscent of the remodeling of the IEL compartment that has been described in celiac disease. However, it is important to note that IELs can be comprised of both conventional and non-conventional T cells, and the latter express a restricted set of TCRs that can recognize a range of self and non-self molecules. These cells have for example been shown to regulate nutrient sensing as well as inflammation. An important follow-up to this work will be to understand the repertoire of IELs in health and disease, for example using V(D)J sequencing approaches. +We were also able to characterize in detail the response of rare cells such as enteroendocrine cells in the context of inflammation. Previous studies have used ex vivo expansion to characterize the transcriptomic profile of the different enteroendocrine cell subsets, but such approaches cannot capture the changes that may occur in disease in these cells. Our work shows an enrichment of ER stress signatures in the context of disease. This finding is particularly relevant given the role of ER stress and the unfolded protein response in the genetic risk for Crohn's disease. The role of ER stress in secretory cells in the intestine, such as Paneth cells or goblet cells, has been long recognized and the enrichment of ER stress in EECs during disease suggests that the secretory function of these cells may also be affected in Crohn's potentially modulating their function and interactions with the nervous system. +Beyond the study of IBD-associated risk genes, single-cell atlases offer unprecedented opportunities to map the emergence of disease associated cell states. Here, we focused specifically on fibroblasts, as these cells have been associated with pathology and therapy resistance but remain poorly characterized in IBD compared to immune or epithelial subsets. For this, we leveraged our transcriptomic data to identify a subset of genes linked to the transition between two myofibroblast subsets with differential abundance in disease and with differential collagen production characteristics. From this, we validated a set of genes, TBX3, RNF168, CHMP1A, which impact collagen production in these cells. These genes may therefore be involved in CD-related fibrotic strictures and suggest novel therapeutic hypotheses for the management of this complication. Altogether, we demonstrate approaches that can leverage single-cell data to both facilitate variant to function assignment and the identification of disease pathway specific targets. We expect that similar future studies focused on other risk genes and compartments could broadly extend our understanding of the functional regulators of CD. +In conclusion, in this study we described the transcriptional perturbations in active CD at an unprecedented level of detail. The resulting analysis and dataset provide a framework for further investigation into the complex dysregulation of the gastrointestinal immune response in CD, and a testing ground for cell type specific differences in this disease. +LIMITATIONS OF THE STUDY +In this study, we report on single-cell analyses performed across 46 Crohn's disease subjects and 25 non-IBD controls in a single center. This number enabled us to deeply characterize the differences associated with both active inflammation and non-inflamed intestinal tissue in Crohn's disease. However, it is important to note that there are many layers of heterogeneity that we were not powered to address here and that would require more targeted collections. For example, understanding the impact of biologics on tissue state will likely require focusing enrollment on a limited number of therapeutic agents and obtaining longitudinal samples pre- and post-treatment. Additionally, it will be critical to enroll participants from a diverse range of ancestries and risk genes to understand the impact of genetics on disease phenotype. +STAR METHODS +RESOURCE AVAILABILITY +Lead Contact: +Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Ramnik J. Xavier (xavier@molbio.mgh.harvard.edu) +Materials Availability: +This study did not generate any novel reagents, all materials are commercially available as listed in the key resource table. +Data and Code Availability: +The datasets generated during the current study are available for download from the controlled-access data repository, Broad DUOS (Accession DUOS-000146 CD_Atlas_2021_GIDER; DUOS-000145 CD_Atlas_2021_PRISM). The analyzed data reported in this paper is available at the Broad Single Cell Portal (SCP1884). +EXPERIMENTAL MODEL AND SUBJECT DETAILS +Patients and tissue sample collection +Subjects were enrolled in either the PRISM (Prospective Registry in IBD Study at MGH, protocol 2004P001067, used for all CD patients and some controls) or the GIDER (GI disease and endoscopy registry, protocol 2015P000275, used for the remaining controls) study at Massachusetts General Hospital (MGH). Informed consent was obtained from all patients in accordance with the respective protocol and sequencing and data storage and publication plans were approved by the MGH IRB and the Office for Research Subject Protection at the Broad Institute. Clinical information and metadata for the samples in this study were provided in Table S1. Healthy controls were recruited at the time of routine colonoscopy. Healthy controls were individuals without a history of inflammatory bowel disease (IBD), a 1st degree relative with IBD, histories of autoimmune disease, immune mediated conditions, infectious colitis, and colon cancer, or a family history of colon cancer, and who were overall healthy with no other disease history. CD patients were included based on having a clinical diagnosis of Crohn's disease, and observed to have active disease via macroscopic assessment from a physician during an endoscopy as part of routine clinical care. Biopsies were obtained during endoscopy, using biopsy forceps that were used in standard of care. The presence or absence of inflammation was visually evaluated by the endoscopist at the time of collection. To ensure this evaluation was consistent across endoscopists, we used the simple endoscopic score for Crohn's disease (SES-CD). This score consists of segmental scores that are then summed to obtain an overall indicator of disease activity. At the patient level, an SES-CD of 0-2 indicates remission/inactive disease while an SES-CD score >= 3 indicates active inflammation, criteria that are consistent with previous studies. Of note, this score has been used in clinical trials and was shown to have limited variability across raters. In patients with active disease, we aimed to collect both inflamed biopsies (segmental score > 0 and visible inflammation) and non-inflamed biopsies (segmental score = 0). Biopsy bites were immediately placed into cryovials containing Advanced DMEM F-12 and placed on wet ice for transport. +METHODS DETAILS +Epithelial Layer Dissociation +On arrival, biopsy bites were washed 2x in cold PBS and in 3x in cold PBS/10mM EDTA. The tissue was then added to 25 mL PBS/10mM EDTA and placed in a rotating incubator at 37 C for 15 minutes. Following incubation, the tissue rested on ice for 10 minutes and was then shaken vigorously for 10-15 seconds. The supernatant was collected as fraction 1 and additional fractions were collected until the supernatant had visible crypts when viewed under the microscope. Tissue was kept on ice in a small amount of PBS/10mM EDTA for further lamina propria digestion, and fraction(s) with visible crypts were combined and spun down at 330g for 3 minutes. Supernatant was removed and the pellet was resuspended in 1mL pre-warmed TrypLE express (Thermo Fisher) for 1 minute. 1mL PBS was added to quench the reaction followed by another 4 mL PBS and the single cell suspension was spun down at 330g for 3 minutes. The pellet was resuspended in 1mL PBS and transferred to a 1.5 mL microcentrifuge tube, spun again at 300g for 3 minutes and resuspended in 50-200mul 0.4% BSA-PBS for 10X single cell loading. +Lamina Propria Layer Dissociation +Tissue saved on ice from epithelial layer digestion was moved into a 5mL snap-cap centrifuge tube with 5mL RPMI 1640 (Gibco, cat no. 11875093) supplemented with 2% FBS, 200mul Liberase TM (2.5 mg/ml, Roche, cat. no. 5401119001, reconstituted in injection-quality sterile water) and 50mul DNase I (10 mg/ml, Roche, cat. no. 10104159001, reconstituted in injection-quality sterile water). Tissue was incubated in a rotating incubator 37 C for 45 minutes. Following incubation, 0.5 mL FBS was added directly to the snap-cap tube which was then vortexed for 20 seconds. Tissue and media were poured over a 70mum filter into a falcon tube and 2% FBS-RPMI was added over the filter up to 30 mL. Sample was then spun down at 450g for 3 minutes. Supernatant was removed and pellet was resuspended in 1 mL 0.4% BSA-PBS and transferred to a microcentrifuge tube. Cell suspension was spun down at 300g for 3 minutes, supernatant was removed and pellet was resuspended in 1 mL ACK Lysing Buffer (Gibco, cat. no. A1049201) and incubated at room temperature for 1 minute. Cell suspension was spun down again at 300g for 3 minutes, washed two additional times in 0.4% BSA-PBS and resuspended in a final dilution of 50-200mul 0.4% BSA-PBS. +Single-Cell Profiling +Epithelial and lamina propria single cell suspensions were counted and, if necessary, diluted to a concentration of 200-2000 cells per mul. 10,000 cells from each sample were then loaded on a Chromium controller (10X Genomics). Samples were processed either with v2 or single-indexed v3.1 chemistry as described below, and chemistry type for each sample is included in Table S1. +For v2 samples, cells were loaded on a Chromium Single Cell A Chip (PN-120236) with gel beads from the Chromium Single Cell 3' Library & Gel Bead Kit v2 (PN-120237) and indexed according to the Chromium i7 Multiplex Kit (PN-120262) instructions. Libraries were sequenced on either a NextSeq or a HiSeq X (both from Illumina), according to manufacturer's instructions (Read 1, Cell barcode and UMI, 26bp, i7 index : 8bp, i5 index : none, Read 2, insert, 98bp). +For single-index v3.1 samples, cells were loaded on a Chromium Next GEM Chip G Single Cell Kit (PN-1000120) with GEMs from the Chromium Next GEM Single Cell 3' GEM, Library & Gel Bead Kit v3.1 (PN-1000121) and indexed according to the Single Index Kit T Set A (PN-1000213) instructions. Libraries were sequenced on either a NextSeq or a HiSeq X (both from Illumina), according to manufacturer's instructions (Read 1, Cell barcode and UMI, 26bp, i7 index : 8bp, i5 index : none, Read 2, insert, 91 or 96bp). +siRNA KD experiments in myofibroblasts +Normal colon-derived intestinal human fibroblasts (CCD-18Co) were obtained from the American Type Culture Collection (CRL-1459). Fibroblasts were maintained in DMEM containing GlutaMAX (Thermo Fisher, Catalog #10566016), supplemented with 10% (vol/vol) heat-inactivated FBS, NEAA (Gibco), penicillin/streptomycin (Corning). Cells were cultured at 37 C with 5% CO2. +Pre-designed pooled duplexes of siRNA oligomers were purchased from Sigma-Aldrich and re-suspended in nuclease-free water at 20muM. Seeded CCD-18Co fibroblasts were transfected with 20nmol siRNA complexed with Lipofectamine RNAiMAX (Thermo Fisher) in Opti-MEM media (Thermo Fisher). 24 hours later, cells were washed with PBS, and replenished with fresh media with or without the addition of 10ng/ml of human TGF-beta (Invivogen) for 24 hours. Cells were washed in PBS, and resuspended in TRIzol reagent (Thermo Fisher) for RNA isolation. +RNA was extracted from fibroblasts in TRIzol reagent following the manufacturer's protocol (Thermo Fisher). Equal amounts of RNA were used to synthesize cDNA with the iScript cDNA synthesis kit (Bio-Rad Laboratories). iTaq Universal SYBR Green Supermix (Bio-Rad Laboratories) was used for qRT-PCR on the C1000 Touch Thermal Cycler (Bio-Rad Laboratories). Gene expression was calculated with the DeltaDeltaCt calculation with Hprt as the reference housekeeping gene. Oligos used for qRT-PCR can be found in Table S6. +Collagen Immunofluorescence +siRNA-transfected CCD18-Co fibroblasts were seeded in a 96-well CellCarrier-96 Ultra microplate (PerkinElmer, #6055302) overnight. The next day, the cells were treated with or without the addition of 10ng/ml of human TGF-beta (Invivogen, rcyc-htgfb1) for 24 hours. +Cells were then fixed in 2% PFA (Electron Microscopy Services, #15710-S) followed by permeabilization with 0.2% Triton X-100. The cells were then washed with PBS and blocked with 4% BSA-PBS. Following blocking, cells were incubated with 5mug/mL anti-Col7a1 (ThermoFisher, #MA5-41570) in 4% BSA-PBS for one hour at room temperature. Cells were then washed with PBS and incubated with 1:500 dilution AF488 (ThermoFisher, #A-21202), 1:5000 dilution of HCS CellMask Red (ThermoFisher, #H32712) and 1:5000 dilution of Hoechst 33342 (ThermoFisher, #H3570) in 4% BSA-PBS for one hour. Cells were then washed with PBS and imaged. +For imaging, the Opera Phenix High-Content/High-Throughput imaging system (Perkin Elmer) was used. 31 different fields were imaged at 6 replicates per sample at 20x water immersion in the confocal setting. Image analysis was performed with the Harmony software (Perkin Elmer). Cell nuclei were identified with Hoechst staining, and each cell boundary demarcated by HCS CellMask Red. Median fluorescence intensity of AF488-labeled Col7a1 was quantified in each individual cell and the median values per sample well obtained followed by subtraction of background fluorescence. +QUANTIFICATION AND STATISTICAL ANALYSIS +Single-cell data processing +After sequencing, BCL files were demultiplexed with Cell Ranger v3.0.2, then fastq files were aligned to the human genome (hg19). CellBender v2-alpha was used to remove systematic biases and background noise (learning_rate= 2e-5), and Scrublet v0.2.1 was used to identify doublets and remove low quality cells (with default settings). Cloud-based Cumulus v1.0 was then used to perform the batch correction (using the Harmony algorithm) on the aggregated gene-count matrices, this was done separately for TI and colon samples. Unless specified otherwise, gene expression was quantified by the default logarithmic expression values in Cumulus, specifically ln(TP100k + 1), where TP100k = 105 * NUMI / CellNUMI, NUMI is the number of UMIs detected for that gene in that cell, and CellNUMI is the total number of UMIs detected in the cell. +To help balance the comparison between inflamed and healthy groups in the colon, we included data from 12 non-IBD patients (24 samples total, which were further layer-separated into 48 total channels) from. For this, we processed these samples together with the rest of the 8 non-IBD samples from the colon location, using the same bioinformatics pipeline, which included Harmony for batch correction. Resulting samples clustered with existing samples from colon. +Scaled mean expression +Expression values presented in Figs. 2A, S4A, and S5D were obtained by scaling the ln(TP100K + 1) expression value by the root mean squared expression to produce an "expression z-score". +Single-cell clustering and ordination +Clustering and UMAP visualization were done by following the Cumulus default settings. Principal coordinates analysis (PCoA) of cell type compositions (Fig. 1D and Fig. S1C-D) was performed using the pco function of the labdsv R package from Bray-Curtis dissimilarities between the compositional profiles. +Cell type identification and signatures +Cell clusters for each location were first manually classified into three compartments based on expression of known marker genes: Epithelial (EPCAM, KRT8, and KRT18), Stromal (CDH5, COL1A1, COL1A2, COL6A2, and VWF), and Immune (CD45/PTPRC, CD3D, CD3G, CD3E, CD79A, CD79B, CD14, CD16, CD68, CD83, CSF1R, FCER1G). +Each compartment was then re-clustered individually per location, and fine-grained cell types were identified using a combination of an automatic cell type annotation step in Cumulus (function infer_cell_types with markers = 'human_immune'), and manual inspection and adjustment based on previously identified markers. Briefly, Epithelial cells were clustered into Enterocytes (RBP2, ANPEP, FABP2), Stem cells (LGR5, ASCL2, SMOC2, RGMB, OLFM4), Goblets (CLCA1, SPDEF, FCGBP, ZG16, MUC2), Paneth cells (DEFA5, DEFA6, REG3A), Tuft cells (LRMP, SH2D6), Enteroendocrine cells (CHGA, CHGB, NEUROD1) and Cycling cells (UBE2C, TOP2A, MKI67, HMGB2). Stromal cells were clustered into Fibroblasts (ADAMDEC1, PDGFRA, BMP4), Myofibroblasts (TAGLN, ACTG2), Lymphatics (CCL21, TFF3), Endothelial cells (CD36, DARC/ACKR1), Pericytes (NOTCH3, MCAM/CD146, RGS5) and Glial cells (FOXD3, MPZ, CDH19, PLP1, SOX10, S100B, ERBB3). Immune cells were first clustered into T cells (CD3D, CD3G, CD3E), B cells (CD79A, MS4A1/CD20, CD19), and Myeloid cells (CD14, CD16, HLA-DR). T cells were further sub-clustered into CD8 T cells (CD8A, CD8B), CD4 T cells (CD4), ILCs (RORC, IL1R1, IL23R, KIT, TNFSF4, PCDH9), NK cells (EOMES, PRF1, NKG7). B cells were sub-clustered into Plasma cells (SDC1, MZB1, SSR4, XBP1), B cells (BANK, MS4A1/CD30, ADAM28, VPREB3) and Germinal Center (GC) B cells (LRMP, GPT2, PAG1). Myeloid cells were sub-clustered into Mast cells (GATA2, CPA3, HPGDS), classical Macrophages (CD163, C1QB, C1QC), classical Monocytes (FCN1, S100A4, S100A6), DC1 (CLEC9A, XCR1) and DC2 (CLEC10A, FCER1A). Some cell clusters were further subdivided if there was evidence of heterogeneity in the UMAP. These are identified by one or two genes whose expression distinguishes these clusters, for example T cells CD4+ IL17A+. A summary of markers used and expression across clusters can be found in Table S2. +Cell type compositional analysis +Cell type composition PCoAs in Fig. 1D were generated using Bray-Curtis dissimilarities between the cell type composition profiles for each channel. PERMANOVA analysis was done using the adonis function in the R package vegan using the same dissimilarity matrix, using 9999 permutations. +Differential cell type abundances were determined as previously described using Dirichlet regression with R package DirichletReg, to account for the compositional nature of cell type counts within a sample. For epithelial cell types, only samples from the epithelial layer or non-separated samples were used, while for stromal and immune cell types, lamina propria and non-separated samples were used. Sample layer separation (separated vs non-separated) was regressed out by testing the formula "Normalized counts ~ Layer separation + Disease status". +IBD risk genes selection +Core IBD risk genes were obtained from Table 1 in Huang, et al, and only genes associated with IBD or CD and which have nonzero expression in at least 3% of cells in at least one cell type were included (Table 1). +Differential expression analysis +Differential expression analysis was performed using MAST. DE analysis was only run for cell types for which there were at least 10 cells in each disease group (healthy, non-inflamed, inflamed). For each cell type and location, low-expression genes were first filtered out (minimum 10% cells with non-zero expression in at least one of the disease groups). To speed up the tests, for cell types with more than 10000 cells, we first sub-sampled the cells using a hierarchical even subsampling algorithm: at each level of the hierarchy (disease group > donor > sample), an even number of cells were sampled from each possible pool at the lower level, such that 10000 cells were sampled in total. This ensured that disease groups, donors, and samples with few total cells were still adequately represented in the sampled dataset. To further speed up the tests, gene expression for each gene was first fitted with an anti-conservative fixed effect model in MAST, with formula "Expression ~ NGenes + Layer + DiseaseGroup". Genes with no disease-related difference were filtered out (nominal P > 0.05 in the discrete and continuous components for both the Non-Inflamed - Healthy and Inflamed - Healthy contrasts, from likelihood ratio tests). Remaining genes were fit with a mixed-effect model in MAST using formula "Expression ~ NGenes + Layer + DiseaseGroup + (1 | Donor) + (1 | Channel)", to account for additional correlations between cells from the same donor and from the same samples. P-values were obtained from likelihood ratio tests. FDR-corrected p-values were calculated from all tested genes (from all cell types and locations), using the P-value from the mixed effect model if available, or from the fixed effect model if not (to avoid selection bias from the fixed effect pre-filter). Unless otherwise specified, all reported coefficients and FDR values are from the discrete component of the MAST model. +Differential expression consistency in CO and TI +To quantify the degree of consistency between DEGs in CO and in TI (Fig. 3D and Table S3), we calculated the expected overlap between the DEG lists, and quantified the "consistency score" of a pair of lists as the ratio between the observed overlap compared to expected. Specifically, for two DEG lists A and B (corresponding to DEG lists in CO and TI) and a total number of genes N, the expected overlap (ignoring direction) was first estimated as E = |A| x |B| / N. A DEG was only considered "consistent" if its direction was the same in the two lists. We therefore first split A into A+ and A- (and likewise for B) for DEGs with positive and negative directions. The consistency score was defined as 2(|A+ B+| + |A- B-|) / E. A p-value was obtained (Table S4) by an upper-tailed Poisson test for the number of consistent DEG pairs, |A+ B+| + |A- B-|, with lambda = E/2. P-values were adjusted using Benjamini-Hochberg FDR correction. +Pathway enrichment analysis +KEGG pathway enrichment analyses were performed by using R package fgsea, fast preranked gene set enrichment analysis (GSEA): minSize=3, maxSize=500, nperm=100,000. Gene sets "c2.cp.kegg.v7.0.symbols.gmt" was in used the analysis, and these gene sets were obtained from the MSigDB collections: https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp. The default fgsea multiple hypothesis correction was used (Benjamini-Hochberg). Fig. 3F contains the 34 pathways that were significant (FDR <0.05) in at least 10% of all cell types per compartment. All pathway enrichment results in Table S5. +Pseudotime analysis in myofibroblasts +Pseudotime trajectory was calculated with Monocle 3 in the TI myofibroblasts. Clustering was performed with the louvain method with k = 500. Genes that have expression changed significantly over pseudotime were extracted using Moran's I test for spatial correlation (q-value < 0.1). The top 200 genes by fraction of myofibroblast cells expressing them were selected from among this significant set and are presented in Fig. 5D. Expression in this heatmap was smoothed using a cubic spline using the smooth.spline function in R with smoothing parameter spar = 1.5. Genes were ordered by the pseudotime of maximum expression. Genes were selected for follow-up by prioritizing genes annotated as transcription factors, or which are DNA or RNA-binding. The following genes were selected for follow up: RNF168, GREM1, ZNF451, ZNF263, EDNRB, PTCH1, TBX3, CYP1B1, CHMP1A, GREM2, APOE, HOPX, RGMA, PKNOX1. +Ligand activity analysis +Ligand activity analysis was performed using nichenetr (An open source R implementation of NicheNet: https://github.com/saeyslab/nichenetr). The function nichenet_seuratobj_aggregate was used to predict ligand-receptor activity in different cell types. Default parameters were used with the exception of expression_pct which was set to 0.05. Top ligands were selected with a Pearson score higher than 0.08. +Supplementary Material +DECLARATION OF INTERESTS +R.J.X. is a co-founder of Celsius Therapeutics and Jnana Therapeutics +This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. +References +Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review +Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies +Genetics and pathogenesis of inflammatory bowel disease +Single-Cell Analyses of Colon and Blood Reveal Distinct Immune Cell Signatures of Ulcerative Colitis and Crohn's Disease +Should We Divide Crohn's Disease Into Ileum-Dominant and Isolated Colonic Diseases? +Inherited determinants of Crohn's disease and ulcerative colitis phenotypes: a genetic association study +Genome-wide association identifies multiple ulcerative colitis susceptibility loci +Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations +Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease +Sequencing of over 100,000 individuals identifies multiple genes and rare variants associated with Crohns disease susceptibility +Pathway paradigms revealed from the genetics of inflammatory bowel disease +Heterogeneity and clonal relationships of adaptive immune cells in ulcerative colitis revealed by single-cell analyses +Colonic epithelial cell diversity in health and inflammatory bowel disease +Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease +Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis +Single-Cell Analysis of Crohn's Disease Lesions Identifies a Pathogenic Cellular Module Associated with Resistance to Anti-TNF Therapy +Single-Cell Sequencing of Developing Human Gut Reveals Transcriptional Links to Childhood Crohn's Disease +Cells of the human intestinal tract mapped across space and time +Colon stroma mediates an inflammation-driven fibroblastic response controlling matrix remodeling and healing +Development and validation of a new, simplified endoscopic activity score for Crohn's disease: the SES-CD +Human intraepithelial lymphocytes +Single-cell analyses of Crohn's disease tissues reveal intestinal intraepithelial T cells heterogeneity and altered subset distributions +Fine-mapping inflammatory bowel disease loci to single-variant resolution +Activation of signal transducer and activator of transcription (STAT) 1 in human chronic inflammatory bowel disease +Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47 +Epithelial hypoxia-inducible factor-1 is protective in murine experimental colitis +Omega-3 fatty acids and low carbohydrate diet for maintenance of remission in Crohn's disease. A randomized controlled multicenter trial. Study Group Members (German Crohn's Disease Study Group) +Ketogenic diet alleviates colitis by reduction of colonic group 3 innate lymphoid cells through altering gut microbiome +Mucin and Toll-like receptors in host defense against intestinal parasites +Mucin dynamics and enteric pathogens +MUC1, the renaissance molecule +Trefoil Factor Peptides and Gastrointestinal Function +Claudins: control of barrier function and regulation in response to oxidant stress +Tight junctions in inflammatory bowel diseases and inflammatory bowel disease associated colorectal cancer +Changes in expression and distribution of claudin 2, 5 and 8 lead to discontinuous tight junctions and barrier dysfunction in active Crohn's disease +Harnessing murine models of Crohn's disease ileitis to advance concepts of pathophysiology and treatment +Claudin Family Participates in the Pathogenesis of Inflammatory Bowel Diseases and Colitis-Associated Colorectal Cancer +Enteroendocrine cell-ssensory sentinels of the intestinal environment and orchestrators of mucosal immunity +High-Resolution mRNA and Secretome Atlas of Human Enteroendocrine Cells +Identification of Enteroendocrine Regulators by Real-Time Single-Cell Differentiation Mapping +Purinergic signaling during intestinal inflammation +Wound repair and regeneration +Fibrosis: from mechanisms to medicines +Tbx3 is a downstream target of the Wnt/beta-catenin pathway and a critical mediator of beta-catenin survival functions in liver cancer +The T-box transcription factor 3 is a promising biomarker and a key regulator of the oncogenic phenotype of a diverse range of sarcoma subtypes +Cross-tissue organization of the fibroblast lineage +The hallmarks of cancer are also the hallmarks of wound healing +NicheNet: modeling intercellular communication by linking ligands to target genes +Altered interactions between circulating and tissue-resident CD8 T cells with the colonic mucosa define colitis associated with immune checkpoint inhibitors +PPARgamma as a new therapeutic target in inflammatory bowel diseases +The Gut Microbiota Mediates the Anti-Seizure Effects of the Ketogenic Diet +Use of Single-Cell-Omic Technologies to Study the Gastrointestinal Tract and Diseases, From Single Cell Identities to Patient Features +Chronic Inflammation Permanently Reshapes Tissue-Resident Immunity in Celiac Disease +A multilayered immune system through the lens of unconventional T cells +gammadelta T cells regulate the intestinal response to nutrient sensing +QRICH1 dictates the outcome of ER stress through transcriptional control of proteostasis +TMEM258 Is a Component of the Oligosaccharyltransferase Complex Controlling ER Stress and Intestinal Inflammation +XBP1 links ER stress to intestinal inflammation and confers genetic risk for human inflammatory bowel disease +IL-1-driven stromal-neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies +Role of alpha1 and alpha2 chains of type IV collagen in early fibrotic lesions of idiopathic interstitial pneumonias and migration of lung fibroblasts +Endoscopic evaluation of Crohn's disease activity: comparison of the CDEIS and the SES-CD +Faecal calprotectin and lactoferrin are reliable surrogate markers of endoscopic response during Crohn's disease treatment +Endoscopic, Radiologic, and Histologic Healing With Vedolizumab in Patients With Active Crohn's Disease +Reliability among central readers in the evaluation of endoscopic findings from patients with Crohn's disease +CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets +Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data +Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq +Differential DARC/ACKR1 expression distinguishes venular from non-venular endothelial cells in murine tissues +Jaw1/LRMP, a germinal centre-associated marker for the immunohistological study of B-cell lymphomas +MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data +Fast gene set enrichment analysis +The Molecular Signatures Database (MSigDB) hallmark gene set collection +The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells +Single-cell measurement of healthy, non-inflamed and inflamed terminal ileum (TI) and colon (CO), biopsies in Crohn's Disease. +(A) Workflow of biopsy collection and scRNA-seq measurements. *Note that some patients contributed samples to multiple locations and/or inflammation statuses. (B-C) UMAP visualization of all cells in TI and CO, colored by cell type compartment (B) and detailed cell types within compartments (C). (D) Principal coordinates analysis (PCoA) of Bray-Curtis dissimilarities from sample-level cell type composition profiles. Top: Colored by layer information: E: epithelium, L: lamina propria, N: not separated. Middle: Colored by location. Bottom: Colored by disease status (Healthy, Non-inflamed or Inflamed). (E) Barplots show significant differences in cell type frequency for non-inflamed (blue) and inflamed (red) samples relative to healthy (green) samples in immune, stromal and epithelial compartments in TI (top-row) and CO (bottom-row). (*adjusted p<0.05, **adjusted p<0.01, red* = overrepresented in inflamed or non-inflamed samples vs. healthy, blue* = underrepresented). The total number of cells contributing to each bar is also shown. In TI, the sample size for Healthy/Non-inflamed/Inflamed was 13/49/16 for the stromal and immune compartments, and 11/45/14 for epithelial. In CO, these were 32/20/6 and 32/18/5 respectively. +Expression profiles across cell types of core IBD risk genes. +(A) Scaled mean expression (Methods) of 20 core IBD risk genes in both TI and colon. (B) Fraction of cells from healthy, non-inflamed, and inflamed samples expressing core IBD genes for cell types with at least 200 cells available. Each point represents the fractions of cells within a cell type expressing that particular gene. Gene-cell type pairs with a difference in fraction of genes expressing greater than 0.3 (dashed lines) are labeled. Note that for readability, cell types are summarized to their category, causing some gene labels to be repeated for each specific cell type. Individually differentially expressed gene-cell type pairs (FDR<0.05) are highlighted (red). +Location- and cell type-specific differential expression in active CD. +(A) Number of differentially-expressed genes between inflamed CD and healthy samples in TI, broken down by cell type (discrete component of a MAST model; FDR < 0.05). (B) Same as (A) but for colon. (C) Relationship between differential expression in TI and colon for each cell compartment. (D) Consistency score (Methods) of differential expression in inflammation for each common cell type between TI and CO. (E) Relationship between inflamed vs healthy and non-inflamed vs healthy samples in TI and colon within each cell compartment. (F) Fraction of cell types in which KEGG pathways are significantly enriched (FDR < 0.05; see Methods) within each compartment and location, split by enrichment direction (all pathway enrichment results in Table S5). +Enteroendocrine Cells (EEC) in TI. +(A) UMAP of 670 EECs in TI, colored by subset. (B) Donor and disease composition among EEC subsets. (C) Expression of markers for major EEC subsets. +Molecular regulators of myofibroblast activation in inflamed tissues. +(A) UMAP of the myofibroblast sub-groups in TI (HHIP+ NPNT+ and GREM1+ GREM2+), overlaid with expression of myofibroblast markers and sub-group markers. (B) Myofibroblast sub-groups clustered into two distinct clusters. (C) Pseudotime analysis of TI myofibroblasts. (D) Expression profiles of 200 selected genes with significant expression changes with respect to pseudotime (Methods). Highlighted genes include sub-group marker genes from (A) for the myofibroblast sub-groups, as well as significant genes in (E). Expression (TP10K) was first smoothed with a cubic spline and standardized to highlight expression differences (Methods). (E) Volcano plots for differential expression of four collagen genes and four HHIP+ NPNT+ marker genes in siRNA knockdown of select genes. Collagen genes were selected based on their expression in this population of cells (COL5A3 and COL7A1), and expression in myofibroblasts in literature. The top 5 genes by FDR are labeled. Dashed red line indicates FDR 0.05. (F) (Left) Fibroblasts were stained for COL7A1 expression (green) and counterstained with DAPI (blue) after knock-down of the indicated genes (left to right: control, TBX3, CHMP1A and RNF168). (Right) MFI of COL7A1 staining in the indicated cells with (pink) and without (gray) TGFbeta treatment. Each dot represents the average of 30 images, n = 6 independent wells. +Gini coefficients for 20 core IBD risk genes in TI and colon. +Functional relevance to IBD from. + Functional relevance in IBD Gene names Gini (TI) Highest mean expression cell type (TI) Gini (CO) Highest mean expression cell type (CO) IL23R Adaptive immunity, Th17 IL23R 0.84 ILCs 0.85 ILCs NKX2-3 Restitution NKX2-3 NKX23 NKX2C 0.81 Endothelial cells CA4+ CD36+ 0.76 Endothelial cells CD36+ CARD9 Microbe-sensing CARD9 0.77 Macrophages PLA2G2D+ 0.77 DC2 CD1D- EBF1 B cells EBF1 COE1 EBF 0.72 Pericytes HIGD1B+ STEAP4+ 0.82 Pericytes HIGD1B+ STEAP4+ IL2RA Adaptive immunity, Treg IL2RA 0.70 Macrophages PLA2G2D+ 0.74 Tregs NOD2 Microbe-sensing NOD2 CARD15 IBD1 0.71 Monocytes S100A8+ S100A9+ 0.73 Monocytes S100A8+ S100A9+ HNF4A UPR, Healing HNF4A HNF4 NR2A1 TCF14 0.68 Epithelial cells HBB+ HBA+ 0.75 Enterocytes TMIGD1+ MEP1A+ PTPN22 Tolerance PTPN22 PTPN8 0.61 T cells OGT+ 0.67 T cells OGT+ LRRK2 Lysosome function LRRK2 PARK8 0.59 Neutrophils S100A8+ S100A9+ 0.65 Monocytes S100A8+ S100A9+ IKZF1 B cells, Treg IKZF1 IK1 IKAROS LYF1 ZNFN1A1 0.51 T cells OGT+ 0.61 T cells OGT+ SLC22A5 (carnitine transporter) SLC22A5 OCTN2 0.54 Epithelial cells METTL12+ MAFB+ 0.49 Enterocytes TMIGD1+ MEP1A+ GPR35 Epithelial barrier GPR35 0.50 Neutrophils S100A8+ S100A9+ 0.52 Enterocytes TMIGD1+ MEP1A+ INPP5E (phosphatase) INPP5E 0.39 T cells OGT+ 0.42 Pericytes HIGD1B+ STEAP4+ PRDM1 B cells, Treg PRDM1 BLIMP1 0.37 Plasma cells 0.43 Macrophages LYVE1+ JAK2 Adaptive immunity, Cytokines JAK2 0.34 Neutrophils S100A8+ S100A9+ 0.44 Monocytes S100A8+ S100A9+ SMAD3 Treg SMAD3 MADH3 0.35 Neutrophils S100A8+ S100A9+ 0.42 T cells OGT+ IFIH1 Microbe-sensing IFIH1 MDA5 RH116 0.29 Neutrophils S100A8+ S100A9+ 0.36 Monocytes S100A8+ S100A9+ TYK2 Adaptive immunity, Cytokines TYK2 0.29 Monocytes S100A8+ S100A9+ 0.32 T cells OGT+ NFKB1 Immune signaling NFKB1 0.23 Neutrophils S100A8+ S100A9+ 0.32 Monocytes S100A8+ S100A9+ EP300 (transcriptional coactivator) EP300 P300 0.22 Tuft cells 0.31 T cells OGT+ +Highlights +scRNAseq atlas of 720k ileal and colonic cells in Crohn's disease (CD) and controls +Compositional and transcriptomic changes across immune, epithelial and stromal cells +Colonic tissues show stronger transcriptomic changes in inflammation and disease +CHMP1A, TBX3, and RNF168 may regulate a CD-associated program in fibroblasts \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41586-023-05769-3.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41586-023-05769-3.txt new file mode 100644 index 0000000..be68d80 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41586-023-05769-3.txt @@ -0,0 +1,366 @@ +An atlas of healthy and injured cell states and niches in the human kidney +Understanding kidney disease relies on defining the complexity of cell types and states, their associated molecular profiles and interactions within tissue neighbourhoods. Here we applied multiple single-cell and single-nucleus assays (>400,000 nuclei or cells) and spatial imaging technologies to a broad spectrum of healthy reference kidneys (45 donors) and diseased kidneys (48 patients). This has provided a high-resolution cellular atlas of 51 main cell types, which include rare and previously undescribed cell populations. The multi-omic approach provides detailed transcriptomic profiles, regulatory factors and spatial localizations spanning the entire kidney. We also define 28 cellular states across nephron segments and interstitium that were altered in kidney injury, encompassing cycling, adaptive (successful or maladaptive repair), transitioning and degenerative states. Molecular signatures permitted the localization of these states within injury neighbourhoods using spatial transcriptomics, while large-scale 3D imaging analysis (around 1.2 million neighbourhoods) provided corresponding linkages to active immune responses. These analyses defined biological pathways that are relevant to injury time-course and niches, including signatures underlying epithelial repair that predicted maladaptive states associated with a decline in kidney function. This integrated multimodal spatial cell atlas of healthy and diseased human kidneys represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations. +A high-resolution kidney cellular atlas of 51 main cell types, including rare and previously undescribed cell populations, represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations. +Main +The human kidneys have vital systemic roles in the preservation of body fluid homeostasis, metabolic waste product removal and blood pressure maintenance. After injury, dynamic acute and chronic changes occur in the renal tubules and surrounding interstitial niche. The balance between successful or maladaptive repair processes may ultimately contribute to the progressive decline in kidney function. Defining the underlying molecular diversity at a single-cell level is key to understanding progression of acute kidney injury (AKI) to chronic kidney disease (CKD), kidney failure, heart disease or death:issues that remain a global concern. +We report a multimodal single-cell and spatial atlas with integrated transcriptomic, epigenomic and imaging data over three major consortia: the Human Biomolecular Atlas Program (HuBMAP), the Kidney Precision Medicine Project (KPMP) and the Human Cell Atlas (HCA). To ensure robust cell state profiles, healthy reference tissues were obtained from multiple sources, and biopsies were collected from patients with AKI and CKD under rigorous quality assurance and control procedures. We define niches for healthy and altered states across different regions of the human kidney spanning the cortex to the papillary tip, and identify gene expression and regulatory modules in altered states associated with worsening kidney function. The resultant atlas greatly expands on existing efforts and will serve as an important resource for investigators and clinicians working towards a better understanding of kidney pathophysiology. +Constructing a kidney cellular atlas +Overview of the technologies used to generate a human kidney cell atlas. +a, Human kidney samplesconsisted of healthy reference, AKI or CKD nephrectomies (Nx), deceased donors (DD) or biopsies. Tissues were processed for one or more assays, including snCv3, scCv3, SNARE2, 3D imaging or spatial transcriptomics (Slide-seq2, Visium). Scale bars, 1 mm (top) and 300 microm (bottom). b, Summary of the samples. Ref, reference. c, Omic RNA data were integrated, as shown by joint UMAP embedding, for alignment of cell type annotations across the three different data modalities. IC, intercalated cells; PC, principal cells; VSM/P, vascular smooth muscle cell or pericyte. +Spatially resolved atlas of molecular cell types. +a, Schematic of the human nephron showing cell types and states. b, UMAP embedding showing cell types (subclass level 3) for snCv3. Insets: overlays for both regional origin and altered-state status. Cyc, cycling; degen, degenerative; trans, transitioning. See Supplementary Table 4 for cell type definitions. c, Heat map of Slide-seq cell type frequencies along the corticomedullary axis (three individuals) (left). Middle, representative tissue puck region showing the transition of ATL to M-TAL segments. Right, corresponding expression of marker genes (scaled). Scale bar, 300 microm. d, Schematic of the renal corpuscle showing resolved cell types. e, The Slide-seq puck area indicated in Extended Data Fig. 4c and predicted cell types for renal corpuscles (top). Bottom, mapped expression values for corresponding marker genes (scaled). Scale bar, 100 microm. f, The average expression values for renal corpuscle cell types for markers shown in e and Extended Data Fig. 4f for all datasets. Ave., average; Exp., expression. g, Visium data on a healthy reference kidney (cortex, top; medulla, bottom). Left, haematoxylin and eosin (H&E)-stained tissue. Right, the per-bead predicted transfer scores for cell types or transcript expression values. Scale bar, 300 microm. Cx, cortex; OM, outer medulla; IM, inner medulla. The black lines outline histologically confirmed medullary rays leading into medulla. +Source Data +To fully examine the molecular profile of kidney cell types, we used droplet-based transcriptomic assays (Chromium v3) for single nuclei (snCv3) and single cells (scCv3) and the multiomic assay for single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq2, or SNARE2) (Supplementary Tables 1-3). Integrative transcriptome analyses were performed on more than 400,000 high-quality nuclei/cells (Methods) from 58 reference tissues (35 donors) and 52 diseased tissues (36 patients) that covered the spectrum of conditions from healthy to AKI and CKD (Fig. 1, Extended Data Figs. 1-3 and Supplementary Fig. 1). Unsupervised clustering was first performed on snCv3 data, permitting the discovery of 100 distinct cell populations, which were annotated to 77 subclasses of epithelial, endothelial, stromal, immune and neural cell types (Fig. 2, Methods, Extended Data Figs. 1 and 2 and Supplementary Tables 4 and 5). To further extend cell type annotations across omic platforms, snCv3 data were used to anchor scCv3 and SNARE2 datasets to the same embedding space, and cell type labels were assigned through integrative clustering (Methods, Extended Data Fig. 3 and Supplementary Tables 6 and 7). For spatial localization of these cell types or states in situ, we applied 3D label-free imaging, multiplex fluorescence imaging (15 individuals) and spatial transcriptomic Slide-seq2 (6 individuals, 67 pucks) and Visium assays (22 individuals, 23 samples) (Fig. 1, Methods and Supplementary Table 2). To ensure consistency and agreement of findings across technologies and minimize procurement- and assay-related biases, multiple samples were processed with more than one assay (Supplementary Table 3 and Extended Data Fig. 1a). Our approach permitted deep and cross-validated molecular profiles for aligned kidney cell types, leveraging the distinct advantages of each technology; for example, the addition of cytosolic transcripts from scCv3, regulatory elements from SNARE2 accessible chromatin, and in situ cell type/state localization and interactions from spatial technologies. +Reference and altered states +We provide a very high level of complexity for all cell types along the depth of the kidney from the cortex to the papillary tip, in each nephron segment and the interstitium (Fig. 2a), identifying 51 canonical human kidney cell types with associated biomarkers (Methods and Supplementary Tables 5-8). This includes cell type epigenetic maps, comprising open chromatin regions and cis-regulatory elements with enriched transcription-factor-binding motifs (Supplementary Fig. 1 and Supplementary Table 9). To spatially localize cell types within the tissue, snCv3 subclasses were used to predict identities in Slide-seq and Visium transcriptomic data at different resolutions (10 microm and 55 microm beads, respectively) (Fig. 2c-g, Methods and Extended Data Fig. 4-5). This enabled us to recapitulate renal corpuscle, tubular, vascular and interstitial cell types with proportions, marker profiles and spatial organizations consistent with expected or observed (Visium) histopathology (Extended Data Fig. 5). Proximity enrichment analysis based on the cell type composition of adjacent Slide-seq beads across 32 cortical and 35 medullary tissue pucks (6 participants) delineated region-specific cellular neighbourhoods (Extended Data Fig. 4d,e), including the renal corpuscle composition of podocytes (PODs), glomerular capillaries (EC-GC), mesangial cells and parietal epithelial cells. These renal corpuscle neighbourhoods localized adjacent to the juxtaglomerular apparatus cells:renin-producing granular (REN) cells and macula densa cells:and endothelial cells of the afferent/efferent arterioles (EC-AEA) leading into and out of the renal corpuscle (Fig. 2e-f). This neighbourhood analysis further confirmed a distinct vascular smooth muscle cell (VSMC) population flanking the afferent/efferent arterioles (Extended Data Fig. 4f). Consistent with these annotations, we validated the appropriate localization of associated cell type markers across platforms (Fig. 2f and Extended Data Fig. 5d-j). In addition to the renal corpuscle, we spatially anchored cell type subpopulations to the cortex or medulla (Fig. 2c and Extended Data Fig. 5a). The transition of the ascending thin limbs (ATL) of the inner medulla to the medullary thick ascending limb (M-TAL) of the outer medullary stripe was observed in Slide-seq (Fig. 2c), along with the transition from descending thin limb (DTL2) and M-TAL in the medulla to the cortical thick ascending limb (C-TAL) in the cortex in Visium (Fig. 2g and Extended Data Fig. 5d). Thus, the unique strengths of each spatial technology enabled the validation of our omic-defined cell types. +A critical and new element of this reference atlas is the characterization of cellular states associated with pathophysiological stress or injury. We carefully defined these altered states on the basis of previous studies and known features of injury (Methods and Supplementary Table 10). We established multiple putative states:namely cycling, transitioning, adaptive (successful or maladaptive repair) and degenerative (damaged or stressed). These altered states were identified for epithelial cells along the nephron, as well as within the stroma and vasculature (Fig. 2a,d). Altered states, from reference and disease tissues in different proportions, were found to exist across technologies (Extended Data Figs. 1 and 3) and showed distinct expression signatures (Supplementary Tables 11-15). +We used several methods to confirm these altered states. Mapping our annotations onto an existing mouse AKI model provided insights into their timecourse after an acute injury event (Extended Data Fig. 6). Degenerative states, coinciding with elevated expression of the known injury markers SPP1, CST3, CLU and IGFBP7 in humans (Supplementary Fig. 2), arose early in mice after injury (Extended Data Fig. 6c-e). These states showed a common expression and regulatory signature across cell types associated with FOS/JUN signalling (Supplementary Fig. 2) and were largely depleted in recovered mouse kidneys, consistent with possible cell death or a progression into repair states. Putative adaptive (successful or maladaptive tubular repair) states were primarily found within the proximal tubule (PT) and TAL subclasses in mouse and human kidneys. Both adaptive epithelial (aEpi) cell types showed expression profiles associated with epithelial differentiation, morphogenesis, mesenchymal differentiation and EMT, while also exhibiting a marked downregulation of transporters critical to their normal function (Extended Data Fig. 7a-c). The adaptive PT (aPT) population both mapped to and correlated with failed repair in rodents (Extended Data Fig. 2g), with characteristic expressions of VCAM1, DCDC2 and HAVCR1 (Extended Data Fig. 7c). Notably, we now identify a similar state within the TAL (aTAL), marked in humans by PROM1 (encoding CD133) and DCDC2 (Supplementary Table 13). These are consistent with CD133+PAX2+ lineage-restricted progenitors that are known to exist in the proximal and distal tubules of the adult kidney. Analysis of the mouse AKI data revealed that these originated predominantly from C-TAL, and followed a similar time course as aPT, persisting 6 weeks after AKI, consistent with a potential failed-repair population. This suggests a common aEpi state, sharing molecular signatures associated with injury and repair, that occurs in higher abundance within the PT and cortical TAL. +Distinct altered states were identified within the stroma (aStr) that were consistent with cell types involved in wound healing and fibrosis after tissue injury (Extended Data Fig. 2i). These cell populations encompass myofibroblasts (MyoF), cycling MyoF (cycMyoF) and a group of adaptive fibroblasts (aFIB) representing potential MyoF progenitors. Their expression signatures included genes encoding periostin (POSTN), fibroblast activation protein alpha (FAP), smooth muscle actin (ACTA2) and collagens (Extended Data Fig. 7d). aStr cells were enriched after mouse AKI, and they persisted at later timepoints (Extended Data Fig. 6d,e). Furthermore, they exhibited high matrisome expression, consistent with their predicted role in extracellular matrix deposition and fibrosis (Extended Data Fig. 7e). Thus, careful annotation of altered states across kidney cell types has provided a means for labelling injury populations. This is important not only for diseased tissues, but also in reference tissues in which they might arise from ischaemic stress during sample acquisition or normal ageing. Key outcomes are the ability to annotate healthy reference cell clusters (Supplementary Fig. 3) as well as providing insights into the pathogenetic mechanisms of disease. +Spatially mapped injury neighbourhoods +Transcriptomically defined injury neighbourhoods. +a, The mean proportion of altered-state expression signatures (see Methods, 10x Visium spatial transcriptomics) for all Visium spots (146,460 total spots over 22 individuals). P values were calculated using Fisher's exact tests over the spot proportions. b, Feature plots of the aEpi cell state. Scale bar, 300 microm. The top bounded region is shown in Extended Data Fig. 7h. c, Colocalization of immune and stromal cells with epithelial cell injury states. The y axis shows the odds ratio of colocalization (40,326 total spots over 22 individuals). P values were calculated using Fisher's exact tests over the colocalization events. Ad/Mal, adaptive/maladaptive representing successful or maladaptive tubular repair. d, The average expression values for healthy reference and altered-state markers across cell types identified using Visium. e, Histology and predicted cell types in a cortical region (CKD) of interstitial fibrosis and neighbouring PT atrophy (altered PT). The pie charts show the proportions of predicted transfer scores for cell type annotations from snCv3 (Fig. 2b). The area corresponds to the bottom bounded region in b. Scale bar, 100 microm. f, The per-bead predicted transfer scores for cell types for area shown in e. Scale bar, 100 microm. *P < 0.01, **P < 1 x 10-5, ***P < 1 x 10-10. Exact P values are provided with the Source Data. +Source Data +For spatial localization of injury, altered states were mapped to Visium data generated on a range of healthy reference, AKI and CKD tissues (Supplementary Tables 2 and 3). As expected, altered cell state signatures were enriched in AKI and CKD samples compared with in reference tissues (Fig. 3a,b). On the basis of cell type colocalization in the relatively larger area of Visium spots, immune and stromal cells colocalized more frequently with altered epithelial cells (Fig. 3c), consistent with increased fibrosis and inflammation around damaged tubules. Furthermore, cell-type-specific altered states in Visium data that showed expression profiles consistent with snCv3/scCv3 (Fig. 3d) were directly mapped to histological areas of injury. For example, stromal (fibroblast (FIB)), aStr (aFIB) and immune cells (monocyte-derived cells (MDCs)) localized to a region of fibrosis within the cortex of a CKD biopsy (Fig. 3e,f). This region abutted dilated and atrophic tubules that showed an aPT signature marked by CDH6 (Extended Data Fig. 7f and Supplementary Table 11). We also found evidence for injury of the medullary tubules (Extended Data Fig. 7g-i), with an area showing intraluminal cellular cast formation, cell sloughing and loss of nuclei that were associated with degenerative CD cells, including degenerative medullary principal cells (dM-PCs) and transitioning principal and intercalated cells. This region increased expression of the degenerative marker DEFB1, which was previously shown to contribute to fibrosis through immune cell recruitment. These results support co-mapping of snCv3/scCv3 reference and altered cell types to histological areas of injury. +Defining cellular niches in renal disease from 3D fluorescence imaging. +a, Maximum-intensity projections of representative biopsies (cortex or medulla) showing classification label examples (insets i-iii). Altered, altered morphology or injury; C-DN, cortical distal nephron; Glom, glomeruli; V, vessels; VB, vascular bundle. Examples of MPO+ and CD68+ are indicated (i). The symbols * and # indicate CD68+ and MPO+ cells, respectively, in (i) and insets. Arrowhead indicates T cell in (iii) and inset. Scale bars, 1 mm (biopsy images), 100 mum (i and ii) and 5 mum (insets). b, Community-based clustering on cell composition for around 20,000 randomly chosen neighbourhoods (15 individuals). The red outline indicates neighbourhoods including the medulla. c, The cellular composition of the neighbourhoods identified in b. d, Pairwise analysis of cells within 1.2 million neighbourhoods (15 individuals); colours are as indicated in c. e, Pearson's coefficients for select interactions, the colour indicates both the value and direction of the correlation. P values were generated using two-sided t-tests. +Source Data +To further uncover in situ cellular niches and injured microenvironments across kidney disease, we performed 3D multiplexed immunofluorescence imaging and label-free cytometry (3DTC) with second harmonic generation for collagen content on KPMP AKI and CKD kidney biopsy samples (Extended Data Fig. 8a and Supplementary Tables 2 and 3). 3DTC defined cellular niches for 1,540,563 cells by neighbourhood analysis of 14 classes of cells covering renal cortical and medullary structures (Fig. 4a, Methods and Extended Data Fig. 8b-i). We identified 14 cellular niches through community detection that included expected niches of cortical or medullary epithelium (N7 and N8 versus N14, N9 and N1, respectively; Fig. 4b,c). The TAL and PT neighbourhoods (N7 and N8) were enriched in areas of injury (Fig. 4c and Extended Data Fig. 8i). Furthermore, areas of injury were associated with infiltrating leukocytes, including CD68+ (myeloid), MPO+ (N) and CD3+ (lymphoid or T) cells (N6, N11 and N13, respectively). Uniquely, CD3+ cells were almost exclusively detected in a subset of neighbourhoods with areas of tissue damage including presumptive epithelial degeneration (loss of markers and simplification) and fibrosis (N13; Fig. 4a (iii) and 4c and Extended Data Fig. 8h), consistent with degenerative epithelial enrichment found using Visium (Fig. 3c). By contrast, myeloid cells were found in cellular diverse niches with cortical or medullary epithelium (N6 and N11; Fig. 4c). This is consistent with the association of M2 macrophages (MAC-M2) with adaptive rather than degenerative epithelia in Visium data (Fig. 3c) and their sustained presence in mouse ischaemia-reperfusion injury (IRI) (Extended Data Fig. 6d). The leukocyte diversity was specific in 3D neighbourhoods, as MPO+ and CD3+ cells were overlapping, whereas CD3+ cells were conspicuously low in neighbourhoods with CD68+ cells (N11 versus N6; Fig. 4c and Extended Data Fig. 8g). As neutrophils colocalized with putative adaptive and degenerative states (Fig. 3c) and transiently infiltrate early in mouse IRI (Extended Data Fig. 6d), neutrophils may infiltrate along with T cells predominantly in areas of acute injury marked by mixed degenerative and adaptive states. Alternatively, myeloid cells (such as MAC-M2) may occur more predominantly within relatively healthy areas showing active repair (adaptive or maladaptive). Overall, the results from spatial transcriptomics, histological correlation and 3DTC demonstrate that altered states were enriched in PT and TAL neighbourhoods, with distinct immune-active cellular niches associated with healthy and injured tubules. +Stages and niches of epithelial repair +Expression and regulatory signatures of adaptive epithelial cells. +a, Trajectory of TAL cells for snCv3, scCv3 and mouse AKI data, showing mouse to human mapping. Top right, latent time heat map from RNA velocity estimates. Bottom right, bar plot of collection groups after IRI across mouse trajectory modules. b, Heat map of smoothened gene expression (conserved or human specific) along the inferred TAL pseudotime. State modules based on the gene expression profiles are shown. M, M-TAL; C, C-TAL; Ad/Mal, adaptive/maladaptive, representing successful or maladaptive tubular repair. c, SNARE2 average accessibilities (access.) (chromVAR) and the proportion accessible for transcription-factor-binding sites (TFBSs) (right), and the averaged gene expression values (log scale) and the proportion expressed for integrated snCv3/scCv3 modules (left). TF, transcription factor. d, Slide-seq fibrotic regions. Top and bottom right, bead locations for a representative region, coloured by predicted subclasses, prediction weights or scaled gene expression values. Marker genes are ITGB6 (aTAL), EGF and SLC12A1 (TAL), CD14 (MAC-M2/MDC), MYH11 (VSMC/MyoF) and COL1A1 (aStr). The bar plot shows the immune subclass counts and the dot plots show the average expression of marker genes generated from three fibrotic regions (two individuals; Extended Data Fig. 11a). Scale bar, 50 mum. e, Visium TAL niches identified from all Visium spots and defined by colocalized cells (Methods and Extended Data Fig. 11b-e), showing the proportion of component cell type signatures. The dot plots show the niche marker gene average expression values. +Source Data +To obtain a deeper understanding of the genetic networks underlying the progression and potential pathology of altered tubular epithelium, we performed trajectory inference on the snCv3/SNARE2 and scCv3 subpopulations (Fig. 5a,b, Methods and Extended Data Fig. 9). Although most degenerative states appeared too disconnected, aEpi trajectories showed dynamic gene expression and regulatory transitions from dedifferentiated to mature functional states (Supplementary Tables 16-21). We further identified transitory states or modules that may be associated with either successful or maladaptive repair. Early repair cells showed expression signatures associated with progenitor states (PROM1), microtubule reorganization (DCDC1) and AKI (HAVCR1, SPP1) (Fig. 5b and Extended Data Fig. 9c,f). The directionality of these repair trajectories was confirmed from RNA velocities estimated from dynamical modelling of transcript splicing kinetics, and the alignment with mouse AKI subpopulations (Fig. 5a and Extended Data Fig. 9b,g). These analyses enabled the identification of TAL repair signatures that were either conserved across species or human specific (Fig. 5b). +Epithelial repair signalling was enriched for several growth factors and pathways with known roles in promoting normal tubulogenesis, as well as maladaptive repair, fibrosis and inflammation. These include Wnt, Notch, TGF-beta, EGF, MAPK (FOS/JUN), JAK/STAT and Rho/Rac signalling (Fig. 5c, Extended Data Fig. 9d and Supplementary Tables 19-21), with dynamic transcription of several pathway regulators mapped to the TAL repair modules (Extended Data Fig. 9h, i). In support of MAPK signalling, PT cells that showed expression of PROM1 were subjacent to phosphorylated JUN (p-JUN) (Extended Data Fig. 9e). Progressively active REL/NF-kappaB signalling along the aTAL and aPT trajectories further expands on previous roles for this pathway in injured PTs (Fig. 5c and Supplementary Table 19). We also found increased cAMP signalling (CREB transcription factors in aPT) capable of promoting dedifferentiation and increased ELF3 activities that are potentially required for mesenchymal-epithelial transition, both indicating that adaptive states may be poised for re-epithelialization. +Through integration of SNARE2 epigenomic profiles with snCv3 transcriptomes, detailed gene regulatory networks (GRNs) were inferred for TAL trajectory modules. Transcription factors with high network importance were identified in each repair state, confirming key roles for several major signalling pathways, including their downstream target genes and processes (Extended Data Fig. 9j and Supplementary Tables 22-24). This highlighted a critical role for TRAP2B (AP-2beta), which was previously found to be required for terminal differentiation of distal tubule cells through activated expression of KCTD1. Both factors were active or expressed within mid-repair states (Fig. 5c) and simulated perturbation of TRAP2B disrupted the repair trajectory transition (Extended Data Fig. 9l,m). We therefore find adaptive epithelial trajectories sharing common molecular profiles that progressively upregulate cytokine signalling involved in tubule regeneration, while also providing molecular links to pathways associated with fibrosis, inflammation and end-stage kidney disease. +Slide-seq, Visium, immunofluorescence staining and RNA in situ hybridization (ISH) experiments confirmed spatial localization of adaptive states into injury niches (Fig. 5d,e and Extended Data Fig. 10). aTAL populations in Slide-seq-processed tissues (3 niches, 2 individuals; Fig. 5d and Extended Data Fig. 11a) were marked by an upregulation of the aTAL marker ITGB6 and downregulated EGF expression, which is known to occur after TAL injury. These were identified adjacent to areas of aStr enrichment, evidenced by elevated COL1A1 expression. These potentially fibrotic regions also showed diverse inflammation for both lymphoid (T cell) and myeloid (MAC-M2/MDC) cell types that co-localized around vessels (Fig. 5d). Analogously, aTAL injury niches were identified in Visium data as spots (55 microm) colocalizing with stromal, lymphoid and myeloid cells (Fig. 5e, Methods and Extended Data Fig. 11b-e). Localization of aTAL states to injured tubules was further confirmed by ISH, in which PROM1-expressing cells showed clear histological evidence of injury, including epithelial simplification (thinning), loss of nuclei and loss of brush border in PTs (Extended Data Fig. 10e). Overall, aTAL, aStr and immune expression profiles from spatial transcriptomics were consistent with those identified from snCV3 and scCv3, providing both validation and spatial co-localization of these cell types and states into niches of ongoing injury and repair. +Maladaptive repair signatures. +a,b, The ligand-receptor signalling strength between TAL states and IMM subclasses (a) or STR subclasses (b). The coloured bars indicate the total signalling strength of the cell group by summarizing signalling pathways. The grey bars indicate the total signalling strength of a signalling pathway by summarizing cell groups. Members of key signaling pathways described in the main text are in bold. c, The average gene expression values for select ligand-receptor combinations using snCv3/scCv3 integrated data. d, Dot plots validating select markers shown in c in the Visium data. e, Unadjusted Kaplan-Meier curves by cell state scores for composite of end-stage renal disease (ESRD) or for 40% drop in eGFR from time of biopsy in the NEPTUNE adult patient cohort (199 patients; Supplementary Table 30). Patients who reached the end point between screening and biopsy were excluded. Enrich., enrichment. P values calculated using log-rank tests for trend are shown (P = 0.021 (aPT), P = 0.003 (aTAL), P = 0.55 (degenerative)). +Given the upregulation of fibrotic cytokine signalling in epithelial repair, these regenerating cells may represent maladaptive states if they accumulate or fail to complete tubulogenesis. We therefore investigated the contribution of these states to cell-cell secreted ligand-receptor interactions within a fibrotic niche (Supplementary Table 25). From spatial assays, this niche may comprise aEpi cells adjacent to normal and altered arteriole cells and fibroblasts, and immune cells that include lymphoid and myeloid cells (Figs. 3-5). Using snCv3 and scCv3 datasets associated with trajectory modules, we identified aTAL repair states as having a higher number of interactions first with immune cells (early repair), then with the stroma (mid-repair; Fig. 6a,b). This was associated with secreted growth factors of the FGF, BMP, WNT, EGF, IGF and TGF-beta families and the gain of interactions with MAC-M2 and T cells (Extended Data Fig. 11f). This indicates that adaptive tubule states may recruit activated fibroblasts and MyoF both primarily and secondarily through their recruitment of immune cells. +We also found additional evidence for the activation of EGF pathway signalling within the adaptive epithelial trajectories, which in itself may lead to activation of TGF-beta signalling and create a niche capable of promoting fibrosis. Consistently, EGF ligands NRG1 and NRG3 both become expressed in aEpi states for a possible role in stromal cells (STR) and MAC-M2 recruitment (Figs. 5d,e and 6c,d). Early and mid-repair TAL states may also recruit or stimulate T cells through expression of the CD226-interacting protein NECTIN2 (Fig. 6c,d). Alternatively, BMP6 signalling from mid-repair states may have a role in preventing fibrosis through possible SMAD1 activation of fibroblast differentiation within aFIB populations (Fig. 6c,d, Extended Data Fig. 11g,h and Supplementary Tables 26-28). BMP6 expression was also detected in repair states of the mouse AKI model at late timepoints when aFIB cells already showed reduced IGF1 expression (Extended Data Fig. 11g). IGF1 secreted from aFIB cells may signal to both stimulate MYOF differentiation and promote regeneration of the repairing epithelial cells through IGF1R (Fig. 6c,d). Given the timing of BMP6 and IGF1 expression after acute injury, BMP6-induced differentiation pathways within the aFIB cells may represent a late aTAL signal to dampen the fibroblast response. We therefore identify state- and niche-dependent signalling for reparative states in proximal and distal tubules that may ultimately influence the extent of fibrosis and inflammation. +Adaptive states can be maladaptive +Although recruitment of stromal and immune cells is necessary for normal wound healing, persistent recruitment by aEpi cells may impair epithelial function or lead to continued release of cytokines promoting disease progression. Consistent with this, we found that aEpi gene signatures that were conserved across snCv3 and scCv3 (Supplementary Table 29) were associated with poor renal function in CKD cases (Extended Data Fig. 12a). Thus, successful or maladaptive repair within the TAL may have a role in the transition to chronic disease. Notably, aTAL signatures underlying early repair states were significantly associated with disease progression using unadjusted and sequentially adjusted survival models within the Nephrotic Syndrome Study Network (NEPTUNE) cohort of 193 patients (Fig. 6e, Methods, Extended Data Fig. 12b and Supplementary Table 30). Furthermore, in an independent cohort of 131 patients with kidney disease in the European Renal cDNA Bank (ERCB) cohort, aEpi scores varied by kidney disease diagnosis relative to living donors. Specifically, patients with diabetes, hypertension and focal segmental glomerular sclerosis had higher aPT and common aPT-aTAL signatures compared with that of living donors after adjusting for age and sex. In the diabetes group, the aPT and common aPT-aTAL signatures remained higher than that of living donors even after adjusting for age, sex and estimated glomerular filtration rate (eGFR; Methods and Supplementary Table 30). Nevertheless, it is important to note that the clinical correlations are based on a small sample size and should therefore be interpreted with care. +These findings indicate that altered TAL functionality, including its GFR-regulatory role through tubuloglomerular feedback, may represent a major contributing factor to progressive kidney failure. Furthermore, causal variants for eGFR and chronic kidney failure were enriched within TAL regulatory regions that were also enriched for oestrogen-related receptor (ESSR) transcription-factor motifs (Extended Data Fig. 12c and Supplementary Table 31). ESRR transcription factors (especially ESRRB), which are key players in TAL ion transporter expression, are central regulators of the TAL expression network (Extended Data Fig. 12d), become inactivated in adaptive states (Fig. 5c) and, in experimental models, could exacerbate AKI and fibrosis. Expression quantitative trait loci (eQTL) associated with kidney function that were previously shown to be enriched primarily in PTs also showed enrichment within the TAL, along with signatures associated with acute injury and fibrosis in a human AKI to CKD progression study (Extended Data Fig. 12e). Thus, we demonstrate both a potential maladaptive role for the aEpi states and a potential central role for the TAL segment in maintaining the health and homeostasis of the human kidney. This is consistent with the finding that the top renal genes showing decline in a mouse ageing cell atlas were associated with the TAL. +Our findings implicate an accumulation of maladaptive epithelia during disease progression that may also be consistent with chronically senescent cells. This is supported by both increased expression of ageing-related genes, stress-response transcription factor activities and an apparent senescence-associated secretory phenotype (SASP) for these cells (Extended Data Fig. 12f,g). As such, we detected CDKN1A (also known as p21cip1), CDKN1B (also known as p27kip1), CDKN2A (also known as p16ink4a) and CCL2 expression in late aPT and aTAL states. Furthermore, expression signatures for reparative processes in aEpi states were downregulated in the CKD (n = 28) over AKI (n = 22) cases used in this study (snCv3/scCv3; Supplementary Table 32). This is distinct from the immune response signatures that were more enriched in AKI cases more globally across cell types (Extended Data Fig. 12h and Supplementary Table 33). Overall, our findings are consistent with pro-inflammatory repair processes that may persist after injury, or may subsequently transition to maladaptive or senescent pro-fibrotic states during disease progression. +Discussion +In contrast to recent work to broadly integrate major healthy kidney cell types across disparate data modalities, here we present a comprehensive spatially resolved healthy and injured single-cell atlas across the corticomedullary axis of the kidney. Signals between tubuli, stroma and immune cells that underlie normal and pathological cell neighbourhoods were identified, including putative adaptive or maladaptive repair signatures within the epithelial segments that may reflect a failure to complete differentiation and tubulogenesis. Spatial analyses identified that these epithelial repair states have elevated cytokine production, increased interactions with the distinct fibrotic and inflammatory cell types, and expression signatures linked to senescence and progression to end-stage kidney disease. Failure of these cells to complete tubulogenesis, which might arise from an incompatible cytokine milieu within the fibrotic niche, in itself might ultimately contribute to a progressive decline in kidney function. In turn, the high-cytokine-producing nature of these cells may further contribute to kidney disease through promotion of fibrosis. We portray a clear role for the relatively understudied TAL segment of the nephron, a region that is critical for maintaining osmotic gradient and blood pressure through tubuloglomerular feedback. The insights, discoveries and interactive data visualization tools provided here will serve as key resources for studies into normal physiology and sex differences, pathways associated with transitions from healthy and injury states, clinical outcomes, disease pathogenesis and targeted interventions. +Methods +Statistics and reproducibility +For 3D imaging and immunofluorescence staining experiments, each staining was repeated on at least two separate individuals or separate regions. For immunofluorescence validation studies, commercially available antibodies were used; 13 out of the 15 tissue samples were also analysed using snCv3 or scCv3. For ISH, 6 tissue samples (4 biopsies and 2 nephrectomies) were analysed. For Slide-seq, 67 tissue pucks (6 individuals) were analysed, with 2 individuals also analysed using snCv3 or Visium. For Visium, 23 kidney tissue sections (22 individuals) were imaged, including 6 that were also analysed using snCv3 or scCv3 and one examined using Slide-seq. Orthogonal validation of spatial transcriptomic annotations revealed similar marker gene expression in snCv3/scCv3 and these technologies, as well as spatial localization that corresponded with histologically validated Visium spot mapping. Although multiomic data from the same samples would be the most informative, this remains technically challenging. However, wherever possible, several technologies were performed on a subset of samples from the same patient and, in some cases, the same tissue block was used to generate multimodal data (Extended Data Fig. 1a and Supplementary Table 3). This heterogeneous sampling approach ensured cell type discovery while minimizing assay-dependent biases or artifacts encountered when using different sources of kidney tissue. We recognize that the heterogeneity of sample sources for several technologies is a potential limitation due logistics and limited patient biopsy material. +Ethical compliance +We have complied with all ethical regulations related to this study. All experiments on human samples followed all relevant guidelines and regulations. Human samples (Supplementary Table 1) collected as part of the KPMP consortium (https://KPMP.org) were obtained with informed consent and approved under a protocol by the KPMP single IRB of the University of Washington Institutional Review Board (IRB 20190213). Samples as part of the HuBMAP consortium were collected by the Kidney Translational Research Center (KTRC) under a protocol approved by the Washington University Institutional Review Board (IRB 201102312). Informed consent was obtained for the use of data and samples for all participants at Washington University, including living patients undergoing partial or total nephrectomy or rejected kidneys from deceased donors. Cortical and papillary biopsy samples from patients with stone disease were obtained with informed consent from Indiana University and approved by the Indiana University Institutional Review Board (IRB 1010002261). For Visium spatial gene expression, reference nephrectomies and kidney biopsy samples were obtained from the KPMP under informed consent or the Biopsy Biobank Cohort of Indiana (BBCI) under waived consent as approved by the Indiana University Institutional Review Board (IRB 1906572234). Living donor biopsies as part of the HCA were obtained with informed consent under the Human Kidney Transplant Transcriptomic Atlas (HKTTA) under the University of Michigan IRB HUM00150968. Deidentified leftover frozen COVID-19 AKI kidney biopsies were obtained from the Johns Hopkins University pathology archive under waived consent approved by the Johns Hopkins Institutional Review Board (IRB 00090103). +Single-cell and single-nucleus human tissue samples +For single-nucleus omic assays, tissues were processed according to a protocol available online (10.17504/protocols.io.568g9hw). For nucleus preparation, around 7 sections of 40 microm thickness were collected and stored in RNAlater solution (RNA assays) or kept on dry ice (accessible chromatin assays) until processing or used fresh. To confirm tissue composition, 5 microm sections flanking these thick sections were obtained for histology and the relative amount of cortex or medulla composition including glomeruli was determined. For single-cell omic assays, tissues used (15 CKD,12 AKI and 18 living donor biopsy cores) were preserved using CryoStor (StemCell Technologies). +Single-cell, single-nucleus and SNARE2 RNA-seq, quality control and clustering +Isolation of single nuclei +Nuclei were isolated from cryosectioned tissues according to a protocol available online (10.17504/protocols.io.ufketkw) with the exception that 4',6-diamidino-2-phenylindole (DAPI) was excluded from the nuclear extraction buffer and used only to stain a subset of nuclei used for counting. Nuclei were used directly for omic assays. +Isolation of single cells +Single cells were isolated from frozen tissues according to a protocol available online (10.17504/protocols.io.7dthi6n). The single-cell suspension was immediately transferred to the University of Michigan Advanced Genomics Core facility for further processing. +10x Chromium v3 RNA-seq analysis +10x single-nucleus RNA-seq and 10x single-cell RNA-seq were performed according to protocols available online (10.17504/protocols.io.86khzcw and 10.17504/protocols.io.7dthi6n, respectively), both using the 10x Chromium Single-Cell 3' Reagent Kit v3. Sample demultiplexing, barcode processing and gene expression quantifications were performed using the 10x Cell Ranger (v.3) pipeline using the GRCh38 (hg38) reference genome with the exception of a subset of scCv3 experiments that used hg19 (indicated in Supplementary Table 1). For single-nucleus data, introns were included in the expression estimates. +SNARE2 dual RNA and ATAC-seq analysis +SNARE-seq2, as outlined previously, was performed according to a protocol available online (10.17504/protocols.io.be5gjg3w). Accessible chromatin and RNA libraries were sequenced separately on the NovaSeq 6000 (Illumina) system (NovaSeq Control Software v.1.6.0 and v.1.7.0) using the 300 cycle and 200 cycle reagent kits, respectively. +SNARE2 data processing +Detailed step-by-step processing for SNARE2 data has been outlined previously. This has now been developed as an automated data processing pipeline that is available at GitHub (https://github.com/huqiwen0313/snarePip). snarePip (v.1.0.1) was used to process all the SNARE2 datasets. The pipeline provides an automated framework for complex single-cell analysis, including quality assessment, doublet removal, cell clustering and identification, robust peak generation and differential accessible region identification, with flexible analysis modules and generation of summary reports for both quality assessment and downstream analysis. The directed acyclic graph was used to incorporate the entirety of the data-processing steps for better error control and reproducibility. For RNA processing, this involved removal of accessible chromatin contaminating reads using cutadapt (v.3.1), dropEst (v.0.8.6) to extract cell barcodes and STAR (version 2.5.2b) to align tagged reads to the genome (GRCh38). For accessible chromatin data, this involved snaptools (v.1.2.3) and minimap (v.2-2.20) for alignment to the genome (GRCh38). +Quality control of sequencing data +10x snRNA-seq (snCv3) +Cell barcodes passing 10x Cell Ranger filters were used for downstream analyses. Mitochondrial transcripts (MT-*) were removed, doublets were identified using the DoubletDetection software (v.2.4.0) and removed. All of the samples were combined across experiments and cell barcodes with greater than 400 and less than 7,500 genes detected were retained for downstream analyses. To further remove low-quality datasets, a gene UMI ratio filter (gene.vs.molecule.cell.filter) was applied using Pagoda2 (https://github.com/hms-dbmi/pagoda2). +10x scRNA-seq (scCv3) +As a quality-control step, a cut-off of <50% mitochondrial reads per cell was applied. The ambient mRNA contamination was corrected using SoupX (v.1.5.0). The mRNA content and the number of genes for doublets are comparatively higher than for single cells. To reduce doublets or multiplets from the analysis, we used a cut-off of >500 and <5,000 genes per cell. +SNARE2 RNA +Cell barcodes for each sample were retained with the following criteria: having an DropEst cell score of greater than 0.9; having greater than 200 UMI detected; having greater than 200 and less than 7,500 genes detected. Doublets identified by both DoubletDetection (v.3.0) and Scrublet (https://github.com/swolock/scrublet; v.0.2.2) were removed. To further remove low-quality datasets, a gene UMI ratio filter (gene.vs.molecule.cell.filter) was applied using Pagoda2. +SNARE2 ATAC +Cell barcodes for each sample that had already passed quality filtering from RNA data were further retained with the following criteria: having transcriptional start site (TSS) enrichment greater than 0.15; having at least 1,000 read fragments and at least 500 UMI; having fragments overlapping the promoter region ratio of greater than 0.15. Samples were retained only if they exhibited greater than 500 dual omic cells after quality filtering. +Clustering snCv3 +Clustering analysis was performed using Pagoda2, whereby counts were normalized to the total number per nucleus, batch variations were corrected by scaling expression of each gene to the dataset-wide average. After variance normalization, all 5,526 significantly variant genes were used for principal component analysis (PCA). Clustering was performed at different k values (50, 100, 200, 500) on the basis of the top 50 principal components, with cluster identities determined using the infomap community detection algorithm. The primary cluster resolution (k = 100) was chosen on the basis of the extent of clustering observed. Principal components and cluster annotations were then imported into Seurat (v.4.0.0) and uniform manifold approximation and projection (UMAP) dimensionality reduction was performed using the top 50 principal components identified using Pagoda2. Subsequent analyses were then performed in Seurat. A cluster decision tree was implemented to determine whether a cluster should be merged, split further or labelled as an altered state. For this, differentially expressed genes between clusters were identified for each resolution using the FindAllMarkers function in Seurat (only.pos = TRUE, max.cells.per.ident = 1000, logfc.threshold = 0.25, min.pct = 0.25). Possible altered states were initially defined for clusters with one or more of the following features: low genes detected, a high number of mitochondrial transcripts, a high number of endoplasmic-reticulum-associated transcripts, upregulation of injury markers (CST3, IGFBP7, CLU, FABP1, HAVCR1, TIMP2, LCN2) or enrichment in AKI or CKD samples. Clusters (k = 100) that showed no distinct markers were assessed for altered-state features; if present, then these clusters were tagged as possible altered states, if absent then clusters were merged on the basis of their cluster resolution at k = 200 or 500. If this merging occurred across major classes (epithelial, endothelial, immune, stromal) at higher k values, then these clusters were instead labelled as ambiguous or low quality (including possible multiplets). For k = 100 clusters (non-epithelial only) that did show distinct markers, their k = 50 subclusters were assessed for distinct marker genes; if present, then these clusters were split further. The remaining split and unsplit clusters were then assessed for altered-state features. If present, they were tagged as possible altered states, if absent they were assessed as the final cluster. Annotations of clusters were based on known positive and negative cell type markers (Supplementary Table 5), the regional distribution of the clusters across the corticomedullary axis and altered state (including cell cycle) features. For separation of EC-DVR from EC-AEA, the combined population was independently clustered using Pagoda2 and clusters associated with medullary sampling were annotated as EC-DVR. For separation of the REN cluster, stromal cells expressing REN were selected on the basis of normalized expression values of greater than 3. Final overall assessment of clustering accuracy was performed using the Single Cell Clustering Assessment Framework (SCCAF v.0.0.10) using the default settings, and compared against that associated with broad cell type classifications (subclass level 1). +Annotating snCv3 clusters +To overcome the challenge of disparate nomenclature for kidney cell annotations, we leveraged a cross-consortium effort to use the extensive knowledge base from human and rodent single-cell gene expression datasets, as well as the domain expertise from pathologists, biologists, nephrologists and ontologists (see also Supplementary Tables 4 and 5 and the HuBMAP ASCT+B Reporter at GitHub (https://hubmapconsortium.github.io/ccf-asct-reporter)). This enabled the adoption of a standardized anatomical and cell type nomenclature for major and minor cell types and their subclasses (Supplementary Table 4), showing distinct and consistent expression profiles of known markers and absence of specific segment markers for some of the cell types (Extended Data Fig. 2a and Supplementary Table 5). The knowledge of the regions dissected and histological composition of snCv3 data further enabled stratification of distinct cortical and outer and inner medullary cell populations (Fig. 2b and Extended Data Fig. 1). The cell type identities and regional locations were confirmed through orthogonal validation using spatial technologies presented here and correlations with existing human or rodent stromal, immune, endothelial and epithelial datasets (Extended Data Fig. 2b-l). +Atlas cell type resolution +Our atlas now includes a higher granularity for the loop of Henle, distal convoluted tubule and collecting duct segments, now resolving three descending thin limb cell types (DTL1, 2, 3); different subpopulations of medullary or cortical thick ascending limb cells (M-TAL/C-TAL); two types of distal convoluted tubule cells (DCT1, 2); intercalated and principal cells of the connecting tubules (CNT-IC and CNT-PC); cortical, outer medullary and inner medullary collecting duct subpopulations (CCD, OMCD, IMCD); and papillary tip epithelial cells abutting the calyx (PapE). Molecular profiles for rare cell types important in homeostasis were annotated, including juxtaglomerular renin-producing granular cells (REN); macula densa cells (MD); and a cell population with enriched Schwann/neuronal (SCI/NEU) genes NRXN1, PLP1 and S100B. Major endothelial cell types were stratified, including endothelial cells of the lymphatics (EC-LYM) and vasa recta (EC-AVR, EC-DVR). Specific stromal and immune cell types were distinguished, including distinct fibroblast populations across the cortico-medullary axis and 12 immune cell types from lymphoid and myeloid lineages. +Integrating snCv3 and SNARE2 datasets +Integration of snCv3 and SNARE RNA data was performed using Seurat (v.4.0.0) using snCv3 as reference. All counts were normalized using sctransform, anchors were identified between datasets based on the snCv3 Pagoda2 principal components. SNARE2 data were then projected onto the snCv3 UMAP structure and snCv3 cell type labels were transferred to SNARE2 using the MapQuery function. Both datasets were then merged and UMAP embeddings were recomputed using the snCv3 projected principal components. Integrated clusters were identified using Pagoda2, with the k-nearest neighbour graph (k = 100) based on the integrated principal components and using the infomap community detection algorithm. The SNARE2 component of the integrated clusters was then annotated to the most overlapping, correlated and/or predicted snCv3 cluster label, with manual inspection of cell type markers used to confirm identities. Integrated clusters that overlapped different classes of cell types were labelled as ambiguous or low-quality clusters. Segregation of EC-AEA, EC-DVR and REN subpopulations was performed as described for snCv3 above. +Integrating snCv3 and scCv3 datasets +Integration of snCv3 and scCv3 data was performed using Seurat v.4.0.0 with snCv3 as a reference. All counts were normalized using sctransform, anchors were identified between datasets based on the snCv3 Pagoda2 principal components. scCv3 data were then projected onto the snCv3 UMAP structure and snCv3 cell type labels were transferred to scCv3 using the MapQuery function. Both datasets were then merged and UMAP embeddings recomputed using the snCv3 projected principal components. Integrated clusters were identified using Pagoda2, with the k-nearest neighbour graph (k = 100) based on the integrated principal components and using the infomap community detection algorithm. The scCv3 component of the integrated clusters was then annotated to the most overlapping or correlated snCv3 subclass, with manual inspection of cell type markers used to confirm identities. Cell types that could not be accurately resolved (PT-S1/PT-S2) were kept merged. Integrated clusters that overlapped different classes of cell types or that were too ambiguous to annotate were considered to be low quality and were removed from the analysis. Segregation of EC-AEA, EC-DVR and REN subpopulations was performed as described above. +Assessment of snCv3, scCv3 and SNARE2 data integration +As described above, we used the demonstrated Seurat v.4.0.0 integration strategy to project query datasets (scCv3, SNARE2 RNA) into the same PCA space as our snCv3 reference. These imputed principal components were used to generate an integrated embedding and integrated clustering through Pagoda2. Query datasets within these integrated clusters were manually annotated on the basis of co-clustering with the reference data, predicted subclass levels and the manual inspection of marker genes. This process was necessary to account for misalignments that occurred for altered states showing more ambiguous marker gene expression profiles, especially for mapping between single-nucleus and single-cell technologies. To assess the accuracy in our alignments, we performed correlation of average expression signatures between the assigned query cell populations and the original reference cell populations (Extended Data Fig. 3e). Although several samples were examined using more than one platform (Supplementary Table 3 and Extended Data Fig. 1a), not all conditions could be covered by all technologies, with AKI/CKD biopsies too limited in size to process with SNARE2 and deeper medullary region capture being less likely for needle biopsies. Despite the differences in patient conditions and regions sampled, we were able to confirm cross-platform sampling with minimal batch contributions for a majority of our subclass (level 3) assignments (77 total). This was demonstrated through integrated bar plots for assay, patient, sex and condition contributions (Extended Data Fig. 3e). The degree to which cells/nuclei between assays were mixed within these subclasses was confirmed using normalized relative entropy weighted by subclass size, with an average assay entropy across subclasses (covered by more than one technology) of 0.71 and an average patient entropy of 0.71 (out of 1). Mixing within the subclasses was also assessed on the cell embeddings (principal components) using the average silhouette width or ASW (scib.metrics.silhouette_batch function of the scIB package v.1.0.3), with an average score of 0.86 for assays and 0.82 for patients (out of 1). Finally, the average of k-nearest neighbour batch effect test (kBET) score per subclass, computed for all patients using the scib.metrics.kBET function of the scIB package, was 0.49 (out of 1), which is consistent with other integration efforts. +Integrating snCv3 with published datasets +Integration with published data was performed using Seurat v.4.0.0 with snCv3 as a reference. All counts were normalized using sctransform, anchors were identified between datasets on the basis of the snCv3 Pagoda2 principal components. Published data were then projected onto the snCv3 UMAP structure and snCv3 cell type labels were transferred to the published dataset using the MapQuery function. Ref. snDrop-seq data are available at the Gene Expression Omnibus (GEO: GSE121862). Ref. single-nucleus RNA-seq and ref. single-cell RNA-seq count matrices and metadata tables were downloaded from the UCSC Cell Browser (Cell Browser dataset IDs human-kidney-atac and kidney-atlas, respectively). +NSForest marker genes +To identify a minimal set of markers that can identify snCv3 clusters and subclasses (subclass.l3), or scCv3 integrated subclasses (subclass.l3), we used the Necessary and Sufficient Forest (NSForest v.2; https://github.com/JCVenterInstitute/NSForest/releases/tag/v2.0) software using the default settings. +Correlation analyses +For correlation of RNA expression values between snCv3 and scCv3, or SNARE2, average scaled expression values were generated, and pairwise correlations were performed using variable genes identified from Pagoda2 analysis of snCv3 (top 5,526 genes). For comparison with mouse single-cell RNA-seq data of healthy reference tissue, raw counts were downloaded from the GEO (GSE129798). For comparison with mouse single-cell RNA-seq from IRI tissue, raw counts were downloaded from the GEO (GSE139107). For human fibroblast and myofibroblast data, raw counts were downloaded from Zenodo (10.5281/zenodo.4059315). For each dataset, raw counts were processed using Seurat: counts for all cell barcodes were scaled by total UMI counts, multiplied by 10,000 and transformed to log space. For comparison with mouse single-cell types of the distal nephron, the precomputed Seurat object was downloaded from the GEO (GSE150338). For mouse bulk distal segment data, normalized counts were downloaded from the GEO (GSE150338) and added to the 'data' slot in a Seurat object. Bulk-sorted immune cell reference data were obtained using the celldex package using the MonacoImmuneData() and ImmGenData() functions and log counts imported into the 'data' slot of Seurat. For correlation against these reference datasets, averaged scaled gene expression values for each cluster were calculated (Seurat) using an intersected set of variable genes identified for each dataset (identified using Padoda2 for snCv3 and Seurat for reference datasets). For immune reference correlations, a list of immune-related genes downloaded from ImmPort (https://immport.org) was used instead of the variable genes. Correlations were plotted using the corrplot package (https://github.com/taiyun/corrplot). Immune annotations within our atlas were further confirmed by manual comparison with recently reported data. +Cross-species alignment of cell types/states +For mouse single-nucleus RNA-seq data from IRI tissue, raw counts were downloaded from the GEO (GSE139107). Integration was performed using Seurat v.4.0.0 with snCv3 as a reference. All counts were normalized using sctransform, anchors were identified between datasets on the basis of the snCv3 Pagoda2 principal components. Mouse data were then projected onto the snCv3 UMAP structure and snCv3 cell type labels were transferred using the MapQuery function. +Computing single-nucleus/cell-level expression scores +To identify markers associated with altered states (degenerative; adaptive:epithelial or aEpi; adaptive:stromal or aStr; cycling), snCv3 and scCv3 data were independently used to identify differentially expressed genes between reference and corresponding altered states for each subclass level 1 (subclass.l1). To ensure general state-level markers, differentially expressed genes were identified using the FindConservedMarkers function (grouping.var = "condition.l1", min.pct = 0.25, max.cells.per.ident = 300) in Seurat. A minimal set of general degenerative conserved genes was identified as enriched (P < 0.05) in the degenerative state of each condition.l1 (reference, AKI and CKD) and in at least 4 out of the 11 (snCv3) or 9 (scCv3) subclass.l1 cell groupings. A minimal set of conserved aEpi genes was identified as enriched (P < 0.05) in the adaptive state of each condition.l1 (reference, AKI and CKD) in both the aPT and aTAL cell populations. This aEpi gene set was then further trimmed to include only those genes that were enriched within the adaptive epithelial population (aPT/aTAL) versus all others using the FindMarkers function and with a minimum P value of 0.05 and average log2-transformed fold change of >0.6. A minimal set of conserved aStr genes was identified as enriched (P < 0.05) in the adaptive state of each condition.l1 (reference, AKI and CKD for snCv2; reference and AKI for scCv3) for stromal cells. To increase representation from MyoF in scCv3 showing a small number of these cells, MyoF-alone enriched genes (average log2-transformed fold change >= 0.6; adjusted P < 0.05) were included for the scCv3 gene set. The aStr gene sets were then further trimmed to include only those genes that were enriched within the adaptive stromal population (aFIB and MYOF) compared with all others using the FindMarkers function and with a minimum P value of 0.05 and average log2-transformed fold change of >0.6. A minimal set of cycling-associated genes was identified as those enriched (adjusted P < 0.05 and average log2-transformed fold change > 0.6) in the cycling state across all associated subclass.l1 cell groupings. +Scores for altered state, extracellular matrix and for gene sets associated with ageing or SASP were computed for each cell from averaged normalized counts using only the genes showing a minimum correlation to the averaged whole gene set of 0.1 (ref. ) (https://github.com/mahmoudibrahim/KidneyMap). Ageing and SASP genes were obtained from ref. (top 20 genes upregulated in ageing kidney), ref. (genes from table S3, group.age A), ref. (SASP genes from figure 2c) or ref. (from table S1 (sheet IR Epithelial SASP), having a positive AVE log2 ratio). +Gene set enrichment analyses (GSEA) +To compute gene set enrichments for aPT and aTAL, conserved genes differentially expressed in the adaptive over reference states were identified as indicated above. Gene set ontologies from the Molecular Signatures Database (MSigDB) were downloaded from https://gsea-msigdb.org and pathway enrichments were computed using fgsea and gage, retaining only Gene Ontology terms that were significant (P < 0.05) for both. Redundant pathways were collapsed using the fgsea function collapsePathways and visualized using ggplot. +SNARE2 accessible chromatin analyses +SNARE2 chromatin data were analysed using Signac (v.1.1.1). Peak calling was performed using the CallPeaks function and MACS (v.3.0.0a6; https://github.com/macs3-project/MACS) separately for clusters, subclass.l1 and subclass.l3 annotations. Peak regions were then combined and used to generate a peak count matrix using the FeatureMatrix function, then used to create a new assay within the SNARE2 Seurat object using the CreateChromatinAssay function. Gene annotation of the peaks was performed using GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86). TSS enrichment, nucleosome signal and blacklist fractions were all computed using Signac. Jaspar motifs (JASPAR2020, all vertebrate) were used to generate a motif matrix and motif object that was added to the Seurat object using the AddMotifs function. For motif activity scores, chromVAR (v.1.12.0; https://greenleaflab.github.io/chromVAR) was performed using the RunChromVAR function. The chromVAR deviation score matrix was then added to a separate assay slot of the Seurat object. To assess the chromatin data, UMAP embeddings were computed from cis-regulatory topics that were identified through latent Dirichlet allocation using CisTopic (v.0.3.0; https://github.com/aertslab/cisTopic) and the runCGSModels function. Only regions accessible in 50 nuclei and nuclei with 200 of these accessible regions were used for cisTopic and downstream analyses. The UMAP coordinates for the remaining nuclei were added to the Seurat object. To ensure high-quality accessible chromatin profiles, only clusters with more than 50 nuclei were retained for downstream analyses (Supplementary Table 7). For joint embedding of SNARE2 accessible chromatin and gene expression, a weighted nearest-neighbour graph was computed using the FindMultiModalNeighbors function (Seurat) based on PCA (RNA) and latent semantic indexing or LSI (accessible chromatin) dimensionality reductions. UMAP dimensionality reduction was performed to visualize the joint embedding. +DAR analyses +Sites that were differentially accessible for a given cell grouping (subclass) were identified against a selection of background cells with the best matched total peak counts, to best account for technical differences in the total accessibility for each cell. For this, the total peaks in each cell were used for estimation of the distribution of total peaks (depth distribution) for the cells belonging to the test cluster, and 10,000 background cells with a similar depth distribution to the test cluster were randomly selected. Differentially accessible sites (DARs) were then identified as significantly enriched in the positive cells over selected background cells using the CalcDiffAccess function (https://github.com/yanwu2014/chromfunks), where P values were calculated using Fisher's exact tests on a hypergeometric distribution and adjusted P values (or q values) were calculated using the Benjamini-Hochberg method. For subclass level 2 DARs, VSM/P clusters were merged and the MD was combined with C-TAL before to DAR calling. Subclasses with >100 DARs with q < 0.01 were used for further analysis. Co-accessibility between all peak regions was computed using Cicero (v.1.8.1). Sites were then linked to genes on the basis of co-accessibility with promoter regions, occurring within 3,000 bp of a gene's TSS, using the RegionGeneLinks function (https://github.com/yanwu2014/chromfunks) and the ChIPSeeker package. DARs associated with markers for each subclass (identified at the subclass.l2 level using snCv3, P < 0.05) and showing q < 0.01 and a log-transformed fold change of >1 were selected for visualization. For this, DAR accessibility (peak counts) was averaged, scaled (trimming values to a minimum of 0 and a maximum of 5) and visualized using the ggHeat plotting function of the SWNE package. Motif enrichment within cell type DARs was computed using the hypergeometric test (FindMotifs function) in Signac. +Transcription factor analyses +To identify active transcription factors from SNARE2 accessible chromatin data, differential activities (or deviation scores) of TFBSs between different populations were assessed using the Find[All]Markers function through logistic regression and using the number of peak counts as a latent variable. Only transcription factors with expression detected within the corresponding cluster, subclass or state grouping were included. For PT and TAL clusters, TFBSs that were differentially active (P < 0.05, average log2-transformed fold change of >0.35) and associated with transcription factors with expression detected in at least 2.5% of nuclei (SNARE2) were identified between clusters. Common aPT/aTAL TFBS activities were identified from an intersection of those differentially active and expressed within adaptive PT and TAL clusters. For aPT and aTAL trajectory modules, TFBSs showing differential activity between modules (adjusted P < 0.05, average log2-transformed fold change of >0.35) and expression detected within at least 2.5% of nuclei/cells (snCv3/scCv3) were identified. For common degenerative state TFBS activities, differentially active TFBSs were identified between reference and degenerative states for each level 1 subclass. Significant degenerative state TFBS activities (P < 0.05, average log2-transformed fold change of >0.35) in three or more subclass.l1 were trimmed to those showing expression detected in more than 20% of the degenerative state nuclei/cells for snCv3/scCv3. +Ligand-receptor interaction analyses +Ligand-receptor analyses were performed on the basis of the CellChat package (v.1.0.0; https://github.com/sqjin/CellChat). Only cells in TAL, immune and stroma of subclass level 2 (immune: cDC, cycMNP, MAC-M2, MAST, MDC, N, ncMON, NKT, pDC, PL, T and B; stroma: MyoF, FIB, dFIB, cycMyoF and aFIB) and interactions for secreted ligands were used to infer the cell-cell communication. For cells in the TAL trajectory, we computed the intercellular cell communication probability between each module and other cell populations using the CellChat function computeCommunProb (see ref. for a detailed description of the method). The overall scaled communication probability was then visualized based on a circle plot using a customized plot_communication function (Code availability). To further understand which signals contribute most to the ligand-receptor (LR) interaction pathways, we generated the pathway enrichment heat map of each interaction for incoming, outgoing and overall signals using the plotSigHeatmap function (Code availability). The contribution of significant LR pairs of each interaction was also identified using netAnalysis_contribution in the CellChat package. +GWAS analyses +To link SNARE2 cell types to kidney genome-wide association study (GWAS) traits and diseases, we first summed the binary peak accessibility profiles for all cells belonging to the same cell type to create a pseudobulk peak-by-subclass accessibility matrix. Pseudobulk analyses give more stable results, especially as SNARE2 accessibility data can be sparse. To ensure sufficient coverage, we used subclass level 2 groupings with the following modifications: VSM/P clusters were merged; MD was combined with C-TAL; subclasses with <100 DARs with q < 0.01 were excluded. We used g-chromVAR (v.0.3.2), an extension of chromVAR for GWAS data, to identify cell types with higher than expected accessibility of genomic regions overlapping GWAS-linked single-nucleotide polymorphisms (SNPs). Running g-chromVAR requires first identifying GWAS-linked SNPs that are more likely to be causal, a process known as fine-mapping. For the chronic kidney failure GWAS traits, we used existing fine-mapped SNPs from the CausalDB database, using the posterior probabilities generated by CAVIARBF. The original GWAS summary statistics files were obtained from an atlas of genetic associations from the UK BioBank. We manually fine-mapped the CKD, eGFR, blood urea nitrogen and gout traits using the same code that was used to generate the CausalDB database (https://github.com/mulinlab/CAUSALdb-finemapping-pip). The summary statistics for all of these traits are available at the CKDGen Consortium site (https://ckdgen.imbi.uni-freiburg.de/). We also manually fine-mapped the hypertension trait and the original summary statistics can be found on the EBI GWAS Catalog. We looked only at causal SNPs with a posterior causal probability of at least 0.05 to ensure that SNPs with low causal probabilities did not cause false-positive signals. Moreover, as g-chromVAR selects a semi-random set of peaks with similar average accessibility and GC content as background peaks, the method has an element of randomness. To ensure stable results, we ran g-chromVAR 20 times and averaged the results. Cluster/trait z-scores were plotted using ggheat (https://github.com/yanwu2014/swne). +To link causal SNPs to genes, we used functions outlined in the chromfunks repository (https://github.com/yanwu2014/chromfunks; /R/link_genes.R). This involved the identification of causal peaks for each cell type and trait (minimum peak Z score of 1, minimum peak posterior probability score of 0.025). Sites were then linked to genes on the basis of co-accessibility (Cicero) with promoter regions, occurring within 3,000 bp of a gene's TSS. Only sites associated with genes detected as expressed in 10% of TAL nuclei/cells (snCv3/scCv3) were included. Motif enrichment within the causal SNP and TAL-associated peaks was performed using the FindMotifs function in Seurat and only motifs for transcription factors expressed in 10% of TAL nuclei/cells (snCv3/scCv3) were included (Supplementary Table 31). For a TAL-associated ESRRB transcription factor subnetwork, peaks were linked to genes using Cicero, then subset to those associated with TAL (C-TAL, M-TAL) marker genes that were identified using the Find[All]Markers function in Seurat for subclass.l3 (P < 0.05). Transcription factors were then linked to gene-associated peaks on the basis of the presence of the motif and correlation of peak and TFBS co-accessibility (chromVAR), using a correlation cut-off of 0.3. Only transcription factors with expression detected within 20% of TAL cells or nuclei (snCv3/scCv3) were included. Eigenvector centralities were then computed using igraph and the transcription-factor-to-gene network was visualized using PlotNetwork in chromfunks. +Disease-associated gene set enrichment analyses +Genes linked with CKDGen consortium GWAS loci for the kidney functional traits eGFR and urinary albumin-creatinine ratio (UACR) were obtained from table S14 of ref. . These included the top 500 genes per trait or only those genes also implicated in monogenic glomerular diseases. eQTLs associated with eGFR, systolic blood pressure and general kidney function were obtained from tables S20, S21 and S22 of ref. , respectively. Genes associated with the transition from acute to chronic organ injury after ischaemia-reperfusion were obtained from ref. from the following supplementary tables: Acute_Human_Specific (table S3, Human specific column); Acute_Mouse_Overlap (table S3, Shared column); Mid_Acute (table S8, cluster 2 genes); Late_Human_Specific (table S9, Human specific column); Late_Mouse_Overlap (table S9, Shared column); Late_Fibrosis (table S6, positive logFC); Late_Recovery (table S6, negative logFC). Each gene set was assessed for its enrichment within combined snCv3 and scCv3 subclass (level 3) differentially expressed genes (adjusted P < 0.05, log-transformed fold change of >0.25). Enrichments were performed using Fisher's exact tests and the resultant -log10[P] values were scaled and visualized using ggplot2. +Patient cohorts used for clinical association analyses +NEPTUNE (193 adult patients) and ERCB (131 patients) expression data were used as validation cohorts to determine the significance between patients with different levels of cell state gene expression. NEPTUNE (NCT01209000) is a multicentre (21 sites) prospective study of children and adults with proteinuria recruited at the time of first clinically indicated kidney biopsy (Supplementary Table 34). The study participants were followed prospectively, every 4 months for the first year, and then biannually thereafter for up to 5 years. At each study visit, medical history, medication use and standard local laboratory test results were recorded, while blood and urine samples were collected for central measurement of serum creatinine and urine protein/creatinine ratio (UPCR) and eGFR (ml per min per 1.73 m2). End-stage kidney disease (ESKD) was defined as initiation of dialysis, receipt of kidney transplant or eGFR <15 ml per min per 1.73 m2 measured at two sequential clinical visits; and the composite end point of kidney functional loss by a combination of ESKD or 40% reduction in eGFR. Genome-wide transcriptome analysis was performed on the research core obtained at the time of a clinically indicated biopsy using RNA-seq by the University of Michigan Advanced Genomics Core using the Illumina HiSeq2000 system. Read counts were extracted from the fastq files using HTSeq (v.0.11). NEPTUNE mRNA-seq data and clinical data are controlled access data and will be available to researchers on request to NEPTUNE-STUDY@umich.edu. +ERCB is the European multicentre study that collects biopsy tissue for gene expression profiling across 28 sites. Transcriptional profiles of biopsies from patients in the ERCB were obtained from the GEO (GSE104954). +Clinical association of cell state scores +The gene expression data from the tubulointerstitial compartment of the kidney biopsies from NEPTUNE patients was used to calculate the composite scores for the genes enriched in degenerative, aPT, aTAL and aStr states. The expression of the genes that were uniquely enriched in the cell state (described above) and that were found in both snCv3 and scCv3 were used to calculate the composite cell state score (Supplementary Table 29). As scCv3 did not efficiently identify all stromal cell types, the union of the enriched genes from scCv3 and snCv3 data were used to calculate the aStr cell state score. We also generated a cell state score for the genes that were commonly enriched in aPT and aTAL cells (common). +For outcome analyses (40% loss of eGFR or ESKD) in the NEPTUNE cohort, patient profiles were binned according to the degree of cell state score by tertile. Clinical outcomes were available on 193 participants with a total of 30 events. Kaplan-Meier analyses were performed using log-rank tests to determine significance between patients in different tertiles of cell state gene expression. Moreover, for the different cell state scores, multivariable adjusted Cox proportional hazard analyses were performed using five statistical models adjusting for different sets of potential confounding effects given the overall few number of events: (1) age, sex and race; (2) baseline eGFR and UPCR; (3) immunosuppressive treatment and FSGS status; (4) eGFR, UPCR and race self-reported as Black (factors that were associated with outcome in this dataset); and (5) immunosuppressive treatment, eGFR and UPCR (Supplementary Table 30). Note that the adjusted models simply assess whether the association with outcome persists after adjusting for common clinical features (that is, confounding effects), but do not assess for prediction accuracy. +In the ERCB cohort, differential expression analyses using multivariable regression modelling were performed between the cell state scores in the disease groups and living donors. Age and sex were used as covariates. The cell state scores for both NEPTUNE and ERCB bulk mRNA transcriptomics data were generated. In brief, the cell state scores were generated by creating Z scores for each of the cell state gene sets and then using the average Z score as the cell state composite score. These analyses found scores for all adaptive epithelial, but not degenerative, states were significantly higher in the patients with diabetic nephropathy patients compared to that of living donors (Supplementary Table 30). After adjusting for sex and age, both aPT and aTAL were significant when scores from patients with diabetic nephropathy were compared with those of living donors and aPT scores were significant even after correcting for the different disease groups. +Sample-level analysis and clustering on clinical association gene sets +To find association of patients based on altered-state gene signatures that were used in clinical association analyses (Supplementary Table 30), we performed sample-level clustering. All of the cells from the same patient in snCv3 and scCv3 were aggregated to get pseudo-bulk count matrices on the basis of the associated clinical gene set. The matrices were further normalized by RPKM followed by t-distributed stochastic neighbour embedding (t-SNE) dimensionality reduction. Groups of patients were then identified based on k-means clustering and density-based spatial clustering (DBSCAN) methods in the reduced space. To associate the patient clusters with clinical features, we calculated the distribution of eGFR in each identified group (Code availability). +To identify gene sets that best differentiate between AKI and CKD patients in the PT and TAL cell populations, we trained a gene-specific logistic regression model based on the sample-level gene expression. The model was used to assess the predictive power that differentiate patients with AKI and CKD in both snCv3 and scCv3 measured by area under the curve (AUC). The genes with AUC > 0.65 on both snCV3 and scCv3 were selected for downstream analysis (Supplementary Table 32). +To identify genes that were differentially expressed between AKI and CKD across all cell types, we aggregated the cells associated with each subclass (level 1) to generate cell-type-specific pseudocounts for each sample and performed differential gene expression analysis based on the DEseq2 method using the estimatePerCellTypeDE function in the Cacoa package (v.0.2.0; https://github.com/kharchenkolab/cacoa). +Pseudotime analysis of PT and TAL cells +To find cells associated with disease progression, we performed trajectory analysis for PT and TAL cells. To get accurate pseudotime and trajectory estimation, we removed degenerative cell populations in both PT and TAL and inferred the trajectory for single nuclei and single cells separately using the Slingshot package (v.2.0.0). We specified normal cell populations as the end points for trajectory inference (S1-S3 in PT and M-TAL in TAL) using the Slingshot parameter end.clus. The correspondent trajectory embedding was visualized using the plotEmbedding function in the Pagoda2 package. +To identify whether the gene expression was statistically significantly associated with the inferred trajectory, we modelled the expression of a gene as a function of the estimated pseudotime by fitting a gam model with cubic spline regression using formula expi = f(t) + epsilon, where t is the pseudotime and f is the function of cubic spline. The model is then compared to a reduced model expi = f(1) + epsilon to get P-value estimates using the F-test. The Benjamin-Hochberg method was used to calculate the adjusted P values. To further identify candidate genes showing potential differences between patients with AKI and CKD, we extended the base gam model by fitting a conditional-smooth interaction using CKD as a reference. +Gene module detection and cell assignment +To identify expression modules for significant gene sets along the estimated trajectories, we applied the module detection algorithm implemented in the WGCNA package (v.1.70-3) based on the smoothed gene expression matrix with parameters softPower = 10 and minModuleSize = 20. The similar modules detected by the original parameters were further merged. In total, we identified five different modules in PT and six modules in TAL cells. For the gene sets in each module, we further performed pathway analysis using the Reactome online tool (https://reactome.org/PathwayBrowser/). The enrichment of clinical associated gene sets for each module (Fig. 6e) was assessed by performing log ratio enrichment tests. To predict the transcription factor activities of PT and TAL subclass genes, we used the DoRothEA package (v.1.7.2) as targets. DoRothEA transcription factors and transcriptional targets were curated from both human and mouse evidence. The transcription factor activity scores for each cell type were calculated based on the run_viper function of the viper package (v.3.15; https://bioconductor.org/packages/release/bioc/html/viper.html). +To identify cells that are associated with each module, we developed a systematic approach. In brief, for the cells in the smoothed expression matrix, we performed dimension reduction using PCA followed by Louvain clustering. This enabled the identification of cell clusters along the trajectory. For the identified cell clusters, we then performed hierarchical clustering to calculate the correlation of each module on the basis of mean gene expression values and further linked the clusters with associated modules by cutting the hierarchical tree. Finally, module labels for each cell were assigned on the basis of its associated clusters. To link single-cell datasets with single-nucleus modules, we performed k-means clustering based on the joint embedding of PT or TAL cells and assigned the cells in scCv3 to modules on the basis of the majority voting from its k's nearest neighbours (Code availability). +To further investigate cluster-free compositional change between disease conditions, we also performed cell density analysis, in which we compared the normalized cell density between AKI and CKD conditions through 2D kernel estimates using Cacoa Package. Z scores were calculated to identify the regions that showed significant differences in cell density. +To validate the direction of modules inferred from human data, we performed joint alignment of the human and mouse trajectories. The individual trajectories inferred separately from these two species (Slingshot, described above) were aligned to generate a joint trajectory using CellAlign (https://github.com/shenorrLab/cellAlign) with parameters winSz = 0.1 and NumPts = 1000. The collection groups (timepoints from injury) derived from mouse data were then projected to human cells based on the joint trajectory. The genes that were conserved or divergent between the two species were specified as overlapping/distinct gene sets that were tested for significance based on a gam model inferred from the trajectory (see above). +RNA velocity analyses +Spliced and unspliced reads were counted from Cell Ranger BAM files for each snCv3 run using velocyto (v.0.17.17) and using the GRCh38 gene annotations prepackaged with the Cell Ranger pipeline. Repetitive elements were downloaded from the UCSC genome browser and masked from these counts. Corresponding loom files were loaded into R using the SeuratWrappers function ReadVelocity and converted to Seurat objects using the as.Seurat function. aPT or aTAL trajectory populations were then subset and RNA velocity estimates were calculated using scVelo (v.0.2.4) through a likelihood-based dynamical model. Velocity embeddings on the trajectory UMAPs were visualized using the pl.velocity_embedding_stream function. Latent times based on transcriptional dynamics predicted from splicing kinetics were computed and the top 300 dynamical genes were plotted using the pl.heatmap function. Top likelihood genes were computed for each TAL module to identify potential drivers for these states. Spliced versus unspliced or latent time scatter plots were generated using the pl.scatter function. +GRN analyses +GRNs associated with TAL trajectory modules were constructed using Celloracle (v.0.9.1) with the default parameters outlined in the provided tutorials (https://morris-lab.github.io/CellOracle.documentation). The base GRN was first constructed from SNARE2 accessible chromatin data. Co-accessible peaks across cell types identified using Cicero (v.1.8.1) were linked to genes through their TSS peaks to identify accessible promoter/enhancer DNA regions. Peaks were then scanned for transcription-factor-binding motifs (gimme.vertebrate.v5.0) to generate a base GRN. snCv3 data were then used to identify TAL state-specific GRNs. To ensure that relevant genes were used, we included genes that varied across the aTAL trajectory (Supplementary Table 17), showed dynamic module-specific transcription from scVelo analyses (Supplementary Table 21), were variably expressed across TAL cells (Pagoda2) or that were associated with differential transcription factor activities (Supplementary Table 20). GRN inference through regularized machine learning regression models was performed to prune inactive (insignificant or weak) connections and to select active edges associated with regulatory connections within each module or state, retaining the top 2,000 edges ranged by edge strength. Network scores for different centrality metrics were then calculated and visualized using Celloracle plotting functions. For in silico transcription factor perturbation analyses, target gene expression was set to 0 and resultant gene expression values were extrapolated or interpolated using the default parameters of Celloracle and according to the provided tutorial. Stromal GRN construction was performed as indicated above, except using a gene subset that included variable STR genes identified using Pagoda2; subclass level 3 markers for FIB, aFIB, MyoF (adjusted P < 0.05); or transcription factors with expression detected in at least 2.5% of nuclei (SNARE2) and having binding sites that were differentially active between STR subclasses (P < 0.05). To ensure BMP target SMADs were represented, SMAD1/5/8 were also included. +SLIDE-seq2 +Puck preparation and sequencing +Tissue pucks were prepared from fresh frozen kidney tissue either embedded in Optimal Cutting Temperature (OCT) compound or frozen in liquid nitrogen and sequenced according to a step-by-step protocol (10.17504/protocols.io.bvv6n69e). Libraries were sequenced on the NovaSeq S2 flowcell (NovaSeq 6000) with a standard loading concentration of 2 nM (read structure: read 1, 42 bp; index 1, 8 bp; read 2, 60 bp; index 2, 0 bp). Demultiplexing, genome alignment and spatial matching was performed using Slide-seq tools (https://github.com/MacoskoLab/slideseq-tools/releases/tag/0.1). +Deconvolution +We used Giotto (v.1.0.3) for handling the slide-seq data and RCTD (v.1.2.0) for the cell type deconvolution. As only reference tissue was used for slide-seq, all degenerative states as well as PapE, NEU, B and N were removed from the snCv3 Seurat object prior to deconvolution. The Seurat object was randomly subsampled to have at most 3,000 cells from each level 2 (l2) subtype and the level 1 (l1) subclasses of ATL and DTL were merged. For each data source, that is, HuBMAP or KPMP (Supplementary Table 2), the counts from all beads across all pucks were pooled and deconvolved hierarchically: first, beads with less than 100 UMIs and genes detected in less than 150 beads were removed. Then, the broad l1 subclass annotations in the Seurat object were used to deconvolve all beads (gene_cutoff = 0.0003, gene_cutoff_reg = 0.00035, fc_cutoff = 0.45, fc_cutoff_reg = 0.6, manually adding REN in the RCTD gene list and merging ATL and DTL subtypes as TL). The prediction weights were normalized to sum to 100 per bead. Beads for which one cell type had a relative weight of 40% or higher were classified as that l1 subclass. Then, for each l1 subclass, all classified beads were further deconvolved using the l2 annotation of that subclass, as well as the remaining subclass l1 annotations (same parameters as l1). Note that, for each l2 deconvolution, the bulk parameters in RCTD were fitted using all beads and then the RCTD object was subsetted to only contain the selected beads for the l2 deconvolution. Classification at subclass l2 was done similar to l1 with the maximum relative weight cut-off of 30% or 50% depending on the stringency needed for an analysis (50% for Figs. 2c,f and Extended Data Fig. 4b and 30% in other analyses). For plotting gene counts, the scaling was performed with the command normalizeGiotto(gObj, scalefactor = 10000, log_norm = T, scale_genes = T, scale_cells = F). The marker gene dot plots were plotted using the DotPlot function in Seurat (v.4.0.0). +Cell type interaction +Coarse cell-cell interactions can be revealed by looking for cell types that tend to be in close proximity. For each puck, we created a neighbourhood graph based on Delaunay triangulation in which each bead is connected by an edge to at least one other neighbouring bead, provided that their distance is smaller than 50 microm. For each pair of cell types, we count the number of times they are connected by edges. Then, the cell type labels are randomly permuted 2,500 times to form the null distribution of the number of connections. The expected number of connections between pairs of cell types is calculated from this simulation and the proximity enrichment is defined as the ratio of the observed over the expected frequency of connections. The network construction and enrichment analysis were performed using Giotto's createSpatialNetwork and cellProximityEnrichment commands, respectively. Those beads with maximum level 2 weight less than 30% were removed. We further excluded spurious beads that were outside of the visual boundary of the tissue (only for the pucks of which the names start with 'Puck_210113') by manually specifying straight lines that follow the tissue boundary. For cortical pucks (Supplementary Table 2), M-PC, C-PC and IMCD labels were relabelled as PC; M-TAL and C-TAL as TAL; and EC-DVR was merged into EC-AEA. Other medullary and cycling subtypes were removed. For medullary pucks, M-PC and C-PC were relabelled as PC; M-TAL and C-TAL as TAL; all DTL subtypes as DTL; and EC-AEA was merged into EC-DVR. Other cortical and cycling subtypes were removed. +To generate the proximity plots in Extended Data Fig. 4, the enrichment values for each cell type pair were averaged across all pucks from the same region and plots were generated using the R package ggGally (v.2.1.2). For the cortex and medulla, respectively, only the connections with mean enrichment values higher than 0.7 and 0.8 were plotted. +10x Visium spatial transcriptomics +Preparation, imaging and sequencing +Human kidney tissue was prepared and imaged according to the Visium Spatial Gene Expression (10x Genomics) manufacturer's protocol (CG000240, Visium Tissue Preparation Guide) and as previously described. Nephrectomy (n = 6), AKI (n = 6) and CKD (n = 11) samples were sectioned at 10 microm thickness from OCT-compound-embedded blocks. These 23 samples represent 22 participants because 2 samples (1 cortex and 1 medulla) were obtained from the same participant with CKD. A Keyence BZ-X810 microscope equipped with a Nikon x10 CFI Plan Fluor objective was used to acquire H&E-stained bright-field mosaics, which were subsequently stitched. mRNA was isolated from stained tissue sections after permeabilization for 12 min. Released mRNA was bound to oligonucleotides in the fiducial capture areas. mRNA was then reverse-transcribed and underwent second strand synthesis, denaturation, cDNA amplification and SPRIselect cDNA cleanup (Visium CG000239 protocol) as part of library preparation. Sequencing was performed on the Illumina NovaSeq 6000 system. +Gene expression analysis +Space Ranger (v.1.0 or higher) with the reference genome GRCh38 was used to perform expression analysis, mapping, counting and clustering. Summary statistics and quality-control metrics are included in Extended Data Fig. 5 and Supplementary Table 2. Normalization was performed using SCTransform. Final data processing was performed in Seurat (v.3.2.3). Expression feature plots depict the intensity of transcript expression in each spot. In each Visium sample, the outermost layer of spots was eliminated from comparative analyses if the edge was manually cut by a razor. +Deconvolution +Using Seurat (v.3.2.0), a transfer score system was used to assess and map the proportion of signatures arising from each 55 microm spot. The transfer score reflects a probability between each spot's signature and its association with a given snCv3 subclass (level 2). The highest probability transfer scores have the highest proportion mapped within each spatial transcriptomics spot pie graph. For cell type feature plots (Figs. 2g and 3f and Extended Data Fig. 7i), subclass level 2 cell type transfer scores were mapped to convey the proportion of signature underlying each spot. For cell state feature plots (Fig. 3b), instead of mapping subclass level 2 cell types, the aEpi cell state annotated in snCv3 was mapped across all spots in the samples. We summed the proportion of signatures arising from all cell types corresponding to each of the 6 cell states in all spots of all samples (Fig. 3a). The proportions of cell state were compared across nephrectomy, AKI and CKD samples using Fisher's exact tests. +Colocalization of epithelial, immune and stromal cells +In all spots across all samples, we categorized spots into healthy, adaptive or degenerative epithelial cell states on the basis of the highest proportion of epithelial cell state signature as calculated in Fig. 3a. For stromal or immune cell type colocalization, we first selected spots with non-zero transfer scores of each cell type in all 23 samples. The presence of stromal or immune cell signature was considered colocalized with an epithelial cell if its stromal or immune transfer score exceeded its mean transfer score across all selected spots. An odds ratio was calculated for colocalization between the healthy, adaptive and degenerative epithelial cell state with stromal or immune cell signature. +Cell state marker expression +To compare marker gene expression associated with the healthy, adaptive and degenerative cell states (Fig. 3d), we first categorized a subset of spots from AKI and CKD samples into 1 of 5 predominant cell types: POD, PT, TAL, CD or FIB. For the PT, TAL and fibroblasts, a spot was selected if the highest proportion of its signature (level 1 mapping) corresponded to one of these cell types. For the CD subset, a spot was selected if the sum of level 1 mapping proportions for the PC and IC contributed most to its signature. POD spots were defined by the presence of a minimum of 20% signature arising from the level 1 POD label. Once the subsets of PT, TAL, fibroblast, CD and POD spots were selected, each spot was further divided into healthy, adaptive or degenerative cell state groups based on the highest proportion of cell state signature as calculated in Fig. 3a. For PODs, the presence of EC-GC signature was considered to be a degenerative equivalent given that a loss of POD markers was associated with an observed gain in EC-GC signatures within DKD samples. +Niche analysis +To examine the diversity of cell types colocalizing with TAL epithelial cells, we selected spots with more than 20% TAL signature and in which the highest proportion of signature arose from level 1 TAL mapping. Using Seurat clustering methodology, selected spots were reclustered after Seurat label transfer scores were substituted in lieu of gene expression. Spots with similar proportions of signature arising from TAL cell types and states, stromal cells and immune cells were clustered into 13 niches. Niches were mapped over the 23 kidney samples and the marker gene expression in each niche was determined. To depict the relative proportion of each cell type, the transfer score average was first computed in each niche. Next, a z score for each cell type was calculated across the niches. +Histological validation +To determine whether the 74 snCv3 subclasses (level 2) were appropriately mapped to histological structures, the proportion of signature in each spot was compared to a histologically validated set of six unsupervised clusters defined by Space Ranger (Extended Data Fig. 5a). These six unsupervised clusters (glomerulus, PT, loop of Henle, distal convoluted tubule, connecting tubule and collecting duct, and the interstitium) had an overall alignment of 97.6% with the underlying histopathologic structures in the H&E image. In each sample, regions of cortex and medulla were defined by histological evaluation, including the presence of glomeruli. Of the 23 samples, 18 samples were composed of only cortex, 4 samples were a combination of cortex and medulla and 1 sample was completely medulla. +Label-free and multifluorescence large-scale 3D imaging +Kidney biopsy cores frozen in OCT from patients with AKI or CKD enrolled in KPMP were used for label-free imaging followed by multiplexed-fluorescence large-scale 3D imaging as outlined in the protocol (10.17504/protocols.io.9avh2e6) and described in a recent publication. Frozen biopsies were sectioned to a thickness of 50 microm using a cryostat and then immediately fixed in 4% fresh paraformaldehyde (PFA) for 24 h and subsequently stored at 4 C in 0.25% PFA. +The first step in imaging consists of label-free imaging with multiphoton microscopy to collect autofluorescence and second harmonic images of the unlabelled tissue mounted in non-hardening mounting medium. Imaging was conducted using a Leica SP8 confocal scan-head mounted to an upright DM6000 microscope. For large-scale imaging of tissues at the sub-micrometer resolution, the Leica Tile Scan function was used to collect a mosaic of smaller image volumes using a high-power, high-numerical aperture objective. Leica LASX software (v.3.5) was then used to stitch these component volumes into a single image volume of the entire sample. The scanner zoom and focus motor control were set to provide voxel dimensions of 0.5 x 0.5 mum laterally and 1 mum axially. +Labelling of tissue for fluorescence microscopy was preceded by washing in phosphate-buffered saline (PBS) and blocking with PBS with 0.1% Triton X-100 (MP Biomedical) and 10% normal donkey serum (Jackson Immuno Research). Antibodies for indirect immunofluorescence were applied first for 8-16 h at room temperature, followed by washing cycles of PBS with 0.1% Triton X-100. An incubation cycle with secondary antibodies occurred next, followed by washing and finally application of directly labelled antibodies. Antibodies targeting markers for tubular cells and structures (aquaporin-1, uromodulin, F-actin) and immune cells (myeloperoxidase, CD68, CD3, siglec 8) were used, in addition to nuclei labelling using DAPI (Supplementary Table 35). After the final washing cycles, the tissue was mounted in Prolong Glass (Thermo Fisher Scientific). +Confocal microscopy was conducted using a Leica x20/0.75 NA multi-immersion objective (adjusted for oil immersion), with excitation sequentially provided by a solid-state laser launch with laser lines at 405 nm, 488 nm, 552 nm and 635 nm. Images in 16 channels (emission spectra collected by PMT detectors adjusted for the following ranges: 410-430 nm, 430-450 nm, 450-470 nm, 470-490 nm, 500-509 nm, 510-519 nm, 520-530 nm, 530-540 nm, 570-590 nm, 590-610 nm, 610-630 nm, 631-651 nm, 643-664 nm, 664-685 nm, 685-706 nm and 706-726 nm) were collected for each focal plane of each panel of the 3D mosaic. The resulting 16-channel image was then spectrally deconvolved (by linear unmixing using the Leica LASX linear unmixing software) to discriminate the eight fluorescent probes in the sample. Validation of the linear unmixing was described previously. +Confocal immunofluorescence microscopy +Human kidney tissue samples from the cortex or medulla were fixed in 4% PFA, cryopreserved in 30% sucrose and frozen in OCT cryomolds, and were cut into 5 mum sections. The sections were post-fixed with 4% PFA for 15 min at room temperature, blocked in blocking buffer (1% BSA, 0.2% skimmed milk, 0.3% Triton X-100 in 1x PBS) for 30 min at room temperature and then immunofluorescence microscopy was performed, first by overnight incubation at 4 C with primary antibodies, followed by labelling with secondary antibodies. The primary antibodies included NRXN-1beta, TUJ1, collagen I and III, synapsin-1, NPSH-1, SLC14A2, UMOD, CD31, CD34, CD11b, PROM1, KIM1, VCAM1, AQP1, AQP2, CD45 and S100 (Supplementary Table 36). After washing, labelling with the secondary antibodies was performed using Alexa-488-conjugated goat anti-mouse IgG, Cy3-conjugated goat anti-rabbit IgG or Cy5-conjugated donkey anti-goat IgG at room temperature for 1 h. After washing, the sections were counterstained with DAPI for nuclear staining. Images were acquired with a Nikon 80i C1 confocal microscope. +In situ hybridization +Human kidney tissues were sectioned with 3 mum from formalin-fixed, paraffin-embedded (FFPE) blocks. In situ detections of PROM1, CST3 and EGF mRNA transcripts were performed with the use of RNAscope Probes Hs-PROM1 (311261, Advanced Cell Diagnostics), Hs-CST3 (528181, Advanced Cell Diagnostics), and Hs-EGF (605771, Advanced Cell Diagnostics) and RNAscope kit (322330, Advanced Cell Diagnostics) according to the manufacturer's protocol. RNAscope Positive Control Probe Hs-UBC (310041, Advanced Cell Diagnostics) was used as a positive control. A horseradish-peroxidase-based signal amplification system (322310, RNAscope 2.0 HD Detection Kit-Brown, Advanced Cell Diagnostics) was used to hybridize with target probes followed by DAB staining. The sections were then counterstained with haematoxylin (3535-16, RICCA Chemical Company). Positive staining was determined by brown dots. After rehydrating, the sections were immersed in periodic acid solution (0.5%, P7875, Sigma-Aldrich) for 5 min, rinsed in three changes of distilled water, incubated with Schiff's reagent (3952016, Sigma-Aldrich) for 15 min and then rinsed in running tap water for 5 min. Nuclei were counterstained with haematoxylin 2 (220-102, Thermo Fisher Scientific) for 2 min. The sections were then rinsed in water, dehydrated in alcohol, cleared in xylene and finally mounted with Cytoseal XYL (8312-4, Thermo Fisher Scientific). +Tissue cytometry and in situ cell classification +Tissue cytometry and analysis were conducted using the Volumetric Tissue Exploration and Analysis (VTEA) software (v.1.0a-r9). VTEA is a 3D image processing workspace that was developed as a plug-in for ImageJ. The version of VTEA, which includes the supervised and unsupervised labelling of cells and combining spatial and features based gating strategies, used here is available at GitHub (https://github.com/icbm-iupui/volumetric-tissue-exploration-analysis) and through the FIJI updater. In this analytical pipeline, each individual nucleus was segmented using intensity thresholding and connected component segmentation built into VTEA and ImageJ. Each surveyed nucleus became a surrogate for a cell, to which the location and marker staining around or within the nucleus could be registered. This captured information could be used to classify cells on the basis of marker intensity or spatial features using scatterplot displays that enable various gating strategies and statistical analysis, including export as .csv files of all segmented cells and the associated features. Cells classified on the basis of marker intensity are summarized in Supplementary Table 37. Gated cells were mapped back directly into the image volumes, which enabled immediate validation of the gates. Moreover, direct gating on the image could be performed, which could trace all of the cells within the chosen region-of-interest back to the data display on the scatter plot. Thus, cell classification could also be performed based on direct annotation of regions-of-interest (ROIs) within the image volumes. Annotated ROIs were determined by the pixel-wise agreement between 3 of 4 experts who performed annotation on each biopsy specimen separately. +Using tissue cytometry, 14 cell classes were defined based on the following features: (1) PT cells: AQP1+ cells in cortex +- brush border staining. (2) C-TAL cells: UMOD+ cells in cortex. (3) Glomerular cells (which encompass PODs, glomerular endothelium and mesangial cells) annotated ROIs based on morphology and F-actin staining. (4) Cortical large and medium vessel cells: annotated ROIs based on morphology and F-actin staining. (5) Cortical distal nephron cells (distal tubules (CD), connecting tubules (CNT) and collecting ducts (C-CD) or cortical distal nephrons): AQP1-UMOD- and annotated ROIs based on unique morphology in cortex. (6) M-TAL cells: UMOD+ cells in the medulla. (7) DTL: AQP1+ cells in the medulla. (8) Medullary collecting ducts: AQP1-UMOD- and annotated ROIs based on unique morphology in the medulla. (9) Vascular bundles in the medulla: annotated ROIs based onunique morphology in the medulla and F-actin staining. (10) Neutrophils: MPO+ cells. (11) Activated macrophages: MPO-CD68+ cells. (12) T cells: CD3+ cells. (13) Cells in altered regions: annotated ROIs based on loss of (unrecognizable) tubular morphology, expanded interstitium, increased fibrosis (by second harmonic generation imaging) and cell infiltrates. (14) Not determined: unable to be classified on the basis of the above criteria. +Using such an approach,1,540,563 cells were labelled from all the biopsies used in this analysis. +3D neighbourhood building and representation +3D neighbourhoods were calculated for every cell in each biopsy using VTEA and a radius of 25 mum (50 voxels in x and y and 25 voxels in z). We reasoned the largest measurable neighbourhood/niche in our 3D approach is limited by the 50 mum thickness of the sections imaged (z dimension). Thus, per Nyquist sampling, the radius used was about 25 mum, which is consistent with previous approaches. For each 3D neighbourhood, VTEA was used to calculate the features: fraction-of-total and sum of each labelled cell per neighbourhood. A list of neighbourhoods, positions in 3D and their features was exported by biopsy sample as .csv files. +Neighbourhood visualization and statistical analysis +CSV files of neighbourhoods by biopsy sample were generated in VTEA and imported into R (v.4.0.4), parsed for the sum of each labelled cell and monotypic neighbourhoods removed. These features were scaled by Z-standardization and used for Louvain community detection (R packages FNN (v.1.1.3) and igraph (v.1.2.6)) and t-SNE manifold projection (R package Rtsne (v.0.15)). To understand the interactions within neighbourhoods, pairwise interactions by neighbourhood were tallied and plotted on a chord plot (R package: circlize (v.0.4.12)) and Pearson's correlation coefficients were calculated and plotted (R packages Hmisc (v.4.5.0) and corrplot (v.0.84)). Subclasses of neighbourhoods, those with at least one cell with a specific label were selected and plotted as network plots (R package igraph (v.1.2.6)) with edges in CD3 and Altered neighbourhoods scaled at 40% of all other subclasses to facilitate visualization. All scripts are provided as an annotated RStudio notebook file (.rmd). +Plots and figures +UMAP, feature, dot and violin plots for snCv3, scCv3, SNARE2 and Visium data were generated using Seurat. Correlation plots were generated using the corrplot package. Genome coverage plots were performed using Signac. Plots for 3D cytometry and neighbourhood analysis were generated in R with circlize, ggplot2 and igraph. +Reporting summary +Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. +Online content +Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-05769-3. +Supplementary information +Source data +Extended data figures and tables +snCv3 cell types and quality metrics. +a. Number of samples processed across technologies assessed both individually and in combination. b. UMAP plots for snCv3 clusters. c. UMAP plots as in (b) showing the corresponding tissue regions, sex, patient identities and conditions. d. Bar and violin plots for snCv3 patients shown in (c). Barplots showing the total number of post-QC nuclei used in the snCv3 clustering analysis, and the proportions that were associated with level 1 subclasses, regions sampled or the health or disease conditions. Violin plots show the percentage of transcripts associated with the mitochondria (Mt) or endoplasmic reticulum (ER), as well as mean genes and mean transcripts detected per patient sample. e. Receiver operating characteristic (ROC) curve showing snCv3 clustering quality as assessed by the descrimination between subclasses (level 1) or clusters (b) using the Single Cell Clustering Assessment Framework (SCCAF). f. Bar and violin plots as in (d) for snCv3 clusters shown in (b), including proportion of nuclei contributed by each patient. + +Source Data + +snCv3 marker genes and comparison with reference data. +a. Dot plot showing averaged marker gene expression values (log scale) and proportion expressed for snCv3 clusters. b. Cell type labels predicted from Lake et. al. 2019 mapped on the snCv3 UMAP embedding. Inset shows the corresponding prediction score values. c. UMAP of Lake et. al. 2019 data mapped to snCv3 embeddings showing subclass level 3 predicted labels. Inset shows the corresponding prediction score values. d. UMAP of Muto et al. 2021 data mapped to snCv3 embeddings showing subclass level 3 predicted labels. Inset shows the corresponding prediction score values. e. Heatmap showing correlation of averaged scaled gene expression values for snCv3 epithelial (reference state) clusters and mouse bulk segmental RNA-seq data from Chen et al., 2021. f. Heatmap showing correlation of averaged scaled gene expression values for snCv3 distal tubule clusters (reference states) and mouse scRNA-seq data from Chen et al., 2021. g. Heatmap showing correlation of averaged scaled gene expression values for snCv3 clusters (reference and altered/adaptive states) and mouse snRNA-seq clusters from Kirita et al., 2020. h. Heatmap showing correlation of averaged scaled gene expression values (reference states) for snCv3 clusters and mouse scRNA-seq clusters from Ransick et al., 2019. i. Heatmap showing correlation of averaged scaled gene expression values for snCv3 stromal clusters (reference and altered/adaptive states) against human scRNA-seq clusters from Kuppe et al., 2020. j. Heatmap showing correlation of averaged scaled gene expression values for snCv3 immune cell clusters and mouse immune cell types from Immgen.org. k. Heatmap showing correlation of averaged scaled gene expression values for snCv3 immune cell clusters and human immune cell types from Monaco et al. 2019. l. UMAP of Stewart et al., 2019 immune single-cell RNA-seq data mapped to snCv3 embeddings showing subclass level 3 predicted labels (top) and the prior published cell type annotations (bottom). Inset shows the corresponding prediction score values. + +Source Data + +scCv3 integration and quality metrics. +a. UMAP plot showing integrated snCv3, scCv3 and SNARE2 (RNA) subclass level 3 annotations. scCv3 and SNARE2 (RNA) datasets were projected onto the snCv3 embeddings. b. UMAP plots as in (a) show mapping of the corresponding sex, patient identities and conditions for scCv3 and SNARE2 datasets. c. Joint embedding of SNARE2 RNA and AC modalities. d. Barplots showing the total number of post-QC nuclei and subclass level 1 cell types detected per scCv3 or SNARE2 patient. Violin plots show the percentage of transcripts associated with the mitochondria (Mt) or endoplasmic reticulum (ER), as well as mean genes, mean transcripts, mean accessible peaks or mean TSS enrichment scores detected per patient. e. Barplots showing the total number of post-QC nuclei/cells per subclass (level 3) combined across platforms (snCv3, scCv3, SNARE2). Patient entropy as well as tissue type, region, condition, sex and assay proportions are shown. Heatmap of correlation values for each scCv3 and SNARE2 subclass against the corresponding snCv3 subclass is shown (top panel). Grey values indicate absence of a comparison where subclasses were not covered by one or more of the technologies. + +Source Data + +Slide-seq predicted cell types. +a. UMI counts per bead for classified beads. Normalized RCTD weights for the beads classified at subclass level 2 (Methods). Region of the tissue associated with beads for each subclass. Frequency of cell types predicted across pucks. b. Dot plot showing expression of cell type markers identified by snCv3 in the classified Slide-seq beads. c. Representative pucks showing subclass level 2 classifications. Cell types are grouped into 3 categories and plotted separately for clarity. Scale bar is 300 microm. d-e. Cell proximity networks for Slide-seq cell types associated with cortical or medullary regions. For panels a, b, d and e all pucks (6 individuals) were combined. f. Left panel: Slide-seq puck area indicated in (c) and predicted cell types for the AEAs and surrounding cell types. Right panel: mapped expression values for corresponding marker genes (scaled). AEA mapping over Visium histology is depicted in Extended Data Fig. 5j, colocalized with REN expression. Scale bar is 100 microm. + +Source Data + +10X Visium predicted cell types. +a. Analysis of subclass (level 2) predictions on 10x Visium spots (23 samples, 22 individuals). The top panel presents the distribution of transfer scores for the subclass (level 2) with the highest score in each spot. The UMI count panel presents the UMI counts associated with these spots. The cell type proportion panel depicts the proportion of transcriptomic signatures for each subclass, corresponding to its transfer score relative to all non-zero transfer scores in that spot. The relative proportion of cell type subclass signatures arising from the cortex or medulla in the 23 samples is shown. The bottom panel reveals the alignment between the predicted cell type subclass and unsupervised clusters that were histologically validated (Methods). b. Dot plot showing gene expression of select cell markers by predicted subclass (level 2) for all 23 Visium samples. c. The proportion of transcriptomic signatures in the 23 samples revealed a similar distribution of cell types across healthy reference nephrectomies, chronic kidney disease (CKD), and acute kidney injury (AKI) samples. d. Cortical (left, I) and medullary (right, U) portions of specimen 21-0063 reveal POD signatures confined to the cortex, while M-TAL signatures were found in the medulla. White arrows denote the connection point between the cortex and medulla portions of the sample. e. A histologic image of the cortex (bounded in d) reveals level 1 cell type mapping of POD, EC-GC, and VSM/P cells to a glomerulus. PT and TAL signatures were seen mapped over distinct regions of tubules. f. Expression of NPHS2 (for glomeruli), ALDOB (for PT), and SLC12A1 (for TAL) in the cortex. g. A histologic image of the medulla (bounded in d) reveals level 1 cell type mapping of a high proportion of TAL cells within the medulla. h. Feature plots showing SLC12A1 but not NPHS2 or ALDOB expression in the medulla. i. Proportion of cortex and medulla cell types for sample 21-0063 (9555 total spots over two sections of the same individual). j. A cortical image in a healthy reference sample (19-M61) showing EC-AEA entering the glomerular corpuscle near the MD. Two glomeruli contain signatures arising predominantly from POD and EC-GC. Two TAL niches are outlined. TAL niche 1 is enriched in healthy cortical TAL signature and TAL niche 8 near the afferent arteriole is enriched for Macula Densa (MD) signature. NPHS2 expression is found within the glomeruli and renin (REN) expression is highest in the EC-AEA. A full level 2 cell type deconvolution is provided in the final panel (right). Scale bars are 300 mum in length. + +Source Data + +Altered states in a mouse model of AKI. +a. UMAP showing mouse AKI (IRI) data with cell types predicted from snCv3. Mouse datasets were projected onto the snCv3 UMAP embeddings (Fig. 2b). Histograms of prediction scores for subclasses (level 1 and 3) are shown. b. UMAP plots as in (a) showing the original cell type annotations and injury groups (time points following IRI) for mouse data. c. Barplot showing the proportion of altered states for each mouse injury group. d. Barplot showing proportion of each injury group for a subset of predicted subclasses. Arrows indicate altered states or immune cells (MAC-M2) that persisted at 6 weeks following injury. e. UMAP as in (a) showing the distribution of reference and altered states over the different injury groups. + +Source Data + +Altered state expression signatures. +a-b. Gene Set Enrichment Analyses (GSEA) for genes upregulated or downregulated in adaptive PT (a) and TAL (b) states compared to reference states. c. Dot plot showing averaged marker gene expression values (log scale) and proportion expressed for snCv3 clusters. d. Dot plot showing averaged marker gene expression values (log scale) and proportion expressed for integrated snCv3/scCv3 reference, degenerative and adaptive stromal clusters. e. Violin plots showing aSTR and ECM (matrisome) scores for snCv3 clusters. f. Visium feature plots of normalized counts for select markers mapped to regions shown in Fig. 3e. Scale bar is 100 microm. g. Visium feature plot of normalized counts for a select marker mapped to region shown in (h). Scale bar is 100 microm. h. Histology and predicted cell types for a medullary region of acute tubular necrosis (cellular cast formation within tubular lumens, loss of brush border, loss of nuclei, and epithelial simplification). Pie charts are proportions of predicted transfer scores. Area corresponds to the upper bounded region in Fig. 3b. Scale bar is 100 microm. i. Predicted transfer scores for area shown in (h). Scale bar is 100 microm. +3D imaging identifies injury neighbourhoods. +a. Maximum intensity projections of immunofluorescence and second harmonic images for 13 example biopsies, scale bars 500 microm. b. Overview of neighbourhood classes as given in Fig. 4b for reference. c. Distribution of neighbourhoods by specimen in neighbourhood clusters plotted in tSNE space from Fig. 4. d. Feature plots of the number of cells per neighbourhood for cortical TAL (C-TAL), altered morphology and proximal tubule (PT). C-TALs and PTs are found in neighbourhoods with altered morphology, cyan and orange vs. red and magenta arrowheads. e-h. Neighbourhoods with at least one cell for the labels indicated were subsetted and neighbourhood graphs generated to indicate the pairwise interaction between cell labels. At right: maximum Z-projections of 3D confocal fluorescence images with white arrow indicating MPO+ cells (e and f) or CD68+ cells (g), orange arrows indicating CD3+ cells and asterisks highlighting fibrosis (white) or areas of altered morphology/injury (yellow). Scale bar = 100 mum. h and i, pairwise subset analysis of CD3+, PT and TAL (orange, magenta and cyan arrows respectively). CD3+ cells cluster in regions of fibrosis (orange arrowhead and white asterisks). UMOD positive casts associate with regions of injury and CD3+ cells (orange asterisk), the tubular epithelium is intact with brush borders (white #), has evidence of epithelial simplification (orange #) or less AQP1 marker and epithelial simplification (red #). Scale bar = 100 microm. + +Source Data + +PT and TAL repair trajectories. +a. Trajectory of PT cells for snCv3 and scCv3 datasets. Bottom UMAPs are coloured by cell density for each condition (AKI/CKD), including the cell density difference between AKI and CKD. b. UMAP of PT subclasses (PT-S1-S3, aPT) with projected RNA velocities, derived from a dynamical model of PT repair modules, visualized as streamlines (Methods). c. Heatmap of smoothed gene expression profiles along the inferred pseudo-time for PT cells. Colour blocks on the left show different repair states or modules identified based on the gene expression profiles. d. Right panel: dot plot of SNARE2 average accessibilities (chromVAR) and proportion accessible for TFBSs showing differential activity in aPT modules. Left panel: dot plot of averaged gene expression values (log scale) and proportion expressed for integrated snCv3/scCv3 modules. e. 3D confocal imaging of a reference kidney tissue section stained for PROM-1 (red), Phopho-c-Jun (p-c-JUN, yellow), F-actin (with FITC phalloidin, green) and DNA with DAPI (cyan) (scale bar 100 microm). Regions of PROM-1 within a glomerulus (G) and a proximal tubule (PT) are indicated and enlarged in the right panels (rendered 3D volumes, scale bar 10 mum). This area shows the association of PROM-1 expression with p-c-Jun+ cells in the tubules. 3D rendering was performed using the Voxx software from the Indiana Center for Biological Microscopy (voxx.sitehost.iu.edu/). f. Top panels: TAL UMAPs as in Fig. 5a (snCv3) showing condition densities as in (a). Bottom panels: changes of smoothed gene expression (snCv3) for representative genes as a function of inferred pseudotime coloured by disease conditions. g. TAL UMAP as in Fig. 5a (snCv3) with projected RNA velocities, derived from a dynamical model for TAL repair modules, visualized as streamlines (Methods). h. Heatmap showing expression value dynamics (snCv3) along latent time inferred from RNA velocities for the top 300 likelihood-ranked genes. Top colour bar indicates aTAL repair modules. i. Scatter plots (u, unspliced; s, spliced; t, latent time) for putative driver genes (snCv3) identified by high likelihoods in the dynamical model. j. Gene regulatory networks associated with TAL repair modules (Methods, see Supplementary Table 23). Eigenvector centrality scores were plotted for select factors with high influence on different states. k. UMAP embedding (snCv3) showing pseudotime gradient and the derived vector field associated with TAL repair. l-m. UMAP embedding showing simulated vector fields following TFAP2B (l) or NR3C1 (m) perturbation. Barplots show inner product calculations (perturbation scores) comparing directionality and size of TAL repair flow vectors and the simulated perturbation vectors. Negative perturbation scores indicate a block in differentiation. +Adaptive epithelia localized to areas of injury. +a. Immunofluorescent (IF) staining of VCAM1, AQP1, KIM1 (HAVCR1) in the aPT (performed on replicate sections from 3 individuals). Scale bars represent 20 microm. b. IF staining of UMOD, PROM1 and KIM1 in the TAL (performed on replicate sections from 3 individuals). Scale bars represent 20 microm. c-e. RNA in situ hybridization (ISH) for PROM1, CST3 or EGF (performed on adjacent sections from 6 individuals). c. ISH for PROM1 and CST3 in adjacent sections. PROM1 is localized to an area showing interstitial fibrosis and tubular atrophy. Scale bar is 100 mum. d. RNA ISH for PROM1 (left panel) and EGF (right panel) in adjacent corticomedullary sections. PROM1 positive epithelial cells seen in injured tubules (epithelial simplification, loss of nuclei) that are EGF negative (blue asterisks, upper inset image) and EGF positive healthy TAL (red asterisks, lower inset image). Scale bar is 100 mum. e. ISH for PROM1 and EGF (healthy TAL) showing PROM1 localization to PT (blue asterisks, left inset) and TAL (red asterisks, right inset) showing histological evidence of injury (epithelial thinning, nuclei loss, brush border loss in PT). Adjacent section (lower panel) shows EGF positivity in healthy TAL cells. Scale bar is 50 mum. +TAL adaptive or maladaptive repair niches. +a. Slide-seq fibrotic/inflammatory niches from Fig. 5d showing full predicted subclass level 3 cell type distributions. Scale bar is 100 mum. b. Visium TAL niches were identified by clustering TAL dominant spots according to Seurat label transfer scores. The UMAP denotes 13 TAL niches which were distributed across the 23 samples (patient inset) and across disease state conditions (condition inset). c. Visium niche cluster compositions. Signature proportions of TAL cell types, injury cell states, stromal cells, and immune cells. Niche 5 contained significant stromal, niche 7 contained lymphoid, and niche 11 contained myeloid cell signatures. Some niches (e.g. 9) had significant contributions from neighbouring non-TAL epithelial cells ("Proportion Other" bar plot). The colocalization score (Methods) for cell types within each niche is based on Seurat label transfer scores and provided as a dot plot. d. A subset of TAL niches (1, 3, 5, 7) were overlaid upon a histologic image of the cortex in sample M19-F52_3, with each niche often represented by multiple contiguous spots. Scale bar is 300 mum in length. e. Representative region (patient 28-12265) showing niche 5 (STR) localized in proximity to interstitial fibrosis, and niche 3 (aTAL) localized adjacent to myeloid cell infiltration. Scale bar is 300 mum. f. Circle plot of ligand-receptor cell cell communications between TAL repair modules or states and immune cell subclasses. Dot size indicates relative proportion of the subclasses and TAL module, edge width represents strength of the communication. g. Dotplots showing expression level and percent expressed for select ligands or receptors within the mouse AKI data. Data were grouped into injury groups less than or equal to 2 days (including control cells) and groups greater than 2 days post-injury. The asterix highlights an IGF1 expression difference found between early and late injury groups of the aFIB population. h. Gene regulatory networks associated with STR cell types (see Supplementary Table 27). Eigenvector centrality scores were plotted for select factors with high influence on different subclasses. Ontologies for target genes downstream of select transcription factors are shown. + +Source Data + +Association of cell state scores with clinical phenotypes. +a. Embedding plots: grouping of patient-level expression profiles for the aTAL, aStr, Degen, and aPT genesets used for clinical outcome association (Supplementary Table 27) for snCv3 (Top) and scCv3 (Bottom). Barplots: the distribution of eGFR among the identified groups. b. Unadjusted Kaplan Meier curves by aStr (P = 0.001) and common aPT and aTAL (P = 0.03) state scores for composite of ESRD or 40% drop in eGFR from time of biopsy in Neptune adult patient cohort (see Supplementary Table 30). A score generated using 100 randomly selected genes failed to show any correlation (P = 0.52) with disease survival. c. Heatmap of causal variants (z-scores) that were enriched in SNARE2 cell-type specific accessible chromatin. Dots represent Z-scores > 2 (or P value < 0.05). Dotplots show averaged ESRRB binding site accessibility or gene expression (log values) and percent accessible or expressed. d. ESRRB subnetwork of TF connections to target genes generated using SNARE2 RNA and AC data, demonstrating a central role for ESRRB in regulating TAL marker genes. Inset shows the ESRRB motif. Boxes represent ESRRB target genes showing causal variant enrichment (c) within linked regulatory regions (AC peaks). e. Heatmap showing enrichment scores (scaled -log10(p values)) for the RNA expression (snCv3/scCv3) of gene sets associated with eQTL linked to kidney function or disease or associated with progression of acute to chronic injury. f. Dot plots of averaged gene expression values (snCv3/scCv3) or TF binding site accessibilities (SNARE) and proportion expressed/accessible. Violin plots show gene expression scores for gene sets associated with aging (Tabula Muris Consortium and Takemon et al.) or SASP (Ruscetti et al. or Basisty et al.). g. Violin plots showing expression scores for gene sets shown in (f) for all non-immune subclasses. h. Bottom: Number of differentially expressed genes between AKI and CKD cases for each major cell type in snCv3 and scCv3 datasets. Top: enrichment of functional gene ontology terms for each major cell type. Colour indicates -log adjusted p-value (derived from GSEA and calculated based on permutation). + +Source Data + +Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. +These authors contributed equally: Blue B. Lake, Rajasree Menon, Seth Winfree, Qiwen Hu, Ricardo Melo Ferreira, Kian Kalhor +Unaffiliated +A list of authors and their affiliations appears at the end of the paper +A full list of members other than authors and their affiliations appears in the Supplementary Information +Change history +9/26/2023 +An amendment to the underlying code was made to enable an author name to appear correctly in PubMed. +Extended data +is available for this paper at 10.1038/s41586-023-05769-3. +Supplementary information +The online version contains supplementary material available at 10.1038/s41586-023-05769-3. +Author contributions +Coordination of manuscript writing and project: B.B.L. and S.J. Contribution to patient recruitment and tissue collection: A.K., A.S.N., C.R.P., D.S., E.H.K., F.P.W., J.C.W., J.R.S., K. Kiryluk, M.K., P.M.P., R.D.T., S.J., S.R. and S.S.W. Contribution to tissue processing: A.K., A.S.N., D.B., D.S., E.A.O., J.R.S., M.F., M.K., M.T.E., P.C.D., S.J., S.R., S.W. and T.M.E.-A. Contribution to RNA data generation: A.G., A.S.N., B.B.L., D.D., E.A.O., E.M., E.Z.M., F.C., J.B.H., J.L.M., K.S.B., K.Z., M.K., M.T.E., N.P., P.C.D., R.M., S.J., S.U. and T.M.E.-A. Contribution to imaging data generation: B.Z., D.B., J.B.H., M.F., M.T.E., P.C.D., S.J., S.W. and T.M.E.-A. Contribution to ATAC data generation: B.B.L., D.D., K.Z., N.P. and S.J. Contribution to data archive/infrastructure: B.B.L., D.D., K.S.B., M.F., M.K., M.T.E., P.C.D., Q.H., R.M., R.M.F., S.W., T.M.E.-A. and X.W. Contribution to data analysis: B.B.L., D.B., E.A.O., J.B.H., J.P.G., K. Kalhor, K.Z., L.H.M., M.F., M.K., M.T.E., P.C.D., P.V.K., Q.H., R.M., R.M.F., S.E., S.J., S.W., T.M.E.-A., T.N., X.W. and Y.W. Contribution to the Azimuth tool: A.H. and R.S. Contribution to data interpretation: A.S.N., A.V., B.B.L., E.A.O., J.B.H., J.P.G., K. Kalhor, K.Z., L.H.M., M.F., M.K., M.T.E., P.C.D., P.J.H., P.V.K., Q.H., R.M., R.M.F., S.E., S.J., S.W. and T.M.E.-A. Contribution to writing the manuscript: B.B.L., K. Kalhor, K.Z., M.K., M.T.E., P.C.D., P.V.K., Q.H., R.M., R.M.F., S.J., S.W. and T.M.E.-A. +Peer review +Peer review information +Nature thanks Carlos Talavera-Lopez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. +Data availability +Processed data, interactive and visualization tools: the snCv3, scCv3, SNARE2, Slide-seq and Visium processed data files are all available for download at the GEO (Superseries GSE183279). snCv3 healthy reference data are available for reference-based single-cell mapping using the Azimuth tool (https://azimuth.hubmapconsortium.org/). All snCv3 and scCv3 processed data can be accessed and viewed at cellxgene (https://cellxgene.cziscience.com/collections/bcb61471-2a44-4d00-a0af-ff085512674c). snCv3 (excluding COVID-AKI and CKD nephrectomy samples), scCv3, Visium (KPMP biopsies) and 3D imaging can all be visualized and examined using the KPMP Data Atlas Explorer (https://atlas.kpmp.org/explorer/). For 3D imaging, the cytometry data, cell classifications, gates and neighbourhood analysis data are available at Zenodo (10.5281/zenodo.7120941). Raw sequencing and imaging data: raw sequencing data are under controlled access (human data) as they are potentially identifiable and can be accessed from the respective sources indicated below (summarized in Supplementary Tables 1 and 2). Raw and processed sequencing and imaging data (snCv3, scCv3, 3D imaging and Visium) generated as part of the KPMP have been deposited (https://atlas.kpmp.org/repository/) and compiled (10.48698/3z31-8924) online. 3D imaging raw data are freely available to download; however, KPMP raw sequencing data (snCv3, scCv3, Visium) have restricted access. These can be requested from KPMP by contacting A.L.D. (info@kpmp.org) and are available by signing a data use agreement (DUA) promising to abide by KPMP security standards and to not re-identify participants, share data outside those named on the DUA Exhibit A or sell the data. Data access is granted to anyone signing the KPMP DUA as is. KPMP will respond to initial data requests within 12-36 h and provide data up to one month after the DUA has been signed. Manuscripts resulting from KPMP controlled access data are requested to go through the KPMP publications and presentations (P&P) committee to ensure that KPMP is acknowledged appropriately and authorship follows ICJME standards. The KPMP P&P committee reviews and approves manuscripts every 2 weeks and, to date, no manuscript has been rejected. Any analysis resulting from KPMP data may be published or shared provided that it does not re-identify KPMP participants. Slide-seq raw sequencing data generated as part of KPMP pilot nephrectomy tissue are available for download from the GEO (Superseries GSE183279). Raw sequencing data (snCv3, SNARE2, Slide-seq) generated as part of the Human Biomolecular Atlas Project (HuBMAP) have been deposited (https://portal.hubmapconsortium.org/) and compiled (10.35079/hbm776.rgsw.867) online. The HuBMAP raw sequencing data have restricted access and are available for download from the database of Genotypes and Phenotypes (dbGaP: phs002249) by requesting for authorized access following instructions on the dbGaP website. The process to request access to this dbGaP study is available online (https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?adddataset=phs002249&page=login). In brief, to download the human sequencing data for this study after obtaining authorization from the NIH DAC, one would go through the SRA (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA671343). snCv3 data not deposited to KPMP or HuBMAP are available from the GEO (GSE183279) or, for Covid AKI raw sequencing files, on request from Washington University Kidney Translational Research Center (KTRC) to S.J. (sanjayjain@wustl.edu) due to patient confidentiality. Response to requests or questions will be addressed within a week. Code access and data use agreement forms can be accessed online (https://research.wustl.edu/core-facilities/ktrc/). Once the executed form is received and approved, data will be distributed within a month. There is no authorship restriction on the use of COVID data. Additional published/public datasets: the following publicly available RNA-seq datasets were used in this study: mouse kidney single cell (GEO: GSE129798); mouse kidney injury single nucleus (GEO: GSE139107); human fibroblast and myofibroblast single cell (Zenodo: 10.5281/zenodo.4059315); mouse distal nephron single cell and bulk distal segment (GEO: GSE150338); human kidney mature immune single cell (https://kidney-atlas.cells.ucsc.edu); and human kidney single nucleus (GEO: GSE151302; https://human-kidney-atac.cells.ucsc.edu). GWAS summary statistics were from the CKDGen Consortium (all eGFR; https://ckdgen.imbi.uni-freiburg.de/files/Wuttke2019), EBI GWAS Catalog (hypertension; https://www.ebi.ac.uk/gwas/efotraits/EFO_0000537) and the CausalDB database (release 1.1 2019-09-29; http://www.mulinlab.org/causaldb). NEPTUNE sequencing and clinical data were obtained from NEPTUNE. Owing to patient confidentiality, these data have restricted access and are available only on request to NEPTUNE-STUDY@umich.edu. ERCB data were obtained from the GEO (GSE104954). Raw sequencing data (scCv3) on living donor biopsies as part of the Chan Zuckerberg Initiative (CZI) and HCA are available from the GEO (GSE169285). Additional Visium spatial transcriptomic data not in the KPMP repository are available from the GEO (GSE171406). Figures: schemata of the human nephron and renal corpuscle were developed by the KPMP and HuBMAP (10.48698/DEM4-0Q93). Source data are provided with this paper. +Code availability +Code to reproduce figures are available to download from GitHub (https://github.com/KPMP/Cell-State-Atlas-2022). No additional custom computational code was generated in this study. +Competing interests +P.V.K. serves on the scientific advisory board to Celsius Therapeutics and Biomage. A.V. is a consultant for Astute and NxStage. C.R.P. is a member of the advisory board of and owns equity in RenalytixAI, and serves as a consultant for Genfit and Novartis. M.K. has grants from JDRF, Astra-Zeneca, NovoNordisc, Eli Lilly, Gilead, Goldfinch Bio, Janssen, Boehringer-Ingelheim, Moderna, European Union Innovative Medicine Initiative, Chan Zuckerberg Initiative, Certa, Chinook, amfAR, Angion Pharmaceuticals, RenalytixAI, Travere Therapeutics, Regeneron, IONIS Pharmaceuticals, Astellas, Poxel and a patent (PCT/EP2014/073413; 'Biomarkers and methods for progression prediction for chronic kidney disease') licensed. F.C. and E.Z.M. are paid consultants for Atlas Bio. F.P.W. receives research support from Astrazeneca, Boeringher-Ingelheim, Vifor Pharma and Whoop. P.M.P. is a consultant for Janssen. S.R. has research funding from AstraZeneca and Bayer Healthcare. S.S.W. is a consultant for GSK, GEHC, JNJ, Strataca, Roth Capital Partners, Venbio, and an expert witness on litigation for Davita and Pfizer. J.R.S. consults for Maze and Goldfinch and receives royalties from Sanfi Genzyme. K.Z. is a co-founder, equity holder and serves on the scientific advisory board of Singlera Genomics. A.S.N. is on the external advisory board for CareDX. L.H.M. is a consultant for Reata Pharmaceuticals, Travere Therapeutics and Calliditas. S.J. is a paid Blue SKy mentor for Meharry Medical College, Nashville and receives royalties from Elsevier. J.L.M. is an employee and shareholder of Solid Biosciences. 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To advance our understanding of the etiology of blinding diseases, we used single-cell RNA-sequencing (scRNA-seq) to analyze the transcriptomes of ~85,000 cells from the fovea and peripheral retina of seven adult human donors. Utilizing computational methods, we identified 58 cell types within 6 classes: photoreceptor, horizontal, bipolar, amacrine, retinal ganglion and non-neuronal cells. Nearly all types are shared between the two retinal regions, but there are notable differences in gene expression and proportions between foveal and peripheral cohorts of shared types. We then used the human retinal atlas to map expression of 636 genes implicated as causes of or risk factors for blinding diseases. Many are expressed in striking cell class-, type-, or region-specific patterns. Finally, we compared gene expression signatures of cell types between human and the cynomolgus macaque monkey, Macaca fascicularis. We show that over 90% of human types correspond transcriptomically to those previously identified in macaque, and that expression of disease-related genes is largely conserved between the two species. These results validate the use of the macaque for modeling blinding disease, and provide a foundation for investigating molecular mechanisms underlying visual processing. +Introduction +The three leading causes of irreversible blindness can be classified as neurodegenerative retinal diseases: age-related macular degeneration, glaucoma and diabetic retinopathy; photoreceptors are lost in age-related macular degeneration and diabetic retinopathy, and retinal ganglion cells (RGCs) are lost in glaucoma. These three groups of diseases affect over 300 million people world-wide, greatly outnumbering those affected by other neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Genetic contributors have been discovered for all of these retinal diseases, largely through genome-wide association studies (GWAS). In few cases, however, do we understand the role of the implicated gene in disease pathogenesis. +Cell classes in human retina. (a) (Top) Sketch of a human eye showing positions of retina and fovea. (Bottom) Sketch of a flat mounted retina showing foveal and peripheral regions. Circle in center is position at which optic nerve exits the eye. (b) Sketch of a peripheral retinal section showing its major cell classes:photoreceptors (PRs), horizontal cells (HCs), bipolar cells (BCs), amacrine cells (ACs), retinal ganglion cells (RGCs) and Muller glia (MG), outer and inner plexiform (synaptic) layers (OPL and IPL), outer and inner nuclear layers (ONL and INL), and ganglion cell layer (GCL). (c) scRNA-seq workflow. Foveal cells were dissociated from <1.5 mm-diameter punches and collected without further processing. Peripheral cells were dissociated from all four quadrants of peripheral retinas, and depleted of rods (CD73+) or enriched for RGCs (CD90+) with magnetic columns before processing. (d) Expression patterns of class-specific markers (rows) in individual cells (columns). Cells are grouped by their classes (color bars, top). Plot shows data from a maximum of 500 randomly selected cells per class. +One main obstacle to gaining such understanding is lack of knowledge about where the implicated genes are expressed; lacking such information, it is difficult to determine mechanisms by which they affect visual function. Another is that substantial differences in structure and gene expression between human and rodent retina have made it difficult to study these genes in animal models. For example, among mammals, only primates have a fovea, the small central region responsible for high acuity vision as well as most chromatic vision - and the region selectively affected in macular degeneration, diabetic macular edema and hereditary maculopathies. As a first step toward addressing these issues, we recently used high throughput single cell RNA-seq (scRNA-seq) to generate a retinal cell atlas from cynomolgus macaque (Macaca fascicularis), a non-human primate that is closely related to humans and frequently used in preclinical ophthalmological studies. We separately profiled peripheral retina and the fovea (Fig. 1a). In each region, we characterized the six classes of retinal cells - photoreceptors, horizontal cells, bipolar cells, amacrine cells, RGCs and Muller glial cells (Fig. 1b) and found molecular signatures that divided them into a total of 62 (fovea) or 70 (periphery) cell types. +Here, to extend this work, we used the macaque atlas as a foundation to generate a comprehensive cell atlas of the adult human retina. We expected that the similarity of macaque to human would aid in identifying cell types, and this was indeed the case. By analyzing a total of 84,982 single cell transcriptomes, we identified 58 cell types in human fovea and 57 types in peripheral retina, nearly all of which were shared between the two regions. For many of these types, however, we documented substantial regional differences in gene expression and proportions. By comparing human and macaque atlases, we found 1:1 matches for >90% of cell types, supporting the use of Macaca fascicularis as a preclinical model. Finally, we mapped the expression of 636 genes implicated in blinding diseases by GWAS studies or as highly penetrant Mendelian mutations underlying a variety of inherited retinal degenerations, each rare but substantial in aggregate. We show that many of the genes queried are selectively expressed in particular retinal cell classes, in particular cell types within a class, or in foveal or peripheral cohorts of shared types. These results provide new insights into mechanisms underlying retinal disease. +Results +Cell classes in human retina +To generate a comprehensive cell atlas of human retina, we obtained eight retinas from seven genetically unrelated human donors with no clinical history of ocular disease (Table S1). We dissected foveal (~1.5 mm diameter centered on the foveal pit, which was visible under a dissecting microscope) and peripheral samples (>5 mm from the fovea) from whole retina, pooling peripheral pieces from all four quadrants. Foveal samples were dissociated into single cells, which were profiled without further processing using high-throughput droplet sequencing. For peripheral samples, in which rod photoreceptors and RGC comprise ~80% and <2% of total cells respectively, we depleted rods using magnetic beads conjugated to anti-CD73 or enriched RGCs using anti-CD90-conjugated beads prior to collection (Fig. 1c), using protocols established in our study on macaque retina. Libraries were prepared from foveal and peripheral samples, and sequenced. Altogether, we obtained 84,982 high-quality transcriptomes, 55,736 from fovea and 29,246 from peripheral retina. The median number of unique transcripts captured per cell was 2,577 and the median number of genes detected was 1,314 (Table S3). +To maximize statistical power, we pooled data from fovea and periphery for initial analysis. Using methods adapted from, we divided the cells into 9 groups based on expression of canonical markers, which were common to both retinal regions (Fig. 1d). We identified the five neuronal classes (9,070 photoreceptors, 2,868 horizontal cells, 25,908 bipolar cells, 13,607 amacrine cells and 11,404 RGCs) as well as four types of non-neuronal cells: 19,896 Muller glia, 1,149 astrocytes, 671 microglia and 409 vascular endothelial cells. +Classification and identification of retinal cell types +We next re-clustered each neuronal class separately to discriminate cell types. We obtained a total of 54 clusters, each corresponding to a putative cell type or possibly a small group of closely related types: 3 photoreceptor, 2 horizontal cell, 12 bipolar cell, 25 amacrine cell, and 12 RGC types. Thus, including the 4 non-neuronal types, we detected a total of 58 cell types in human retina. Of them, 49 contained cells from at least 6 of the 7 donors (Supplemental Fig. 1), indicating that the heterogeneity does not result from individual variations or batch effects. We took advantage of the evolutionary proximity between humans and macaques and utilized previously defined macaque retina cell types to train a multi-class supervised classification algorithm. This enabled us to relate most human clusters to macaque types, based on their expression patterns of orthologous genes. Many of the human types were further characterized by assessing their expression of key genes reported previously. +Photoreceptors +Photoreceptor, horizontal and bipolar cell types. (a-c). Photoreceptors. (a) Cell clusters visualized using t-distributed stochastic neighbor embedding (t-SNE). Dots represent individual cells and are color coded by their cluster assignments (text labels). (b) Transcriptional correspondence between human and macaque cell types summarized as a "confusion matrix." In this and subsequent confusion matrices, the color and size of each dot reflect the percentage of cells in a given human cluster (columns) mapped to a corresponding macaque type (rows). (c) Dot plots showing expression of select marker genes. In this and subsequent gene expression plots, dot size represents the percentage of cells in a cluster with non-zero expression of a select gene; and color intensity represents the average expression of a gene within expressing cells. (d-f) Horizontal cells. Clusters (d), correspondence to macaque types (e) and expression of key genes (f) shown as in (a-c). (g-i) Bipolar cells. Clusters (g), correspondence to macaque types (h) and expression of key genes (i) shown as in (a-c). +The two subclasses of photoreceptor cells in vertebrate retinas are rods, specialized for high-sensitivity vision at low light levels, and cones, which mediate chromatic vision. Rods and cones express rhodopsin and cone opsins, respectively. Humans and many old world monkeys, such as macaques, are trichromats, with three cone types, each expressing a single opsin (S-, M- or L-opsin) tuned to short-, medium- or long-wavelengths, respectively. We found three clear photoreceptor clusters: rods, which selectively express rhodopsin; S-cones, which selectively express S-opsin; and M and L cones, which selectively express and M or L-opsin (Fig. 2a-c). The inability to distinguish M from L opsin results from their nearly identical coding sequences (98% nucleotide identity), the presence of multiple copies of the M-opsin gene in some individuals, and the high frequency of recombination between these two closely linked genes (see Peng et al., 2019 for discussion). The three human photoreceptor types mapped to their macaque counterparts with high confidence (Fig. 2b). +Horizontal cells +Most primates, including macaques, have two horizontal cell types, H1 and H2. Based on morphological criteria, Kolb and colleagues argued for a third horizontal cell type in human retina, with H3 differing from H1 in having larger somata and dendritic arbors. We identified two horizontal cell types (Fig. 2d), which corresponded to the macaque H1 and H2 types, respectively (Fig. 2e). Attempts to further subdivide the two types by increasing the sensitivity of the clustering algorithm failed to reveal a third type with a distinguishing molecular signature. Similar to macaque H1 and H2, human H1 and H2 were distinguished from each other by selectively expressing transcription factors LHX1 and ISL1, respectively (Fig. 2f). +Bipolar cells +Bipolar cells are divided into three subclasses: ON and OFF cone bipolars, which release neurotransmitter in response to increases and decreases in illumination of cones, respectively; and rod bipolars, which generate ON responses to stimulation of rods. In macaques, ON and OFF bipolars are characterized by expression of genes that encode the metabotropic glutamate receptor 6, GRM6, and the kainate-type glutamate receptor, GRIK1, respectively; rod bipolars are distinguished from cone bipolars by expression of PKCalpha, encoding protein kinase Calpha. These expression patterns were conserved in human bipolars, allowing us to divide 12 bipolar clusters into 1 ON rod, 5 ON cone, and 6 OFF cone types (Fig. 2g-i). The counterparts of all 12 macaque types were found in human retina and named based on this correspondence (Fig. 2h). Notably, the provisionally named "OFFx" type, first identified and named in our analysis of macaque retina, was also present in human retina as a distinct cluster (Fig. 2h,i). +Amacrine cells +Amacrine and retinal ganglion cell types. (a-d) Amacrine cells. Clusters visualized by tSNE (a), correspondence to macaque types (c) and expression of key genes (d) shown as in Fig. 2. Known amacrine types (SAC, VG3-AC, Aii, SEG) are conserved between macaque and human. Genes encoding neuropeptides are shown in bold in d. (b) Top, dendrogram showing transcriptomic relationships among AC clusters; Bottom, violin plots representing the distribution of expression of GABAergic (GAD1, GAD2) and Glycinergic (SLC6A9) in each AC cluster. (e-g) Retinal ganglion cell clusters visualized by tSNE (e), correspondence to macaque types (f) and expression of key genes (g) shown as in Fig. 2. +Most amacrine cells are inhibitory neurons utilizing GABA or glycine as neurotransmitters. By assessing the expression of marker genes for GABAergic (glutamate carboxylase, GAD1 and GAD2) and glycinergic (SLC6A9, encoding the high affinity glycine transporter GLYT1) amacrines, we identified 16 putative GABAergic and 8 putative glycinergic amacrine cell types among a total of 25 types (Fig. 3a,b). One type (C14) expressed none of these three genes at high levels, and might correspond to a non-GABAergic non-Glycinergic (nGnG) type identified in mouse . One of the glycinergic types (C17) also expressed GAD2, raising the possibility that it uses both transmitters. Several known amacrine types were detected based on key marker genes (Fig. 3d), including SLC17A8 for VG3 amacrine (an excitatory type that co-releases glycine and glutamate), SLC18A3 for cholinergic starburst amacrines, TH for catecholaminergic CAI/CAII amacrines, and GJD2 for AII amacrines, which mediate transmission of rod signals to RGCs. Those and many other AC types mapped faithfully to macaque types (Fig. 3c). Many AC types also expressed neuropeptides (bold in Fig. 3d), with some predominantly in single types (e.g. NPW in C7 and VIP in C24, and others expressed by multiple types (e.g. CARTPT and PENK). In several instances, more than one neuropeptide was detected in the same AC type - for example, thyrotropin-releasing hormone (TRH) and Natriuretic Peptide B (NPPB) in C9, and Proenkephalin (PENK) and Cholecystokinin (CCK) in C15. Thus, human amacrines appear to use a variety of neurotransmitters and neuromodulators, as has been demonstrated for amacrines in other mammalian and non-mammalian retinas . +Retinal ganglion cells +The predominant ganglion cell types in primate retina are ON and OFF midget RGCs, together accounting for >80% of RGCs in human (by morphological criteria) and macaque retina (by morphological and molecular criteria). Next most abundant in both species are ON and OFF parasol RGCs, totaling ~10% of all RGCs. Based on abundance, four RGC clusters appeared likely to correspond to these types (Fig. 3e). Mapping to the macaque atlas confirmed their identities (Fig. 3f). The midget and parasol RGCs comprised 86% (44% ON and 42% OFF) and 10% (4% ON and 6% OFF) of all RGCs in our dataset, respectively. +The remaining 8 clusters ranged in abundance from 0.1% to 1.6% of all RGCs. They included two types that expressed the transcription factor, FOXP2 (hRGC6 and 7), one of which also expressed FOXP1 (Fig. 3g); these might be related to mouse FoxP2 + FoxP1- and FoxP2 + FoxP1 + F-RGCs. We also detected two RGC clusters that expressed melanopsin (OPN4), the canonical marker of intrinsically photosensitive RGCs (ipRGCs; hRGC5 and hRGC12; Fig. 3g). Recent morphological and physiological studies have demonstrated 2-4 human ipRGC types. We speculate that hRGC12, which expressed the highest level of OPN4 (Fig. S2) corresponds to M1, which expresses highest levels of OPN4 in mice; others could be included in hRGC5 or be too rare to detect. +Non-neuronal cells +Non-neuronal cell types. (a) Visualization of four non-neuronal types using t-SNE. (b) Genes differentially expressed between Muller glia and astrocytes. Genes shown exhibited >1.5 log fold change. +Four clusters of non-neuronal cells were identified as Muller glia, astrocytes, microglia, and endothelial cells based on expression of known markers (Fig. 1d). The Muller cell, the intrinsic glial cell of the retina, was the most abundant type among them (Fig. 4a). Astrocytes, which are largely confined to the ganglion cell and nerve fiber layers, were transcriptomically similar to Muller glia (Fig. 1d), but the two types were readily distinguished by selective expression of multiple genes (Fig. 4b). +Comparison of human and macaque retinal cell types +Comparison of gene expression between corresponding human and macaque cell types. (a-f) Dot plots showing similarities and differences in type-specific DE gene expression among corresponding human and macaque types (columns) in PRs (a), HCs (b), BCs (c), ACs (d), RGCs (e), and non-neuronal types (f). (g) Conservation of type-specific marker genes between human and macaque. Graph shows the proportion of DE genes in human (log fold change >0.5 and adjusted p < 0.001 for each type compared to all other types within the class) that are also DE in macaque within shared types (columns). +As noted above, the evolutionary proximity of human and macaque enabled us to name most human clusters based on their striking transcriptional correspondence with types characterized in macaque. We next assessed the extent to which gene expression are conserved among corresponding types between the two species. We compared the expression of type-specific marker genes in 34 corresponding types : 3 photoreceptor, 2 horizontal cell, 12 bipolar cell, 7 amacrine cell, 7 RGC, and 3 non-neuronal types (Fig. 5a-f, see Methods for details). As expected, all corresponding types expressed at least some common type-specific "marker" genes. +In some cases, however, type-specific genes were expressed selectively and at high levels in only one of the two species. Examples of such genes include: (a) EPHX2 by macaque but not human cones; (b) GPATCH1 and CRHBP by human but not macaque cones; (c) CA8 by macaque but not human OFF parasol RGCs; (d) FABP4 by human but not macaque OFF parasol RGCs; (e) SCGN by macaque but not human bipolar types DB1 and DB6; (f) RBPMS2 by human but not macaque midget RGCs; and (g) RGR by human but not macaque Muller glia (Fig. 5a-f). As another metric of similarity, we identified genes differentially expressed by each shared human and macaque type (log fold change >=0.5, <0.001 adjusted p value for each type compared to other types within the class). We then calculated the proportion of DE genes in human that were also DE genes in macaque. The five pairs with the largest proportion of shared DE genes were 2 photoreceptor types, 2 non-neuronal types, and one bipolar type (Fig. 5g). +Histological validation of gene expression difference between human and macaque. (a) SCGN (green) does not label any CHX10-positive BCs (magenta) in human retina (top two panels), but does label a subset of BCs in macaque retina (bottom two panels). Circles outline the somatic positions of SCGN-positive cells. Boxed areas in top panels are shown at higher magnification in lower panels . The SCGN-positive CHX-10 negative cells in human retina are likely to be amacrine cells. (b) RBPMS detected immunohistochemically (top two rows) is similar in human and macaque GCL, while the expression of RBPMS2 detected by in situ hybridization (bottom two rows) is unique to human RGCs, but not found in macaque RGCs. Human RGCs are labeled with SLC17A6 (magenta). Figures were generated using Zeiss ZEN (https://www.zeiss.com/microscopy/en_us/products/microscope-software/zen.html), and ImageJ (https://imagej.nih.gov/ij/). Scale bars are 20 mum in a (top panels) , and b; 10 mum in a (bottom panels) . +We used histological methods to validate some of these differences. Labeling with anti-secretagogin (SCGN) plus anti-VSX2 (CHX10), which labels all bipolar cells, confirmed that SCGN is expressed by bipolar cells in macaque retina but not in human retina (Fig. 6a). Conversely, RBPMS2 was expressed by human but not macaque midget RGCs, while the canonical markers, RBPMS and SLC17A6 (VGLUT2) were expressed by most or all RGCs in both species (Fig. 6b). Together, these comparisons demonstrate predominant but not complete conservation of gene expression by corresponding cell types in human and macaque retina. +Comparison of foveal and peripheral retinal cells +For analyses presented so far, we pooled data from fovea and periphery. We next compared the regions with each other. Nearly all (57/58) cell types were present in both regions. One GABAergic amacrine type, C18, was found only in the fovea. +Differences between corresponding cell types in fovea and periphery. (a) Bar plot showing the number of differential expression (DE) genes (log fold change >1 and adjusted p value < 0.001) per matched cell types between fovea and periphery (x-axis). (b) Violin plots showing the expression of select DE genes in relevant foveal and peripheral cell types. (c-f) Box-and-whisker plots showing proportions of cell types in fovea and peripheral retina for BCs (c), HCs (d), ACs(e), RGCs (f). Black horizontal line, median; bars, interquartile range; vertical lines, minimum and maximum, dots are values from individual donors; n = 6 foveal, and 2 peripheral samples. +For all corresponding types, however, some genes were differentially expressed between foveal and peripheral cohorts. Of 47 types for which there were enough cells in both regions (>20) to enable a comparison, the number of differentially expressed genes ranged from 5 to 100 (log fold change >1; adjusted p-value < 0.001). The types with the most differences by these criteria were RGCs (5 types), non-neuronal cells (3 types) and M/L cones (Fig. 7a). Examples include EPB41L2 and VTN expressed by foveal, but not peripheral cones; TTR expressed at higher levels by foveal than peripheral bipolar type DB3b and DB4; TULP1 expressed by peripheral but not foveal bipolar type FMB and DB2; and RND3 expressed by peripheral but not foveal ON parasol RGCs (Fig. 7b). +In many cases, proportions of cell types also differed between fovea and periphery. Several differences were consistent with previous reports, such as the relatively lower proportion of S cones among all cones in the fovea compared to the periphery; the depletion of astrocytes from fovea (0.9% of all non-neuronal cells in fovea and 12% in periphery); and the higher ratio of cone bipolar to rod bipolar cells in the fovea compared to the periphery (rod BCs were 35% of peripheral BCs but only 3% of foveal BCs, Fig. 7c). Other differences have not, to our knowledge, been noted previously. +The H1:H2 ratio was nearly ~4-fold higher in the fovea (7.3:1) than in peripheral retina, 1.9:1; Fig. 7d). The ratio of GABAergic to Glycinergic AC types was higher in fovea (1.8:1) than in the periphery (1.1:1). Several AC clusters showed enrichment in either fovea (e.g., C8, 12 and 23) or peripheral retina (e.g., C1, 2, and 4) (Fig. 7e). The OFF parasol is the only RGC type enriched in fovea using the same criteria, while 5 out of the 8 rare RGC types (hRGC cluster 7, 8, 9, 10, 12) were more abundant in peripheral retina than in fovea (Fig. 7f). Foveal enrichment of H1 horizontal cells and OFF parasol RGCs was also observed in the cynomolgus macaque. +Expression of genes implicated in retinal disease +We used the cell atlas to assess retinal expression of 1,756 genes associated with diseases in which vision loss results primarily from death or dysfunction of retinal cells. They include retinitis pigmentosa, cone-rod dystrophy, Leber congenital amaurosis, congenital stationary night blindness, hereditary maculopathy, Leber hereditary optic neuropathy, dominant optic atrophy, open angle glaucoma, age-related macular degeneration, diabetic retinopathy, diabetic macular edema and Macular Telangiectasia type 2. Of these, 624 genes showed robust expression (detected in more than 20% cells of any class, foveal or peripheral, with average expression level >0.5). We evaluated these, as well as some that fell below threshold but are clinically interesting, further. +Expression of disease-related genes in retinal cell classes. (a-l) Heat maps show expression of genes implicated in each of 12 groups of blinding diseases described in the text. Color represents the scaled expression level of genes among all cell classes. Thus, the heat maps accurately reflect the order of expression among cell classes for each gene but not the absolute levels of expression. Asterisks mark genes that were included because of clinical interest but fell below the threshold noted to the text. +We assessed expression of these genes in 9 cell classes: rods, cones, HCs, BCs, ACs, RGCs, Muller glia, astrocytes, microglia and endothelial cells. All genes are listed in Fig. S3, and examples are shown in Fig. 8. Note that the retinal pigment epithelium, which plays a critical role in the pathogenesis of many retinal diseases was not included in our atlas (see Discussion). We summarize these groups here, beginning with diseases for which monogenic high penetrance causes have been identified. We then discuss disorders for which few monogenic causes are known, but numerous susceptibility factors have been implicated primarily through genome-wide association studies (GWAS). +Retinitis Pigmentosa (RP) is a diffuse photoreceptor dystrophy that also affects the pigment epithelium. It manifests as night blindness with progressive visual field loss. Clinical features in the macula often include loss of foveal reflex, abnormalities at the vitreoretinal interface, and cystoid macular edema. Other typical findings include arteriolar narrowing, waxy pallor to the optic disc, and variable amounts of bone-spicule pigment changes. Consistent with the predominant functional deficits of night blindness, most genes implicated as monogenic causes of RP were predominantly expressed in rods (Figs. 8a, S3). Potentially consistent with other clinical findings of RP, some genes were also expressed in vascular endothelium and RGCs (e.g., RPGR and TOPORS), while others were expressed at highest levels in RGCs (e.g., SLC25A46, SLC7A14 and RP9) or Muller glia (e.g., RGR and RLBP1). As noted above, some genes in this and other disease groups (e.g., RPE65) are likely to act in and be expressed at higher levels by retinal pigment epithelial cells, which were not included in our dataset. +Cone-rod dystrophy affects both photoreceptor classes and patients with this condition demonstrate expanding central scotomas often leading to severe visual impairment. Consistent with this pathology, causative genes were expressed in both rods and cones (e.g., CRX, RAX2), with expression often higher in the latter (e.g., GNAT2, PDE6H) (Fig. 8b). +Lebers Congenital Amaurosis (LCA) is a severe group of inherited retinal dystrophies characterized by nystagmus, sluggish or absent pupillary light reflexes and blindness, often in the first year of life. Genes mutated in the most prevalent forms of LCA were expressed in both rod and cone photoreceptors, consistent with the characteristic early absence of retina-wide rod and cone photoreceptor function demonstrable by electroretinogram (ERG). Several (CEP290, GUCY2D and CRB1) were also expressed in RGCs (Fig. 8c). +Congenital Stationary Night Blindness (CSNB), a lifelong, nonprogressive abnormality of scotopic vision, disrupts transmission through the rod pathway by disabling neurotransmission from rods to rod bipolar cells. In most cases, cell loss is minimal. Genes implicated in CSNB were generally expressed either in rods (e.g., GNAT1, SLC24A1) or bipolar cells (e.g., GRM6, TRPM1). CACNA1F, which harbors mutations in the majority of cases of incomplete X-linked CSNB, was expressed in both photoreceptor and bipolar cells, consistent with its wider phenotypic spectrum encompassing X-linked progressive cone-rod dystrophy, optic atrophy, and Aland Island eye disease (AIED) (Fig. 8d). Finally, although, NYX and LRIT3, which harbor CSNB mutations were expressed at levels too low to meet our set screening threshold, they were preferentially expressed in bipolar cells and photoreceptors, respectively. +Macular dystrophies, including Stargardt Disease, Vitelliform degenerations, Pattern Dystrophies, Sorsby Macular Dystrophy and Familial drusen, are slowly progressive retinal degenerations that account for a significant proportion of cases of central vision loss among adults under the age of 50. Genes involved in these dystrophies were generally enriched in photoreceptors (e.g., PRPH2, PROM1), but others were also expressed in non-neural cells - e.g., EFEMP1 selectively in Muller Glia and TIMP3 selectively in vascular endothelium (Fig. 8e). TIMP3, which is mutated in Sorsby Macular Dystrophy, encodes a protein involved in matrix remodeling and suppression of retinal angiogenesis. Although the primary site of pathogenesis in this disease is believed to be at the level of retinal pigment epithelium or Bruch's membrane, our results suggest that it might also act within retinal vasculature. +Leber's hereditary optic neuropathy (LHON) is the most common inherited mitochondrial disease with ophthalmic manifestations. It is caused by mutations in genes primarily encoding respiratory complex chain 1 proteins (e.g., ND1, ND4, ND6), leading to defects in NADH-ubiquinone oxidoreductase chains that may impair glutamate transport and increase production of reactive oxygen species. The result of these impairments is RGC dysfunction and, eventually, apoptosis, and atrophy of the retinal nerve fiber layer. Consistent with this pathogenesis, all LHON genes were predominantly expressed in RGCs (Fig. 8f). +Autosomal Dominant Optic Atrophy, the most common hereditary optic neuropathy, is characterized by gradual loss of visual acuity that is generally bilateral and symmetric. RGC degeneration, particularly in the papillomacular bundle, has been implicated as the primary mechanism of disease. Consistent with this pattern of expression, causative genes such as OPA1 and OPA3 were predominantly expressed in RGCs (Fig. 8g). +Inherited Vitreoretinopathies include Familial Exudative Vitreoretinopathy (FEVR), Norrie Disease and Coats Disease. Most genes mutated in these disorders were expressed in retinal vascular endothelium (Fig. 8h). In addition, however, NDP and LRP5 were also expressed in Muller glia and in astrocytes, a finding compatible with observations of NDP expression in a subset of cortical astrocytes. +Age Related Macular Degeneration (AMD) is broadly classified into non-exudative ("dry") and exudative or neovascular ("wet") types. In dry AMD, mild forms are characterized by drusen accumulation between retinal pigment epithelium and Bruch's membrane, which can progress to late forms with large patches of atrophic outer retina. In wet AMD, aberrant angiogenesis originating either within the choroid or retina leads to often catastrophic sub- or intraretinal hemorrhage. HTRA1, a major susceptibility gene for neovascular AMD, was expressed at high levels in HCs and Muller glia (Fig. 8i). While the disease-related effects of this gene are thought be exerted in the retinal pigment epithelium, its expression in HCs and Muller glia suggests additional sites of action. Alleles in the CFH gene and other complement pathway genes have also emerged as risk factors for AMD; we noted expression of multiple complement genes (CFH, CFI, C2, C3) in different cell classes (Fig. 8i). +Diabetic retinopathy (DR) and Diabetic Macular Edema (DME) together represent the leading cause of blindness and visual disability among working-age adults of all races living in industrialized nations. While many genes implicated in diabetic retinopathy - classically considered a predominantly microvascular complication of diabetes mellitus - were expressed in non-neuronal retinal cell classes (particularly, vascular endothelial cells), a large proportion, such as HS6ST3, DPP10, and VEGFB, were almost exclusively expressed in RGCs (Fig. 8j). +Macular Telangiectasia type 2 (Mac Tel 2) is a rare retinal neurodegenerative condition that leads to late-onset progressive central vision loss. Early clinical findings, including retinal discoloration and capillary telangiectasis, are limited to the perifoveal region. Photoreceptor loss and foveal atrophy occur as the disease progresses. Mac Tel 2 is currently considered to be a primary neurodegenerative condition of the retina with secondary vascular involvement rather than (as previously hypothesized) a primary vasculopathy. Several risk alleles and genes in proximity to SNPs identified by GWAS studies implicate Muller cell dysfunction and dysregulation of serine metabolism in pathogenesis. We found expression patterns compatible with these hypotheses. For example, PHGDH and PSPH, encoding enzymes involved in L-serine synthesis, were selectively expressed in RGCs with PHGDH also expressed in Muller glia. Finally, to gain insight into the protective effects of CNTF, which is currently in phase 2 clinical trials for Mac Tel 2, we investigated the expression of CNTFR. We found expression in RGCs and Muller glia as well as amacrine cells and astrocytes (Fig. 8k). +Open Angle Glaucoma, an optic neuropathy characterized by loss of RGCs, is one of the most common causes of vision loss world-wide and the leading cause of irreversible blindness among African Americans. Among genes implicated in glaucoma, either by GWAS or as rare Mendelian alleles, most were expressed predominantly within RGCs (e.g., OPTN, TMCO1, TBK1) (Fig. 8l). Other genes, including FOXC1, CYP1B1, LMX1B and MYOC were not expressed substantially in any neural retina cell classes, consistent with their proposed role predominantly in the anterior segment with mutations leading to high intraocular pressure, a major risk factor for glaucoma (Fig. S3). Indeed, in a parallel study, we have demonstrated expression of these genes in cells of the aqueous humor outflow pathways. +In general, these patterns of expression match those we previously documented for macaque. For example, of the 94 genes shown in Fig. 8, 85 were profiled in macaque and of these, 78 (or 92%) were expressed at highest levels in the same cell class in both species. +Differential expression of disease-related genes by region and cell type. (a) Violin and superimposed box plots showing differential expression of select disease genes between foveal and peripheral cell classes. (b) Heat maps showing expressions of select disease genes in four major RGC types. Cell types are segregated by their regions: fovea versus periphery. (c) Heat map showing expression of GRM6 and TRPM1, two previously reported congenital stationary night blindness genes, in ON bipolar cells (blue bar, top), but not in OFF bipolar cells (red bar, top). +We next compared foveal and peripheral cohorts of cell classes in which genes were highly expressed (Fig. 9a,b). Several patterns were consistent with clinical features of the associated conditions. For example, many genes with causative mutations leading to Retinitis Pigmentosa, including RHO, NRL, and NR2E3 demonstrated foveal rod enrichment (Figs. 9a and S3). RP1 was preferentially expressed in foveal rods and cones. In contrast, PDE6H, a cone-rod dystrophy gene, demonstrated preferential expression in peripheral rods. Genes implicated in macular dystrophies were generally enriched in the fovea compared to the peripheral retina. ABCA4, which harbors causative mutations leading to Stargardt disease, was enriched in foveal photoreceptors (Fig. S3); EFEMP1, implicated in Doyne Honeycomb Dystrophy, was predominantly expressed in foveal Muller glia. OPTN and APOE, implicated in Open Angle Glaucoma, demonstrated class-specific foveal enrichment among RGCs and Muller glia, respectively. In contrast, VEGFA, polymorphisms of which have been linked to severity of Diabetic Retinopathy, was expressed at higher levels in the periphery compared to fovea. +Finally, we assessed type-specific expression in RGCs for genes implicated in dominant optic atrophy, diabetic retinopathy, diabetic macular edema, Mac Tel 2 and primary open angle glaucoma, and type-specific expression in bipolar cells for genes implicated in CSNB (Fig. 9b,c). Genes with type-specific RGC expression patterns included the glaucoma-associated genes SIX6, which was enriched in midget ganglion cells; CAV2 and POU6F2 enriched in ON parasol RGCs and AFAP1, enriched in peripheral ON parasol RGCs. (POU6F2 was expressed at highest levels in RGC types 5, 11 and 12, which include ipRGCs.) Several but not all genes implicated in Mac Tel 2 (e.g. PHGDH, PSPH, LINC00461 and GBAS) were enriched in foveal RGCs. Patterns of expression differed, however, with PHGDH expressed primarily in foveal midget RGCS, LINC00461 primarily in foveal parasol RGCs, and PSPG and GBAS in both. MRPL19, one of the few genes implicated specifically in DME, which affects the fovea by clinical definition, was expressed preferentially in foveal RGCs. +Discussion +We used high-throughput single-cell RNA-seq to generate a cell atlas of the adult human retina. From 55,736 foveal and 29,246 peripheral retinal cells, we identified 58 cell types. We then used the cell atlas to compare fovea with peripheral retina, and human with macaque retinal cell types. Finally, we probed region-, cell class-, and cell type-specific expression of genes associated with blinding retinal diseases. Together, our atlas provides a roadmap for human retinal research and paves the way for further research on the pathology of ocular diseases. +Human cell atlas +Non-diseased retina is seldom excised during ocular surgery, so tissue must be obtained postmortem. Given that cell viability declines and transcriptomic profiles change after death, with dramatic alterations after 10 hours post-mortem, data quality hinges in large part on the time between death and tissue processing. For example, Lukowski et al. showed that rods began to degenerate and their expression of MALAT1:a long non-coding RNA:decreased at this point. In our dataset, retinas from 6 of the 7 donors were obtained within 6.5 hr post-mortem, all rod photoreceptors clustered together, and MALAT1 levels were high. These results affirm the high quality of the cells from which the atlas was generated. +We and others have recently reported results of scRNA-seq studies on human retina, (see Table S2). However, our initial study was focused on bipolar cells, and some groups used fetal rather than adult cells, and/or did not distinguish foveal from peripheral cells. Four of these groups did, however, use adult retina and separated fovea from peripheral retina. These studies generated valuable data, but were disadvantaged in that rods comprise a large fraction of all cells (>70%), reducing power to distinguish cell types among less abundant classes. This problem is most severe for RGCs, which comprise <2% of retinal cells. We circumvented these limitations by depleting rods in some samples (using anti-CD73) to enrich other neuronal classes, and by selecting RGCs in other samples (using anti-CD90). These strategies allowed us to distinguish more types within classes than in previous studies. For example, we were able to characterize vGlut3 excitatory ACs (0.7% of total retinal cells), ipRGCs (0.02% of total retinal cells), S cones (0.07% of total retinal cells), and the primate-specific OFFx bipolar type. Thus, our cell atlas represents the most complete classification to date of cell types in adult human retina. +Comparison of fovea and peripheral retina +A major hurdle for studying human retinal biology and diseases is that retinas of accessible animal models differ from human retina in critical respects. Perhaps most important is that among mammals, only primates have a fovea or macula. The fovea comprises only ~1% of retinal area in humans, but accounts for most of our high-acuity vision, much of our chromatic vision, and supplies ~50% of the visual input to the cortex. Moreover, the fovea, and the macula within which it is embedded, are the principal site of pathology among diseases such as age-related macular degeneration, diabetic macular edema, hereditary macular dystrophy, and macular telangiectasia. Lacking fovea and macula, it is unsurprising that rodent models of these diseases have severe limitations. +Our results address this issue in two ways. First, by generating a human cell atlas, and a comprehensive database on expression of disease-related genes, we provide a foundation for both translational and basic studies. Second, by documenting close similarities between human retinal cell types and the macaque types we described recently, we both validate the use of this non-human primate model and point out some important differences that will need to be considered in interpreting studies of non-human primates. Although some differences could result from imperfections in gene and transcript annotation, it is likely that the vast majority are genuine. +The structural and functional difference between the fovea and peripheral retina could result from the existence of specialized foveal cell types. We show however, that nearly all retinal cell types are shared between fovea and periphery in human retina. Instead, there are substantial regional differences in gene expression and abundance between foveal and peripheral cohorts of shared types. Limitations to the comparison include low cell number in periphery for some cell types, and potential bias introduced by the methods we used to deplete rods and enrich RGCs from peripheral samples. Nonetheless, many of the differences in abundance we observed were consistent with those reported by others based on morphological analysis, and we reported histological validation of some of the DE genes in a recent study. Thus, in humans as in macaques, the fovea and peripheral retina are composed of similar cell types, with the structural and functional differences between them likely arising from differences in abundance of shared types and specific aspects of gene expression. +Mapping disease genes to cell classes and types +We analyzed expression of 636 genes associated with retinal diseases, chosen from an initial list of 1,756. Although long, the list is incomplete, because retinal pigment epithelium, which plays a major role in the pathogenesis of many retinal diseases such as retinitis pigmentosa and age-related macular degeneration, was not included in our dataset. Moreover, our criterion for inclusion was expression in more than 20% of cells of at least one class in either fovea or peripheral retina, so genes expressed at slightly lower levels or in only a few minor types within a class would have been excluded. We added 12 genes that fell below threshold to the 624 that met the criterion based on their known clinical relevance, but others remain to be analyzed. +We investigated cell-class and cell-type specific expression patterns for disease-associated genes meeting the above criteria; expression patterns for many of them supported prior reports of pathogenetic mechanisms (detailed in Results). For example, many genes harboring mutations implicated in retinal degenerations characterized predominantly by night blindness-such as RHO, SLC24A1, and GNAT1-were confirmed to be selectively expressed in rods. Similarly, genes characterized by optic nerve degeneration or atrophy - such as OPA1, OPTN and WFS1 - were selectively expressed in RGCs. These patterns are expected and many have been documented. +In contrast, several disease-associated genes were expressed by cells not definitively linked to previously reported phenotypes or pathogenic mechanisms. For example, many RP-associated genes were expressed in RGCs. While loss of RGCs could explain waxy pallor of the optic disc:a common clinical finding in RP:the molecular underpinnings leading to this finding remain incompletely understood. Our observations that some RP-associated genes such as IDH3B, ACBD5 and ASRGL1 were highly expressed in RGCs motivate further investigation. Similarly, several genes implicated in diabetic retinopathy - classically considered a retinal vasculopathy- were differentially expressed by RGCs (e.g AKR1B1, HS6ST3 and MPRIP), suggesting that a primary neuropathic process may also be involved. This conclusion is complicated by the fact that we detect more genes in RGCs than in other classes, likely because of their large size. On the other hand, this confound would not affect the intriguing result that we documented RGC type-selectivity in expression of many disease-associated genes. Of particular interest are selective expression patterns of genes implicated in POAG, given speculation that specific RGC types may exhibit selective vulnerability in this disease. +Together, our results offer new insights into many rare and common retinal diseases, and may contribute to a more comprehensive understanding of their pathogenesis and the discovery of novel therapeutic targets. +Materials and Methods +Human tissue +Acquisition and use of post-mortem human tissue samples was approved by the Human Study Subject Committees of Harvard Medical School (DFCI Protocol Number: 13-416 and MEE - NHSR Protocol Number 18-034 H) and in compliance with the National Human Genome Research Institute (NHGRI) policies. Informed consent was obtained from participants if they were enrolled antemortem or their legal guardians if post-mortem. All human eyes used for sequencing and histological studies were collected 3-14 hours post mortem through the Rapid Autopsy Program, Massachusetts General Hospital, with all but one collected <=6.5 hours post mortem (Table S1). The globe was immediately transported back to the lab in a humid chamber. Hemisection was performed to remove the anterior chamber, and the posterior pole was immersed in Ames equilibrated with 95% O2/5% CO2 before further dissection and dissociation. All donors were confirmed to have no history or clinical evidence of ocular disease or intraocular surgery. +For histological studies, dissected retinal tissues were fixed in ice-cold 4% PFA for 2hrs. For transcriptomic analysis, retinal cells were dissociated as described in the next section. +Single cell isolation, library preparation and sequencing +Single cell libraries were generated by minor modifications of methods developed for macaque retina. Briefly, a ~1.5 mm diameter circular region centered on the foveal pit was dissected from the retina, and peripheral retinal pieces were pooled from all retinal quadrants. Dissected tissues were digested with papain (Worthington, LS003126) for 30 min at 37 C. Following digestion, samples were dissociated and triturated into single cell suspensions with 0.04% bovine serum albumin (BSA) in Ames. Dissociated cells from digested peripheral retinas were incubated with CD90 microbeads (Miltenyi Biotec, 130-096-253; 1 ml per 107 cells) to enrich RGCs or with anti-CD73 (BD Biosciences, clone AD2; 5 ml per 107 cells) followed by anti-mouse IgG1 microbeads (Miltenyi Biotec, 130-047-102; 10 ml per 107 cells) to deplete rods. Incubations were at room temperature for 10 min. CD90 positive cells or CD73 negative cells were selected via large cell columns through a MiniMACS Separator (Miltenyi Biotec). Foveal samples were used without further processing. Single cell suspensions were diluted to 500-1800 cells/microL in 0.04% BSA/Ames for loading into 10X Chromium Single Cell v2 or v3 Chips. Following collection, cDNA libraries were prepared following the manufacturer's protocol, and sequenced on the Illumina HiSeq. 2500 (Paired end reads: Read 1, 26 bp, Read 2, 98 bp). +Bioinformatics analysis +Clustering +Sequencing reads were demultiplexed and aligned to a human transcriptomic reference (GRCh38) with the Cell Ranger software (version 2.1.0, 10X Genomics for the v2 samples, and version 3.0.2 for the v3 samples) for each 10X channel separately. The resulting digital gene expression (DGE) matrices representing the transcript counts for each gene (rows) in each cell (columns) were combined for all samples (foveal and peripheral), and analyzed further using the R statistical language following methods described by Peng et al., with minor modifications, as follows. +A threshold of 600 detected genes per cell was applied to filter out low quality cells or debris from the combined DGE matrix. DGE matrix was normalized, log-transformed where expression values Ei,j for gene i in cell j were calculated following. Highly variable genes (HVGs) were identified as in, and used for dimensionality reduction. Batch correction was performed using the linear regression approach adapted from the R package 'Seurat'. Principal component analysis (PCA) was performed and statistically significant PCs were estimated using Random Matrix Theory. Cell clustering was performed using Louvain algorithm with Jaccard correction. The work flow was first performed to group cells into major cell classes. In addition to the nine cell classes described in the main text, we detected small numbers of epithelial cells and melanocytes (<100 cells); they were not considered further, because of the likelihood that they arise from non-retinal contaminants. For each of the neuronal classes, a second round of clustering was performed to assign cells into molecularly distinct types within that class. We noted a significant higher number of genes/transcripts detected in RGCs than other cell classes (the averaged number in RGC is 2.4~5 fold of other classes), so the gene expression matrix was re-normalized when analyzing RGCs. +Some low quality cells or doublets became apparent only after clustering. We identified and eliminated them before re-running the analysis. Doublets were detected in two ways: (1) During initial analysis, clusters exhibiting high expression levels of canonical markers from more than cell class were further investigated. If no uniquely expressed marker genes were found (as assessed by statistical testing with R package 'MAST'), and the average number of genes in these clusters was higher than observed in related clusters, the cluster was classified as comprising doublets. (2) When analyzing types within one class, no pre-filtering was applied. Clusters were identified as comprising doublets if its cells lacked uniquely expressed marker gene and had on average higher numbers of transcripts/genes per cell than related types. Clusters of low quality cells were identified by low numbers of transcripts detected and high proportion of reads mapped to mitochondrial genes. The rationale is that damaged cells "leak" cytoplasmic RNAs but retain organelle-bound mitochondrial RNAs. These quality indices (average genes/transcripts per cell and proportion of mitochondrial gene transcripts) are shown for each collection in Table S3, along with the number of cells in which >600 gene/cell were detected. On average, 17% of the cells that passed the initial filter of 600 genes/cell were later identified as low quality cells or doublets. The median number of genes and transcripts detected per cell was 1314 and 2577, respectively. +To visualize the data in 2D space, we applied t-distributed stochastic neighbor embedding (t-SNE) to the normalized cell factors using the functions quantileAlignSNF and runTSNE (with default setting of perplexity =30) from the R package 'liger', which uses an integrative non-negative matrix factorization framework. Averaged expression matrix of HVGs for each cluster were calculated to build dendrogram (Fig. 3b upper panel, hierarchical agglomerative clustering of Euclidean distance metric with complete linkage), which revealed transcriptomic relationships among types. Neighboring clusters on this dendrogram were iteratively merged if no more than five DE gene was found showing >=1.1 log fold change with adjusted p value < 0.001 using the R package 'MAST'. +Comparing human and macaque cell types +We sought correspondence between human and macaque cell types using the multi-class classification approach described earlier. Briefly, for each cell class in macaque, we trained a multi-class classifier using the R package 'xgboost' to map cells to discrete type labels based on their transcriptional signatures. This classifier was then applied to each human cell of the same class to assign it a macaque type label based on its expression of 1:1 gene orthologs, but in a manner completely agnostic to its cluster identity. Confusion matrices (e.g. Fig. 2b,e,h) were used to identify correspondences between macaque and human types within each class. Shared types were defined as those exhibiting a near 1:1 correspondence using this classification approach. +Each pair of shared human and macaque types were assessed for DE genes compared to other types of the same class (Fig. 5g). Only genes expressed in >20% of cells in either type and exhibiting a >=0.5 log fold difference were considered for analysis. The statistic criteria is less stringent comparing to that of the fovea vs peripheral comparison within human cells (next section) to compensate for the across species differences. DE genes were selected as those satisfying an adjusted p < 0.001 cutoff according to the 'MAST' test (56). For each shared type i this resuled in two DE lists, one each for human (hDEi) and macaque (mDEi) respectively. The proportion of type-specific DE genes that were shared across species (pDEi) was computed for each type i as,where # means number of. +Comparing foveal and peripheral cells +To evaluate the extent of similarity between foveal and peripheral cell types in human retina (Fig. 7a), we measured the number of DE genes identified between foveal and peripheral cells for each human type. Only types with at least 20 cells in both regions were considered. DE genes in this comparison were those with differences in expression >=1*log fold change and adjusted p-value < 0.001. +Immunohistochemistry and fluorescence in situ hybridization +Procedures for tissue preparation, immunohistochemistry and in situ hybridization have been described in. Briefly, eyes were fixed in ice-cold 4% paraformaldehyde, rinsed with PBS, immersed in 30% sucrose overnight at 4 C, embedded in Tissue Freezing Medium (EMS) and cryosectioned at 20 mum. For immunohistochemistry, antibodies were diluted in 3% donkey serum (Jackson, 017-000-121), and 0.3% Triton-X in PBS. Antibodies used for immunostaining were as follows: goat anti anti-CHX10 (1:300, Santa Cruz); rabbit anti-TFAP2A (1:500, DSHB); rabbit anti-Secretagogin (1:10,000; BioVendor). For in situ hybridization, sections were mounted on Superfrost slides (Thermo Scientific), treated with 1.5 mg/mL of proteinase K (NEB, P8107S), and then post-fixed and treated with acetic anhydride for deacetylation. Probe detection was performed with anti-DIG HRP (1:1000) and anti-DNP HRP (1:500), followed by tyramide amplification. +Image acquisition and processing +Images were acquired on Zeiss LSM 710 confocal microscopes with 405, 488-515, 568, and 647 lasers, processed using Zeiss ZEN software suites, and analyzed using ImageJ (NIH). Images were acquired with 16x, 40x or 63x oil lens at the resolution of 1,024 x 1,024 pixels, a step size of 0.5-1.5 microm, and 90 microm pinhole size. ImageJ (NIH) software was used to generate maximum intensity projections. Adobe Photoshop CC was used for adjustments to brightness and contrast. +Mapping disease genes +SNP-trait associations (N = 980) were downloaded on 09/03/2019 from the NHGRI-EBI GWAS Catalog for the traits, "open-angle glaucoma" (n = 108), "intraocular pressure measurement" (n = 504),"diabetic retinopathy" (n = 138), "age-related macular degeneration" (n = 230) and Macular Telangiectasia Type 2 (n = 5). A list of genes and loci associated with retinal diseases was downloaded on 09/03/2019 from the Retinal Information Network, last updated on 07/01/2019 (RetNet, https://sph.uth.edu/retnet/). +Supplementary information +Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. +These authors contributed equally: Wenjun Yan, Yi-Rong Peng and Tave van Zyl. +Supplementary information +is available for this paper at 10.1038/s41598-020-66092-9. +Author contributions +W.Y., Y.-R.P., T.v.Z., and J.R.S. conceived and designed experiments, analyzed data and wrote the paper. Y.-R.P. and T.v.Z., performed experiments. W.Y. performed bioinformatic analyses. K.S. and A.R. advised on computational approaches. D.J. provided human tissue. +Data availability +The raw and processed single cell RNAseq data reported in this study can be accessed at GEO: GSE148077. Data can be visualized at the Broad Institute's Single Cell Portal: https://singlecell.broadinstitute.org/single_cell/study/SCP839. +Competing interests +A.R. is an equity holder and founder of Celsius Therapeutics, a founder of Immunitas, and an SAB member in Syros Pharmaceuticals, Neogene Therapeutics, Asimov, and Thermo Fisher Scientific. J.R.S. is a consultant for Biogen. +References +Age-related macular degeneration +The pathophysiology and treatment of glaucoma: a review +Duh, E. J., Sun, J. K. & Stitt, A. W. Diabetic retinopathy: current understanding, mechanisms, and treatment strategies. JCI Insight2, 10.1172/jci.insight.93751 (2017). +Genome-wide meta-analysis for severe diabetic retinopathy +A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants +A multiethnic genome-wide association study of primary open-angle glaucoma identifies novel risk loci +Macular edema. 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Susceptibility of retinal ganglion cell types in experimental glaucoma +Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics +Comprehensive identification and spatial mapping of habenular neuronal types using single-cell RNA-seq +Population structure and eigenanalysis +Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity +MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data +Satb1 Regulates Contactin 5 to Pattern Dendrites of a Mammalian Retinal Ganglion Cell +The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019 \ No newline at end of file From ff50014ec79184148f54077f85e04443f4c46548 Mon Sep 17 00:00:00 2001 From: ugur Date: Fri, 7 Nov 2025 15:59:31 +0000 Subject: [PATCH 2/9] Add gpt-4.1 as model --- cellsem_agent/agents/annotator/annotator_agent.py | 3 ++- cellsem_agent/agents/paper_celltype/paper_celltype_agent.py | 5 +++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/cellsem_agent/agents/annotator/annotator_agent.py b/cellsem_agent/agents/annotator/annotator_agent.py index 2912f73..8b22ba5 100644 --- a/cellsem_agent/agents/annotator/annotator_agent.py +++ b/cellsem_agent/agents/annotator/annotator_agent.py @@ -134,7 +134,8 @@ class TextAnnotationResult(BaseModel): annotator_agent = Agent( - model="openai:gpt-5", + # model="openai:gpt-5", + model="openai:gpt-4.1", # model="openai:gpt-4o", # model="openai:gpt-4o-2024-11-20", deps_type=AnnotatorDependencies, diff --git a/cellsem_agent/agents/paper_celltype/paper_celltype_agent.py b/cellsem_agent/agents/paper_celltype/paper_celltype_agent.py index c959dd1..2a52bae 100644 --- a/cellsem_agent/agents/paper_celltype/paper_celltype_agent.py +++ b/cellsem_agent/agents/paper_celltype/paper_celltype_agent.py @@ -45,7 +45,8 @@ class BiocurationOutput(BaseModel): cell_type_annotations: List[CellTypeEntry] = Field(..., description="A list of extracted cell type annotations.") celltype_agent = Agent( - model="openai:gpt-5", + # model="openai:gpt-5", + model="openai:gpt-4.1", # model="openai:gpt-4o-2024-11-20", deps_type=PaperCTDependencies, result_type=BiocurationOutput, @@ -54,4 +55,4 @@ class BiocurationOutput(BaseModel): ) # celltype_agent.tool(get_full_text) -# celltype_agent.tool(read_json) \ No newline at end of file +# celltype_agent.tool(read_json) From c63d025564539342c726dab193aac12f8945106a Mon Sep 17 00:00:00 2001 From: ugur Date: Fri, 7 Nov 2025 16:00:48 +0000 Subject: [PATCH 3/9] Update caching for multiple input datasets --- .../cxg_annotate/cxg_annotate_graph_v2.py | 146 +++++++++++------- 1 file changed, 89 insertions(+), 57 deletions(-) diff --git a/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py b/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py index 48bc8da..f232682 100644 --- a/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py +++ b/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py @@ -91,71 +91,103 @@ async def run(self, ctx: GraphRunContext[State]) -> End: ) ) - cache_dir = os.path.join(RESOURCES_DIR, "cache") - os.makedirs(cache_dir, exist_ok=True) + base_cache_dir = os.path.join(RESOURCES_DIR, "cache") + os.makedirs(base_cache_dir, exist_ok=True) batch_size = 4 all_groundings = [] - for i in range(0, len(annotations), batch_size): - batch_index = i // batch_size - batch = annotations[i : i + batch_size] - batch_cache_path = os.path.join( - cache_dir, f"groundings_batch_{batch_index}.json" + + annotations_by_dataset: dict[str, list[dict]] = {} + for annotation in annotations: + dataset_name = annotation.get("dataset_name", "unknown_dataset") + annotations_by_dataset.setdefault(dataset_name, []).append(annotation) + + for dataset_name in ctx.state.dataset_names: + dataset_annotations = annotations_by_dataset.get(dataset_name, []) + if not dataset_annotations: + continue + + dataset_cache_dir = os.path.join( + base_cache_dir, + normalise_file_name(dataset_name), ) + os.makedirs(dataset_cache_dir, exist_ok=True) - if os.path.exists(batch_cache_path): - print(f"Loading cached results for batch {batch_index}") - with open(batch_cache_path, "r") as f: - batch_groundings = [ - TextAnnotation(**entry) for entry in json.load(f) - ] - else: - print( - "Processing batch: ", - i // batch_size + 1, - " of ", - (len(annotations) + batch_size - 1) // batch_size, - ) - expansions_json = json.dumps( - [annotation["enrichment"].model_dump() for annotation in batch], - indent=2, + for batch_index, batch_start in enumerate( + range(0, len(dataset_annotations), batch_size) + ): + batch = dataset_annotations[batch_start : batch_start + batch_size] + expected_inputs = [ + annotation.get("annotation_text", "") or "" for annotation in batch + ] + batch_cache_path = os.path.join( + dataset_cache_dir, f"batch_{batch_index}.json" ) - agent_response = await annotator_agent.run(expansions_json) - batch_groundings = agent_response.output.annotations - with open(batch_cache_path, "w") as f: - json.dump( - [entry.model_dump() for entry in batch_groundings], f, indent=2 - ) - all_groundings.extend(batch_groundings) - # update batch annotations with grounding results - for annotation in batch: - # convert enrichment to json to make df mode readable - annotation["enrichment"] = annotation["enrichment"].model_dump() - if "grounding_cl_id" not in annotation: - related_groundings = [ - gr - for gr in batch_groundings - if gr.input_name == annotation["annotation_text"] - ] - if related_groundings: - valid_grounding = next( - ( - g - for g in related_groundings - if "NO MATCH" not in g.cl_id - ), - None, + batch_groundings: list[TextAnnotation] + cache_hit = False + if os.path.exists(batch_cache_path): + with open(batch_cache_path, "r") as f: + cached_payload = json.load(f) + if isinstance(cached_payload, list): + cached_inputs = [ + entry.get("input_name", "") for entry in cached_payload + ] + if cached_inputs == expected_inputs: + batch_groundings = [ + TextAnnotation(**entry) for entry in cached_payload + ] + cache_hit = True + + if not cache_hit: + print( + "Processing batch: ", + batch_index + 1, + " of ", + (len(dataset_annotations) + batch_size - 1) // batch_size, + ) + expansions_json = json.dumps( + [annotation["enrichment"].model_dump() for annotation in batch], + indent=2, + ) + agent_response = await annotator_agent.run(expansions_json) + batch_groundings = agent_response.output.annotations + with open(batch_cache_path, "w") as f: + json.dump( + [entry.model_dump() for entry in batch_groundings], + f, + indent=2, ) - if valid_grounding: - grounding_to_use = valid_grounding - else: - grounding_to_use = related_groundings[0] - annotation["grounding_cl_id"] = grounding_to_use.cl_id - annotation["grounding_cl_label"] = grounding_to_use.cl_label - else: - annotation["grounding_cl_id"] = "" - annotation["grounding_cl_label"] = "" + + all_groundings.extend(batch_groundings) + # update batch annotations with grounding results + for annotation in batch: + # convert enrichment to json to make df mode readable + annotation["enrichment"] = annotation["enrichment"].model_dump() + if "grounding_cl_id" not in annotation: + related_groundings = [ + gr + for gr in batch_groundings + if gr.input_name == annotation["annotation_text"] + ] + if related_groundings: + valid_grounding = next( + ( + g + for g in related_groundings + if "NO MATCH" not in g.cl_id + ), + None, + ) + if valid_grounding: + grounding_to_use = valid_grounding + else: + grounding_to_use = related_groundings[0] + annotation["grounding_cl_id"] = grounding_to_use.cl_id + annotation["grounding_cl_label"] = grounding_to_use.cl_label + else: + annotation["grounding_cl_id"] = "" + annotation["grounding_cl_label"] = "" # Create output directory os.makedirs(OUTPUT_DIR, exist_ok=True) From eda4759e3fad2297cbb2e8d2253193fcea7feaf1 Mon Sep 17 00:00:00 2001 From: ugur Date: Mon, 10 Nov 2025 10:21:59 +0000 Subject: [PATCH 4/9] Update expansion caching logic --- .../cxg_annotate/cxg_annotate_graph_v2.py | 189 ++++++++++-------- 1 file changed, 106 insertions(+), 83 deletions(-) diff --git a/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py b/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py index f232682..d56dfa7 100644 --- a/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py +++ b/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py @@ -82,6 +82,9 @@ async def run(self, ctx: GraphRunContext[State]) -> End: ) # delete tissue_context of all enrichments annotation["enrichment"].tissue_context = "" + # reset previous grounding results so reruns don't mix states + annotation["grounding_cl_id"] = None + annotation["grounding_cl_label"] = None # Sort annotations by article_id_doi, then annotation_text annotations.sort( @@ -164,30 +167,26 @@ async def run(self, ctx: GraphRunContext[State]) -> End: for annotation in batch: # convert enrichment to json to make df mode readable annotation["enrichment"] = annotation["enrichment"].model_dump() - if "grounding_cl_id" not in annotation: - related_groundings = [ - gr - for gr in batch_groundings - if gr.input_name == annotation["annotation_text"] - ] - if related_groundings: - valid_grounding = next( - ( - g - for g in related_groundings - if "NO MATCH" not in g.cl_id - ), - None, - ) - if valid_grounding: - grounding_to_use = valid_grounding - else: - grounding_to_use = related_groundings[0] - annotation["grounding_cl_id"] = grounding_to_use.cl_id - annotation["grounding_cl_label"] = grounding_to_use.cl_label - else: - annotation["grounding_cl_id"] = "" - annotation["grounding_cl_label"] = "" + related_groundings = [ + gr + for gr in batch_groundings + if gr.input_name == annotation["annotation_text"] + ] + if related_groundings: + valid_grounding = next( + ( + g + for g in related_groundings + if "NO MATCH" not in g.cl_id + ), + None, + ) + if valid_grounding: + grounding_to_use = valid_grounding + else: + grounding_to_use = related_groundings[0] + annotation["grounding_cl_id"] = grounding_to_use.cl_id + annotation["grounding_cl_label"] = grounding_to_use.cl_label # Create output directory os.makedirs(OUTPUT_DIR, exist_ok=True) @@ -235,37 +234,57 @@ async def run(self, ctx: GraphRunContext[State]) -> GetGroundings: if not os.path.exists(EXPANSIONS_DIR): os.makedirs(EXPANSIONS_DIR) article_to_annotations = ctx.state.article_to_annotations - articles = sorted( - str(a) if a is not None else "" for a in set(article_to_annotations.keys()) - ) - index = 1 - for article_pmc in articles: - print(f"Processing article: {article_pmc} - {index}/{len(articles)}") - index += 1 - # get all annotations for this article - article_annotations = article_to_annotations[article_pmc] - - for batch_index in range( - 0, len(article_annotations), ANNOTATIONS_BATCH_SIZE - ): - batch = article_annotations[ - batch_index : batch_index + ANNOTATIONS_BATCH_SIZE - ] - dataset_cache = os.path.join( - EXPANSIONS_DIR, - f"{normalise_file_name(article_pmc)}_batch_{batch_index // ANNOTATIONS_BATCH_SIZE}.json", + annotations_by_dataset_and_article: dict[str, dict[str, list[dict]]] = {} + for article_pmc, article_annotations in article_to_annotations.items(): + for annotation in article_annotations: + dataset_name = annotation.get("dataset_name", "unknown_dataset") + dataset_articles = annotations_by_dataset_and_article.setdefault( + dataset_name, {} ) - cc_labels = [{"cc.label": ann["annotation_text"]} for ann in batch] + dataset_articles.setdefault(article_pmc, []).append(annotation) - if not os.path.exists(dataset_cache): - full_text_path = os.path.join( - PUBLICATIONS_DIR, f"{normalise_file_name(article_pmc)}.txt" + for dataset_name in ctx.state.dataset_names: + dataset_articles = annotations_by_dataset_and_article.get(dataset_name, {}) + if not dataset_articles: + continue + + dataset_cache_dir = os.path.join( + EXPANSIONS_DIR, normalise_file_name(dataset_name) + ) + os.makedirs(dataset_cache_dir, exist_ok=True) + + articles = sorted( + str(a) if a is not None else "" for a in dataset_articles.keys() + ) + index = 1 + for article_pmc in articles: + print( + f"[{dataset_name}] Processing article: {article_pmc} - {index}/{len(articles)}" + ) + index += 1 + article_annotations = dataset_articles[article_pmc] + + for batch_index in range( + 0, len(article_annotations), ANNOTATIONS_BATCH_SIZE + ): + batch = article_annotations[ + batch_index : batch_index + ANNOTATIONS_BATCH_SIZE + ] + dataset_cache = os.path.join( + dataset_cache_dir, + f"{normalise_file_name(article_pmc)}_batch_{batch_index // ANNOTATIONS_BATCH_SIZE}.json", ) - if os.path.exists(full_text_path): - with open(full_text_path, "r", encoding="utf-8") as f: - paper_full_text = f.read() + cc_labels = [{"cc.label": ann["annotation_text"]} for ann in batch] + + if not os.path.exists(dataset_cache): + full_text_path = os.path.join( + PUBLICATIONS_DIR, f"{normalise_file_name(article_pmc)}.txt" + ) + if os.path.exists(full_text_path): + with open(full_text_path, "r", encoding="utf-8") as f: + paper_full_text = f.read() - prompt_instructions = f""" + prompt_instructions = f""" You are tasked with extracting cell type information from the provided academic paper content, and the provided JSON data. @@ -299,45 +318,49 @@ async def run(self, ctx: GraphRunContext[State]) -> GetGroundings: Do not ask for confirmation. Provide the output as a JSON array of `CellTypeEntry` objects. """ - agent_response = await celltype_agent.run(prompt_instructions) + agent_response = await celltype_agent.run( + prompt_instructions + ) - for entry in agent_response.output.cell_type_annotations: + for entry in agent_response.output.cell_type_annotations: + entry_copy = entry.model_copy() + print( + f"Name: {entry.name}, Full Name: {entry.full_name}, Synonyms: {entry.paper_synonyms}, Tissue Context: {entry.tissue_context}" + ) + # add entry to the related article_annotations + for ann in article_annotations: + if ann["annotation_text"] == entry.name: + ann["enrichment"] = entry_copy.model_copy() + + expansions = agent_response.output.cell_type_annotations print( - f"Name: {entry.name}, Full Name: {entry.full_name}, Synonyms: {entry.paper_synonyms}, Tissue Context: {entry.tissue_context}" + f"Saving results to cache for dataset {dataset_name}, article: {article_pmc}" ) - # add entry to the related article_annotations - for ann in article_annotations: - if ann["annotation_text"] == entry.name: - ann["enrichment"] = entry - break - - # ctx.state.paper_expansion[article_pmc] = agent_response.output.cell_type_annotations - expansions = agent_response.output.cell_type_annotations - print(f"Saving results to cache for article: {article_pmc}") - with open(dataset_cache, "w") as cache_file: - json.dump( - [entry.model_dump() for entry in expansions], - cache_file, - indent=2, + with open(dataset_cache, "w") as cache_file: + json.dump( + [entry.model_dump() for entry in expansions], + cache_file, + indent=2, + ) + else: + print( + f"Error: Full text file not found for article for name expansion: {article_pmc}" ) else: print( - f"Error: Full text file not found for article for name expansion: {article_pmc}" + f"[{dataset_name}] Using cached data for article: {article_pmc}" ) - else: - print(f"Using cached data for article: {article_pmc}") - with open(dataset_cache, "r") as cache_file: - cached_data = json.load(cache_file) - for cached_entry in cached_data: - for ann in article_annotations: - if ann["annotation_text"] == cached_entry["name"]: - ann["enrichment"] = CellTypeEntry(**cached_entry) - print( - "Using cached enrichment data for annotation:", - ann["annotation_text"], - ) - break - # ctx.state.paper_expansion[article_pmc] = [CellTypeEntry(**entry) for entry in cached_data] + with open(dataset_cache, "r") as cache_file: + cached_data = json.load(cache_file) + for cached_entry in cached_data: + cached_model = CellTypeEntry(**cached_entry) + for ann in article_annotations: + if ann["annotation_text"] == cached_model.name: + ann["enrichment"] = cached_model.model_copy() + print( + "Using cached enrichment data for annotation:", + ann["annotation_text"], + ) return GetGroundings() From dccaf9ee740e22ad504231ef25120d51d82f2be4 Mon Sep 17 00:00:00 2001 From: Caroline Eastwood Date: Wed, 12 Nov 2025 19:52:08 +0000 Subject: [PATCH 5/9] Tested 30 datasets, agent created a script to assess the granularity of the agent's mappings compared to author 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cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_devcel_2020_11_010.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_isci_2021_103115.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_jcmgh_2022_02_007.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_jhep_2023_12_023.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41467-021-22368-w.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41467-022-32972-z.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41590-020-0602-z.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s42003-020-0922-4.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s42255-022-00531-x.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1073_pnas_2103240118.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1073_pnas_2200914119.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1093_hmg_ddab140.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1126_science_aat5031.txt create mode 100644 cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_15252_embj_2018100811.txt diff --git a/cellsem_agent/agents/annotator/annotator_agent.py b/cellsem_agent/agents/annotator/annotator_agent.py index 8b22ba5..a71b50b 100644 --- a/cellsem_agent/agents/annotator/annotator_agent.py +++ b/cellsem_agent/agents/annotator/annotator_agent.py @@ -1,6 +1,7 @@ """ Ontology based Annotator Agent. """ + import logging from typing import List, Optional @@ -134,8 +135,8 @@ class TextAnnotationResult(BaseModel): annotator_agent = Agent( - # model="openai:gpt-5", - model="openai:gpt-4.1", + model="openai:gpt-5", + # model="openai:gpt-4.1", # model="openai:gpt-4o", # model="openai:gpt-4o-2024-11-20", deps_type=AnnotatorDependencies, diff --git a/cellsem_agent/agents/paper_celltype/paper_celltype_agent.py b/cellsem_agent/agents/paper_celltype/paper_celltype_agent.py index 2a52bae..ca41567 100644 --- a/cellsem_agent/agents/paper_celltype/paper_celltype_agent.py +++ b/cellsem_agent/agents/paper_celltype/paper_celltype_agent.py @@ -1,6 +1,7 @@ """ Agent for Cell Ontology. """ + import logging from pydantic_ai import Agent @@ -8,13 +9,13 @@ cell_logger.setLevel(logging.INFO) console = logging.StreamHandler() console.setLevel(logging.INFO) -formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') +formatter = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s") console.setFormatter(formatter) cell_logger.addHandler(console) cell_logger.propagate = False -from .paper_celltype_config import PaperCTDependencies +from .paper_celltype_config import PaperCTDependencies from .paper_celltype_tools import get_full_text, read_json SYSTEM_PROMPT = """ @@ -35,18 +36,31 @@ from pydantic import BaseModel, Field from typing import List, Optional + class CellTypeEntry(BaseModel): name: str = Field(..., description="The exact cc.label from the input JSON.") - full_name: Optional[str] = Field(None, description="The expanded or reconstructed full name of the cell type as defined in the paper.") - paper_synonyms: Optional[str] = Field(None, description="Synonyms mentioned in the paper, separated by semicolons.") - tissue_context: Optional[str] = Field(None, description="Exact quoted tissue(s) or anatomical terms from the paper where the cell type was identified.") + full_name: Optional[str] = Field( + None, + description="The expanded or reconstructed full name of the cell type as defined in the paper.", + ) + paper_synonyms: Optional[str] = Field( + None, description="Synonyms mentioned in the paper, separated by semicolons." + ) + tissue_context: Optional[str] = Field( + None, + description="Exact quoted tissue(s) or anatomical terms from the paper where the cell type was identified.", + ) + class BiocurationOutput(BaseModel): - cell_type_annotations: List[CellTypeEntry] = Field(..., description="A list of extracted cell type annotations.") + cell_type_annotations: List[CellTypeEntry] = Field( + ..., description="A list of extracted cell type annotations." + ) + celltype_agent = Agent( - # model="openai:gpt-5", - model="openai:gpt-4.1", + model="openai:gpt-5", + # model="openai:gpt-4.1", # model="openai:gpt-4o-2024-11-20", deps_type=PaperCTDependencies, result_type=BiocurationOutput, diff --git a/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py b/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py index d56dfa7..ef0899f 100644 --- a/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py +++ b/cellsem_agent/graphs/cxg_annotate/cxg_annotate_graph_v2.py @@ -439,6 +439,8 @@ def load_cxg_annotations(): df = pd.read_csv(tsv_path, sep="\t") for _, row in df.iterrows(): + if pd.isna(row["reference"]): + continue paper_doi = str(row["reference"]).replace("https://doi.org/", "DOI:") annotation = { "annotation_text": row["author_cell_type"], diff --git a/cellsem_agent/graphs/cxg_annotate/report_generator.py b/cellsem_agent/graphs/cxg_annotate/report_generator.py new file mode 100644 index 0000000..aeb9972 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/report_generator.py @@ -0,0 +1,74 @@ +import os +import pandas as pd +from oaklib import get_adapter + +def analyze_groundings(output_dir): + """ + Analyzes all groundings.tsv files in the output directory and generates a report. + + Args: + output_dir (str): The path to the directory containing the dataset output folders. + """ + report_lines = ["# Annotation Granularity Report"] + good_examples = [] + + # Setup ontology adapter + try: + cl_adapter = get_adapter("ols:cl") + except Exception as e: + print(f"Could not initialize OLS adapter for CL: {e}") + cl_adapter = None + + for dataset_folder in os.listdir(output_dir): + dataset_path = os.path.join(output_dir, dataset_folder) + if os.path.isdir(dataset_path): + groundings_file = os.path.join(dataset_path, "groundings.tsv") + if os.path.exists(groundings_file): + report_lines.append(f"\n## Dataset: {dataset_folder}") + df = pd.read_csv(groundings_file, sep='\t') + + improved_count = 0 + + for _, row in df.iterrows(): + author_cl_id = row.get('cl_id') + agent_cl_id = row.get('grounding_cl_id') + + if pd.notna(author_cl_id) and pd.notna(agent_cl_id) and author_cl_id != agent_cl_id: + if cl_adapter: + try: + # Check if author's term is an ancestor of the agent's term + if author_cl_id in cl_adapter.ancestors(agent_cl_id): + improved_count += 1 + good_examples.append({ + "dataset": dataset_folder, + "annotation_text": row['annotation_text'], + "author_mapping": f"{row['cl_label']} ({author_cl_id})", + "agent_mapping": f"{row['grounding_cl_label']} ({agent_cl_id})", + "enrichment": row['enrichment'] + }) + except Exception as e: + print(f"Could not process ontology check for {author_cl_id} and {agent_cl_id}: {e}") + + + report_lines.append(f"Found {improved_count} instances of improved granularity.") + + report_lines.append("\n# Good Examples of Improved Granularity") + for ex in good_examples: + report_lines.append(f"\n### Dataset: {ex['dataset']}") + report_lines.append(f"- **Annotation Text:** {ex['annotation_text']}") + report_lines.append(f"- **Author's Mapping:** {ex['author_mapping']}") + report_lines.append(f"- **Agent's Mapping:** {ex['agent_mapping']}") + report_lines.append(f"- **Enrichment Info:** `{ex['enrichment']}`") + + + report_content = "\n".join(report_lines) + report_file_path = os.path.join(output_dir, "granularity_report.md") + with open(report_file_path, "w") as f: + f.write(report_content) + + print(f"Report generated at {report_file_path}") + +if __name__ == "__main__": + current_dir = os.path.dirname(os.path.abspath(__file__)) + output_directory = os.path.join(current_dir, "resources", "output") + analyze_groundings(output_directory) diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/DOI_10_1016_j_jcmgh_2022_02_007_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/DOI_10_1016_j_jcmgh_2022_02_007_batch_0.json new file mode 100644 index 0000000..0ed0833 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/DOI_10_1016_j_jcmgh_2022_02_007_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "SI_earlyAE", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "SI_AE2", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "SI_tuft", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "C_earlyACC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "C_goblet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/DOI_10_1016_j_jcmgh_2022_02_007_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/DOI_10_1016_j_jcmgh_2022_02_007_batch_1.json new file mode 100644 index 0000000..14b723d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/DOI_10_1016_j_jcmgh_2022_02_007_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "SI_matureAE", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "C_BEST4", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "SI_intermAE", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "C_lateACC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "SI_goblet", 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a/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_6.json new file mode 100644 index 0000000..253d41a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_6.json @@ -0,0 +1,32 @@ +[ + { + "name": "NKT--Mac-Doublet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "CholMucus", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "MatB", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Fibroblast", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Hepato--Mac", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_7.json new file mode 100644 index 0000000..d881592 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_7.json @@ -0,0 +1,32 @@ +[ + { + "name": "pDC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Mac--Fibro-Doublet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "RBC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "MAST", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "MatB--CD4T-Doublet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_8.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_8.json new file mode 100644 index 0000000..41915ae --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_8.json @@ -0,0 +1,32 @@ +[ + { + "name": "Mac--B-Doublet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "MatB--RBC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "CD4T--RBC-Doublet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "cNK--RBC-Doublet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "NKT", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_0.json new file mode 100644 index 0000000..0e0a3c4 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "", + "tissue_context": "human kidney; nephron segments and interstitium; cortex; medulla; renal tubules" + }, + { + "name": "stroma cells", + "full_name": "stromal cells", + "paper_synonyms": "STR", + "tissue_context": "stroma and vasculature; interstitium; cortex" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "", + "tissue_context": "human kidney; nephron segments and interstitium; cortex; medulla; renal tubules" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "IMM", + "tissue_context": "human kidney; cortex; medulla; kidney biopsy samples" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "", + "tissue_context": "human kidney; nephron segments and interstitium; cortex; medulla; renal tubules" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_1.json new file mode 100644 index 0000000..4da4513 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "endothelial cells", + "full_name": "endothelial cells", + "paper_synonyms": "EC; endothelium", + "tissue_context": "renal corpuscle; glomerular capillaries (EC-GC); afferent/efferent arterioles (EC-AEA); lymphatics (EC-LYM); vasa recta (EC-AVR, EC-DVR)" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla; interstitium; interstitial fibrosis; areas of tissue damage; around vessels" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "epithelium; tubular epithelium", + "tissue_context": "nephron segments; renal tubules; cortex; medulla" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla; interstitium; interstitial fibrosis; areas of tissue damage; around vessels" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "epithelium; tubular epithelium", + "tissue_context": "nephron segments; renal tubules; cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_10.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_10.json new file mode 100644 index 0000000..f4a32ac --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_10.json @@ -0,0 +1,32 @@ +[ + { + "name": "Proximal Tubule Epithelial Cell Segment 1 / Segment 2", + "full_name": "proximal tubule (PT) epithelial cell, segment 1/segment 2", + "paper_synonyms": "PT-S1; PT-S2; PT", + "tissue_context": "cortex" + }, + { + "name": "Proximal Tubule Epithelial Cell Segment 3", + "full_name": "proximal tubule (PT) epithelial cell, segment 3", + "paper_synonyms": "PT-S3; PT", + "tissue_context": "corticomedullary sections; cortex" + }, + { + "name": "Cortical Thick Ascending Limb Cell", + "full_name": "cortical thick ascending limb (C-TAL) cell", + "paper_synonyms": "C-TAL; TAL", + "tissue_context": "cortex" + }, + { + "name": "Outer Medullary Collecting Duct Principal Cell", + "full_name": "outer medullary collecting duct principal cell", + "paper_synonyms": "OMCD; PC; M-PC", + "tissue_context": "outer medulla; collecting duct" + }, + { + "name": "Fibroblast", + "full_name": "fibroblast (FIB)", + "paper_synonyms": "FIB", + "tissue_context": "interstitium; cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_11.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_11.json new file mode 100644 index 0000000..9692232 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_11.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Outer Medullary Collecting Duct Principal Cell", + "full_name": "Degenerative Outer Medullary Collecting Duct Principal Cell", + "paper_synonyms": "OMCD; PC; degenerative medullary principal cells; dM-PCs", + "tissue_context": "outer medulla; medulla; collecting duct" + }, + { + "name": "T Cell", + "full_name": "T cell", + "paper_synonyms": "CD3+ cells; lymphoid (T) cells; T", + "tissue_context": "cortex; medulla; areas of tissue damage; fibrosis" + }, + { + "name": "Plasma Cell", + "full_name": "Plasma cell", + "paper_synonyms": "PL", + "tissue_context": "" + }, + { + "name": "Connecting Tubule Principal Cell", + "full_name": "Connecting tubule principal cell", + "paper_synonyms": "CNT-PC; CNT; PC", + "tissue_context": "connecting tubules (CNT); cortical distal nephrons; cortex" + }, + { + "name": "Distal Convoluted Tubule Cell Type 1", + "full_name": "Distal convoluted tubule cell type 1", + "paper_synonyms": "DCT1", + "tissue_context": "distal convoluted tubule" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_12.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_12.json similarity index 71% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_12.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_12.json index f064292..4ac7e52 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_12.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_12.json @@ -3,30 +3,30 @@ "name": "Degenerative Fibroblast", "full_name": "degenerative fibroblast", "paper_synonyms": "FIB", - "tissue_context": "cortex; interstitium" + "tissue_context": "interstitium; cortex" }, { "name": "Degenerative Cortical Thick Ascending Limb Cell", "full_name": "degenerative cortical thick ascending limb cell", - "paper_synonyms": "C-TAL; TAL", + "paper_synonyms": "C-TAL; cortical TAL", "tissue_context": "cortex; cortical thick ascending limb (C-TAL)" }, { "name": "Vascular Smooth Muscle Cell", "full_name": "vascular smooth muscle cell", "paper_synonyms": "VSMC; VSM/P", - "tissue_context": "afferent/efferent arterioles" + "tissue_context": "afferent/efferent arterioles; renal corpuscle" }, { "name": "Schwann Cell / Neural", "full_name": "Schwann/neuronal cell", - "paper_synonyms": "SCI/NEU", - "tissue_context": "kidney" + "paper_synonyms": "Schwann/neuronal; SCI/NEU", + "tissue_context": "human kidney" }, { "name": "Descending Thin Limb Cell Type 3", "full_name": "descending thin limb cell type 3", "paper_synonyms": "DTL3; DTL", - "tissue_context": "medulla; descending thin limb (DTL)" + "tissue_context": "medulla" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_13.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_13.json similarity index 76% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_13.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_13.json index f9306a3..aab497b 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_13.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_13.json @@ -1,9 +1,9 @@ [ { "name": "Glomerular Capillary Endothelial Cell", - "full_name": "glomerular capillaries", + "full_name": "glomerular capillary endothelial cell", "paper_synonyms": "EC-GC", - "tissue_context": "renal corpuscle" + "tissue_context": "renal corpuscle; glomeruli" }, { "name": "Vascular Smooth Muscle Cell / Pericyte", @@ -15,18 +15,18 @@ "name": "Cycling Mononuclear Phagocyte", "full_name": "cycling mononuclear phagocyte", "paper_synonyms": "cycMNP", - "tissue_context": "human kidney" + "tissue_context": null }, { "name": "Myofibroblast", "full_name": "myofibroblast", "paper_synonyms": "MyoF", - "tissue_context": "interstitium" + "tissue_context": "interstitium; cortex" }, { "name": "Degenerative Peritubular Capilary Endothelial Cell", "full_name": null, "paper_synonyms": null, - "tissue_context": "vasculature" + "tissue_context": null } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_14.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_14.json similarity index 62% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_14.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_14.json index 2082db6..bbe03a3 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_14.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_14.json @@ -1,32 +1,32 @@ [ { "name": "Non-classical Monocyte", - "full_name": "Non-classical Monocyte", + "full_name": "non-classical monocyte", "paper_synonyms": "ncMON", "tissue_context": "human kidney" }, { "name": "Cycling Endothelial Cell", - "full_name": "Cycling Endothelial Cell", - "paper_synonyms": null, + "full_name": "endothelial cell, cycling state", + "paper_synonyms": "", "tissue_context": "vasculature; human kidney" }, { "name": "Classical Dendritic Cell", - "full_name": "Classical Dendritic Cell", + "full_name": "classical dendritic cell", "paper_synonyms": "cDC", "tissue_context": "human kidney" }, { "name": "Lymphatic Endothelial Cell", - "full_name": "endothelial cells of the lymphatics", + "full_name": "endothelial cell of the lymphatics", "paper_synonyms": "EC-LYM", "tissue_context": "lymphatics; human kidney" }, { "name": "Distal Convoluted Tubule Cell Type 2", - "full_name": "Distal Convoluted Tubule Cell Type 2", + "full_name": "distal convoluted tubule cell 2", "paper_synonyms": "DCT2", - "tissue_context": "distal convoluted tubule; human kidney" + "tissue_context": "distal convoluted tubule" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_15.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_15.json similarity index 89% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_15.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_15.json index ac26006..70f47bb 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_15.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_15.json @@ -15,13 +15,13 @@ "name": "Connecting Tubule Cell", "full_name": "connecting tubule cell", "paper_synonyms": "CNT", - "tissue_context": "connecting tubules" + "tissue_context": "connecting tubule and collecting duct" }, { "name": "Mast Cell", "full_name": "mast cell", "paper_synonyms": "MAST", - "tissue_context": "cortex; medulla" + "tissue_context": null }, { "name": "Degenerative Vascular Smooth Muscle Cell", diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_16.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_16.json new file mode 100644 index 0000000..994fbfe --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_16.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Ascending Thin Limb Cell", + "full_name": "Degenerative ascending thin limb cell", + "paper_synonyms": "ATL", + "tissue_context": "inner medulla" + }, + { + "name": "Renin-positive Juxtaglomerular Granular Cell", + "full_name": "Juxtaglomerular renin-producing granular cell", + "paper_synonyms": "REN; renin-producing granular cell", + "tissue_context": "juxtaglomerular apparatus; afferent/efferent arterioles" + }, + { + "name": "B Cell", + "full_name": "B cell", + "paper_synonyms": "B", + "tissue_context": "renal cortical and medullary structures" + }, + { + "name": "Degenerative Cortical Intercalated Cell Type A", + "full_name": "Degenerative cortical intercalated cell", + "paper_synonyms": "IC", + "tissue_context": "cortex" + }, + { + "name": "Degenerative Connecting Tubule Cell", + "full_name": "Degenerative connecting tubule cell", + "paper_synonyms": "CNT", + "tissue_context": "cortex; cortical distal nephron" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_17.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_17.json similarity index 58% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_17.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_17.json index 79ce036..61d2be4 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_17.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_17.json @@ -1,9 +1,9 @@ [ { "name": "Adaptive / Maladaptive / Repairing Fibroblast", - "full_name": "adaptive (successful or maladaptive repair) fibroblast", - "paper_synonyms": "aFIB; aStr", - "tissue_context": "interstitium; region of fibrosis within the cortex of a CKD biopsy" + "full_name": "adaptive fibroblast", + "paper_synonyms": "aFIB", + "tissue_context": "cortex; interstitium" }, { "name": "Parietal Epithelial Cell", @@ -14,19 +14,19 @@ { "name": "Cycling Connecting Tubule Cell", "full_name": "cycling connecting tubule cell", - "paper_synonyms": "CNT", - "tissue_context": "connecting tubule; cortical distal nephron" + "paper_synonyms": "CNT; Cyc", + "tissue_context": "connecting tubule" }, { "name": "Degenerative Inner Medullary Collecting Duct Cell", "full_name": "degenerative inner medullary collecting duct cell", - "paper_synonyms": "IMCD", - "tissue_context": "inner medullary collecting duct" + "paper_synonyms": "IMCD; CD", + "tissue_context": "inner medulla" }, { "name": "Inner Medullary Collecting Duct Cell", "full_name": "inner medullary collecting duct cell", "paper_synonyms": "IMCD", - "tissue_context": "inner medullary collecting duct" + "tissue_context": "inner medulla" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_18.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_18.json new file mode 100644 index 0000000..103cb25 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_18.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Endothelial Cell", + "full_name": "Degenerative Endothelial Cell", + "paper_synonyms": "EC-AEA; EC-GC; EC-LYM; EC-AVR; EC-DVR; EC", + "tissue_context": "afferent/efferent arterioles; glomerular capillaries; lymphatics; vasa recta" + }, + { + "name": "Degenerative Medullary Fibroblast", + "full_name": "Degenerative Medullary Fibroblast", + "paper_synonyms": "FIB", + "tissue_context": "medulla; interstitium; corticomedullary axis" + }, + { + "name": "Connecting Tubule Intercalated Cell Type A", + "full_name": "Connecting Tubule Intercalated Cell Type A", + "paper_synonyms": "CNT-IC; IC; CNT", + "tissue_context": "connecting tubules (CNT)" + }, + { + "name": "Cycling Distal Convoluted Tubule Cell", + "full_name": "Cycling Distal Convoluted Tubule Cell", + "paper_synonyms": "DCT; DCT1; DCT2", + "tissue_context": "distal convoluted tubule (DCT1, 2)" + }, + { + "name": "Degenerative Podocyte", + "full_name": "Degenerative Podocyte", + "paper_synonyms": "POD; PODs", + "tissue_context": "renal corpuscle; glomeruli" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_19.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_19.json similarity index 50% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_19.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_19.json index 03b56eb..0a005e3 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_19.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_19.json @@ -1,31 +1,31 @@ [ { "name": "Plasmacytoid Dendritic Cell", - "full_name": "Plasmacytoid dendritic cell", + "full_name": "plasmacytoid dendritic cell", "paper_synonyms": "pDC", - "tissue_context": "human kidney" + "tissue_context": "human kidney; cortex; medulla" }, { "name": "Degenerative Descending Thin Limb Cell Type 3", - "full_name": "Degenerative descending thin limb cell type 3", - "paper_synonyms": "DTL3; DTL", + "full_name": "degenerative descending thin limb cell type 3", + "paper_synonyms": "DTL3", "tissue_context": "medulla" }, { "name": "Degenerative Distal Convoluted Tubule Cell", - "full_name": "Degenerative distal convoluted tubule cell", + "full_name": "degenerative distal convoluted tubule cell", "paper_synonyms": "DCT", - "tissue_context": "distal convoluted tubule; cortex" + "tissue_context": "cortex; distal convoluted tubule" }, { "name": "Cycling Myofibroblast", - "full_name": "Cycling myofibroblast", + "full_name": "cycling myofibroblast", "paper_synonyms": "cycMyoF", - "tissue_context": "stroma; interstitium" + "tissue_context": "interstitium; interstitial fibrosis; cortex" }, { "name": "Papillary Tip Epithelial Cell", - "full_name": "Papillary tip epithelial cell", + "full_name": "papillary tip epithelial cell", "paper_synonyms": "PapE", "tissue_context": "papillary tip; calyx" } diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_2.json similarity index 58% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_4.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_2.json index 2d290c0..f9e9557 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_4.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_2.json @@ -1,32 +1,32 @@ [ { - "name": "immune cells", - "full_name": "immune cells", - "paper_synonyms": "IMM", - "tissue_context": "human kidney; cortex; medulla" - }, - { - "name": "immune cells", - "full_name": "immune cells", - "paper_synonyms": "IMM", - "tissue_context": "human kidney; cortex; medulla" + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "human kidney; cortex; medulla; nephron" }, { "name": "immune cells", "full_name": "immune cells", - "paper_synonyms": "IMM", + "paper_synonyms": "leukocytes; IMM", "tissue_context": "human kidney; cortex; medulla" }, { "name": "epithelial cells", "full_name": "epithelial cells", "paper_synonyms": null, - "tissue_context": "human kidney; cortex; medulla; nephron segments; tubules" + "tissue_context": "human kidney; cortex; medulla; nephron" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes; IMM", + "tissue_context": "human kidney; cortex; medulla" }, { "name": "immune cells", "full_name": "immune cells", - "paper_synonyms": "IMM", + "paper_synonyms": "leukocytes; IMM", "tissue_context": "human kidney; cortex; medulla" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_20.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_20.json new file mode 100644 index 0000000..c9b066e --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_20.json @@ -0,0 +1,32 @@ +[ + { + "name": "Cycling Natural Killer Cell / Natural Killer T Cell", + "full_name": "cycling natural killer cell / natural killer T cell", + "paper_synonyms": "NKT", + "tissue_context": "cortex; medulla" + }, + { + "name": "PT", + "full_name": "proximal tubule", + "paper_synonyms": "proximal tubule (PT)", + "tissue_context": "proximal tubule (PT)" + }, + { + "name": "FIB", + "full_name": "fibroblast", + "paper_synonyms": "fibroblast (FIB)", + "tissue_context": "interstitium; cortex" + }, + { + "name": "TAL", + "full_name": "thick ascending limb", + "paper_synonyms": "thick ascending limb (TAL)", + "tissue_context": "medullary thick ascending limb (M-TAL); cortical thick ascending limb (C-TAL)" + }, + { + "name": "IMM", + "full_name": "immune", + "paper_synonyms": "immune; immune cells", + "tissue_context": "cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_21.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_21.json similarity index 50% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_21.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_21.json index 6976d42..0e10449 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_21.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_21.json @@ -3,30 +3,30 @@ "name": "IC", "full_name": "intercalated cells", "paper_synonyms": null, - "tissue_context": "connecting tubules; collecting duct" + "tissue_context": "connecting tubules (CNT-IC and CNT-PC)" }, { "name": "EC", "full_name": "endothelial cells", "paper_synonyms": null, - "tissue_context": "glomerular capillaries; afferent/efferent arterioles; lymphatics; vasa recta" + "tissue_context": "glomerular capillaries (EC-GC); afferent/efferent arterioles (EC-AEA); lymphatics (EC-LYM); vasa recta (EC-AVR, EC-DVR)" }, { "name": "IMM", "full_name": "immune cells", - "paper_synonyms": "leukocytes", - "tissue_context": "cortex; medulla" + "paper_synonyms": null, + "tissue_context": "cortex; medulla; interstitium" }, { "name": "DTL", "full_name": "descending thin limb", "paper_synonyms": null, - "tissue_context": "loop of Henle; medulla" + "tissue_context": "medulla" }, { "name": "IMM", "full_name": "immune cells", - "paper_synonyms": "leukocytes", - "tissue_context": "cortex; medulla" + "paper_synonyms": null, + "tissue_context": "cortex; medulla; interstitium" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_22.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_22.json new file mode 100644 index 0000000..fa7c770 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_22.json @@ -0,0 +1,32 @@ +[ + { + "name": "POD", + "full_name": "podocytes", + "paper_synonyms": "PODs", + "tissue_context": "renal corpuscle" + }, + { + "name": "ATL", + "full_name": "ascending thin limbs", + "paper_synonyms": "", + "tissue_context": "inner medulla" + }, + { + "name": "IMM", + "full_name": "immune", + "paper_synonyms": "immune cells", + "tissue_context": "cortex; medulla" + }, + { + "name": "PC", + "full_name": "principal cells", + "paper_synonyms": "", + "tissue_context": "collecting duct; medulla" + }, + { + "name": "IMM", + "full_name": "immune", + "paper_synonyms": "immune cells", + "tissue_context": "cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_23.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_23.json similarity index 76% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_23.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_23.json index dbd1359..a8bafa0 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_23.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_23.json @@ -15,18 +15,18 @@ "name": "DCT", "full_name": "distal convoluted tubule", "paper_synonyms": null, - "tissue_context": "cortex" + "tissue_context": "cortex; cortical distal nephrons" }, { "name": "VSM/P", "full_name": "vascular smooth muscle cell or pericyte", - "paper_synonyms": "vascular smooth muscle cell; pericyte; VSMC", + "paper_synonyms": "VSMC", "tissue_context": "afferent/efferent arterioles; renal corpuscle" }, { "name": "NEU", "full_name": "neuronal cell", - "paper_synonyms": "Schwann/neuronal; SCI/NEU", - "tissue_context": "human kidney" + "paper_synonyms": "SCI/NEU; Schwann/neuronal", + "tissue_context": null } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_24.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_24.json new file mode 100644 index 0000000..cd664c1 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_24.json @@ -0,0 +1,32 @@ +[ + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla" + }, + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla" + }, + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla" + }, + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "cortex; medulla" + }, + { + "name": "PEC", + "full_name": "parietal epithelial cells", + "paper_synonyms": "", + "tissue_context": "renal corpuscle" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_25.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_25.json new file mode 100644 index 0000000..abea734 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_25.json @@ -0,0 +1,32 @@ +[ + { + "name": "IMM", + "full_name": "immune cells", + "paper_synonyms": "leukocytes; immune cells", + "tissue_context": "cortex; medulla; interstitium" + }, + { + "name": "PapE", + "full_name": "papillary tip epithelial cells abutting the calyx", + "paper_synonyms": "", + "tissue_context": "papillary tip; calyx" + }, + { + "name": "dPT", + "full_name": "degenerative proximal tubule", + "paper_synonyms": "", + "tissue_context": "proximal tubule (PT)" + }, + { + "name": "aPT", + "full_name": "adaptive proximal tubule", + "paper_synonyms": "", + "tissue_context": "proximal tubule (PT); cortex" + }, + { + "name": "M-FIB", + "full_name": "medullary fibroblast", + "paper_synonyms": "", + "tissue_context": "medulla; interstitium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_26.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_26.json new file mode 100644 index 0000000..4c408c9 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_26.json @@ -0,0 +1,32 @@ +[ + { + "name": "MD", + "full_name": "macula densa cells", + "paper_synonyms": "Macula Densa", + "tissue_context": "juxtaglomerular apparatus; renal corpuscle" + }, + { + "name": "NKC/T", + "full_name": "T cells", + "paper_synonyms": "", + "tissue_context": "areas of tissue damage including presumptive epithelial degeneration and fibrosis" + }, + { + "name": "tPC-IC", + "full_name": "transitioning principal and intercalated cells", + "paper_synonyms": "", + "tissue_context": "medullary region of acute tubular necrosis" + }, + { + "name": "EC-DVR", + "full_name": "endothelial cells of the vasa recta", + "paper_synonyms": "", + "tissue_context": "vasa recta; medullary sampling" + }, + { + "name": "M-TAL", + "full_name": "medullary thick ascending limb", + "paper_synonyms": "", + "tissue_context": "medullary thick ascending limb (M-TAL) of the outer medullary stripe; in the medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_27.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_27.json new file mode 100644 index 0000000..423d6f7 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_27.json @@ -0,0 +1,32 @@ +[ + { + "name": "C-IC-A", + "full_name": "cortical intercalated cell A", + "paper_synonyms": "", + "tissue_context": "cortex" + }, + { + "name": "dM-TAL", + "full_name": "degenerative medullary thick ascending limb cell", + "paper_synonyms": "", + "tissue_context": "medulla; outer medullary stripe" + }, + { + "name": "EC-AVR", + "full_name": "endothelial cell of the vasa recta", + "paper_synonyms": "", + "tissue_context": "vasa recta; medulla" + }, + { + "name": "MAC-M2", + "full_name": "M2 macrophage", + "paper_synonyms": "M2 macrophages", + "tissue_context": "cortex; vessels" + }, + { + "name": "cycPT", + "full_name": "cycling proximal tubule cell", + "paper_synonyms": "", + "tissue_context": "proximal tubule" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_28.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_28.json new file mode 100644 index 0000000..3e2d860 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_28.json @@ -0,0 +1,32 @@ +[ + { + "name": "M-IC-A", + "full_name": "medullary intercalated cell", + "paper_synonyms": null, + "tissue_context": "medulla" + }, + { + "name": "aTAL1", + "full_name": "adaptive thick ascending limb 1", + "paper_synonyms": "aTAL", + "tissue_context": "cortex; corticomedullary sections" + }, + { + "name": "EC-AEA", + "full_name": "endothelial cells of the afferent/efferent arterioles", + "paper_synonyms": null, + "tissue_context": "afferent/efferent arterioles; renal corpuscle; cortex" + }, + { + "name": "DTL2", + "full_name": "descending thin limb cell type 2", + "paper_synonyms": null, + "tissue_context": "medulla" + }, + { + "name": "N", + "full_name": "neutrophils", + "paper_synonyms": "MPO+ cells", + "tissue_context": "cortex; medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_29.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_29.json new file mode 100644 index 0000000..7ca59ec --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_29.json @@ -0,0 +1,32 @@ +[ + { + "name": "DTL1", + "full_name": "descending thin limb 1", + "paper_synonyms": "DTL; descending thin limb", + "tissue_context": "medulla" + }, + { + "name": "MDC", + "full_name": "monocyte-derived cells", + "paper_synonyms": "MDCs", + "tissue_context": "cortex" + }, + { + "name": "C-PC", + "full_name": "cortical principal cell", + "paper_synonyms": "PC; principal cells", + "tissue_context": "cortex" + }, + { + "name": "EC-PTC", + "full_name": "endothelial cell PTC", + "paper_synonyms": "EC; endothelial cells", + "tissue_context": "" + }, + { + "name": "PT-S1/2", + "full_name": "proximal tubule S1/2", + "paper_synonyms": "PT; proximal tubule", + "tissue_context": "cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_3.json similarity index 50% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_3.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_3.json index d873918..75f3f99 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_3.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_3.json @@ -2,31 +2,31 @@ { "name": "epithelial cells", "full_name": "epithelial cells", - "paper_synonyms": "epithelium; tubular epithelium", - "tissue_context": "nephron; cortex; outer medulla; inner medulla; papillary tip" + "paper_synonyms": "", + "tissue_context": "nephron; cortex; outer medulla; inner medulla; papillary tip; interstitium" }, { "name": "epithelial cells", "full_name": "epithelial cells", - "paper_synonyms": "epithelium; tubular epithelium", - "tissue_context": "nephron; cortex; outer medulla; inner medulla; papillary tip" + "paper_synonyms": "", + "tissue_context": "nephron; cortex; outer medulla; inner medulla; papillary tip; interstitium" }, { "name": "stroma cells", "full_name": "stromal cells", - "paper_synonyms": "stroma; STR", - "tissue_context": "interstitium; cortex; medulla" + "paper_synonyms": "STR; stroma", + "tissue_context": "stroma; interstitium; cortex; medulla" }, { "name": "neural cells", - "full_name": "neural cell types", - "paper_synonyms": "neuronal; Schwann/neuronal; SCI/NEU", - "tissue_context": "human kidney" + "full_name": "neural cells", + "paper_synonyms": "NEU; SCI/NEU; Schwann/neuronal", + "tissue_context": "human kidney; cortex; medulla" }, { "name": "immune cells", "full_name": "immune cells", - "paper_synonyms": "leukocytes; IMM", - "tissue_context": "cortex; medulla" + "paper_synonyms": "IMM; leukocytes", + "tissue_context": "cortex; medulla; interstitium" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_30.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_30.json similarity index 63% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_30.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_30.json index 8142e01..da4fb0f 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_30.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_30.json @@ -3,30 +3,30 @@ "name": "PT-S3", "full_name": "proximal tubule S3", "paper_synonyms": "proximal tubule (PT)", - "tissue_context": "proximal tubule (PT); cortex" + "tissue_context": "proximal tubule (PT)" }, { "name": "C-TAL", "full_name": "cortical thick ascending limb", "paper_synonyms": "thick ascending limb (TAL)", - "tissue_context": "cortex; cortical thick ascending limb (C-TAL)" + "tissue_context": "cortex" }, { "name": "M-PC", "full_name": "medullary principal cell", - "paper_synonyms": "principal cells (PC)", - "tissue_context": "medulla; Medullary collecting ducts" + "paper_synonyms": "principal cell (PC)", + "tissue_context": "medulla" }, { "name": "dM-PC", "full_name": "degenerative medullary principal cell", - "paper_synonyms": "dM-PCs", + "paper_synonyms": "degenerative medullary principal cells (dM-PCs)", "tissue_context": "medulla; collecting duct" }, { "name": "T", "full_name": "T cell", - "paper_synonyms": "CD3+ cells; lymphoid or T cells", + "paper_synonyms": "CD3+ cells", "tissue_context": "cortex; medulla" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_31.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_31.json similarity index 62% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_31.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_31.json index 804860f..9fe6999 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_31.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_31.json @@ -9,18 +9,18 @@ "name": "dFIB", "full_name": "degenerative fibroblast", "paper_synonyms": null, - "tissue_context": "stroma; interstitium; cortex" + "tissue_context": "stroma; interstitium" }, { "name": "dC-TAL", - "full_name": "degenerative cortical thick ascending limb", - "paper_synonyms": "thick ascending limb (TAL); cortical thick ascending limb (C-TAL)", - "tissue_context": "cortical thick ascending limb (C-TAL); cortex" + "full_name": "degenerative cortical thick ascending limb cell", + "paper_synonyms": null, + "tissue_context": "cortex; cortical thick ascending limb (C-TAL)" }, { "name": "VSMC", "full_name": "vascular smooth muscle cell", - "paper_synonyms": "VSM/P; pericyte", + "paper_synonyms": "VSM/P", "tissue_context": "afferent/efferent arterioles; renal corpuscle" }, { diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_32.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_32.json new file mode 100644 index 0000000..f9e2ede --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_32.json @@ -0,0 +1,32 @@ +[ + { + "name": "DTL3", + "full_name": "descending thin limb 3", + "paper_synonyms": "DTL", + "tissue_context": "AQP1+ cells in the medulla" + }, + { + "name": "EC-GC", + "full_name": "glomerular capillaries", + "paper_synonyms": "", + "tissue_context": "renal corpuscle; glomerulus; cortex" + }, + { + "name": "VSMC/P", + "full_name": "vascular smooth muscle cell or pericyte", + "paper_synonyms": "VSM/P", + "tissue_context": "afferent/efferent arterioles; renal corpuscle" + }, + { + "name": "cycMNP", + "full_name": "cycling MNP", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "MYOF", + "full_name": "myofibroblasts", + "paper_synonyms": "MyoF", + "tissue_context": "stroma; interstitium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_33.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_33.json similarity index 79% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_33.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_33.json index 46b97bb..eb194a6 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_33.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_33.json @@ -1,7 +1,7 @@ [ { "name": "dEC-PTC", - "full_name": "endothelial cells", + "full_name": "degenerative endothelial cell", "paper_synonyms": null, "tissue_context": "vasculature; human kidney" }, @@ -13,7 +13,7 @@ }, { "name": "cycEC", - "full_name": "cycling endothelial cells", + "full_name": "cycling endothelial cell", "paper_synonyms": null, "tissue_context": "vasculature; human kidney" }, @@ -27,6 +27,6 @@ "name": "EC-LYM", "full_name": "endothelial cells of the lymphatics", "paper_synonyms": null, - "tissue_context": "lymphatics; human kidney" + "tissue_context": "human kidney; lymphatics" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_34.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_34.json new file mode 100644 index 0000000..f0879e8 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_34.json @@ -0,0 +1,32 @@ +[ + { + "name": "MC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "IC-B", + "full_name": "intercalated cell", + "paper_synonyms": "IC; CNT-IC", + "tissue_context": "connecting tubules (CNT-IC and CNT-PC)" + }, + { + "name": "MAST", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "dVSMC", + "full_name": "vascular smooth muscle cell", + "paper_synonyms": "VSMC; VSM/P", + "tissue_context": "afferent/efferent arterioles" + }, + { + "name": "dATL", + "full_name": "ascending thin limb", + "paper_synonyms": "ATL", + "tissue_context": "inner medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_35.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_35.json new file mode 100644 index 0000000..226653a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_35.json @@ -0,0 +1,32 @@ +[ + { + "name": "REN", + "full_name": "juxtaglomerular renin-producing granular cell", + "paper_synonyms": "renin-producing granular cells; juxtaglomerular renin-producing granular cells", + "tissue_context": "juxtaglomerular apparatus; afferent/efferent arterioles; renal corpuscle" + }, + { + "name": "B", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "aTAL2", + "full_name": "adaptive thick ascending limb cell 2", + "paper_synonyms": "adaptive TAL; aTAL", + "tissue_context": "cortical thick ascending limb (C-TAL); cortex; injured tubules" + }, + { + "name": "dC-IC-A", + "full_name": "degenerative cortical intercalated cell A", + "paper_synonyms": "intercalated cells (IC)", + "tissue_context": "cortex" + }, + { + "name": "dCNT", + "full_name": "degenerative connecting tubule cell", + "paper_synonyms": "connecting tubules (CNT)", + "tissue_context": "connecting tubule" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_36.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_36.json similarity index 50% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_36.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_36.json index 5067213..dcc308d 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_36.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_36.json @@ -3,30 +3,30 @@ "name": "aFIB", "full_name": "adaptive fibroblast", "paper_synonyms": null, - "tissue_context": "interstitium; stroma; cortex" + "tissue_context": "cortex; interstitium; stroma" }, { "name": "cycCNT", - "full_name": "cycling connecting tubule", - "paper_synonyms": null, - "tissue_context": "connecting tubule; cortical distal nephron; cortex" + "full_name": "cycling connecting tubule cell", + "paper_synonyms": "CNT", + "tissue_context": "connecting tubule; cortex" }, { "name": "dIMCD", - "full_name": "degenerative inner medullary collecting duct", + "full_name": "degenerative inner medullary collecting duct cell", "paper_synonyms": null, - "tissue_context": "inner medulla; collecting duct" + "tissue_context": "inner medulla" }, { "name": "IMCD", - "full_name": "inner medullary collecting duct", + "full_name": "inner medullary collecting duct cell", "paper_synonyms": null, "tissue_context": "inner medulla; collecting duct" }, { "name": "dEC", "full_name": "degenerative endothelial cell", - "paper_synonyms": null, + "paper_synonyms": "EC", "tissue_context": "vasculature" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_37.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_37.json new file mode 100644 index 0000000..437d954 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_37.json @@ -0,0 +1,32 @@ +[ + { + "name": "dM-FIB", + "full_name": "degenerative medullary fibroblast", + "paper_synonyms": "FIB", + "tissue_context": "stroma; interstitium; medulla" + }, + { + "name": "cycDCT", + "full_name": "cycling distal convoluted tubule", + "paper_synonyms": "DCT", + "tissue_context": "distal convoluted tubule" + }, + { + "name": "dPOD", + "full_name": "degenerative podocyte", + "paper_synonyms": "PODs", + "tissue_context": "renal corpuscle; glomeruli; cortex" + }, + { + "name": "pDC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "dDTL3", + "full_name": "degenerative descending thin limb 3", + "paper_synonyms": "DTL3; DTL; descending thin limb", + "tissue_context": "medulla" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_38.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_38.json similarity index 55% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_38.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_38.json index 1f3759a..f6421c3 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_38.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_38.json @@ -2,31 +2,31 @@ { "name": "dDCT", "full_name": "degenerative distal convoluted tubule cells", - "paper_synonyms": "DCT", - "tissue_context": "distal convoluted tubule; cortex" + "paper_synonyms": null, + "tissue_context": "human kidney; distal convoluted tubule" }, { "name": "cycMYOF", "full_name": "cycling myofibroblasts", - "paper_synonyms": "MyoF; cycMyoF; myofibroblasts", - "tissue_context": "stroma; interstitium; fibrosis within the cortex" + "paper_synonyms": "cycMyoF; cycling MyoF", + "tissue_context": "stroma; interstitium" }, { "name": "cycNKC/T", "full_name": "cycling T cells", - "paper_synonyms": "T; T cells", - "tissue_context": "cortex; medulla; areas of tissue damage; fibrosis" + "paper_synonyms": null, + "tissue_context": "human kidney" }, { "name": "CCD-IC-A", "full_name": "cortical collecting duct intercalated cells", - "paper_synonyms": "CCD; C-CD; IC; intercalated cells", + "paper_synonyms": null, "tissue_context": "cortex; collecting duct" }, { "name": "OMCD-IC-A", "full_name": "outer medullary collecting duct intercalated cells", - "paper_synonyms": "OMCD; IC; intercalated cells", + "paper_synonyms": null, "tissue_context": "outer medulla; collecting duct" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_39.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_39.json new file mode 100644 index 0000000..a3fb839 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_39.json @@ -0,0 +1,32 @@ +[ + { + "name": "CCD-PC", + "full_name": "cortical collecting duct principal cell", + "paper_synonyms": "CCD; cortical collecting duct; PC; principal cells", + "tissue_context": "cortex; cortical collecting duct" + }, + { + "name": "OMCD-PC", + "full_name": "outer medullary collecting duct principal cell", + "paper_synonyms": "OMCD; outer medullary collecting duct; PC; principal cells", + "tissue_context": "outer medulla; outer medullary collecting duct" + }, + { + "name": "dOMCD-PC", + "full_name": "degenerative outer medullary collecting duct principal cell", + "paper_synonyms": "OMCD; outer medullary collecting duct; PC; principal cells", + "tissue_context": "medulla; outer medulla; outer medullary collecting duct" + }, + { + "name": "CNT-PC", + "full_name": "connecting tubule principal cell", + "paper_synonyms": "CNT; connecting tubule; PC; principal cells", + "tissue_context": "connecting tubules" + }, + { + "name": "DCT1", + "full_name": "distal convoluted tubule cell 1", + "paper_synonyms": "DCT; distal convoluted tubule", + "tissue_context": "distal convoluted tubule" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_4.json new file mode 100644 index 0000000..36cec85 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "kidney; cortex; medulla; interstitium" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "kidney; cortex; medulla; interstitium" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "kidney; cortex; medulla; interstitium" + }, + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": "", + "tissue_context": "kidney; cortex; medulla; renal tubules; interstitium" + }, + { + "name": "immune cells", + "full_name": "immune cells", + "paper_synonyms": "leukocytes", + "tissue_context": "kidney; cortex; medulla; interstitium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_40.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_40.json similarity index 56% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_40.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_40.json index 8ba8d9a..e8d3782 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_40.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_40.json @@ -3,12 +3,12 @@ "name": "DCT2", "full_name": "distal convoluted tubule cell 2", "paper_synonyms": "DCT", - "tissue_context": "distal convoluted tubule" + "tissue_context": "human kidney; distal convoluted tubule" }, { "name": "CNT-IC-A", "full_name": "connecting tubule intercalated cell", - "paper_synonyms": "CNT-IC; IC; CNT", - "tissue_context": "connecting tubule" + "paper_synonyms": "CNT-IC; IC", + "tissue_context": "human kidney; connecting tubules" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_5.json new file mode 100644 index 0000000..db71bc0 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_5.json @@ -0,0 +1,32 @@ +[ + { + "name": "epithelial cells", + "full_name": "epithelial cells", + "paper_synonyms": null, + "tissue_context": "nephron; renal tubules; cortex; medulla; corticomedullary axis" + }, + { + "name": "Degenerative Proximal Tubule Epithelial Cell", + "full_name": "degenerative proximal tubule (PT) epithelial cell", + "paper_synonyms": "PT; degen", + "tissue_context": "cortex; proximal tubule (PT)" + }, + { + "name": "Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell", + "full_name": "adaptive proximal tubule (aPT) epithelial cell (successful or maladaptive tubular repair)", + "paper_synonyms": "aPT; aEpi; Ad/Mal; PT", + "tissue_context": "cortex; proximal tubule (PT); tubules" + }, + { + "name": "Medullary Fibroblast", + "full_name": "medullary fibroblast", + "paper_synonyms": "FIB", + "tissue_context": "medulla; interstitium; stroma" + }, + { + "name": "Macula Densa Cell", + "full_name": "macula densa cell", + "paper_synonyms": "MD", + "tissue_context": "juxtaglomerular apparatus; renal corpuscle; cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_6.json new file mode 100644 index 0000000..4ef9b66 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_6.json @@ -0,0 +1,32 @@ +[ + { + "name": "Natural Killer Cell / Natural Killer T Cell", + "full_name": "Natural killer cell / natural killer T cell", + "paper_synonyms": "NKT", + "tissue_context": "human kidney; cortex; medulla" + }, + { + "name": "Transitional Principal-Intercalated Cell", + "full_name": "Transitional principal\u2013intercalated cell", + "paper_synonyms": "transitioning principal and intercalated cells; PC, principal cells; IC, intercalated cells", + "tissue_context": "medullary; collecting duct" + }, + { + "name": "Descending Vasa Recta Endothelial Cell", + "full_name": "Descending vasa recta endothelial cell", + "paper_synonyms": "EC-DVR", + "tissue_context": "medulla; vascular bundles in the medulla" + }, + { + "name": "Medullary Thick Ascending Limb Cell", + "full_name": "Medullary thick ascending limb cell", + "paper_synonyms": "M-TAL; thick ascending limb (TAL)", + "tissue_context": "inner medulla; outer medulla; outer medullary stripe" + }, + { + "name": "Cortical Collecting Duct Intercalated Cell Type A", + "full_name": "Cortical collecting duct intercalated cell type A", + "paper_synonyms": "CCD; IC; collecting duct (CD)", + "tissue_context": "cortex; cortical collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_7.json new file mode 100644 index 0000000..ff7e731 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_7.json @@ -0,0 +1,32 @@ +[ + { + "name": "Degenerative Medullary Thick Ascending Limb Cell", + "full_name": "degenerative medullary thick ascending limb cell", + "paper_synonyms": "TAL; M-TAL", + "tissue_context": "inner medulla; outer medullary stripe" + }, + { + "name": "Ascending Vasa Recta Endothelial Cell", + "full_name": "ascending vasa recta endothelial cell", + "paper_synonyms": "EC-AVR", + "tissue_context": "vascular bundles in the medulla" + }, + { + "name": "M2 Macrophage", + "full_name": "M2 macrophage", + "paper_synonyms": "MAC-M2", + "tissue_context": "region of fibrosis within the cortex; co-localized around vessels" + }, + { + "name": "Cycling Proximal Tubule Epithelial Cell", + "full_name": "cycling proximal tubule epithelial cell", + "paper_synonyms": "PT", + "tissue_context": "cortical epithelium (N7 and N8)" + }, + { + "name": "Outer Medullary Collecting Duct Intercalated Cell Type A", + "full_name": "outer medullary collecting duct intercalated cell", + "paper_synonyms": "OMCD; IC", + "tissue_context": "outer medulla; collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_8.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_8.json similarity index 52% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_8.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_8.json index 8d31d92..7adc68a 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_8.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_8.json @@ -1,32 +1,32 @@ [ { "name": "Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell", - "full_name": "adaptive/maladaptive repairing thick ascending limb epithelial cell", - "paper_synonyms": "aTAL; adaptive TAL; adaptive epithelial (aEpi)", - "tissue_context": "cortical thick ascending limb (C-TAL); medullary thick ascending limb (M-TAL); cortex; medulla" + "full_name": "adaptive (successful or maladaptive repair) thick ascending limb cell (aTAL)", + "paper_synonyms": "aTAL; adaptive epithelial (aEpi); Ad/Mal", + "tissue_context": "cortex; medulla; cortical thick ascending limb (C-TAL); medullary thick ascending limb (M-TAL)" }, { "name": "Afferent / Efferent Arteriole Endothelial Cell", - "full_name": "endothelial cell of the afferent/efferent arterioles", + "full_name": "endothelial cell of the afferent/efferent arterioles (EC-AEA)", "paper_synonyms": "EC-AEA", - "tissue_context": "afferent/efferent arterioles; renal corpuscle; glomerular corpuscle; Macula Densa (MD)" + "tissue_context": "afferent/efferent arterioles; renal corpuscle; cortex; macula densa (MD)" }, { "name": "Descending Thin Limb Cell Type 2", - "full_name": "descending thin limb cell type 2", + "full_name": "descending thin limb cell type 2 (DTL2)", "paper_synonyms": "DTL2", "tissue_context": "medulla" }, { "name": "Neutrophil", "full_name": "neutrophil", - "paper_synonyms": "N; MPO+ (N)", - "tissue_context": "cortex; medulla" + "paper_synonyms": "N", + "tissue_context": "cortex; medulla; areas of injury" }, { "name": "Podocyte", - "full_name": "podocyte", - "paper_synonyms": "PODs", + "full_name": "podocyte (POD)", + "paper_synonyms": "POD; PODs", "tissue_context": "renal corpuscle; glomerulus; cortex" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_9.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_9.json similarity index 82% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_9.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_9.json index 3a344df..b5873d6 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_9.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/DOI_10_1038_s41586-023-05769-3_batch_9.json @@ -1,13 +1,13 @@ [ { "name": "Ascending Thin Limb Cell", - "full_name": "ascending thin limb (ATL) cell", + "full_name": "ascending thin limb cell", "paper_synonyms": "ATL", "tissue_context": "inner medulla; outer medullary stripe" }, { "name": "Descending Thin Limb Cell Type 1", - "full_name": "descending thin limb cell type 1 (DTL1)", + "full_name": "descending thin limb cell type 1", "paper_synonyms": "DTL1", "tissue_context": "medulla" }, @@ -20,7 +20,7 @@ { "name": "Cortical Collecting Duct Principal Cell", "full_name": "cortical collecting duct principal cell", - "paper_synonyms": "PC; C-PC", + "paper_synonyms": "C-PC; PC", "tissue_context": "cortex" }, { diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_0.json similarity index 81% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_3.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_0.json index 67b6cbc..9637d7b 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_3.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_0.json @@ -1,30 +1,30 @@ [ { - "name": "Endothelial", - "full_name": "endothelial cells", + "name": "Inhibitory_4", + "full_name": "inhibitory neurons", "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" }, { - "name": "Parvalbumin interneurons", - "full_name": "parvalbumin interneurons", + "name": "Oligodendrocytes", + "full_name": "oligodendrocytes", "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" }, { - "name": "Inhibitory_4", - "full_name": "inhibitory neurons", + "name": "Excitatory_2", + "full_name": "excitatory neurons", "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" }, { - "name": "Oligodendrocytes", - "full_name": "oligodendrocytes", + "name": "Inhibitory_2", + "full_name": "inhibitory neurons", "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" }, { - "name": "Excitatory_2", + "name": "Excitatory_4", "full_name": "excitatory neurons", "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_1.json similarity index 55% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_4.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_1.json index 54c0573..8969c45 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_4.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_1.json @@ -1,32 +1,32 @@ [ { - "name": "Inhibitory_2", - "full_name": "inhibitory neurons", - "paper_synonyms": null, + "name": "Excitatory_1", + "full_name": "excitatory neuron", + "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" }, { - "name": "Excitatory_4", - "full_name": "excitatory neurons", - "paper_synonyms": null, + "name": "Inhibitory_1", + "full_name": "inhibitory neuron", + "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" }, { - "name": "Excitatory_1", - "full_name": "excitatory neurons", - "paper_synonyms": null, + "name": "Astrocytes", + "full_name": "astrocytes", + "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" }, { - "name": "Inhibitory_1", - "full_name": "inhibitory neurons", - "paper_synonyms": null, + "name": "Excitatory_3", + "full_name": "excitatory neuron", + "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" }, { - "name": "Astrocytes", - "full_name": "astrocytes", - "paper_synonyms": null, + "name": "Excitatory_5", + "full_name": "excitatory neuron", + "paper_synonyms": "", "tissue_context": "frontal cortex; occipital cortex" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_2.json similarity index 66% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_5.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_2.json index 45a716a..a3eae85 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_5.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_2.json @@ -1,16 +1,4 @@ [ - { - "name": "Excitatory_3", - "full_name": "excitatory neuron 3", - "paper_synonyms": "excitatory neurons", - "tissue_context": "frontal cortex; occipital cortex" - }, - { - "name": "Excitatory_5", - "full_name": "excitatory neuron 5", - "paper_synonyms": "excitatory neurons", - "tissue_context": "frontal cortex; occipital cortex" - }, { "name": "Microglia", "full_name": "microglia", @@ -20,7 +8,7 @@ { "name": "OPCs", "full_name": "oligodendrocyte progenitor cells", - "paper_synonyms": "oligodendrocyte progenitor cells; OPCs", + "paper_synonyms": "OPCs", "tissue_context": "frontal cortex; occipital cortex" }, { @@ -28,5 +16,17 @@ "full_name": "endothelial cells", "paper_synonyms": null, "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_8", + "full_name": "excitatory neurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Inhibitory_3", + "full_name": "inhibitory neurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_3.json similarity index 71% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_6.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_3.json index 1b87d7a..4114dd8 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_6.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_3.json @@ -1,16 +1,4 @@ [ - { - "name": "Excitatory_8", - "full_name": "excitatory neuron 8", - "paper_synonyms": null, - "tissue_context": "frontal cortex; occipital cortex" - }, - { - "name": "Inhibitory_3", - "full_name": "inhibitory neuron 3", - "paper_synonyms": null, - "tissue_context": "frontal cortex; occipital cortex" - }, { "name": "Excitatory_9", "full_name": "excitatory neuron 9", @@ -28,5 +16,11 @@ "full_name": "excitatory neuron 10", "paper_synonyms": null, "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Excitatory_7", + "full_name": "excitatory neuron 7", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_0.json new file mode 100644 index 0000000..c3e5db2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "Fibroblasts ADAMDEC1", + "full_name": "ADAMDEC1+ fibroblasts", + "paper_synonyms": "ADAMDEC+ Fibroblast clusters", + "tissue_context": "terminal ileum; colon; lamina propria" + }, + { + "name": "Endothelial cells CD36", + "full_name": "CD36+ endothelial cells", + "paper_synonyms": "", + "tissue_context": "terminal ileum; colon; lamina propria" + }, + { + "name": "Myofibroblasts HHIP NPNT", + "full_name": "HHIP+ NPNT+ myofibroblasts", + "paper_synonyms": "Myofibroblasts HHIP+ NPNT+", + "tissue_context": "terminal ileum; colon; lamina propria" + }, + { + "name": "Fibroblasts SMOC2 PTGIS", + "full_name": "SMOC2+ PTGIS+ fibroblasts", + "paper_synonyms": "SMOC2+ PTGIS+ Fibroblast clusters", + "tissue_context": "terminal ileum; colon; lamina propria" + }, + { + "name": "Endothelial cells DARC", + "full_name": "DARC/ACKR1+ endothelial cells", + "paper_synonyms": "ACKR1; DARC/ACKR1", + "tissue_context": "terminal ileum; colon; lamina propria" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_1.json similarity index 80% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_1.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_1.json index ad43237..f99bb74 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_1.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_1.json @@ -3,7 +3,7 @@ "name": "Fibroblasts NPY SLITRK6", "full_name": "Fibroblasts", "paper_synonyms": null, - "tissue_context": "terminal ileum; colon" + "tissue_context": "terminal ileum; colon; lamina propria" }, { "name": "Myofibroblasts GREM1 GREM2", @@ -15,7 +15,7 @@ "name": "Endothelial cells CA4 CD36", "full_name": "Endothelial cells CA4+ CD36+", "paper_synonyms": null, - "tissue_context": "terminal ileum; colon" + "tissue_context": "terminal ileum" }, { "name": "Glial cells", @@ -27,6 +27,6 @@ "name": "Fibroblasts SFRP2 SLPI", "full_name": "Fibroblasts", "paper_synonyms": null, - "tissue_context": "terminal ileum; colon" + "tissue_context": "terminal ileum; colon; lamina propria" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_2.json similarity index 55% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_2.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_2.json index d0c8490..2fdbd69 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_2.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_2.json @@ -3,30 +3,30 @@ "name": "Endothelial cells LTC4S SEMA3G", "full_name": "Endothelial cells", "paper_synonyms": null, - "tissue_context": "terminal ileum; colon; lamina propria" + "tissue_context": "lamina propria; terminal ileum; colon" }, { "name": "Pericytes HIGD1B STEAP4", - "full_name": "Pericytes HIGD1B+ STEAP4+", + "full_name": "Pericytes", "paper_synonyms": null, - "tissue_context": "terminal ileum; colon; lamina propria" + "tissue_context": "lamina propria; terminal ileum; colon" }, { "name": "Activated fibroblasts CCL19 ADAMADEC1", - "full_name": "ADAMDEC+ Fibroblast clusters", + "full_name": "Fibroblasts", "paper_synonyms": null, - "tissue_context": "terminal ileum; colon; lamina propria" + "tissue_context": "lamina propria; terminal ileum; colon" }, { "name": "Lymphatics", "full_name": "Lymphatics", "paper_synonyms": "lymphatic endothelial cells", - "tissue_context": "terminal ileum; colon; lamina propria" + "tissue_context": "lamina propria; terminal ileum; colon" }, { "name": "Fibroblasts KCNN3 LY6H", "full_name": "Fibroblasts", "paper_synonyms": null, - "tissue_context": "terminal ileum; colon; lamina propria" + "tissue_context": "lamina propria; terminal ileum; colon" } ] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_3.json new file mode 100644 index 0000000..b1ab529 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_3.json @@ -0,0 +1,8 @@ +[ + { + "name": "Pericytes RERGL NTRK2", + "full_name": "Pericytes", + "paper_synonyms": "", + "tissue_context": "terminal ileum (TI); colon (CO)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_0.json new file mode 100644 index 0000000..4840cec --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "T cells", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "Diff. Keratinocytes", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "Macrophages+DC", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "EpSC and undiff. progenitors", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "Secretory-reticular fibroblasts", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "human dermis" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_1.json new file mode 100644 index 0000000..52dd9f2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "Pericytes", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human dermis; sun-protected area in healthy human donors" + }, + { + "name": "Pro-inflammatory fibroblasts", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human dermis; sun-protected area in healthy human donors" + }, + { + "name": "Secretory-papillary fibroblasts", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human dermis; sun-protected area in healthy human donors" + }, + { + "name": "Mesenchymal fibroblasts", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human dermis; sun-protected area in healthy human donors" + }, + { + "name": "Vascular EC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human dermis; sun-protected area in healthy human donors" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_2.json new file mode 100644 index 0000000..a954079 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/DOI_10_1038_s42003-020-0922-4_batch_2.json @@ -0,0 +1,20 @@ +[ + { + "name": "Melanocytes", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Lymphatic EC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Erythrocytes", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_0.json new file mode 100644 index 0000000..0a7e3a8 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "Hepato-Doublet", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "Kupffer", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "Stellate-Doublet", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "P-Hepato", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "C-Hepato", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_1.json new file mode 100644 index 0000000..459282b --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "cvLSEC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Prolif-Mac", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "C-Hepato2", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "I-Hepato", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Chol", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_2.json new file mode 100644 index 0000000..e5f79da --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "ppLSEC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Monocyte", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Kupffer-Doublet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "P-Hepato2", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "cvEndo", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_3.json new file mode 100644 index 0000000..796b0fa --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "Stellate", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Prolif", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "CD4T", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "cvLSEC-Doublet", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "aStellate", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_4.json new file mode 100644 index 0000000..6517eb3 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/DOI_10_1016_j_jhep_2023_12_023_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": 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+] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/DOI_10_1038_s41590-020-0602-z_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/DOI_10_1038_s41590-020-0602-z_batch_3.json new file mode 100644 index 0000000..90f61e8 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/DOI_10_1038_s41590-020-0602-z_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "Th17", + "full_name": "T helper 17 cell", + "paper_synonyms": "TH17; T helper (TH) 17 cells", + "tissue_context": "mesenteric lymph nodes (mLN); lamina propria of cecum, transverse colon and sigmoid colon" + }, + { + "name": "cDC2", + "full_name": "conventional dendritic cell 2", + "paper_synonyms": null, + "tissue_context": "mesenteric lymph nodes (mLN); lamina propria of cecum, transverse 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a/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_2.json new file mode 100644 index 0000000..b608cb8 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "TA", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Enterocytes", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "SM", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Goblets", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Fibs", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_3.json new file mode 100644 index 0000000..2a1c3ae --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "ACKR1+ endothelium", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Pericytes", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Enteroendocrines", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Paneth cells", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Glial cells", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_4.json new file mode 100644 index 0000000..24c513a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": "Lymphatics", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "Immune cells", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": 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"name": "Immune cells", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Immune cells", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Endothelium", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_6.json new file mode 100644 index 0000000..f8b1e48 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_6.json @@ -0,0 +1,32 @@ +[ + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_7.json new file mode 100644 index 0000000..f8b1e48 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_7.json @@ -0,0 +1,32 @@ +[ + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_8.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_8.json new file mode 100644 index 0000000..1ecce3e --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/DOI_10_1016_j_cell_2019_08_008_batch_8.json @@ -0,0 +1,14 @@ +[ + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Stroma", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_0.json new file mode 100644 index 0000000..a03ba16 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "alpha", + "full_name": "alpha cell", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "beta_major", + "full_name": "beta cell", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "endothelial", + "full_name": "endothelial cell", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "delta", + "full_name": "delta cell", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "stellates", + "full_name": "stellate cell", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_1.json new file mode 100644 index 0000000..8594d63 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "beta_minor", + "full_name": "beta cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "duct_major", + "full_name": "ductal cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "immune_stellates", + "full_name": "immune and stellate cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "acinar_minor_mhcclassII", + "full_name": "MHC Class II acinar cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "hybrid", + "full_name": "Hybrid cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_2.json new file mode 100644 index 0000000..456c604 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/DOI_10_1038_s42255-022-00531-x_batch_2.json @@ -0,0 +1,26 @@ +[ + { + "name": "acinar", + "full_name": "acinar cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "epsilon", + "full_name": "epsilon cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets; islets of Langerhans" + }, + { + "name": "duct_acinar_related", + "full_name": "ductal-acinar related cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + }, + { + "name": "pp", + "full_name": "PP cells", + "paper_synonyms": null, + "tissue_context": "pancreatic islets" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_0.json similarity index 94% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_0.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_0.json index 5fa4db0..77e5f59 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_0.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_0.json @@ -25,7 +25,7 @@ }, { "name": "SV2C LAMP5 Interneurons", - "full_name": "SV2C LAMP5 Interneurons", + "full_name": "SV2C LAMP5 interneurons", "paper_synonyms": "", "tissue_context": "frontal cortex" } diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_1.json similarity index 90% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_1.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_1.json index 7cae50b..ee41b50 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_1.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_1.json @@ -8,19 +8,19 @@ { "name": "L2-L3 Intratelencephalic", "full_name": "L2-L3 intratelencephalic", - "paper_synonyms": null, + "paper_synonyms": "", "tissue_context": "frontal cortex" }, { "name": "L3-L5 Intratelencephalic Type 2", "full_name": "L3-L5 intratelencephalic type 2", - "paper_synonyms": null, + "paper_synonyms": "", "tissue_context": "frontal cortex" }, { "name": "L6 Intratelencephalic - Type 2", "full_name": "L6 intratelencephalic type 2", - "paper_synonyms": null, + "paper_synonyms": "", "tissue_context": "frontal cortex" }, { diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_2.json similarity index 72% rename from cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_2.json rename to cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_2.json index 4e2443e..5e92ca7 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_2.json +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_2.json @@ -1,31 +1,31 @@ [ { "name": "L5-L6 Near Projecting", - "full_name": "L5-L6 near projecting", + "full_name": "L5-L6 near projecting neuronal cluster", "paper_synonyms": null, "tissue_context": "frontal cortex" }, { "name": "Somatostatin Interneurons", - "full_name": "Somatostatin Interneurons", + "full_name": "somatostatin interneurons", "paper_synonyms": null, "tissue_context": "frontal cortex" }, { "name": "Microglia", - "full_name": "Microglia", + "full_name": "microglia", "paper_synonyms": null, "tissue_context": "frontal cortex; occipital cortex" }, { "name": "VIP Interneurons", - "full_name": "VIP Interneurons", + "full_name": "VIP interneurons", "paper_synonyms": null, "tissue_context": "frontal cortex" }, { "name": "L5 Extratelencephalic", - "full_name": "L5 Extratelencephalic", + "full_name": "L5 extratelencephalic neurons", "paper_synonyms": null, "tissue_context": "frontal cortex" } diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_3.json new file mode 100644 index 0000000..247d8bd --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/DOI_10_1007_s00401-023-02599-5_batch_3.json @@ -0,0 +1,14 @@ +[ + { + "name": "Endothelial", + "full_name": "endothelial cells", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + }, + { + "name": "Parvalbumin interneurons", + "full_name": "Parvalbumin interneurons", + "paper_synonyms": null, + "tissue_context": "frontal cortex; occipital cortex" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_0.json new file mode 100644 index 0000000..1132b7c --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "Plasmablast", + "full_name": "plasmablast", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "NK", + "full_name": "NK cell", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "B memory", + "full_name": "memory B cell", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cell (PBMC)" + }, + { + "name": "B naive", + "full_name": "naive B cell", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cell (PBMC)" + }, + { + "name": "cDC", + "full_name": "conventional dendritic cell", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_1.json new file mode 100644 index 0000000..d10dcea --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "HSPC", + "full_name": "Hematopoietic stem and progenitor cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMCs)" + }, + { + "name": "CD4+ T naive", + "full_name": "CD4+ Naive T cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMCs)" + }, + { + "name": "Platelet", + "full_name": "platelet", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMCs)" + }, + { + "name": "CD8+ Tem", + "full_name": "CD8+ Effector Memory cell", + "paper_synonyms": "CD8+ TEM", + "tissue_context": "peripheral blood mononuclear cells (PBMCs)" + }, + { + "name": "CD4+ CTL", + "full_name": "CD4+ Cytotoxic T cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMCs)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_10.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_10.json new file mode 100644 index 0000000..e3721a2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_10.json @@ -0,0 +1,32 @@ +[ + { + "name": "Lymphoid_T/NK", + "full_name": "lymphoid T and NK cells", + "paper_synonyms": "T cell; NK cell", + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Hematopoietic_Mega", + "full_name": "hematopoietic megakaryocyte", + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Lymphoid_T/NK", + "full_name": "lymphoid T and NK cells", + "paper_synonyms": "T cell; NK cell", + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Lymphoid_T/NK", + "full_name": "lymphoid T and NK cells", + "paper_synonyms": "T cell; NK cell", + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Myeloid", + "full_name": "myeloid cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL); lung parenchyma" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_11.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_11.json new file mode 100644 index 0000000..d5bb636 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_11.json @@ -0,0 +1,32 @@ +[ + { + "name": "Myeloid", + "full_name": "myeloid cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL); lung parenchyma" + }, + { + "name": "Lymphoid_T/NK", + "full_name": "lymphoid T and NK cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Myeloid", + "full_name": "myeloid cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL); lung parenchyma" + }, + { + "name": "Lymphoid_T/NK", + "full_name": "lymphoid T and NK cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Lymphoid_T/NK", + "full_name": "lymphoid T and NK cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMC); bronchoalveolar lavage (BAL)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_12.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_12.json new file mode 100644 index 0000000..130d1f7 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_12.json @@ -0,0 +1,32 @@ +[ + { + "name": "Lymphoid_T/NK", + "full_name": "T and NK cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Hematopoietic_R", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Lymphoid_B", + "full_name": "B cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Lymphoid_T/NK", + "full_name": "T and NK cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Lymphoid_T/NK", + "full_name": "T and NK cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_13.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_13.json new file mode 100644 index 0000000..4610c7e --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_13.json @@ -0,0 +1,26 @@ +[ + { + "name": "Lymphoid_T/NK", + "full_name": "Lymphoid T and NK cells", + "paper_synonyms": "T cell; NK cell", + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Lymphoid_T/NK", + "full_name": "Lymphoid T and NK cells", + "paper_synonyms": "T cell; NK cell", + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "Myeloid_G", + "full_name": "myeloid cells", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL); lung parenchyma" + }, + { + "name": "Myeloid_G", + "full_name": "myeloid cells", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL); lung parenchyma" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_2.json new file mode 100644 index 0000000..7da3371 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "Classical Monocyte", + "full_name": "classical monocyte", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC)" + }, + { + "name": "pDC", + "full_name": "plasmacytoid dendritic cell", + "paper_synonyms": "plasmacytoid dendritic cells", + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "T/NK proliferative", + "full_name": "proliferative T and NK cells", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC)" + }, + { + "name": "Non-classical Monocyte", + "full_name": "non-classical monocyte", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC)" + }, + { + "name": "NK CD56bright", + "full_name": "CD56-bright NK cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_3.json new file mode 100644 index 0000000..8f815a4 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "CD4+ Tcm", + "full_name": "CD4+ Central Memory T cell", + "paper_synonyms": "CD4+ TCM; CD4+ Central Memory T cell", + "tissue_context": "peripheral blood mononuclear cell (PBMC)" + }, + { + "name": "Treg", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "RBC", + "full_name": "red blood cell", + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "B intermediate", + "full_name": "intermediate B cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC)" + }, + { + "name": "CD4+ T activated", + "full_name": "CD4+ activated T cell", + "paper_synonyms": "activated T cells", + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_4.json new file mode 100644 index 0000000..0603842 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": "CD8+ T naive", + "full_name": "CD8+ Naive T cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMCs); bronchoalveolar lavage (BAL)" + }, + { + "name": "CD8+ T activated", + "full_name": "Activated CD8+ T cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMCs); bronchoalveolar lavage (BAL)" + }, + { + "name": "NK activated", + "full_name": "Activated NK cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMCs); bronchoalveolar lavage (BAL)" + }, + { + "name": "MAIT", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Neutrophil", + "full_name": "Neutrophil", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cells (PBMCs); bronchoalveolar lavage (BAL)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_5.json new file mode 100644 index 0000000..466ad0d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_5.json @@ -0,0 +1,32 @@ +[ + { + "name": "immature Neutrophil", + "full_name": "immature neutrophil", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "NK cell", + "full_name": "NK cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "B cell", + "full_name": "B cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "B cell", + "full_name": "B cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "CD4+ T cell", + "full_name": "CD4+ T cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_6.json new file mode 100644 index 0000000..14ba1bf --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_6.json @@ -0,0 +1,32 @@ +[ + { + "name": "CD8+ T cell", + "full_name": "CD8+ T cell", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cells (PBMCs); bronchoalveolar lavage (BAL)" + }, + { + "name": "CD4+ T cell", + "full_name": "CD4+ T cell", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cells (PBMCs); bronchoalveolar lavage (BAL)" + }, + { + "name": "CD14+ Monocyte", + "full_name": "CD14+ monocytes", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cells (PBMCs)" + }, + { + "name": "Other T", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "CD16+ Monocyte", + "full_name": "monocytes", + "paper_synonyms": "", + "tissue_context": "peripheral blood mononuclear cells (PBMCs)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_7.json new file mode 100644 index 0000000..8d00fc7 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_7.json @@ -0,0 +1,32 @@ +[ + { + "name": "NK cell", + "full_name": "NK cell", + 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a/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_8.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_8.json new file mode 100644 index 0000000..044b0b0 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/DOI_10_1016_j_isci_2021_103115_batch_8.json @@ -0,0 +1,32 @@ +[ + { + "name": "CD8+ T cell", + "full_name": "CD8+ T cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "CD8+ T cell", + "full_name": "CD8+ T cell", + "paper_synonyms": null, + "tissue_context": "peripheral blood mononuclear cell (PBMC); bronchoalveolar lavage (BAL)" + }, + { + "name": "CD8+ T cell", + "full_name": "CD8+ T cell", + "paper_synonyms": 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b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique/DOI_10_1038_s41598-020-66092-9_batch_2.json @@ -0,0 +1,14 @@ +[ + { + "name": "DB6", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human retina; fovea; peripheral retina" + }, + { + "name": "RB1", + "full_name": null, + "paper_synonyms": "rod bipolar cells; rod BCs", + "tissue_context": "human retina; fovea; peripheral retina" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_0.json new file mode 100644 index 0000000..f01b833 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "CD4 T cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "TA", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "IgA plasma cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Goblet cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "IgG plasma cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_1.json new file mode 100644 index 0000000..e0b3bfc --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "Cycling B cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "S4 fibroblasts", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "crypt", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "early enterocyte", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Memory B cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_2.json new file mode 100644 index 0000000..8745952 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "FCER2 B cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Tfh", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "cDC2", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Arterial endothelial cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "IL2RG+ enterocyte (M cell)", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_3.json new file mode 100644 index 0000000..68fb6e9 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "enterocyte", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "enteroendocrine", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Venous endothelial cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Activated T", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Monocyte", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_4.json new file mode 100644 index 0000000..f148125 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": "S1 fibroblasts", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "cDC1", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "myofibroblast", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "pericyte", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Treg", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_5.json new file mode 100644 index 0000000..ee9b9de --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_5.json @@ -0,0 +1,32 @@ +[ + { + "name": "Lymphatic endothelial cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "B cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Activated B cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "pDC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Cycling plasma cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_6.json new file mode 100644 index 0000000..9a54959 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_6.json @@ -0,0 +1,32 @@ +[ + { + "name": "gd T/NK cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "mast cells", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Paneth cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "BEST4 enterocyte", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "activated DC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_7.json new file mode 100644 index 0000000..97cddb2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_7.json @@ -0,0 +1,32 @@ +[ + { + "name": "S2 fibroblasts", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "CD8 T cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Macrophage", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Glial cell", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "Cycling myeloid cells", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_8.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_8.json new file mode 100644 index 0000000..4871f13 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/DOI_10_1016_j_devcel_2020_11_010_batch_8.json @@ -0,0 +1,8 @@ +[ + { + "name": "Tuft", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json new file mode 100644 index 0000000..eded8e9 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "Iris_PPE", + "full_name": "iris posterior pigmented epithelium", + "paper_synonyms": "posterior pigmented epithelium; PPE", + "tissue_context": "iris; posterior pigmented epithelium; posterior chamber" + }, + { + "name": "Uveal_Melanocyte", + "full_name": "uveal melanocyte", + "paper_synonyms": "", + "tissue_context": "anterior border layer and stroma of the iris; ciliary muscle" + }, + { + "name": "Iris_Fibro", + "full_name": "iris stromal fibroblast", + "paper_synonyms": "iris fibroblasts", + "tissue_context": "stroma and anterior border layer of the iris" + }, + { + "name": "Iris_APE", + "full_name": "iris anterior pigmented epithelium", + "paper_synonyms": "anterior pigmented epithelium; APE; iris dilator muscle", + "tissue_context": "iris; iris dilator muscle; anterior pigmented epithelium" + }, + { + "name": "Macrophage", + "full_name": "macrophage", + "paper_synonyms": "clump cells", + "tissue_context": "central cornea; limbus; iris stroma; ciliary stroma" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json new file mode 100644 index 0000000..df5aff5 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json @@ -0,0 +1,26 @@ +[ + { + "name": "Iris_Sphincter", + "full_name": "iris sphincter muscle cell", + "paper_synonyms": null, + "tissue_context": "iris; iris sphincter muscle" + }, + { + "name": "Schwann", + "full_name": "Schwann cell", + "paper_synonyms": null, + "tissue_context": "iris stroma; ciliary body" + }, + { + "name": "Lymphocyte", + "full_name": "lymphocyte", + "paper_synonyms": null, + "tissue_context": "cornea; iris; ciliary body; corneoscleral wedge (CSW)" + }, + { + "name": "Vasc_Endo", + "full_name": "vascular endothelium", + "paper_synonyms": null, + "tissue_context": "iris stroma; ciliary stroma; external limbus; conjunctival stroma" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_0.json new file mode 100644 index 0000000..c672041 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "PC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "PT", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "TAL", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "DCT2", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "DCT1", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_1.json new file mode 100644 index 0000000..0b50bf2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "ICA", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "ICB", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "PEC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "CNT", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "ENDO", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_2.json new file mode 100644 index 0000000..db1d2fe --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/DOI_10_1038_s41467-021-22368-w_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "MES", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "PODO", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "PT_VCAM1", + "full_name": "VCAM1", + "paper_synonyms": null, + "tissue_context": "adult human kidney; proximal tubule epithelial cells" + }, + { + "name": "LEUK", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + }, + { + "name": "FIB", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "adult human kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_0.json new file mode 100644 index 0000000..cd98f40 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "Thick ascending limb of Loop of Henle", + "full_name": "loop of Henle", + "paper_synonyms": "LOH; loop of Henle", + "tissue_context": "human kidney; nephron; mature kidney; fetal kidney" + }, + { + "name": "Peritubular capillary endothelium 2", + "full_name": "peritubular capillary endothelium 2", + "paper_synonyms": "PCE; peritubular capillaries; PCap", + "tissue_context": "human kidney; peritubular capillaries" + }, + { + "name": "Proximal tubule", + "full_name": "proximal tubule", + "paper_synonyms": "PT", + "tissue_context": "human kidney; cortex; cortico-medullary; mature kidney; fetal kidney" + }, + { + "name": "NK cell", + "full_name": "natural killer cell", + "paper_synonyms": "NK", + "tissue_context": "mature kidney; fetal kidney; human kidney" + }, + { + "name": "Proliferating Proximal Tubule", + "full_name": "proximal tubule", + "paper_synonyms": "PT", + "tissue_context": "human kidney; cortex; cortico-medullary" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_1.json new file mode 100644 index 0000000..5abd1f6 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "CD4 T cell", + "full_name": "CD4 T cell", + "paper_synonyms": "CD4 T", + "tissue_context": "mature kidney; fetal kidney" + }, + { + "name": "Podocyte", + "full_name": "podocyte", + "paper_synonyms": "Podo", + "tissue_context": "cortical/cortico-medullary; cortex; glomeruli" + }, + { + "name": "Transitional urothelium", + "full_name": "transitional epithelium of ureter", + "paper_synonyms": "TE; transitional epithelium; transitional epithelium of ureter", + "tissue_context": "medulla/pelvic pseudodepth; ureter; kidney pelvis" + }, + { + "name": "B cell", + "full_name": "B cell", + "paper_synonyms": "B", + "tissue_context": "cortical samples; mature kidney; fetal kidney" + }, + { + "name": "Ascending vasa recta endothelium", + "full_name": "ascending vasa recta endothelium", + "paper_synonyms": "AVRE", + "tissue_context": "mature kidney; vasa recta (VR)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_2.json new file mode 100644 index 0000000..0f6df49 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "Type A intercalated cell", + "full_name": "type A intercalated cell", + "paper_synonyms": "intercalated cells (IC); IC (A+B); IC", + "tissue_context": "collecting duct" + }, + { + "name": "Peritubular capillary endothelium 1", + "full_name": "peritubular capillary endothelium", + "paper_synonyms": "peritubular capillaries (PCap); PCE", + "tissue_context": "peritubular capillaries (PCap)" + }, + { + "name": "Pelvic epithelium", + "full_name": "pelvic epithelium", + "paper_synonyms": "PE", + "tissue_context": "kidney pelvis; medulla/pelvis" + }, + { + "name": "Glomerular endothelium", + "full_name": "glomerular endothelium", + "paper_synonyms": "GE; glomerular endothelial cells (GE)", + "tissue_context": "glomeruli; cortex; cortical/cortico-medullary" + }, + { + "name": "Indistinct intercalated cell", + "full_name": "intercalated cell", + "paper_synonyms": "intercalated cells (IC); IC", + "tissue_context": "collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_3.json new file mode 100644 index 0000000..424aa72 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "Type B intercalated cell", + "full_name": "type B intercalated cell", + "paper_synonyms": "IC; intercalated cells; IC (A+B)", + "tissue_context": "collecting duct; human kidney" + }, + { + "name": "Descending vasa recta endothelium", + "full_name": "descending vasa recta endothelium", + "paper_synonyms": "DVRE", + "tissue_context": "vasa recta; human kidney" + }, + { + "name": "CD8 T cell", + "full_name": "CD8 T cell", + "paper_synonyms": "CD8 T", + "tissue_context": "mature kidney; fetal kidney; human kidney" + }, + { + "name": "Epithelial progenitor cell", + "full_name": "epithelial progenitor cell", + "paper_synonyms": "EPC", + "tissue_context": "developing nephron epithelium; fetal kidney" + }, + { + "name": "MNP-a/classical monocyte derived", + "full_name": "mononuclear phagocyte a/classical monocyte-derived", + "paper_synonyms": "MNPa", + "tissue_context": "mature kidney; inner regions of the kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_4.json new file mode 100644 index 0000000..08ebf02 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": "Neutrophil", + "full_name": "neutrophil", + "paper_synonyms": "NO", + "tissue_context": "mature kidney; kidney pelvis; pelvic epithelium" + }, + { + "name": "Connecting tubule", + "full_name": "connecting nephron tubule", + "paper_synonyms": "CNT", + "tissue_context": "nephron epithelial cells; mature kidney; fetal kidney" + }, + { + "name": "Myofibroblast", + "full_name": "myofibroblast", + "paper_synonyms": "MFib", + "tissue_context": "human kidney; mature kidney; fetal kidney" + }, + { + "name": "MNP-c/dendritic cell", + "full_name": "mononuclear phagocyte c (dendritic cell)", + "paper_synonyms": "MNPc; classical myeloid DC; cDC", + "tissue_context": "mature kidney" + }, + { + "name": "NKT cell", + "full_name": "natural killer T cell", + "paper_synonyms": "NKT", + "tissue_context": "mature kidney; human kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_5.json new file mode 100644 index 0000000..31968cc --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_5.json @@ -0,0 +1,32 @@ +[ + { + "name": "MNP-d/Tissue macrophage", + "full_name": "mononuclear phagocyte d/Tissue macrophage", + "paper_synonyms": "MNPd", + "tissue_context": "mature human kidney; cortex; medulla/pelvis" + }, + { + "name": "Fibroblast", + "full_name": "fibroblast", + "paper_synonyms": "Fib", + "tissue_context": "human kidney; stroma" + }, + { + "name": "Distinct proximal tubule 1", + "full_name": "distinct proximal tubule 1", + "paper_synonyms": "dPT", + "tissue_context": "mature human kidney; cortical/cortico-medullary" + }, + { + "name": "MNP-b/non-classical monocyte derived", + "full_name": "mononuclear phagocyte b/non-classical monocyte derived", + "paper_synonyms": "MNPb", + "tissue_context": "mature human kidney; deeper regions of the kidney; medulla/pelvis" + }, + { + "name": "Distinct proximal tubule 2", + "full_name": "distinct proximal tubule 2", + "paper_synonyms": "dPT", + "tissue_context": "mature human kidney; cortical/cortico-medullary" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_6.json new file mode 100644 index 0000000..e9ebbda --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_6.json @@ -0,0 +1,32 @@ +[ + { + "name": "Principal cell", + "full_name": "principal cell", + "paper_synonyms": "PC", + "tissue_context": "mature human kidneys; collecting duct" + }, + { + "name": "Plasmacytoid dendritic cell", + "full_name": "plasmacytoid dendritic cell", + "paper_synonyms": "pDC", + "tissue_context": "mature kidney; fetal kidney" + }, + { + "name": "Mast cell", + "full_name": "mast cell", + "paper_synonyms": "Mast", + "tissue_context": "mature kidney; fetal kidney" + }, + { + "name": "Peritubular capillary endothelium", + "full_name": "peritubular capillary endothelium", + "paper_synonyms": "PCE", + "tissue_context": "human kidney; peritubular capillaries" + }, + { + "name": "Intercalated cell", + "full_name": "intercalated cells (IC)", + "paper_synonyms": "IC; IC (A+B); type A and B intercalated cells", + "tissue_context": "mature human kidneys; collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_7.json new file mode 100644 index 0000000..5371a63 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/DOI_10_1126_science_aat5031_batch_7.json @@ -0,0 +1,14 @@ +[ + { + "name": "Intercalated cell", + "full_name": "intercalated cell", + "paper_synonyms": "IC", + "tissue_context": "human kidney; collecting duct" + }, + { + "name": "Intercalated cell", + "full_name": "intercalated cell", + "paper_synonyms": "IC", + "tissue_context": "human kidney; collecting duct" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_7.json deleted file mode 100644 index 3d2cc42..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1007_s00401-023-02599-5_batch_7.json +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "name": "Excitatory_7", - "full_name": "excitatory neurons", - "paper_synonyms": null, - "tissue_context": "frontal cortex; occipital cortex" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_0.json deleted file mode 100644 index 5ff2f37..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_0.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "Fibroblasts ADAMDEC1", - "full_name": "ADAMDEC1+ fibroblasts", - "paper_synonyms": "ADAMDEC+ fibroblasts", - "tissue_context": "terminal ileum (TI); colon (CO)" - }, - { - "name": "Endothelial cells CD36", - "full_name": "CD36+ endothelial cells", - "paper_synonyms": null, - "tissue_context": "terminal ileum (TI); colon (CO)" - }, - { - "name": "Myofibroblasts HHIP NPNT", - "full_name": "HHIP+ NPNT+ myofibroblasts", - "paper_synonyms": null, - "tissue_context": "terminal ileum (TI); colon (CO)" - }, - { - "name": "Fibroblasts SMOC2 PTGIS", - "full_name": "SMOC2+ PTGIS+ fibroblasts", - "paper_synonyms": "SMOC2+ PTGIS+ fibroblasts", - "tissue_context": "terminal ileum (TI); colon (CO)" - }, - { - "name": "Endothelial cells DARC", - "full_name": "DARC+ endothelial cells", - "paper_synonyms": "ACKR1", - "tissue_context": "terminal ileum (TI); colon (CO)" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_3.json deleted file mode 100644 index 5d30844..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1016_j_immuni_2023_01_002_batch_3.json +++ /dev/null @@ -1,8 +0,0 @@ -[ - { - "name": "Pericytes RERGL NTRK2", - "full_name": "Pericytes", - "paper_synonyms": null, - "tissue_context": "terminal ileum; colon; lamina propria" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_0.json deleted file mode 100644 index 7278454..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_0.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "epithelial cells", - "full_name": "epithelial cells", - "paper_synonyms": null, - "tissue_context": "human kidney; cortex; medulla; nephron segments; interstitium" - }, - { - "name": "stroma cells", - "full_name": "stromal cells", - "paper_synonyms": "STR; aStr", - "tissue_context": "human kidney; cortex; medulla; interstitium" - }, - { - "name": "epithelial cells", - "full_name": "epithelial cells", - "paper_synonyms": null, - "tissue_context": "human kidney; cortex; medulla; nephron segments; interstitium" - }, - { - "name": "immune cells", - "full_name": "immune cells", - "paper_synonyms": "IMM; leukocytes", - "tissue_context": "human kidney; cortex; medulla" - }, - { - "name": "epithelial cells", - "full_name": "epithelial cells", - "paper_synonyms": null, - "tissue_context": "human kidney; cortex; medulla; nephron segments; interstitium" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_1.json deleted file mode 100644 index 5c2362f..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_1.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "endothelial cells", - "full_name": "endothelial cells", - "paper_synonyms": "EC", - "tissue_context": "human kidney; renal corpuscle; glomerular capillaries (EC-GC); afferent/efferent arterioles (EC-AEA); endothelial cells of the lymphatics (EC-LYM); vasa recta (EC-AVR, EC-DVR)" - }, - { - "name": "immune cells", - "full_name": "immune cells", - "paper_synonyms": "leukocytes; IMM", - "tissue_context": "human kidney; cortex; medulla; interstitium" - }, - { - "name": "epithelial cells", - "full_name": "epithelial cells", - "paper_synonyms": "", - "tissue_context": "human kidney; nephron; cortex; medulla" - }, - { - "name": "immune cells", - "full_name": "immune cells", - "paper_synonyms": "leukocytes; IMM", - "tissue_context": "human kidney; cortex; medulla; interstitium" - }, - { - "name": "epithelial cells", - "full_name": "epithelial cells", - "paper_synonyms": "", - "tissue_context": "human kidney; nephron; cortex; medulla" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_10.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_10.json deleted file mode 100644 index 1744ee1..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_10.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "Proximal Tubule Epithelial Cell Segment 1 / Segment 2", - "full_name": "Proximal tubule epithelial cell, segments 1 and 2", - "paper_synonyms": "PT-S1; PT-S2; PT-S1/PT-S2; PT", - "tissue_context": "proximal tubule (PT); cortex" - }, - { - "name": "Proximal Tubule Epithelial Cell Segment 3", - "full_name": "Proximal tubule epithelial cell, segment 3", - "paper_synonyms": "PT-S3; PT", - "tissue_context": "proximal tubule (PT)" - }, - { - "name": "Cortical Thick Ascending Limb Cell", - "full_name": "Cortical thick ascending limb cell", - "paper_synonyms": "C-TAL; cortical TAL", - "tissue_context": "cortex; cortical thick ascending limb (C-TAL)" - }, - { - "name": "Outer Medullary Collecting Duct Principal Cell", - "full_name": "Outer medullary collecting duct principal cell", - "paper_synonyms": "OMCD; principal cells (PC); medullary principal cell (M-PC)", - "tissue_context": "outer medulla; outer medullary collecting duct (OMCD)" - }, - { - "name": "Fibroblast", - "full_name": "Fibroblast", - "paper_synonyms": "FIB", - "tissue_context": "interstitium; stroma" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_11.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_11.json deleted file mode 100644 index 6a2dcfd..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_11.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "Degenerative Outer Medullary Collecting Duct Principal Cell", - "full_name": "Degenerative outer medullary collecting duct principal cell", - "paper_synonyms": "degenerative medullary principal cells; dM-PCs", - "tissue_context": "outer medulla; collecting duct" - }, - { - "name": "T Cell", - "full_name": "T cell", - "paper_synonyms": "T; CD3+ cells", - "tissue_context": "cortex; medulla" - }, - { - "name": "Plasma Cell", - "full_name": "Plasma cell", - "paper_synonyms": "PL", - "tissue_context": "human kidney" - }, - { - "name": "Connecting Tubule Principal Cell", - "full_name": "Connecting tubule principal cell", - "paper_synonyms": "CNT-PC", - "tissue_context": "connecting tubule; cortex" - }, - { - "name": "Distal Convoluted Tubule Cell Type 1", - "full_name": "Distal convoluted tubule cell type 1", - "paper_synonyms": "DCT1", - "tissue_context": "distal convoluted tubule; cortex" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_16.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_16.json deleted file mode 100644 index 5cbeb62..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_16.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "Degenerative Ascending Thin Limb Cell", - "full_name": "degenerative ascending thin limb (ATL) cell", - "paper_synonyms": "ATL", - "tissue_context": "ascending thin limbs (ATL) of the inner medulla" - }, - { - "name": "Renin-positive Juxtaglomerular Granular Cell", - "full_name": "juxtaglomerular renin-producing granular (REN) cell", - "paper_synonyms": "renin-producing granular (REN) cells; REN; juxtaglomerular renin-producing granular cells (REN)", - "tissue_context": "juxtaglomerular apparatus; afferent/efferent arterioles (EC-AEA); renal corpuscle" - }, - { - "name": "B Cell", - "full_name": "B cell", - "paper_synonyms": "B", - "tissue_context": "" - }, - { - "name": "Degenerative Cortical Intercalated Cell Type A", - "full_name": "degenerative cortical intercalated cell type A", - "paper_synonyms": "IC; intercalated cells", - "tissue_context": "cortex" - }, - { - "name": "Degenerative Connecting Tubule Cell", - "full_name": "degenerative connecting tubule (CNT) cell", - "paper_synonyms": "CNT", - "tissue_context": "connecting tubule; cortex" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_18.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_18.json deleted file mode 100644 index 4efe6b9..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_18.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "Degenerative Endothelial Cell", - "full_name": "degenerative endothelial cell", - "paper_synonyms": "EC", - "tissue_context": "vasculature; afferent/efferent arterioles; glomerular capillaries; vasa recta; lymphatics" - }, - { - "name": "Degenerative Medullary Fibroblast", - "full_name": "degenerative medullary fibroblast", - "paper_synonyms": "FIB", - "tissue_context": "medulla; interstitium" - }, - { - "name": "Connecting Tubule Intercalated Cell Type A", - "full_name": "connecting tubule intercalated cell", - "paper_synonyms": "CNT-IC; IC", - "tissue_context": "connecting tubules (CNT)" - }, - { - "name": "Cycling Distal Convoluted Tubule Cell", - "full_name": "cycling distal convoluted tubule cell", - "paper_synonyms": "DCT; Cyc", - "tissue_context": "distal convoluted tubule (DCT)" - }, - { - "name": "Degenerative Podocyte", - "full_name": "degenerative podocyte", - "paper_synonyms": "POD", - "tissue_context": "renal corpuscle; glomeruli" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_2.json deleted file mode 100644 index b6837c8..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_2.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "epithelial cells", - "full_name": "epithelial cells", - "paper_synonyms": null, - "tissue_context": "human kidney; nephron segments; renal tubules; cortex; outer medulla; inner medulla; papillary tip" - }, - { - "name": "immune cells", - "full_name": "immune cells", - "paper_synonyms": "leukocytes", - "tissue_context": "human kidney; cortex; medulla; areas of injury; interstitial fibrosis" - }, - { - "name": "epithelial cells", - "full_name": "epithelial cells", - "paper_synonyms": null, - "tissue_context": "human kidney; nephron segments; renal tubules; cortex; outer medulla; inner medulla; papillary tip" - }, - { - "name": "immune cells", - "full_name": "immune cells", - "paper_synonyms": "leukocytes", - "tissue_context": "human kidney; cortex; medulla; areas of injury; interstitial fibrosis" - }, - { - "name": "immune cells", - "full_name": "immune cells", - "paper_synonyms": "leukocytes", - "tissue_context": "human kidney; cortex; medulla; areas of injury; interstitial fibrosis" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_20.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_20.json deleted file mode 100644 index 264ab2a..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_20.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "Cycling Natural Killer Cell / Natural Killer T Cell", - "full_name": "Cycling natural killer cell / natural killer T cell", - "paper_synonyms": "NKT", - "tissue_context": "areas of injury; renal cortical and medullary structures" - }, - { - "name": "PT", - "full_name": "proximal tubule", - "paper_synonyms": null, - "tissue_context": "cortex" - }, - { - "name": "FIB", - "full_name": "fibroblast", - "paper_synonyms": null, - "tissue_context": "interstitium; cortex" - }, - { - "name": "TAL", - "full_name": "thick ascending limb", - "paper_synonyms": "C-TAL; M-TAL", - "tissue_context": "cortex; medulla; outer medullary stripe" - }, - { - "name": "IMM", - "full_name": "immune cell", - "paper_synonyms": "immune", - "tissue_context": "renal cortical and medullary structures" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_22.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_22.json deleted file mode 100644 index 8586ce1..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_22.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "POD", - "full_name": "podocyte", - "paper_synonyms": "PODs", - "tissue_context": "renal corpuscle; glomerulus; cortex" - }, - { - "name": "ATL", - "full_name": "ascending thin limb", - "paper_synonyms": null, - "tissue_context": "inner medulla" - }, - { - "name": "IMM", - "full_name": "immune cells", - "paper_synonyms": "leukocytes", - "tissue_context": "cortex; medulla; areas of injury; region of fibrosis" - }, - { - "name": "PC", - "full_name": "principal cells", - "paper_synonyms": null, - "tissue_context": "collecting duct; connecting tubules; cortex; outer medulla; inner medulla" - }, - { - "name": "IMM", - "full_name": "immune cells", - "paper_synonyms": "leukocytes", - "tissue_context": "cortex; medulla; areas of injury; region of fibrosis" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_24.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_24.json deleted file mode 100644 index 1b0f75d..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_24.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "IMM", - "full_name": "immune cell", - "paper_synonyms": "leukocytes", - "tissue_context": "cortex; medulla; kidney biopsy samples" - }, - { - "name": "IMM", - "full_name": "immune cell", - "paper_synonyms": "leukocytes", - "tissue_context": "cortex; medulla; kidney biopsy samples" - }, - { - "name": "IMM", - "full_name": "immune cell", - "paper_synonyms": "leukocytes", - "tissue_context": "cortex; medulla; kidney biopsy samples" - }, - { - "name": "IMM", - "full_name": "immune cell", - "paper_synonyms": "leukocytes", - "tissue_context": "cortex; medulla; kidney biopsy samples" - }, - { - "name": "PEC", - "full_name": "parietal epithelial cell", - "paper_synonyms": null, - "tissue_context": "renal corpuscle" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_25.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_25.json deleted file mode 100644 index d9130b0..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_25.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "IMM", - "full_name": "immune cells", - "paper_synonyms": null, - "tissue_context": "renal cortical and medullary structures" - }, - { - "name": "PapE", - "full_name": "papillary tip epithelial cells abutting the calyx", - "paper_synonyms": null, - "tissue_context": "papillary tip; calyx" - }, - { - "name": "dPT", - "full_name": "degenerative proximal tubule cells", - "paper_synonyms": null, - "tissue_context": "proximal tubule (PT)" - }, - { - "name": "aPT", - "full_name": "adaptive proximal tubule cells", - "paper_synonyms": null, - "tissue_context": "proximal tubule (PT)" - }, - { - "name": "M-FIB", - "full_name": "medullary fibroblasts", - "paper_synonyms": null, - "tissue_context": "medulla; interstitium" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_26.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_26.json deleted file mode 100644 index ec0df29..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_26.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "MD", - "full_name": "macula densa cells", - "paper_synonyms": null, - "tissue_context": "renal corpuscle; afferent/efferent arterioles" - }, - { - "name": "NKC/T", - "full_name": "T cells", - "paper_synonyms": "T", - "tissue_context": "areas of tissue damage; fibrosis; around vessels" - }, - { - "name": "tPC-IC", - "full_name": "transitioning principal and intercalated cells", - "paper_synonyms": "principal cells (PC); intercalated cells (IC)", - "tissue_context": "medullary tubules; collecting duct" - }, - { - "name": "EC-DVR", - "full_name": "endothelial cells of the vasa recta", - "paper_synonyms": null, - "tissue_context": "vasa recta; medulla" - }, - { - "name": "M-TAL", - "full_name": "medullary thick ascending limb", - "paper_synonyms": "thick ascending limb (TAL)", - "tissue_context": "inner medulla; outer medullary stripe" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_27.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_27.json deleted file mode 100644 index 6d108f3..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_27.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "C-IC-A", - "full_name": "cortical intercalated cell", - "paper_synonyms": "IC", - "tissue_context": "connecting tubules (CNT); collecting ducts" - }, - { - "name": "dM-TAL", - "full_name": "thick ascending limb", - "paper_synonyms": "TAL", - "tissue_context": "cortex; outer medulla; inner medulla" - }, - { - "name": "EC-AVR", - "full_name": "endothelial cell, vasa recta", - "paper_synonyms": "EC", - "tissue_context": "vasa recta" - }, - { - "name": "MAC-M2", - "full_name": "M2 macrophage", - "paper_synonyms": "M2 macrophages", - "tissue_context": "cortex; medulla" - }, - { - "name": "cycPT", - "full_name": "cycling proximal tubule cell", - "paper_synonyms": "PT; cycling", - "tissue_context": "proximal tubule (PT)" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_28.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_28.json deleted file mode 100644 index 39e6def..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_28.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "M-IC-A", - "full_name": "intercalated cells", - "paper_synonyms": "IC", - "tissue_context": "connecting tubules (CNT-IC and CNT-PC); cortical, outer medullary and inner medullary collecting duct subpopulations (CCD, OMCD, IMCD)" - }, - { - "name": "aTAL1", - "full_name": "adaptive thick ascending limb 1", - "paper_synonyms": "aTAL; aEpi", - "tissue_context": "thick ascending limb (TAL); cortical thick ascending limb (C-TAL)" - }, - { - "name": "EC-AEA", - "full_name": "endothelial cells of the afferent/efferent arterioles", - "paper_synonyms": "AEA", - "tissue_context": "afferent/efferent arterioles; renal corpuscle" - }, - { - "name": "DTL2", - "full_name": "descending thin limb 2", - "paper_synonyms": "DTL", - "tissue_context": "descending thin limb (DTL2); medulla" - }, - { - "name": "N", - "full_name": "neutrophils", - "paper_synonyms": "MPO+ cells", - "tissue_context": "cortical or medullary epithelium (N6 and N11)" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_29.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_29.json deleted file mode 100644 index 7fcf21c..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_29.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "DTL1", - "full_name": "descending thin limb cell type 1", - "paper_synonyms": null, - "tissue_context": "DTL: AQP1+ cells in the medulla." - }, - { - "name": "MDC", - "full_name": "monocyte-derived cells", - "paper_synonyms": null, - "tissue_context": "monocyte-derived cells (MDCs) localized to a region of fibrosis within the cortex of a CKD biopsy" - }, - { - "name": "C-PC", - "full_name": "cortical principal cell", - "paper_synonyms": null, - "tissue_context": "cortex" - }, - { - "name": "EC-PTC", - "full_name": "endothelial cell", - "paper_synonyms": null, - "tissue_context": null - }, - { - "name": "PT-S1/2", - "full_name": "proximal tubule S1/S2", - "paper_synonyms": "PT-S1/PT-S2", - "tissue_context": "proximal tubule (PT)" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_32.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_32.json deleted file mode 100644 index e1ced84..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_32.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "DTL3", - "full_name": "descending thin limb 3", - "paper_synonyms": "descending thin limb; DTL", - "tissue_context": "medulla" - }, - { - "name": "EC-GC", - "full_name": "glomerular capillary endothelial cell", - "paper_synonyms": "glomerular capillaries; EC-GC", - "tissue_context": "renal corpuscle; glomeruli" - }, - { - "name": "VSMC/P", - "full_name": "vascular smooth muscle cell or pericyte", - "paper_synonyms": "VSM/P; VSMC; vascular smooth muscle cell; pericyte", - "tissue_context": "afferent/efferent arterioles" - }, - { - "name": "cycMNP", - "full_name": "cycling", - "paper_synonyms": null, - "tissue_context": "cortex; medulla" - }, - { - "name": "MYOF", - "full_name": "myofibroblast", - "paper_synonyms": "MyoF", - "tissue_context": "interstitium" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_34.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_34.json deleted file mode 100644 index 4d38755..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_34.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "MC", - "full_name": null, - "paper_synonyms": null, - "tissue_context": null - }, - { - "name": "IC-B", - "full_name": "intercalated cells B", - "paper_synonyms": "IC; intercalated cells", - "tissue_context": "connecting tubules (CNT); collecting duct" - }, - { - "name": "MAST", - "full_name": null, - "paper_synonyms": null, - "tissue_context": null - }, - { - "name": "dVSMC", - "full_name": "degenerative vascular smooth muscle cell", - "paper_synonyms": "VSMC; vascular smooth muscle cell; VSM/P", - "tissue_context": "afferent/efferent arterioles; renal corpuscle" - }, - { - "name": "dATL", - "full_name": "degenerative ascending thin limb", - "paper_synonyms": "ATL; ascending thin limbs", - "tissue_context": "inner medulla; outer medullary stripe" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_35.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_35.json deleted file mode 100644 index c66ce14..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_35.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "REN", - "full_name": "juxtaglomerular renin-producing granular cells", - "paper_synonyms": "renin-producing granular cells", - "tissue_context": "juxtaglomerular apparatus; afferent/efferent arterioles; renal corpuscle" - }, - { - "name": "B", - "full_name": null, - "paper_synonyms": null, - "tissue_context": null - }, - { - "name": "aTAL2", - "full_name": "adaptive thick ascending limb 2", - "paper_synonyms": "adaptive TAL", - "tissue_context": "C-TAL; cortex; corticomedullary sections" - }, - { - "name": "dC-IC-A", - "full_name": "degenerative cortical intercalated cell", - "paper_synonyms": null, - "tissue_context": "cortex" - }, - { - "name": "dCNT", - "full_name": "degenerative connecting tubule", - "paper_synonyms": null, - "tissue_context": "connecting tubule; cortex" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_37.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_37.json deleted file mode 100644 index 257ac91..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_37.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "dM-FIB", - "full_name": "degenerative medullary fibroblast", - "paper_synonyms": "FIB", - "tissue_context": "medulla" - }, - { - "name": "cycDCT", - "full_name": "cycling distal convoluted tubule cell", - "paper_synonyms": "DCT", - "tissue_context": "distal convoluted tubule; cortex" - }, - { - "name": "dPOD", - "full_name": "degenerative podocyte", - "paper_synonyms": "POD", - "tissue_context": "renal corpuscle; glomerulus" - }, - { - "name": "pDC", - "full_name": "plasmacytoid dendritic cell", - "paper_synonyms": "", - "tissue_context": "cortex; medulla" - }, - { - "name": "dDTL3", - "full_name": "degenerative descending thin limb cell type 3", - "paper_synonyms": "DTL3", - "tissue_context": "descending thin limb; medulla" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_39.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_39.json deleted file mode 100644 index df6a6fc..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_39.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "CCD-PC", - "full_name": "cortical collecting duct principal cell", - "paper_synonyms": "PC; principal cells; CCD; cortical collecting duct", - "tissue_context": "cortical, outer medullary and inner medullary collecting duct subpopulations (CCD, OMCD, IMCD)" - }, - { - "name": "OMCD-PC", - "full_name": "outer medullary collecting duct principal cell", - "paper_synonyms": "PC; principal cells; OMCD; outer medullary collecting duct", - "tissue_context": "cortical, outer medullary and inner medullary collecting duct subpopulations (CCD, OMCD, IMCD)" - }, - { - "name": "dOMCD-PC", - "full_name": "degenerative outer medullary collecting duct principal cell", - "paper_synonyms": "PC; principal cells; OMCD; outer medullary collecting duct; degenerative medullary principal cells (dM-PCs)", - "tissue_context": "an area showing intraluminal cellular cast formation, cell sloughing and loss of nuclei that were associated with degenerative CD cells, including degenerative medullary principal cells (dM-PCs)" - }, - { - "name": "CNT-PC", - "full_name": "connecting tubule principal cell", - "paper_synonyms": "PC; principal cells; CNT; connecting tubules", - "tissue_context": "intercalated and principal cells of the connecting tubules (CNT-IC and CNT-PC)" - }, - { - "name": "DCT1", - "full_name": "distal convoluted tubule cell (type 1)", - "paper_synonyms": "DCT; distal convoluted tubule", - "tissue_context": "two types of distal convoluted tubule cells (DCT1, 2)" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_5.json deleted file mode 100644 index 4e6d863..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_5.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "epithelial cells", - "full_name": "epithelial cells", - "paper_synonyms": null, - "tissue_context": "across different regions of the human kidney spanning the cortex to the papillary tip; along the nephron" - }, - { - "name": "Degenerative Proximal Tubule Epithelial Cell", - "full_name": "degenerative proximal tubule epithelial cell", - "paper_synonyms": null, - "tissue_context": "proximal tubule (PT)" - }, - { - "name": "Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell", - "full_name": "adaptive (successful or maladaptive tubular repair) proximal tubule epithelial cell", - "paper_synonyms": "aPT; adaptive epithelial (aEpi)", - "tissue_context": "proximal tubule (PT); cortex" - }, - { - "name": "Medullary Fibroblast", - "full_name": "medullary fibroblast", - "paper_synonyms": "FIB", - "tissue_context": "medulla; outer medulla; inner medulla" - }, - { - "name": "Macula Densa Cell", - "full_name": "macula densa cell", - "paper_synonyms": "MD", - "tissue_context": "juxtaglomerular apparatus cells; afferent/efferent arterioles (EC-AEA); renal corpuscle" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_6.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_6.json deleted file mode 100644 index 2c449cf..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_6.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "Natural Killer Cell / Natural Killer T Cell", - "full_name": "Natural Killer Cell / Natural Killer T Cell", - "paper_synonyms": "NKT", - "tissue_context": "cortex; medulla" - }, - { - "name": "Transitional Principal-Intercalated Cell", - "full_name": "transitioning principal and intercalated cells", - "paper_synonyms": "", - "tissue_context": "medulla; collecting duct" - }, - { - "name": "Descending Vasa Recta Endothelial Cell", - "full_name": "endothelial cell of the descending vasa recta", - "paper_synonyms": "EC-DVR", - "tissue_context": "medulla; vasa recta" - }, - { - "name": "Medullary Thick Ascending Limb Cell", - "full_name": "medullary thick ascending limb cell", - "paper_synonyms": "M-TAL; TAL", - "tissue_context": "outer medullary stripe; inner medulla" - }, - { - "name": "Cortical Collecting Duct Intercalated Cell Type A", - "full_name": "cortical collecting duct intercalated cell", - "paper_synonyms": "CCD; IC; C-CD", - "tissue_context": "cortex; collecting duct" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_7.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_7.json deleted file mode 100644 index b3405d8..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41586-023-05769-3_batch_7.json +++ /dev/null @@ -1,32 +0,0 @@ -[ - { - "name": "Degenerative Medullary Thick Ascending Limb Cell", - "full_name": "Degenerative Medullary Thick Ascending Limb Cell", - "paper_synonyms": "M-TAL; TAL", - "tissue_context": "outer medullary stripe; medulla" - }, - { - "name": "Ascending Vasa Recta Endothelial Cell", - "full_name": "Ascending Vasa Recta Endothelial Cell", - "paper_synonyms": "EC-AVR", - "tissue_context": "vasa recta; medulla" - }, - { - "name": "M2 Macrophage", - "full_name": "M2 Macrophage", - "paper_synonyms": "MAC-M2", - "tissue_context": "cortex; fibrotic regions" - }, - { - "name": "Cycling Proximal Tubule Epithelial Cell", - "full_name": "Cycling Proximal Tubule Epithelial Cell", - "paper_synonyms": "PT; Cyc", - "tissue_context": "cortex" - }, - { - "name": "Outer Medullary Collecting Duct Intercalated Cell Type A", - "full_name": "Outer Medullary Collecting Duct Intercalated Cell Type A", - "paper_synonyms": "OMCD; IC", - "tissue_context": "outer medulla; collecting duct" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41598-020-66092-9_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41598-020-66092-9_batch_0.json deleted file mode 100644 index 5503965..0000000 --- a/cellsem_agent/graphs/cxg_annotate/resources/expansions/DOI_10_1038_s41598-020-66092-9_batch_0.json +++ /dev/null @@ -1,14 +0,0 @@ -[ - { - "name": "H1", - "full_name": "H1 horizontal cell", - "paper_synonyms": null, - "tissue_context": "fovea; peripheral retina; human retina" - }, - { - "name": "H2", - "full_name": "H2 horizontal cell", - "paper_synonyms": null, - "tissue_context": "fovea; peripheral retina; human retina" - } -] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json new file mode 100644 index 0000000..ca55291 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "K_Epi-Basal", + "full_name": "corneal basal epithelial cells", + "paper_synonyms": null, + "tissue_context": "central cornea; corneal epithelium" + }, + { + "name": "K_Epi-Wing", + "full_name": "corneal epithelium wing cells", + "paper_synonyms": "polygonal suprabasal cells", + "tissue_context": "central cornea; corneal epithelium" + }, + { + "name": "K_Fibro", + "full_name": "corneal fibroblasts", + "paper_synonyms": "stromal keratocytes; corneal stromal keratocytes", + "tissue_context": "central cornea; corneal stroma" + }, + { + "name": "K_Epi-Superficial", + "full_name": "corneal epithelium superficial cells", + "paper_synonyms": "superficial-most squamous epithelial cells", + "tissue_context": "central cornea; Corneal superficial epithelium" + }, + { + "name": "K_Endo", + "full_name": "corneal endothelium", + "paper_synonyms": "corneal endothelial cells", + "tissue_context": "central cornea; corneal endothelium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json new file mode 100644 index 0000000..d736544 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json @@ -0,0 +1,14 @@ +[ + { + "name": "K_Epi-TA", + "full_name": "corneal epithelium transit amplifying cell", + "paper_synonyms": "TA; transit amplifying cells", + "tissue_context": "central cornea; corneal epithelium; basal cell layer" + }, + { + "name": "Immune", + "full_name": "immune cells", + "paper_synonyms": "", + "tissue_context": "central cornea; iris stroma; ciliary body" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_0.json new file mode 100644 index 0000000..a508d76 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "retinal rod cell type A", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "unannotated", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "retinal rod cell type B", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "retinal bipolar neuron type B", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "retinal rod cell type C", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_1.json new file mode 100644 index 0000000..09f310a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "unspecified", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "retinal bipolar neuron type C", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "Muller cell", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "retinal cone cell", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "retinal bipolar neuron type A", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_2.json new file mode 100644 index 0000000..d624a6d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/DOI_10_15252_embj_2018100811_batch_2.json @@ -0,0 +1,26 @@ +[ + { + "name": "amacrine cell", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "retinal bipolar neuron type D", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "retinal ganglion cell", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + }, + { + "name": "microglial cell", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json new file mode 100644 index 0000000..69ede3f --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "K_Epi-Wing", + "full_name": "corneal epithelial wing cell", + "paper_synonyms": "wing cells; polygonal suprabasal cells", + "tissue_context": "central cornea; corneal epithelium" + }, + { + "name": "Limbal_Epi-Superficial", + "full_name": "limbal superficial epithelial cell", + "paper_synonyms": "", + "tissue_context": "limbus; ocular surface epithelium; corneoscleral wedge (CSW)" + }, + { + "name": "Conj_Epi-Superficial", + "full_name": "conjunctival superficial epithelial cell", + "paper_synonyms": "", + "tissue_context": "conjunctival epithelium; corneoscleral wedge (CSW)" + }, + { + "name": "Conj_Epi-Basal", + "full_name": "conjunctival basal epithelial cell", + "paper_synonyms": "", + "tissue_context": "conjunctival epithelium; corneoscleral wedge (CSW)" + }, + { + "name": "K_Epi-Superficial", + "full_name": "corneal superficial epithelial cell", + "paper_synonyms": "superficial-most squamous epithelial cells", + "tissue_context": "central cornea; corneal superficial epithelium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json new file mode 100644 index 0000000..71f4ed7 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "K_Epi-Basal", + "full_name": "corneal epithelial basal cells", + "paper_synonyms": null, + "tissue_context": "central cornea; corneal epithelium" + }, + { + "name": "Conj_Epi-Wing", + "full_name": "conjunctival epithelial wing cells", + "paper_synonyms": null, + "tissue_context": "conjunctival epithelium; corneoscleral wedge (CSW)" + }, + { + "name": "Limbal_Epi-Basal", + "full_name": "limbal epithelial basal cells", + "paper_synonyms": null, + "tissue_context": "limbus" + }, + { + "name": "Limbal_Epi-Wing", + "full_name": "limbal epithelial wing cells", + "paper_synonyms": null, + "tissue_context": "limbus" + }, + { + "name": "TM_Fibro", + "full_name": "trabecular meshwork fibroblasts", + "paper_synonyms": "TM fibroblasts", + "tissue_context": "trabecular meshwork (TM); iridocorneal angle" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_2.json new file mode 100644 index 0000000..94b57c6 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "Conj_Melanocyte", + "full_name": "conjunctival melanocyte", + "paper_synonyms": "", + "tissue_context": "conjunctiva; basal layer of the conjunctiva" + }, + { + "name": "Lymphatic_Endo", + "full_name": "lymphatic endothelium", + "paper_synonyms": "conjunctival lymphatic endothelium", + "tissue_context": "subepithelial vessels within the conjunctival stroma; conjunctival subepithelial stroma" + }, + { + "name": "Uveal_Fibro", + "full_name": "uveal fibroblast", + "paper_synonyms": "ciliary fibroblasts", + "tissue_context": "uveal base of the TM; iris root; ciliary muscle" + }, + { + "name": "Schwann", + "full_name": "Schwann cell", + "paper_synonyms": "", + "tissue_context": "iris stroma; ciliary body" + }, + { + "name": "Sclera_Fibro", + "full_name": "scleral fibroblast", + "paper_synonyms": "", + "tissue_context": "sclera; limbus; corneoscleral wedge (CSW)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_3.json new file mode 100644 index 0000000..1b6f9b6 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "Uveal_Melanocyte", + "full_name": "uveal melanocyte", + "paper_synonyms": null, + "tissue_context": "ciliary muscle; anterior border layer and stroma of the iris; ciliary stroma" + }, + { + "name": "Ciliary_Muscle", + "full_name": "ciliary muscle cells", + "paper_synonyms": "CM", + "tissue_context": "ciliary muscle; ciliary body (CB)" + }, + { + "name": "CC_VenEndo", + "full_name": "Collector Channel/Venous Endothelium", + "paper_synonyms": null, + "tissue_context": "collector channels; aqueous veins; scleral venous plexuses; corneoscleral wedge (CSW)" + }, + { + "name": "Lymphocyte", + "full_name": "lymphocyte", + "paper_synonyms": null, + "tissue_context": "central cornea; iris stroma; ciliary body (CB); corneoscleral wedge (CSW)" + }, + { + "name": "K_Endo", + "full_name": "corneal endothelium", + "paper_synonyms": "corneal endothelial cells", + "tissue_context": "central cornea; corneal endothelium" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_4.json new file mode 100644 index 0000000..99c2193 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_4.json @@ -0,0 +1,32 @@ +[ + { + "name": "VascEndo", + "full_name": "vascular endothelium", + "paper_synonyms": "Vasc_Endo", + "tissue_context": "iris stroma; ciliary stroma; subepithelial stromal tissues of the external limbus; conjunctival stroma" + }, + { + "name": "Pericyte", + "full_name": "pericytes", + "paper_synonyms": null, + "tissue_context": "trabecular meshwork (TM); ciliary body (CB); corneoscleral wedge (CSW)" + }, + { + "name": "K_Fibro", + "full_name": "corneal fibroblasts", + "paper_synonyms": "K_Fibro; stromal keratocytes", + "tissue_context": "central cornea; cornea" + }, + { + "name": "Goblet", + "full_name": "goblet cells", + "paper_synonyms": null, + "tissue_context": "conjunctival epithelium; conjunctiva; ocular surface" + }, + { + "name": "Schlemm_Endo", + "full_name": "Schlemm canal endothelium", + "paper_synonyms": "SC endothelium", + "tissue_context": "limbus; iridocorneal angle; aqueous humor outflow pathways" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_5.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_5.json new file mode 100644 index 0000000..3150d5d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/DOI_10_1073_pnas_2200914119_batch_5.json @@ -0,0 +1,20 @@ +[ + { + "name": "Macrophage", + "full_name": "macrophage", + "paper_synonyms": "Mo; clump cells", + "tissue_context": "central cornea; corneoscleral wedge (CSW); iris stroma; ciliary body (CB)" + }, + { + "name": "FibroX", + "full_name": "FibroX", + "paper_synonyms": "", + "tissue_context": "limbus" + }, + { + "name": "Mast", + "full_name": "mast cell", + "paper_synonyms": "", + "tissue_context": "iris stroma; corneoscleral wedge (CSW)" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_0.json new file mode 100644 index 0000000..5f9a282 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "PT", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "proximal tubule; kidney" + }, + { + "name": "TAL1", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "DCT2", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "DCT1", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "TAL2", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_1.json new file mode 100644 index 0000000..df399f6 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "ICA", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "ICB", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "PEC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "ENDO", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "ATL", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_2.json new file mode 100644 index 0000000..d969fc9 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "PTVCAM1", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "PODO", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "PC", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "MES", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + }, + { + "name": "FIB", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_3.json new file mode 100644 index 0000000..b85cccf --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/DOI_10_1038_s41467-022-32972-z_batch_3.json @@ -0,0 +1,8 @@ +[ + { + "name": "LEUK", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "kidney; proximal tubule" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/DOI_10_1016_j_celrep_2018_09_006_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/DOI_10_1016_j_celrep_2018_09_006_batch_0.json new file mode 100644 index 0000000..058b342 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/DOI_10_1016_j_celrep_2018_09_006_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "spinous", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "basal2", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "melanocyte", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "mitotic", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "channel", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/DOI_10_1016_j_celrep_2018_09_006_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/DOI_10_1016_j_celrep_2018_09_006_batch_1.json new file mode 100644 index 0000000..6a4b381 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/DOI_10_1016_j_celrep_2018_09_006_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "basal1", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "immune", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "WNT1", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "folicular", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + }, + { + "name": "granular", + "full_name": null, + "paper_synonyms": null, + "tissue_context": null + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_0.json new file mode 100644 index 0000000..eaa0882 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "cone", + "full_name": "cone photoreceptor cell", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina; foveal" + }, + { + "name": "RGC", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina" + }, + { + "name": "Muller", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina" + }, + { + "name": "cone-on-BC", + "full_name": "cone photoreceptor cell", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina" + }, + { + "name": "cone-off-BC", + "full_name": "cone photoreceptor cell", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_1.json new file mode 100644 index 0000000..156ec3d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "amacrine", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human neural retina; central retina" + }, + { + "name": "pericyte", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human neural retina; central retina" + }, + { + "name": "endothelial", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human neural retina; central retina" + }, + { + "name": "horizontal", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human neural retina; central retina" + }, + { + "name": "rod", + "full_name": null, + "paper_synonyms": null, + "tissue_context": "human neural retina; central retina" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_2.json new file mode 100644 index 0000000..2b960c4 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/DOI_10_1093_hmg_ddab140_batch_2.json @@ -0,0 +1,26 @@ +[ + { + "name": "astrocyte", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina" + }, + { + "name": "rod-BC", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina" + }, + { + "name": "cone-off-BC-BC3A", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina" + }, + { + "name": "microglia", + "full_name": "", + "paper_synonyms": "", + "tissue_context": "human neural retina; central retina" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_0.json new file mode 100644 index 0000000..fd27cb4 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "PC", + "full_name": "principal cells", + "paper_synonyms": null, + "tissue_context": "human kidney; benign adjacent kidney" + }, + { + "name": "CNT", + "full_name": "connecting tubule", + "paper_synonyms": "connecting duct", + "tissue_context": "human kidney" + }, + { + "name": "DCT", + "full_name": "distal convoluted tubule", + "paper_synonyms": null, + "tissue_context": "human kidney" + }, + { + "name": "TAL", + "full_name": "thick ascending limb", + "paper_synonyms": null, + "tissue_context": "medullary regions" + }, + { + "name": "Macro", + "full_name": "macrophages", + "paper_synonyms": null, + "tissue_context": "benign adjacent kidney; ccRCC tumor samples" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_1.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_1.json new file mode 100644 index 0000000..7c00f9a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_1.json @@ -0,0 +1,32 @@ +[ + { + "name": "IC-A", + "full_name": "intercalated cell A", + "paper_synonyms": null, + "tissue_context": "benign adjacent kidney; distal tubule" + }, + { + "name": "PT-C", + "full_name": "proximal tubule C", + "paper_synonyms": null, + "tissue_context": "benign adjacent kidney; cortex; medullary; proximal tubule" + }, + { + "name": "IC-B", + "full_name": "intercalated cell B", + "paper_synonyms": null, + "tissue_context": "benign adjacent kidney; distal tubule" + }, + { + "name": "AVR", + "full_name": "ascending vasa recta", + "paper_synonyms": null, + "tissue_context": "benign adjacent kidney; ccRCC tumor tissues" + }, + { + "name": "PT-B", + "full_name": "proximal tubule B", + "paper_synonyms": null, + "tissue_context": "benign kidney; cortex; medullary; proximal tubule" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_2.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_2.json new file mode 100644 index 0000000..2bcb395 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_2.json @@ -0,0 +1,32 @@ +[ + { + "name": "Mono", + "full_name": "monocytes", + "paper_synonyms": null, + "tissue_context": "benign kidney; ccRCC" + }, + { + "name": "tAL", + "full_name": "thin ascending limb cells", + "paper_synonyms": null, + "tissue_context": "benign renal tissues" + }, + { + "name": "IC-PC", + "full_name": "intercalated cell\u2013principal cell", + "paper_synonyms": "transitional cell type between PC and IC cells", + "tissue_context": "benign adjacent kidney; distal tubule" + }, + { + "name": "vSMC", + "full_name": "vascular smooth muscle cells", + "paper_synonyms": null, + "tissue_context": "benign kidney samples" + }, + { + "name": "GC", + "full_name": "glomerular capillaries", + "paper_synonyms": null, + "tissue_context": "benign adjacent tissue" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_3.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_3.json new file mode 100644 index 0000000..4e242bf --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_3.json @@ -0,0 +1,32 @@ +[ + { + "name": "AEA-DVR", + "full_name": "afferent/efferent arterioles/descending vasa recta", + "paper_synonyms": "AEA/DVR", + "tissue_context": "benign kidney; benign adjacent kidney" + }, + { + "name": "NKcell", + "full_name": "natural killer cells", + "paper_synonyms": "NK", + "tissue_context": "benign kidney; benign adjacent kidney; ccRCC tumor samples" + }, + { + "name": "DL", + "full_name": "descending limb", + "paper_synonyms": null, + "tissue_context": "benign kidney" + }, + { + "name": "Tcell", + "full_name": "T cells", + "paper_synonyms": null, + "tissue_context": "ccRCC tumor samples; benign adjacent kidney" + }, + { + "name": "UC", + "full_name": "uncharacterized", + "paper_synonyms": null, + "tissue_context": "benign adjacent tissue; benign adjacent kidney" + } +] \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_4.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/DOI_10_1073_pnas_2103240118_batch_4.json new file mode 100644 index 0000000..48772d8 --- /dev/null +++ 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a/cellsem_agent/graphs/cxg_annotate/resources/expansions/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_0.json b/cellsem_agent/graphs/cxg_annotate/resources/expansions/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_0.json new file mode 100644 index 0000000..989920a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/expansions/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/DOI_10_1016_j_immuni_2023_01_002_batch_0.json @@ -0,0 +1,32 @@ +[ + { + "name": "Stem cells OLFM4 PCNA", + "full_name": "Stem cells", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon" + }, + { + "name": "Enterocytes TMIGD1 MEP1A", + "full_name": "Enterocytes TMIGD1+ MEP1A+", + "paper_synonyms": null, + "tissue_context": "colon" + }, + { + "name": "Stem cells OLFM4", + "full_name": "Stem cells", + "paper_synonyms": null, + "tissue_context": "terminal ileum; colon" + }, + { + "name": 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https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Kupffer Kupffer cell CL:0000091 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +ActMac macrophage CL:0000235 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD4T CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD3T-lrNK hepatic pit cell CL:2000054 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +C-Hepato centrilobular region hepatocyte CL:0019029 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MHCII macrophage CL:0000235 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD8T-cNK natural killer cell CL:0000623 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Stellate hepatic stellate cell CL:0000632 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Kupffer--LSEC-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Tcell T cell CL:0000084 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Neutrophil neutrophil CL:0000775 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Chol intrahepatic cholangiocyte CL:0002538 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cNK natural killer cell CL:0000623 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cDC conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Hepato hepatocyte CL:0000182 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +P-Hepato2 periportal region hepatocyte CL:0019026 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cvLSEC--T-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cvEndo vein endothelial cell CL:0002543 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Monocyte monocyte CL:0000576 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Prolif unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +AntiB plasma cell CL:0000786 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +NKT--Mac-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CholMucus intrahepatic cholangiocyte CL:0002538 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MatB mature B cell CL:0000785 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Fibroblast fibroblast CL:0000057 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Hepato--Mac unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +pDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Mac--Fibro-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +RBC erythrocyte CL:0000232 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MAST mast cell CL:0000097 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MatB--CD4T-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +Mac--B-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +MatB--RBC unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +CD4T--RBC-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +cNK--RBC-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad +NKT natural killer cell CL:0000623 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/7d4d0da4-655e-438a-a2ec-b4371e2b80fc.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique.tsv new file mode 100644 index 0000000..106999d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique.tsv @@ -0,0 +1,14 @@ +author_cell_type CL_label CL_ID reference dataset_version +T cells T cell CL:0000084 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Diff. Keratinocytes keratinocyte CL:0000312 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Macrophages+DC macrophage CL:0000235 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +EpSC and undiff. progenitors stem cell of epidermis CL:1000428 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Secretory-reticular fibroblasts skin fibroblast CL:0002620 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Pericytes pericyte CL:0000669 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Pro-inflammatory fibroblasts skin fibroblast CL:0002620 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Secretory-papillary fibroblasts skin fibroblast CL:0002620 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Mesenchymal fibroblasts skin fibroblast CL:0002620 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Vascular EC endothelial cell of vascular tree CL:0002139 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Melanocytes melanocyte CL:0000148 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Lymphatic EC endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad +Erythrocytes erythrocyte CL:0000232 https://doi.org/10.1038/s42003-020-0922-4 https://datasets.cellxgene.cziscience.com/ba0fb2d9-28c9-4149-8591-694e0b7d9c31.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique.tsv new file mode 100644 index 0000000..99be474 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique.tsv @@ -0,0 +1,31 @@ +author_cell_type CL_label CL_ID reference dataset_version +Hepato-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Kupffer Kupffer cell CL:0000091 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Stellate-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +P-Hepato periportal region hepatocyte CL:0019026 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +C-Hepato centrilobular region hepatocyte CL:0019029 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +cvLSEC endothelial cell of pericentral hepatic sinusoid CL:0019022 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Prolif-Mac macrophage CL:0000235 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +C-Hepato2 centrilobular region hepatocyte CL:0019029 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +I-Hepato midzonal region hepatocyte CL:0019028 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Chol intrahepatic cholangiocyte CL:0002538 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +ppLSEC endothelial cell of periportal hepatic sinusoid CL:0019021 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Monocyte monocyte CL:0000576 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Kupffer-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +P-Hepato2 periportal region hepatocyte CL:0019026 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +cvEndo vein endothelial cell CL:0002543 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Stellate hepatic stellate cell CL:0000632 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Prolif unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +CD4T CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +cvLSEC-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +aStellate hepatic stellate cell CL:0000632 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Chol-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +cvLSEC--Mac unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Tcell-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +VSMC vascular associated smooth muscle cell CL:0000359 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +lrNK hepatic pit cell CL:2000054 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Chol--Kupffer-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +AntiB plasma cell CL:0000786 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Fibroblast fibroblast CL:0000057 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +Chol--Stellate-Doublet unknown unknown https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad +CholMucus intrahepatic cholangiocyte CL:0002538 https://doi.org/10.1016/j.jhep.2023.12.023 https://datasets.cellxgene.cziscience.com/4b5895d7-6d92-471a-b13a-5c59a000ddc4.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique.tsv new file mode 100644 index 0000000..7e5aa4a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique.tsv @@ -0,0 +1,15 @@ +author_cell_type CL_label CL_ID reference dataset_version +naive B cell naive B cell CL:0000788 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +memory B cell memory B cell CL:0000787 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +gamma-delta T cell gamma-delta T cell CL:0000798 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +plasmablast plasmablast CL:0000980 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +regulatory T cell regulatory T cell CL:0000815 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +CD4-positive, alpha-beta memory T cell CD4-positive, alpha-beta memory T cell CL:0000897 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +CD8-positive, alpha-beta memory T cell CD8-positive, alpha-beta memory T cell CL:0000909 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +naive CD8+ T cell naive thymus-derived CD8-positive, alpha-beta T cell CL:0000900 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +naive CD4+ T cell naive thymus-derived CD4-positive, alpha-beta T cell CL:0000895 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +mucosal invariant T cell (MAIT) mucosal invariant T cell CL:0000940 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +TissueResMemT memory T cell CL:0000813 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +double-positive T cell (DPT) double-positive, alpha-beta thymocyte CL:0000809 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +double negative T cell (DNT) double negative thymocyte CL:0002489 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad +TCRVbeta13.1pos T cell CL:0000084 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/e6ef9f09-bf7f-49bf-900c-457c02675411.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique.tsv new file mode 100644 index 0000000..a8c36fd --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique.tsv @@ -0,0 +1,26 @@ +author_cell_type CL_label CL_ID reference dataset_version +B cell IgA Plasma IgA plasma cell CL:0000987 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +B cell memory memory B cell CL:0000787 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +CD8 T CD8-positive, alpha-beta T cell CL:0000625 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +gd T gamma-delta T cell CL:0000798 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Mast mast cell CL:0000097 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +ILC innate lymphoid cell CL:0001065 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Macrophage colon macrophage CL:0009038 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +NK natural killer cell CL:0000623 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Follicular B cell follicular B cell CL:0000843 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +B cell IgG Plasma B cell CL:0000236 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Tcm memory T cell CL:0000813 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +B cell cycling B cell CL:0000236 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Treg regulatory T cell CL:0000815 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +LYVE1 Macrophage macrophage CL:0000235 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Th1 T-helper 1 cell CL:0000545 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Th17 T-helper 17 cell CL:0000899 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +cDC2 conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +cycling gd T gamma-delta T cell CL:0000798 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Monocyte monocyte CL:0000576 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +cDC1 conventional dendritic cell CL:0000990 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Activated CD4 T CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +pDC plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Tfh T follicular helper cell CL:0002038 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +Lymphoid DC dendritic cell CL:0000451 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad +cycling DCs dendritic cell CL:0000451 https://doi.org/10.1038/s41590-020-0602-z https://datasets.cellxgene.cziscience.com/9a696bfb-7cd5-4c27-89f5-f7979ae12111.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique.tsv new file mode 100644 index 0000000..a85289d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique.tsv @@ -0,0 +1,11 @@ +author_cell_type CL_label CL_ID reference dataset_version +non-classical monocyte non-classical monocyte CL:0000875 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +classical monocyte classical monocyte CL:0000860 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +NK_CD16hi CD16-positive, CD56-dim natural killer cell, human CL:0000939 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +NK_CD56loCD16lo natural killer cell CL:0000623 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +plasmacytoid dendritic cell plasmacytoid dendritic cell CL:0000784 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +conventional dendritic cell conventional dendritic cell CL:0000990 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +platelet platelet CL:0000233 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +NK_CD56hiCD16lo CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +granulocyte granulocyte CL:0000094 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad +intermediate monocyte intermediate monocyte CL:0002393 https://doi.org/10.1016/j.cell.2021.02.018 https://datasets.cellxgene.cziscience.com/73024e1c-c5e4-48d2-9ca4-49d2f845c8b9.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique.tsv new file mode 100644 index 0000000..fc82a89 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique.tsv @@ -0,0 +1,43 @@ +author_cell_type CL_label CL_ID reference dataset_version +Plasma cells plasma cell CL:0000786 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Doublets unknown unknown https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +T cells alpha-beta T cell CL:0000789 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +ILC innate lymphoid cell CL:0001065 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Progenitors progenitor cell CL:0011026 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +B cells B cell CL:0000236 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +CD36+ endothelium endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +MNP mononuclear phagocyte CL:0000113 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Cycling unknown unknown https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Mast cells mast cell CL:0000097 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +TA transit amplifying cell of small intestine CL:0009012 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Enterocytes enterocyte of epithelium proper of ileum CL:1000342 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +SM smooth muscle fiber of ileum CL:1000278 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Goblets ileal goblet cell CL:1000326 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Fibs fibroblast CL:0000057 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +ACKR1+ endothelium endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Pericytes pericyte CL:0000669 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Enteroendocrines enteroendocrine cell of small intestine CL:0009006 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Paneth cells paneth cell of epithelium of small intestine CL:1000343 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Glial cells glial cell CL:0000125 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Lymphatics endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells plasma cell CL:0000786 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells alpha-beta T cell CL:0000789 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells innate lymphoid cell CL:0001065 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells B cell CL:0000236 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Endothelium endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells mononuclear phagocyte CL:0000113 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Immune cells mast cell CL:0000097 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Endothelium endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma progenitor cell CL:0011026 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma endothelial cell CL:0000115 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma unknown unknown https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma transit amplifying cell of small intestine CL:0009012 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma enterocyte of epithelium proper of ileum CL:1000342 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma smooth muscle fiber of ileum CL:1000278 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma ileal goblet cell CL:1000326 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma fibroblast CL:0000057 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma pericyte CL:0000669 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma enteroendocrine cell of small intestine CL:0009006 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma paneth cell of epithelium of small intestine CL:1000343 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma glial cell CL:0000125 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad +Stroma endothelial cell of lymphatic vessel CL:0002138 https://doi.org/10.1016/j.cell.2019.08.008 https://datasets.cellxgene.cziscience.com/73904545-a97e-4b0b-9599-a8636359ef00.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique.tsv new file mode 100644 index 0000000..ca11e4a --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique.tsv @@ -0,0 +1,15 @@ +author_cell_type CL_label CL_ID reference dataset_version +alpha pancreatic A cell CL:0000171 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +beta_major type B pancreatic cell CL:0000169 https://doi.org/10.1038/s42255-022-00531-x https://datasets.cellxgene.cziscience.com/111d6e7d-d3d2-48fd-907a-4d3f8c77ee93.h5ad +endothelial endothelial cell CL:0000115 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alpha-beta T cell CL:0000913 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD4+ T cell CD4-positive, alpha-beta cytotoxic T cell CL:0000934 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD14+ Monocyte classical monocyte CL:0000860 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +Other T lymphocyte CL:0000542 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +CD16+ Monocyte non-classical monocyte CL:0000875 https://doi.org/10.1016/j.isci.2021.103115 https://datasets.cellxgene.cziscience.com/767750a4-5bb1-4093-9882-639bf4d285fd.h5ad +NK cell CD16-negative, CD56-bright natural killer cell, human CL:0000938 https://doi.org/10.1016/j.isci.2021.103115 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CL:0005009 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +PT epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +TAL kidney loop of Henle thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +DCT2 kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +DCT1 kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +ICA renal alpha-intercalated cell CL:0005011 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +ICB renal beta-intercalated cell CL:0002201 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +PEC parietal epithelial cell CL:1000452 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +CNT kidney connecting tubule epithelial cell CL:1000768 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +ENDO kidney capillary endothelial cell CL:1000892 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +MES mesangial cell CL:0000650 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +PODO podocyte CL:0000653 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +PT_VCAM1 epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +LEUK leukocyte CL:0000738 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad +FIB fibroblast CL:0000057 https://doi.org/10.1038/s41467-021-22368-w https://datasets.cellxgene.cziscience.com/ff2e21de-0848-4346-8f8b-4e1741ec4b39.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique.tsv new file mode 100644 index 0000000..e8bfc59 --- /dev/null +++ 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epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +CD4 T cell CD4-positive, alpha-beta T cell CL:0000624 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Podocyte podocyte CL:0000653 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Transitional urothelium urothelial cell CL:0000731 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +B cell B cell CL:0000236 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Ascending vasa recta endothelium vasa recta ascending limb cell CL:1001131 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Type A intercalated cell renal alpha-intercalated cell CL:0005011 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Peritubular capillary endothelium 1 capillary endothelial cell CL:0002144 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Pelvic epithelium kidney epithelial cell CL:0002518 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Glomerular endothelium glomerular endothelial cell CL:0002188 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Indistinct intercalated cell renal intercalated cell CL:0005010 https://doi.org/10.1126/science.aat5031 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https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Neutrophil neutrophil CL:0000775 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Connecting tubule kidney connecting tubule epithelial cell CL:1000768 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Myofibroblast myofibroblast cell CL:0000186 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +MNP-c/dendritic cell dendritic cell CL:0000451 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +NKT cell mature NK T cell CL:0000814 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +MNP-d/Tissue macrophage kidney resident macrophage CL:1000698 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Fibroblast kidney interstitial fibroblast CL:1000692 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Distinct proximal tubule 1 epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +MNP-b/non-classical monocyte derived non-classical monocyte CL:0000875 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Distinct proximal tubule 2 epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1126/science.aat5031 https://datasets.cellxgene.cziscience.com/7dafa492-6129-4dff-a794-17bdefde3575.h5ad +Principal cell renal principal cell CL:0005009 https://doi.org/10.1126/science.aat5031 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https://datasets.cellxgene.cziscience.com/112a469f-6838-49f9-9f96-470744e79e8d.h5ad +IMM non-classical monocyte CL:0000875 https://datasets.cellxgene.cziscience.com/112a469f-6838-49f9-9f96-470744e79e8d.h5ad +IMM conventional dendritic cell CL:0000990 https://datasets.cellxgene.cziscience.com/112a469f-6838-49f9-9f96-470744e79e8d.h5ad +DTL kidney loop of Henle thin descending limb epithelial cell CL:1001111 https://datasets.cellxgene.cziscience.com/112a469f-6838-49f9-9f96-470744e79e8d.h5ad +VSM/P renal interstitial pericyte CL:1001318 https://datasets.cellxgene.cziscience.com/112a469f-6838-49f9-9f96-470744e79e8d.h5ad +ATL kidney loop of Henle thin ascending limb epithelial cell CL:1001107 https://datasets.cellxgene.cziscience.com/112a469f-6838-49f9-9f96-470744e79e8d.h5ad +IMM neutrophil CL:0000775 https://datasets.cellxgene.cziscience.com/112a469f-6838-49f9-9f96-470744e79e8d.h5ad +PapE papillary tips cell CL:1000597 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a/cellsem_agent/graphs/cxg_annotate/resources/input/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique.tsv new file mode 100644 index 0000000..25b9d93 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique.tsv @@ -0,0 +1,15 @@ +author_cell_type CL_label CL_ID reference dataset_version +retinal rod cell type A retinal rod cell CL:0000604 https://doi.org/10.15252/embj.2018100811 https://datasets.cellxgene.cziscience.com/74a5e56b-15f0-4965-b6de-7e8dd689d74f.h5ad +unannotated unknown unknown https://doi.org/10.15252/embj.2018100811 https://datasets.cellxgene.cziscience.com/74a5e56b-15f0-4965-b6de-7e8dd689d74f.h5ad +retinal rod cell type B retinal rod cell CL:0000604 https://doi.org/10.15252/embj.2018100811 https://datasets.cellxgene.cziscience.com/74a5e56b-15f0-4965-b6de-7e8dd689d74f.h5ad +retinal bipolar neuron type B OFF-bipolar cell CL:0000750 https://doi.org/10.15252/embj.2018100811 https://datasets.cellxgene.cziscience.com/74a5e56b-15f0-4965-b6de-7e8dd689d74f.h5ad +retinal rod cell type C retinal rod cell CL:0000604 https://doi.org/10.15252/embj.2018100811 https://datasets.cellxgene.cziscience.com/74a5e56b-15f0-4965-b6de-7e8dd689d74f.h5ad +unspecified unknown unknown https://doi.org/10.15252/embj.2018100811 https://datasets.cellxgene.cziscience.com/74a5e56b-15f0-4965-b6de-7e8dd689d74f.h5ad +retinal bipolar neuron type C ON-bipolar cell CL:0000749 https://doi.org/10.15252/embj.2018100811 https://datasets.cellxgene.cziscience.com/74a5e56b-15f0-4965-b6de-7e8dd689d74f.h5ad +Muller cell Mueller cell CL:0000636 https://doi.org/10.15252/embj.2018100811 https://datasets.cellxgene.cziscience.com/74a5e56b-15f0-4965-b6de-7e8dd689d74f.h5ad +retinal cone cell retinal cone cell CL:0000573 https://doi.org/10.15252/embj.2018100811 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a/cellsem_agent/graphs/cxg_annotate/resources/input/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique.tsv new file mode 100644 index 0000000..e3056ac --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique.tsv @@ -0,0 +1,29 @@ +author_cell_type CL_label CL_ID reference dataset_version +K_Epi-Wing corneal epithelial cell CL:0000575 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad +Limbal_Epi-Superficial basal cell CL:0000646 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad +Conj_Epi-Superficial conjunctival epithelial cell CL:1000432 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad 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https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad +Pericyte pericyte CL:0000669 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad +K_Fibro fibroblast CL:0000057 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad +Goblet goblet cell CL:0000160 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad +Schlemm_Endo endothelial cell CL:0000115 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad +Macrophage macrophage CL:0000235 https://doi.org/10.1073/pnas.2200914119 https://datasets.cellxgene.cziscience.com/8b0c2c6a-902e-41dd-ad1c-60165d552360.h5ad +FibroX fibroblast CL:0000057 https://doi.org/10.1073/pnas.2200914119 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https://datasets.cellxgene.cziscience.com/46d8d92b-32e0-4ca5-9907-4dbf519c7fc3.h5ad +PEC parietal epithelial cell CL:1000452 https://doi.org/10.1038/s41467-022-32972-z https://datasets.cellxgene.cziscience.com/46d8d92b-32e0-4ca5-9907-4dbf519c7fc3.h5ad +ENDO endothelial cell CL:0000115 https://doi.org/10.1038/s41467-022-32972-z https://datasets.cellxgene.cziscience.com/46d8d92b-32e0-4ca5-9907-4dbf519c7fc3.h5ad +ATL kidney loop of Henle thin ascending limb epithelial cell CL:1001107 https://doi.org/10.1038/s41467-022-32972-z https://datasets.cellxgene.cziscience.com/46d8d92b-32e0-4ca5-9907-4dbf519c7fc3.h5ad +PTVCAM1 epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1038/s41467-022-32972-z https://datasets.cellxgene.cziscience.com/46d8d92b-32e0-4ca5-9907-4dbf519c7fc3.h5ad +PODO podocyte CL:0000653 https://doi.org/10.1038/s41467-022-32972-z https://datasets.cellxgene.cziscience.com/46d8d92b-32e0-4ca5-9907-4dbf519c7fc3.h5ad +PC renal principal cell CL:0005009 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a/cellsem_agent/graphs/cxg_annotate/resources/input/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique.tsv new file mode 100644 index 0000000..eeabc08 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique.tsv @@ -0,0 +1,15 @@ +author_cell_type CL_label CL_ID reference dataset_version +cone retinal cone cell CL:0000573 https://doi.org/10.1093/hmg/ddab140 https://datasets.cellxgene.cziscience.com/4d7c53aa-892b-4c73-8616-a97a5be4fb2c.h5ad +RGC retinal ganglion cell CL:0000740 https://doi.org/10.1093/hmg/ddab140 https://datasets.cellxgene.cziscience.com/4d7c53aa-892b-4c73-8616-a97a5be4fb2c.h5ad +Muller Mueller cell CL:0000636 https://doi.org/10.1093/hmg/ddab140 https://datasets.cellxgene.cziscience.com/4d7c53aa-892b-4c73-8616-a97a5be4fb2c.h5ad +cone-on-BC cone retinal bipolar cell CL:0000752 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b/cellsem_agent/graphs/cxg_annotate/resources/input/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique.tsv new file mode 100644 index 0000000..6f96780 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique.tsv @@ -0,0 +1,26 @@ +author_cell_type CL_label CL_ID reference dataset_version +PC renal principal cell CL:0005009 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +CNT kidney collecting duct cell CL:1001225 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +DCT kidney distal convoluted tubule epithelial cell CL:1000849 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +TAL kidney loop of Henle thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Macro macrophage CL:0000235 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +IC-A renal alpha-intercalated cell CL:0005011 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +PT-C epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +IC-B renal beta-intercalated cell CL:0002201 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +AVR vasa recta ascending limb cell CL:1001131 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +PT-B epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Mono monocyte CL:0000576 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +tAL kidney loop of Henle thick ascending limb epithelial cell CL:1001106 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +IC-PC columnar/cuboidal epithelial cell CL:0000075 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +vSMC vascular associated smooth muscle cell CL:0000359 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +GC glomerular capillary endothelial cell CL:1001005 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +AEA-DVR vasa recta descending limb cell CL:1001285 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +NKcell natural killer cell CL:0000623 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +DL vasa recta descending limb cell CL:1001285 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Tcell T cell CL:0000084 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +UC unknown unknown https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Peri pericyte CL:0000669 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Bcell B cell CL:0000236 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +PT-A epithelial cell of proximal tubule CL:0002306 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Podo podocyte CL:0000653 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad +Mesangial mesangial cell CL:0000650 https://doi.org/10.1073/pnas.2103240118 https://datasets.cellxgene.cziscience.com/c0b7ba53-d527-44ae-aa8c-1b7eef19017e.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/input/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique.tsv b/cellsem_agent/graphs/cxg_annotate/resources/input/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique.tsv new file mode 100644 index 0000000..3e1c383 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/input/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique.tsv @@ -0,0 +1,18 @@ +author_cell_type CL_label CL_ID reference dataset_version +Stem cells OLFM4 PCNA stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Enterocytes TMIGD1 MEP1A enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Stem cells OLFM4 stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Goblet cells MUC2 TFF1- goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Stem cells OLFM4 GSTA1 stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Goblet cells MUC2 TFF1 goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Epithelial Cycling cells epithelial cell CL:0000066 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Stem cells OLFM4 LGR5 stem cell CL:0000034 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Enterocytes BEST4 enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +L cells type L enteroendocrine cell CL:0002279 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Epithelial cells METTL12 MAFB epithelial cell CL:0000066 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Paneth cells paneth cell CL:0000510 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Enterocytes TMIGD1 MEP1A GSTA1 enterocyte CL:0000584 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Goblet cells SPINK4 goblet cell CL:0000160 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Epithelial HBB HBA epithelial cell CL:0000066 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Tuft cells tuft cell CL:0002204 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad +Enterochromaffin cells type EC enteroendocrine cell CL:0000577 https://doi.org/10.1016/j.immuni.2023.01.002 https://datasets.cellxgene.cziscience.com/b7c3c27c-97c2-4983-97df-1d537d138a43.h5ad diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..1d11e9b --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,46 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +BEST4+ CL:4030026 BEST4+ enterocyte DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'BEST4+', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030026 BEST4+ enterocyte +BEST4+ CL:0002254 epithelial cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'BEST4+', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030026 BEST4+ enterocyte +C_BEST4 CL:4030026 BEST4+ enterocyte DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_BEST4', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030026 BEST4+ enterocyte +C_EEC CL:0009042 enteroendocrine cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_EEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell +C_ISC CL:0009043 intestinal crypt stem cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_ISC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002250 intestinal crypt stem cell +C_TA CL:0009011 transit amplifying cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_TA', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0009010 transit amplifying cell +C_earlyACC CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_earlyACC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047018 early colonocyte +C_earlyCC CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_earlyCC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:4047018 early colonocyte +C_goblet CL:0009039 colon goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_goblet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell +C_lateACC CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_lateACC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000622 acinar cell +C_lateCC CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_lateCC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000064 ciliated cell +C_secretory_prog CL:0011026 progenitor cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_secretory_prog', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0011026 progenitor cell +C_tuft CL:0009041 tuft cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell +EEC CL:0009042 enteroendocrine cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'EEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell +EEC CL:0009006 enteroendocrine cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'EEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell +FAE CL:1000353 microfold cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'FAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4052011 follicle associated enterocyte +ISC CL:0009043 intestinal crypt stem cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'ISC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002250 intestinal crypt stem cell +ISC CL:0009017 intestinal crypt stem cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'ISC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002250 intestinal crypt stem cell +SI_6-? CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_6-?', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +SI_AE2 CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_AE2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +SI_BEST4 CL:0002254 epithelial cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_BEST4', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047051 small intestine BEST4+ enterocyte +SI_EEC CL:0009006 enteroendocrine cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_EEC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0009006 enteroendocrine cell of small intestine +SI_FAE CL:1000353 microfold cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_FAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4052011 follicle associated enterocyte +SI_ISC CL:0009017 intestinal crypt stem cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_ISC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009017 intestinal crypt stem cell of small intestine +SI_TA CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_TA', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0009012 transit amplifying cell of small intestine +SI_TA2 CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_TA2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009012 transit amplifying cell of small intestine +SI_earlyAE CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_earlyAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047019 early enterocyte +SI_goblet CL:1000495 small intestine goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_goblet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000495 small intestine goblet cell +SI_intermAE CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_intermAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000334 enterocyte of epithelium of small intestine +SI_matureAE CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_matureAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000334 enterocyte of epithelium of small intestine +SI_paneth CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_paneth', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000343 paneth cell of epithelium of small intestine +SI_secretory CL:1000495 small intestine goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_secretory', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1001598 small intestine secretory cell +SI_secretory CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_secretory', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1001598 small intestine secretory cell +SI_secretory_prog CL:0011026 progenitor cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_secretory_prog', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0011026 progenitor cell +SI_tuft CL:0019032 intestinal tuft cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009080 tuft cell of small intestine +TA CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'TA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009010 transit amplifying cell +TA CL:0009011 transit amplifying cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'TA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009010 transit amplifying cell +absorptive CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'absorptive', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} +absorptive CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'absorptive', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} +goblet CL:0009039 colon goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'goblet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} +goblet CL:1000495 small intestine goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'goblet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell +paneth CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'paneth', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000510 paneth cell +secretory_prog CL:0011026 progenitor cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'secretory_prog', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0011026 progenitor cell +tuft CL:0019032 intestinal tuft cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell +tuft CL:0009041 tuft cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..33be62d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,43 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +BEST4+ CL:4030026 BEST4+ enterocyte DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'BEST4+', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030026 BEST4+ enterocyte TRUE +BEST4+ CL:0002254 epithelial cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'BEST4+', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030026 BEST4+ enterocyte FALSE +C_BEST4 CL:4030026 BEST4+ enterocyte DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_BEST4', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030026 BEST4+ enterocyte TRUE +C_EEC CL:0009042 enteroendocrine cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_EEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell FALSE +C_ISC CL:0009043 intestinal crypt stem cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_ISC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002250 intestinal crypt stem cell FALSE +C_TA CL:0009011 transit amplifying cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_TA', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0009010 transit amplifying cell FALSE +C_earlyACC CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_earlyACC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047018 early colonocyte FALSE +C_earlyCC CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_earlyCC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:4047018 early colonocyte FALSE +C_goblet CL:0009039 colon goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_goblet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell FALSE +C_lateACC CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_lateACC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000622 acinar cell FALSE +C_lateCC CL:0002071 enterocyte of epithelium of large intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_lateCC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000064 ciliated cell FALSE +C_secretory_prog CL:0011026 progenitor cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_secretory_prog', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0011026 progenitor cell TRUE +C_tuft CL:0009041 tuft cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'C_tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell FALSE +EEC CL:0009042 enteroendocrine cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'EEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell FALSE +EEC CL:0009006 enteroendocrine cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'EEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell FALSE +FAE CL:1000353 microfold cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'FAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4052011 follicle associated enterocyte FALSE +ISC CL:0009043 intestinal crypt stem cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'ISC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002250 intestinal crypt stem cell FALSE +ISC CL:0009017 intestinal crypt stem cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'ISC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002250 intestinal crypt stem cell FALSE +SI_6-? CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_6-?', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +SI_AE2 CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_AE2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +SI_BEST4 CL:0002254 epithelial cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_BEST4', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047051 small intestine BEST4+ enterocyte FALSE +SI_EEC CL:0009006 enteroendocrine cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_EEC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0009006 enteroendocrine cell of small intestine TRUE +SI_FAE CL:1000353 microfold cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_FAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4052011 follicle associated enterocyte FALSE +SI_ISC CL:0009017 intestinal crypt stem cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_ISC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009017 intestinal crypt stem cell of small intestine TRUE +SI_TA CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_TA', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0009012 transit amplifying cell of small intestine TRUE +SI_TA2 CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_TA2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009012 transit amplifying cell of small intestine TRUE +SI_earlyAE CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_earlyAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047019 early enterocyte FALSE +SI_goblet CL:1000495 small intestine goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_goblet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000495 small intestine goblet cell TRUE +SI_intermAE CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_intermAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000334 enterocyte of epithelium of small intestine TRUE +SI_matureAE CL:1000334 enterocyte of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_matureAE', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000334 enterocyte of epithelium of small intestine TRUE +SI_paneth CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_paneth', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000343 paneth cell of epithelium of small intestine TRUE +SI_secretory CL:1000495 small intestine goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_secretory', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1001598 small intestine secretory cell FALSE +SI_secretory CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_secretory', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1001598 small intestine secretory cell FALSE +SI_secretory_prog CL:0011026 progenitor cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_secretory_prog', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0011026 progenitor cell TRUE +SI_tuft CL:0019032 intestinal tuft cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'SI_tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009080 tuft cell of small intestine FALSE +TA CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'TA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009010 transit amplifying cell FALSE +TA CL:0009011 transit amplifying cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'TA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009010 transit amplifying cell FALSE +goblet CL:1000495 small intestine goblet cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'goblet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell FALSE +paneth CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'paneth', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000510 paneth cell FALSE +secretory_prog CL:0011026 progenitor cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'secretory_prog', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0011026 progenitor cell TRUE +tuft CL:0019032 intestinal tuft cell DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell FALSE +tuft CL:0009041 tuft cell of colon DOI:10.1016/j.jcmgh.2022.02.007 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique {'name': 'tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..9b99217 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,46 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +ActMac CL:0000235 macrophage DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'ActMac', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage +AntiB CL:0000786 plasma cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'AntiB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000946 antibody secreting cell +Arterial CL:1000413 endothelial cell of artery DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Arterial', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1000413 endothelial cell of artery +C-Hepato CL:0019029 centrilobular region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'C-Hepato', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte +C-Hepato2 CL:0019029 centrilobular region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'C-Hepato2', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte +CD3T-lrNK CL:2000054 hepatic pit cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD3T-lrNK', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047101 liver-resident natural killer cell +CD4T CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD4T', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD4T--RBC-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD4T--RBC-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD8T CL:0000625 CD8-positive, alpha-beta T cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD8T', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell +CD8T-cNK CL:0000623 natural killer cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD8T-cNK', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell +Chol CL:0002538 intrahepatic cholangiocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Chol', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1000488 cholangiocyte +CholMucus CL:0002538 intrahepatic cholangiocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CholMucus', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047056 large mucus secreting cholangiocyte +Fibroblast CL:0000057 fibroblast DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Hepato CL:0000182 hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Hepato', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte +Hepato--Mac unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Hepato--Mac', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +I-Hepato CL:0019028 midzonal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'I-Hepato', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte +Kupffer CL:0000091 Kupffer cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Kupffer', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000091 Kupffer cell +Kupffer--LSEC-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Kupffer--LSEC-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000091 Kupffer cell +LAM-like CL:0000235 macrophage DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'LAM-like', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:4033086 lipid-associated macrophage +MAST CL:0000097 mast cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MAST', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell +MHCII CL:0000235 macrophage DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MHCII', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000145 professional antigen presenting cell +Mac--B-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Mac--B-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +Mac--Fibro-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Mac--Fibro-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +MatB CL:0000785 mature B cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MatB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000785 mature B cell +MatB--CD4T-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MatB--CD4T-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +MatB--RBC unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MatB--RBC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte +Monocyte CL:0000576 monocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte +NKT CL:0000623 natural killer cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'NKT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000814 mature NK T cell +NKT--Mac-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'NKT--Mac-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +Neutrophil CL:0000775 neutrophil DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000775 neutrophil +P-Hepato CL:0019026 periportal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'P-Hepato', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte +P-Hepato2 CL:0019026 periportal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'P-Hepato2', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte +Prolif unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Prolif', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +RBC CL:0000232 erythrocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'RBC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte +Stellate CL:0000632 hepatic stellate cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Stellate', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +Tcell CL:0000084 T cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Tcell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000084 T cell +cDC CL:0000990 conventional dendritic cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cDC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000990 conventional dendritic cell +cNK CL:0000623 natural killer cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cNK', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000623 natural killer cell +cNK--RBC-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cNK--RBC-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +cvEndo CL:0002543 vein endothelial cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cvEndo', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +cvLSEC CL:0019022 endothelial cell of pericentral hepatic sinusoid DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cvLSEC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0019022 endothelial cell of pericentral hepatic sinusoid +cvLSEC--T-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cvLSEC--T-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +lrNK CL:2000054 hepatic pit cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'lrNK', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:4047101 liver-resident natural killer cell +pDC CL:0000784 plasmacytoid dendritic cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'pDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell +ppLSEC CL:0019021 endothelial cell of periportal hepatic sinusoid DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'ppLSEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0019021 endothelial cell of periportal hepatic sinusoid diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..b7fbfcc --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,46 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +ActMac CL:0000235 macrophage DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'ActMac', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage TRUE +AntiB CL:0000786 plasma cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'AntiB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000946 antibody secreting cell FALSE +Arterial CL:1000413 endothelial cell of artery DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Arterial', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1000413 endothelial cell of artery TRUE +C-Hepato CL:0019029 centrilobular region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'C-Hepato', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte FALSE +C-Hepato2 CL:0019029 centrilobular region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'C-Hepato2', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte FALSE +CD3T-lrNK CL:2000054 hepatic pit cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD3T-lrNK', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047101 liver-resident natural killer cell FALSE +CD4T CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD4T', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell TRUE +CD4T--RBC-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD4T--RBC-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell FALSE +CD8T CL:0000625 CD8-positive, alpha-beta T cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD8T', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell TRUE +CD8T-cNK CL:0000623 natural killer cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CD8T-cNK', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell TRUE +Chol CL:0002538 intrahepatic cholangiocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Chol', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1000488 cholangiocyte FALSE +CholMucus CL:0002538 intrahepatic cholangiocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'CholMucus', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047056 large mucus secreting cholangiocyte FALSE +Fibroblast CL:0000057 fibroblast DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +Hepato CL:0000182 hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Hepato', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte TRUE +Hepato--Mac unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Hepato--Mac', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +I-Hepato CL:0019028 midzonal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'I-Hepato', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte FALSE +Kupffer CL:0000091 Kupffer cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Kupffer', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000091 Kupffer cell TRUE +Kupffer--LSEC-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Kupffer--LSEC-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000091 Kupffer cell FALSE +LAM-like CL:0000235 macrophage DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'LAM-like', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:4033086 lipid-associated macrophage FALSE +MAST CL:0000097 mast cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MAST', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell TRUE +MHCII CL:0000235 macrophage DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MHCII', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000145 professional antigen presenting cell FALSE +Mac--B-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Mac--B-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +Mac--Fibro-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Mac--Fibro-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +MatB CL:0000785 mature B cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MatB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000785 mature B cell TRUE +MatB--CD4T-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MatB--CD4T-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell FALSE +MatB--RBC unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'MatB--RBC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte FALSE +Monocyte CL:0000576 monocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte TRUE +NKT CL:0000623 natural killer cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'NKT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000814 mature NK T cell FALSE +NKT--Mac-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'NKT--Mac-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +Neutrophil CL:0000775 neutrophil DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000775 neutrophil TRUE +P-Hepato CL:0019026 periportal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'P-Hepato', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte FALSE +P-Hepato2 CL:0019026 periportal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'P-Hepato2', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte FALSE +Prolif unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Prolif', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +RBC CL:0000232 erythrocyte DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'RBC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte TRUE +Stellate CL:0000632 hepatic stellate cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Stellate', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +Tcell CL:0000084 T cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'Tcell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000084 T cell TRUE +cDC CL:0000990 conventional dendritic cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cDC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE +cNK CL:0000623 natural killer cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cNK', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000623 natural killer cell TRUE +cNK--RBC-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cNK--RBC-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +cvEndo CL:0002543 vein endothelial cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cvEndo', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell FALSE +cvLSEC CL:0019022 endothelial cell of pericentral hepatic sinusoid DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cvLSEC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0019022 endothelial cell of pericentral hepatic sinusoid TRUE +cvLSEC--T-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'cvLSEC--T-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +lrNK CL:2000054 hepatic pit cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'lrNK', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:4047101 liver-resident natural killer cell FALSE +pDC CL:0000784 plasmacytoid dendritic cell DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'pDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell TRUE +ppLSEC CL:0019021 endothelial cell of periportal hepatic sinusoid DOI:10.1016/j.jhep.2023.12.023 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique {'name': 'ppLSEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0019021 endothelial cell of periportal hepatic sinusoid TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv index bfa994a..fc2ea85 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -1,203 +1,203 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label -ATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ATL', 'full_name': 'ascending thin limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell -Adaptive / Maladaptive / Repairing Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Fibroblast', 'full_name': 'adaptive (successful or maladaptive repair) fibroblast', 'paper_synonyms': 'aFIB; aStr', 'tissue_context': ''} CL:0000057 fibroblast -Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell', 'full_name': 'adaptive (successful or maladaptive tubular repair) proximal tubule epithelial cell', 'paper_synonyms': 'aPT; adaptive epithelial (aEpi)', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule -Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell', 'full_name': 'adaptive/maladaptive repairing thick ascending limb epithelial cell', 'paper_synonyms': 'aTAL; adaptive TAL; adaptive epithelial (aEpi)', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell -Afferent / Efferent Arteriole Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Afferent / Efferent Arteriole Endothelial Cell', 'full_name': 'endothelial cell of the afferent/efferent arterioles', 'paper_synonyms': 'EC-AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole -Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Thin Limb Cell', 'full_name': 'ascending thin limb (ATL) cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell -Ascending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Vasa Recta Endothelial Cell', 'full_name': 'Ascending Vasa Recta Endothelial Cell', 'paper_synonyms': 'EC-AVR', 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell +ATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ATL', 'full_name': 'ascending thin limbs', 'paper_synonyms': '', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +Adaptive / Maladaptive / Repairing Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Fibroblast', 'full_name': 'adaptive fibroblast', 'paper_synonyms': 'aFIB', 'tissue_context': ''} CL:0000057 fibroblast +Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell', 'full_name': 'adaptive proximal tubule (aPT) epithelial cell (successful or maladaptive tubular repair)', 'paper_synonyms': 'aPT; aEpi; Ad/Mal; PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell', 'full_name': 'adaptive (successful or maladaptive repair) thick ascending limb cell (aTAL)', 'paper_synonyms': 'aTAL; adaptive epithelial (aEpi); Ad/Mal', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +Afferent / Efferent Arteriole Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Afferent / Efferent Arteriole Endothelial Cell', 'full_name': 'endothelial cell of the afferent/efferent arterioles (EC-AEA)', 'paper_synonyms': 'EC-AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole +Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Thin Limb Cell', 'full_name': 'ascending thin limb cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +Ascending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Vasa Recta Endothelial Cell', 'full_name': 'ascending vasa recta endothelial cell', 'paper_synonyms': 'EC-AVR', 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell B CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'B', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell B Cell CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'B Cell', 'full_name': 'B cell', 'paper_synonyms': 'B', 'tissue_context': ''} CL:0000236 B cell -C-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-IC-A', 'full_name': 'cortical intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell -C-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-PC', 'full_name': 'cortical principal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell +C-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-IC-A', 'full_name': 'cortical intercalated cell A', 'paper_synonyms': '', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell +C-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-PC', 'full_name': 'cortical principal cell', 'paper_synonyms': 'PC; principal cells', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell C-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-TAL', 'full_name': 'cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell -CCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-IC-A', 'full_name': 'cortical collecting duct intercalated cells', 'paper_synonyms': 'CCD; C-CD; IC; intercalated cells', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell -CCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-PC', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; CCD; cortical collecting duct', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell +CCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-IC-A', 'full_name': 'cortical collecting duct intercalated cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell +CCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-PC', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'CCD; cortical collecting duct; PC; principal cells', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell CNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT', 'full_name': 'connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell -CNT-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-IC-A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC; CNT', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell -CNT-PC CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-PC', 'full_name': 'connecting tubule principal cell', 'paper_synonyms': 'PC; principal cells; CNT; connecting tubules', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell -Classical Dendritic Cell CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Classical Dendritic Cell', 'full_name': 'Classical Dendritic Cell', 'paper_synonyms': 'cDC', 'tissue_context': ''} CL:0000990 conventional dendritic cell +CNT-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-IC-A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell +CNT-PC CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-PC', 'full_name': 'connecting tubule principal cell', 'paper_synonyms': 'CNT; connecting tubule; PC; principal cells', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell +Classical Dendritic Cell CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Classical Dendritic Cell', 'full_name': 'classical dendritic cell', 'paper_synonyms': 'cDC', 'tissue_context': ''} CL:0000990 conventional dendritic cell Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Cell', 'full_name': 'connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell -Connecting Tubule Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Intercalated Cell Type A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell -Connecting Tubule Principal Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Principal Cell', 'full_name': 'Connecting tubule principal cell', 'paper_synonyms': 'CNT-PC', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell -Cortical Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Intercalated Cell Type A', 'full_name': 'cortical collecting duct intercalated cell', 'paper_synonyms': 'CCD; IC; C-CD', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell -Cortical Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Principal Cell', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'PC; C-PC', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell -Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Thick Ascending Limb Cell', 'full_name': 'Cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; cortical TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell -Cycling Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Connecting Tubule Cell', 'full_name': 'cycling connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell -Cycling Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Distal Convoluted Tubule Cell', 'full_name': 'cycling distal convoluted tubule cell', 'paper_synonyms': 'DCT; Cyc', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell -Cycling Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Endothelial Cell', 'full_name': 'Cycling Endothelial Cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Connecting Tubule Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Intercalated Cell Type A', 'full_name': 'Connecting Tubule Intercalated Cell Type A', 'paper_synonyms': 'CNT-IC; IC; CNT', 'tissue_context': ''} CL:4030020 kidney connecting tubule alpha-intercalated cell +Connecting Tubule Principal Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Principal Cell', 'full_name': 'Connecting tubule principal cell', 'paper_synonyms': 'CNT-PC; CNT; PC', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell +Cortical Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Intercalated Cell Type A', 'full_name': 'Cortical collecting duct intercalated cell type A', 'paper_synonyms': 'CCD; IC; collecting duct (CD)', 'tissue_context': ''} CL:4030015 kidney collecting duct alpha-intercalated cell +Cortical Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Principal Cell', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'C-PC; PC', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell +Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Thick Ascending Limb Cell', 'full_name': 'cortical thick ascending limb (C-TAL) cell', 'paper_synonyms': 'C-TAL; TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell +Cycling Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Connecting Tubule Cell', 'full_name': 'cycling connecting tubule cell', 'paper_synonyms': 'CNT; Cyc', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +Cycling Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Distal Convoluted Tubule Cell', 'full_name': 'Cycling Distal Convoluted Tubule Cell', 'paper_synonyms': 'DCT; DCT1; DCT2', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +Cycling Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Endothelial Cell', 'full_name': 'endothelial cell, cycling state', 'paper_synonyms': '', 'tissue_context': ''} CL:0000115 endothelial cell Cycling Mononuclear Phagocyte CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Mononuclear Phagocyte', 'full_name': 'cycling mononuclear phagocyte', 'paper_synonyms': 'cycMNP', 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte -Cycling Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Myofibroblast', 'full_name': 'Cycling myofibroblast', 'paper_synonyms': 'cycMyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell -Cycling Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Cycling natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:4033071 cycling natural killer cell -Cycling Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Proximal Tubule Epithelial Cell', 'full_name': 'Cycling Proximal Tubule Epithelial Cell', 'paper_synonyms': 'PT; Cyc', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Cycling Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Myofibroblast', 'full_name': 'cycling myofibroblast', 'paper_synonyms': 'cycMyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell +Cycling Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Natural Killer Cell / Natural Killer T Cell', 'full_name': 'cycling natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:4033071 cycling natural killer cell +Cycling Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Proximal Tubule Epithelial Cell', 'full_name': 'cycling proximal tubule epithelial cell', 'paper_synonyms': 'PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule DCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT', 'full_name': 'distal convoluted tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell -DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT1', 'full_name': 'distal convoluted tubule cell (type 1)', 'paper_synonyms': 'DCT; distal convoluted tubule', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule +DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT1', 'full_name': 'distal convoluted tubule cell 1', 'paper_synonyms': 'DCT; distal convoluted tubule', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule DCT2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT2', 'full_name': 'distal convoluted tubule cell 2', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule DTL CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL', 'full_name': 'descending thin limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell -DTL1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL1', 'full_name': 'descending thin limb cell type 1', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell -DTL2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL2', 'full_name': 'descending thin limb 2', 'paper_synonyms': 'DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell -DTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL3', 'full_name': 'descending thin limb 3', 'paper_synonyms': 'descending thin limb; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell -Degenerative Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Ascending Thin Limb Cell', 'full_name': 'degenerative ascending thin limb (ATL) cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell -Degenerative Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Connecting Tubule Cell', 'full_name': 'degenerative connecting tubule (CNT) cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell -Degenerative Cortical Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Intercalated Cell Type A', 'full_name': 'degenerative cortical intercalated cell type A', 'paper_synonyms': 'IC; intercalated cells', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell -Degenerative Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Thick Ascending Limb Cell', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell -Degenerative Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Descending Thin Limb Cell Type 3', 'full_name': 'Degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell -Degenerative Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Distal Convoluted Tubule Cell', 'full_name': 'Degenerative distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell -Degenerative Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Endothelial Cell', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell +DTL1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL1', 'full_name': 'descending thin limb 1', 'paper_synonyms': 'DTL; descending thin limb', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +DTL2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL2', 'full_name': 'descending thin limb cell type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +DTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL3', 'full_name': 'descending thin limb 3', 'paper_synonyms': 'DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Degenerative Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Ascending Thin Limb Cell', 'full_name': 'Degenerative ascending thin limb cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +Degenerative Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Connecting Tubule Cell', 'full_name': 'Degenerative connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +Degenerative Cortical Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Intercalated Cell Type A', 'full_name': 'Degenerative cortical intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell +Degenerative Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Thick Ascending Limb Cell', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; cortical TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell +Degenerative Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Descending Thin Limb Cell Type 3', 'full_name': 'degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Degenerative Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Distal Convoluted Tubule Cell', 'full_name': 'degenerative distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +Degenerative Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Endothelial Cell', 'full_name': 'Degenerative Endothelial Cell', 'paper_synonyms': 'EC-AEA; EC-GC; EC-LYM; EC-AVR; EC-DVR; EC', 'tissue_context': ''} CL:0000115 endothelial cell Degenerative Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Fibroblast', 'full_name': 'degenerative fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast -Degenerative Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Inner Medullary Collecting Duct Cell', 'full_name': 'degenerative inner medullary collecting duct cell', 'paper_synonyms': 'IMCD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell -Degenerative Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Fibroblast', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast -Degenerative Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Thick Ascending Limb Cell', 'full_name': 'Degenerative Medullary Thick Ascending Limb Cell', 'paper_synonyms': 'M-TAL; TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell -Degenerative Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'degenerative medullary principal cells; dM-PCs', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell +Degenerative Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Inner Medullary Collecting Duct Cell', 'full_name': 'degenerative inner medullary collecting duct cell', 'paper_synonyms': 'IMCD; CD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell +Degenerative Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Fibroblast', 'full_name': 'Degenerative Medullary Fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast +Degenerative Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Thick Ascending Limb Cell', 'full_name': 'degenerative medullary thick ascending limb cell', 'paper_synonyms': 'TAL; M-TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell +Degenerative Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'paper_synonyms': 'OMCD; PC; degenerative medullary principal cells; dM-PCs', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell Degenerative Peritubular Capilary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell -Degenerative Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Podocyte', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'POD', 'tissue_context': ''} CL:0000653 podocyte -Degenerative Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Proximal Tubule Epithelial Cell', 'full_name': 'degenerative proximal tubule epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Degenerative Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Podocyte', 'full_name': 'Degenerative Podocyte', 'paper_synonyms': 'POD; PODs', 'tissue_context': ''} CL:0000653 podocyte +Degenerative Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Proximal Tubule Epithelial Cell', 'full_name': 'degenerative proximal tubule (PT) epithelial cell', 'paper_synonyms': 'PT; degen', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule Degenerative Vascular Smooth Muscle Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Vascular Smooth Muscle Cell', 'full_name': 'degenerative vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell -Descending Thin Limb Cell Type 1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 1', 'full_name': 'descending thin limb cell type 1 (DTL1)', 'paper_synonyms': 'DTL1', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell -Descending Thin Limb Cell Type 2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 2', 'full_name': 'descending thin limb cell type 2', 'paper_synonyms': 'DTL2', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Descending Thin Limb Cell Type 1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 1', 'full_name': 'descending thin limb cell type 1', 'paper_synonyms': 'DTL1', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +Descending Thin Limb Cell Type 2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 2', 'full_name': 'descending thin limb cell type 2 (DTL2)', 'paper_synonyms': 'DTL2', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 3', 'full_name': 'descending thin limb cell type 3', 'paper_synonyms': 'DTL3; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell -Descending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Vasa Recta Endothelial Cell', 'full_name': 'endothelial cell of the descending vasa recta', 'paper_synonyms': 'EC-DVR', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell +Descending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Vasa Recta Endothelial Cell', 'full_name': 'Descending vasa recta endothelial cell', 'paper_synonyms': 'EC-DVR', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell Distal Convoluted Tubule Cell Type 1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 1', 'full_name': 'Distal convoluted tubule cell type 1', 'paper_synonyms': 'DCT1', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule -Distal Convoluted Tubule Cell Type 2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 2', 'full_name': 'Distal Convoluted Tubule Cell Type 2', 'paper_synonyms': 'DCT2', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule +Distal Convoluted Tubule Cell Type 2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 2', 'full_name': 'distal convoluted tubule cell 2', 'paper_synonyms': 'DCT2', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule EC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell -EC-AEA CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AEA', 'full_name': 'endothelial cells of the afferent/efferent arterioles', 'paper_synonyms': 'AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole -EC-AVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AVR', 'full_name': 'endothelial cell, vasa recta', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell -EC-DVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-DVR', 'full_name': 'endothelial cells of the vasa recta', 'paper_synonyms': None, 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell -EC-GC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-GC', 'full_name': 'glomerular capillary endothelial cell', 'paper_synonyms': 'glomerular capillaries; EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell +EC-AEA CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AEA', 'full_name': 'endothelial cells of the afferent/efferent arterioles', 'paper_synonyms': None, 'tissue_context': ''} CL:1000412 endothelial cell of arteriole +EC-AVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AVR', 'full_name': 'endothelial cell of the vasa recta', 'paper_synonyms': '', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell +EC-DVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-DVR', 'full_name': 'endothelial cells of the vasa recta', 'paper_synonyms': '', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell +EC-GC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-GC', 'full_name': 'glomerular capillaries', 'paper_synonyms': '', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell EC-LYM CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-LYM', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': None, 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel -EC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-PTC', 'full_name': 'endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell -FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'FIB', 'full_name': 'fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast -Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': 'Fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast -Glomerular Capillary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Glomerular Capillary Endothelial Cell', 'full_name': 'glomerular capillaries', 'paper_synonyms': 'EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell +EC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-PTC', 'full_name': 'endothelial cell PTC', 'paper_synonyms': 'EC; endothelial cells', 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell +FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'FIB', 'full_name': 'fibroblast', 'paper_synonyms': 'fibroblast (FIB)', 'tissue_context': ''} CL:0000057 fibroblast +Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': 'fibroblast (FIB)', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast +Glomerular Capillary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Glomerular Capillary Endothelial Cell', 'full_name': 'glomerular capillary endothelial cell', 'paper_synonyms': 'EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC', 'full_name': 'intercalated cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005010 renal intercalated cell -IC-B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC-B', 'full_name': 'intercalated cells B', 'paper_synonyms': 'IC; intercalated cells', 'tissue_context': ''} CL:0002201 renal beta-intercalated cell -IMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMCD', 'full_name': 'inner medullary collecting duct', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell -IMM CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -IMM CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +IC-B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC-B', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC; CNT-IC', 'tissue_context': ''} CL:0005010 renal intercalated cell +IMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMCD', 'full_name': 'inner medullary collecting duct cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell +IMM CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte +IMM CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Inner Medullary Collecting Duct Cell', 'full_name': 'inner medullary collecting duct cell', 'paper_synonyms': 'IMCD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell Intercalated Cell Type B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Intercalated Cell Type B', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell -Lymphatic Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Lymphatic Endothelial Cell', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': 'EC-LYM', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel -M-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-FIB', 'full_name': 'medullary fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:4030022 renal medullary fibroblast -M-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-IC-A', 'full_name': 'intercalated cells', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell -M-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-PC', 'full_name': 'medullary principal cell', 'paper_synonyms': 'principal cells (PC)', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell -M-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-TAL', 'full_name': 'medullary thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell -M2 Macrophage CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M2 Macrophage', 'full_name': 'M2 Macrophage', 'paper_synonyms': 'MAC-M2', 'tissue_context': ''} CL:0000890 alternatively activated macrophage +Lymphatic Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Lymphatic Endothelial Cell', 'full_name': 'endothelial cell of the lymphatics', 'paper_synonyms': 'EC-LYM', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel +M-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-FIB', 'full_name': 'medullary fibroblast', 'paper_synonyms': '', 'tissue_context': ''} CL:4030022 renal medullary fibroblast +M-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-IC-A', 'full_name': 'medullary intercalated cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1001432 kidney collecting duct intercalated cell +M-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-PC', 'full_name': 'medullary principal cell', 'paper_synonyms': 'principal cell (PC)', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell +M-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-TAL', 'full_name': 'medullary thick ascending limb', 'paper_synonyms': '', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell +M2 Macrophage CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M2 Macrophage', 'full_name': 'M2 macrophage', 'paper_synonyms': 'MAC-M2', 'tissue_context': ''} CL:0000890 alternatively activated macrophage MAC-M2 CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MAC-M2', 'full_name': 'M2 macrophage', 'paper_synonyms': 'M2 macrophages', 'tissue_context': ''} CL:0000890 alternatively activated macrophage MAST CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MAST', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell MC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell -MD CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MD', 'full_name': 'macula densa cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000850 macula densa epithelial cell -MDC CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MDC', 'full_name': 'monocyte-derived cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0011031 monocyte-derived dendritic cell -MYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MYOF', 'full_name': 'myofibroblast', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell +MD CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MD', 'full_name': 'macula densa cells', 'paper_synonyms': 'Macula Densa', 'tissue_context': ''} CL:1000850 macula densa epithelial cell +MDC CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MDC', 'full_name': 'monocyte-derived cells', 'paper_synonyms': 'MDCs', 'tissue_context': ''} CL:0000782 myeloid dendritic cell +MYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MYOF', 'full_name': 'myofibroblasts', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell Macula Densa Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Macula Densa Cell', 'full_name': 'macula densa cell', 'paper_synonyms': 'MD', 'tissue_context': ''} CL:1000850 macula densa epithelial cell Mast Cell CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Mast Cell', 'full_name': 'mast cell', 'paper_synonyms': 'MAST', 'tissue_context': ''} CL:0000097 mast cell -Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Fibroblast', 'full_name': 'medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast -Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Thick Ascending Limb Cell', 'full_name': 'medullary thick ascending limb cell', 'paper_synonyms': 'M-TAL; TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell +Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Fibroblast', 'full_name': 'medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast +Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Thick Ascending Limb Cell', 'full_name': 'Medullary thick ascending limb cell', 'paper_synonyms': 'M-TAL; thick ascending limb (TAL)', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell Mesangial Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Mesangial Cell', 'full_name': 'mesangial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000650 mesangial cell Monocyte-derived Cell CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Monocyte-derived Cell', 'full_name': 'monocyte-derived cell', 'paper_synonyms': 'MDCs', 'tissue_context': ''} NO MATCH found Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Myofibroblast', 'full_name': 'myofibroblast', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell N CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'N', 'full_name': 'neutrophils', 'paper_synonyms': 'MPO+ cells', 'tissue_context': ''} CL:0000775 neutrophil -NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NEU', 'full_name': 'neuronal cell', 'paper_synonyms': 'Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0000540 neuron -NKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NKC/T', 'full_name': 'T cells', 'paper_synonyms': 'T', 'tissue_context': ''} CL:0000084 T cell -Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Natural Killer Cell / Natural Killer T Cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:0000623 natural killer cell -Neutrophil CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'neutrophil', 'paper_synonyms': 'N; MPO+ (N)', 'tissue_context': ''} CL:0000775 neutrophil -Non-classical Monocyte CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Non-classical Monocyte', 'full_name': 'Non-classical Monocyte', 'paper_synonyms': 'ncMON', 'tissue_context': ''} CL:0000875 non-classical monocyte -OMCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-IC-A', 'full_name': 'outer medullary collecting duct intercalated cells', 'paper_synonyms': 'OMCD; IC; intercalated cells', 'tissue_context': ''} CL:1000717 kidney outer medulla collecting duct intercalated cell -OMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-PC', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; OMCD; outer medullary collecting duct', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell -Outer Medullary Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'full_name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'paper_synonyms': 'OMCD; IC', 'tissue_context': ''} CL:4030015 kidney collecting duct alpha-intercalated cell -Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; principal cells (PC); medullary principal cell (M-PC)', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell -PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PC', 'full_name': 'principal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell -PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PEC', 'full_name': 'parietal epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell -PL CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PL', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} -POD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'POD', 'full_name': 'podocyte', 'paper_synonyms': 'PODs', 'tissue_context': ''} -PT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT', 'full_name': 'proximal tubule', 'paper_synonyms': None, 'tissue_context': ''} -PT-S1/2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S1/2', 'full_name': 'proximal tubule S1/S2', 'paper_synonyms': 'PT-S1/PT-S2', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NEU', 'full_name': 'neuronal cell', 'paper_synonyms': 'SCI/NEU; Schwann/neuronal', 'tissue_context': ''} CL:0000540 neuron +NKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NKC/T', 'full_name': 'T cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000084 T cell +Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:0000623 natural killer cell +Neutrophil CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'neutrophil', 'paper_synonyms': 'N', 'tissue_context': ''} CL:0000775 neutrophil +Non-classical Monocyte CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Non-classical Monocyte', 'full_name': 'non-classical monocyte', 'paper_synonyms': 'ncMON', 'tissue_context': ''} CL:0000875 non-classical monocyte +OMCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-IC-A', 'full_name': 'outer medullary collecting duct intercalated cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000717 kidney outer medulla collecting duct intercalated cell +OMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-PC', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; outer medullary collecting duct; PC; principal cells', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell +Outer Medullary Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'full_name': 'outer medullary collecting duct intercalated cell', 'paper_synonyms': 'OMCD; IC', 'tissue_context': ''} CL:1000717 kidney outer medulla collecting duct intercalated cell +Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Principal Cell', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; PC; M-PC', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell +PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PC', 'full_name': 'principal cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0005009 renal principal cell +PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PEC', 'full_name': 'parietal epithelial cells', 'paper_synonyms': '', 'tissue_context': ''} CL:1000452 parietal epithelial cell +PL CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PL', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +POD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'POD', 'full_name': 'podocytes', 'paper_synonyms': 'PODs', 'tissue_context': ''} CL:0000653 podocyte +PT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT', 'full_name': 'proximal tubule', 'paper_synonyms': 'proximal tubule (PT)', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +PT-S1/2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S1/2', 'full_name': 'proximal tubule S1/2', 'paper_synonyms': 'PT; proximal tubule', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule PT-S3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S3', 'full_name': 'proximal tubule S3', 'paper_synonyms': 'proximal tubule (PT)', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 -PapE CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PapE', 'full_name': 'papillary tip epithelial cells abutting the calyx', 'paper_synonyms': None, 'tissue_context': ''} CL:0000731 urothelial cell -Papillary Tip Epithelial Cell CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Papillary Tip Epithelial Cell', 'full_name': 'Papillary tip epithelial cell', 'paper_synonyms': 'PapE', 'tissue_context': ''} CL:0000731 urothelial cell +PapE CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PapE', 'full_name': 'papillary tip epithelial cells abutting the calyx', 'paper_synonyms': '', 'tissue_context': ''} CL:1000597 papillary tips cell +Papillary Tip Epithelial Cell CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Papillary Tip Epithelial Cell', 'full_name': 'papillary tip epithelial cell', 'paper_synonyms': 'PapE', 'tissue_context': ''} CL:1000597 papillary tips cell Parietal Epithelial Cell CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Parietal Epithelial Cell', 'full_name': 'parietal epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell Peritubular Capilary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell Plasma Cell CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasma Cell', 'full_name': 'Plasma cell', 'paper_synonyms': 'PL', 'tissue_context': ''} CL:0000786 plasma cell -Plasmacytoid Dendritic Cell CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasmacytoid Dendritic Cell', 'full_name': 'Plasmacytoid dendritic cell', 'paper_synonyms': 'pDC', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell -Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Podocyte', 'full_name': 'podocyte', 'paper_synonyms': 'PODs', 'tissue_context': ''} CL:0000653 podocyte -Proximal Tubule Epithelial Cell Segment 1 / Segment 2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 1 / Segment 2', 'full_name': 'Proximal tubule epithelial cell, segments 1 and 2', 'paper_synonyms': 'PT-S1; PT-S2; PT-S1/PT-S2; PT', 'tissue_context': ''} CL:1000838 kidney proximal convoluted tubule epithelial cell -Proximal Tubule Epithelial Cell Segment 3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 3', 'full_name': 'Proximal tubule epithelial cell, segment 3', 'paper_synonyms': 'PT-S3; PT', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 -REN CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'REN', 'full_name': 'juxtaglomerular renin-producing granular cells', 'paper_synonyms': 'renin-producing granular cells', 'tissue_context': ''} CL:0000648 kidney granular cell -Renin-positive Juxtaglomerular Granular Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Renin-positive Juxtaglomerular Granular Cell', 'full_name': 'juxtaglomerular renin-producing granular (REN) cell', 'paper_synonyms': 'renin-producing granular (REN) cells; REN; juxtaglomerular renin-producing granular cells (REN)', 'tissue_context': ''} CL:0000648 kidney granular cell +Plasmacytoid Dendritic Cell CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasmacytoid Dendritic Cell', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': 'pDC', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell +Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Podocyte', 'full_name': 'podocyte (POD)', 'paper_synonyms': 'POD; PODs', 'tissue_context': ''} CL:0000653 podocyte +Proximal Tubule Epithelial Cell Segment 1 / Segment 2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 1 / Segment 2', 'full_name': 'proximal tubule (PT) epithelial cell, segment 1/segment 2', 'paper_synonyms': 'PT-S1; PT-S2; PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Proximal Tubule Epithelial Cell Segment 3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 3', 'full_name': 'proximal tubule (PT) epithelial cell, segment 3', 'paper_synonyms': 'PT-S3; PT', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 +REN CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'REN', 'full_name': 'juxtaglomerular renin-producing granular cell', 'paper_synonyms': 'renin-producing granular cells; juxtaglomerular renin-producing granular cells', 'tissue_context': ''} CL:0000648 kidney granular cell +Renin-positive Juxtaglomerular Granular Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Renin-positive Juxtaglomerular Granular Cell', 'full_name': 'Juxtaglomerular renin-producing granular cell', 'paper_synonyms': 'REN; renin-producing granular cell', 'tissue_context': ''} CL:0000648 kidney granular cell SC/NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'SC/NEU', 'full_name': 'Schwann/neuronal', 'paper_synonyms': 'SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell -Schwann Cell / Neural CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Schwann Cell / Neural', 'full_name': 'Schwann/neuronal cell', 'paper_synonyms': 'SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell -T CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T', 'full_name': 'T cell', 'paper_synonyms': 'CD3+ cells; lymphoid or T cells', 'tissue_context': ''} CL:0000084 T cell -T Cell CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T Cell', 'full_name': 'T cell', 'paper_synonyms': 'T; CD3+ cells', 'tissue_context': ''} CL:0000084 T cell -TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'C-TAL; M-TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell -Transitional Principal-Intercalated Cell CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Transitional Principal-Intercalated Cell', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found -VSM/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSM/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'vascular smooth muscle cell; pericyte; VSMC', 'tissue_context': ''} NO MATCH found -VSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSM/P; pericyte', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell -VSMC/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC; vascular smooth muscle cell; pericyte', 'tissue_context': ''} CL:0008034 mural cell +Schwann Cell / Neural CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Schwann Cell / Neural', 'full_name': 'Schwann/neuronal cell', 'paper_synonyms': 'Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell +T CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T', 'full_name': 'T cell', 'paper_synonyms': 'CD3+ cells', 'tissue_context': ''} CL:0000084 T cell +T Cell CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T Cell', 'full_name': 'T cell', 'paper_synonyms': 'CD3+ cells; lymphoid (T) cells; T', 'tissue_context': ''} CL:0000084 T cell +TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +Transitional Principal-Intercalated Cell CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Transitional Principal-Intercalated Cell', 'full_name': 'Transitional principal–intercalated cell', 'paper_synonyms': 'transitioning principal and intercalated cells; PC, principal cells; IC, intercalated cells', 'tissue_context': ''} CL:1001225 kidney collecting duct cell +VSM/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSM/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSMC', 'tissue_context': ''} CL:0008034 mural cell +VSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell +VSMC/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P', 'tissue_context': ''} CL:4033054 perivascular cell Vascular Smooth Muscle Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell -Vascular Smooth Muscle Cell / Pericyte CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell / Pericyte', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC', 'tissue_context': ''} CL:0008034 mural cell +Vascular Smooth Muscle Cell / Pericyte CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell / Pericyte', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC', 'tissue_context': ''} CL:4033054 perivascular cell aFIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aFIB', 'full_name': 'adaptive fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast -aPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aPT', 'full_name': 'adaptive proximal tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule -aTAL1 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL1', 'full_name': 'adaptive thick ascending limb 1', 'paper_synonyms': 'aTAL; aEpi', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell -aTAL2 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL2', 'full_name': 'adaptive thick ascending limb 2', 'paper_synonyms': 'adaptive TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +aPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aPT', 'full_name': 'adaptive proximal tubule', 'paper_synonyms': '', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +aTAL1 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL1', 'full_name': 'adaptive thick ascending limb 1', 'paper_synonyms': 'aTAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +aTAL2 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL2', 'full_name': 'adaptive thick ascending limb cell 2', 'paper_synonyms': 'adaptive TAL; aTAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell cDC CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000990 conventional dendritic cell -cycCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycCNT', 'full_name': 'cycling connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell -cycDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycDCT', 'full_name': 'cycling distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell -cycEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycEC', 'full_name': 'cycling endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell -cycMNP CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMNP', 'full_name': 'cycling', 'paper_synonyms': None, 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte -cycMYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMYOF', 'full_name': 'cycling myofibroblasts', 'paper_synonyms': 'MyoF; cycMyoF; myofibroblasts', 'tissue_context': ''} CL:0000186 myofibroblast cell -cycNKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycNKC/T', 'full_name': 'cycling T cells', 'paper_synonyms': 'T; T cells', 'tissue_context': ''} CL:4033069 cycling T cell -cycPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycPT', 'full_name': 'cycling proximal tubule cell', 'paper_synonyms': 'PT; cycling', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule -dATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dATL', 'full_name': 'degenerative ascending thin limb', 'paper_synonyms': 'ATL; ascending thin limbs', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell -dC-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-IC-A', 'full_name': 'degenerative cortical intercalated cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell -dC-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-TAL', 'full_name': 'degenerative cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL); cortical thick ascending limb (C-TAL)', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell -dCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dCNT', 'full_name': 'degenerative connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell -dDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDCT', 'full_name': 'degenerative distal convoluted tubule cells', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell -dDTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDTL3', 'full_name': 'degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell -dEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell -dEC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC-PTC', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +cycCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycCNT', 'full_name': 'cycling connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +cycDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycDCT', 'full_name': 'cycling distal convoluted tubule', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +cycEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycEC', 'full_name': 'cycling endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +cycMNP CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMNP', 'full_name': 'cycling MNP', 'paper_synonyms': '', 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte +cycMYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMYOF', 'full_name': 'cycling myofibroblasts', 'paper_synonyms': 'cycMyoF; cycling MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell +cycNKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycNKC/T', 'full_name': 'cycling T cells', 'paper_synonyms': None, 'tissue_context': ''} CL:4033069 cycling T cell +cycPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycPT', 'full_name': 'cycling proximal tubule cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +dATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dATL', 'full_name': 'ascending thin limb', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +dC-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-IC-A', 'full_name': 'degenerative cortical intercalated cell A', 'paper_synonyms': 'intercalated cells (IC)', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell +dC-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-TAL', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell +dCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dCNT', 'full_name': 'degenerative connecting tubule cell', 'paper_synonyms': 'connecting tubules (CNT)', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +dDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDCT', 'full_name': 'degenerative distal convoluted tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +dDTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDTL3', 'full_name': 'degenerative descending thin limb 3', 'paper_synonyms': 'DTL3; DTL; descending thin limb', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell +dEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell +dEC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC-PTC', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell dFIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dFIB', 'full_name': 'degenerative fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast -dIMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dIMCD', 'full_name': 'degenerative inner medullary collecting duct', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell +dIMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dIMCD', 'full_name': 'degenerative inner medullary collecting duct cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell dM-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-FIB', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast -dM-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-PC', 'full_name': 'degenerative medullary principal cell', 'paper_synonyms': 'dM-PCs', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell -dM-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell -dOMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dOMCD-PC', 'full_name': 'degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; OMCD; outer medullary collecting duct; degenerative medullary principal cells (dM-PCs)', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell -dPOD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPOD', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'POD', 'tissue_context': ''} CL:0000653 podocyte -dPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPT', 'full_name': 'degenerative proximal tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule -dVSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dVSMC', 'full_name': 'degenerative vascular smooth muscle cell', 'paper_synonyms': 'VSMC; vascular smooth muscle cell; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell -endothelial cells CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'endothelial cells', 'full_name': 'endothelial cells', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell +dM-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-PC', 'full_name': 'degenerative medullary principal cell', 'paper_synonyms': 'degenerative medullary principal cells (dM-PCs)', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell +dM-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-TAL', 'full_name': 'degenerative medullary thick ascending limb cell', 'paper_synonyms': '', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell +dOMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dOMCD-PC', 'full_name': 'degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; outer medullary collecting duct; PC; principal cells', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell +dPOD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPOD', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'PODs', 'tissue_context': ''} CL:0000653 podocyte +dPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPT', 'full_name': 'degenerative proximal tubule', 'paper_synonyms': '', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +dVSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dVSMC', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell +endothelial cells CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'endothelial cells', 'full_name': 'endothelial cells', 'paper_synonyms': 'EC; endothelium', 'tissue_context': ''} CL:0000115 endothelial cell epithelial cells CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -epithelial cells CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell -immune cells CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'IMM', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte -immune cells CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +epithelial cells CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +epithelial cells CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +immune cells CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte +immune cells CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte ncMON CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ncMON', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000875 non-classical monocyte -neural cells CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'neural cells', 'full_name': 'neural cell types', 'paper_synonyms': 'neuronal; Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell -pDC CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'pDC', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell -stroma cells CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': 'stromal cells', 'paper_synonyms': 'stroma; STR', 'tissue_context': ''} CL:0000499 stromal cell -stroma cells CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000499 stromal cell -tPC-IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'tPC-IC', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': 'principal cells (PC); intercalated cells (IC)', 'tissue_context': ''} CL:1001225 kidney collecting duct cell +neural cells CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'neural cells', 'full_name': 'neural cells', 'paper_synonyms': 'NEU; SCI/NEU; Schwann/neuronal', 'tissue_context': ''} CL:0002319 neural cell +pDC CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'pDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell +stroma cells CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': 'stromal cells', 'paper_synonyms': 'STR; stroma', 'tissue_context': ''} CL:0000499 stromal cell +stroma cells CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': 'stromal cells', 'paper_synonyms': 'STR; stroma', 'tissue_context': ''} CL:0000499 stromal cell +tPC-IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'tPC-IC', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': '', 'tissue_context': ''} CL:1001225 kidney collecting duct cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/groundings.tsv index e2771ac..b6b46ab 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/groundings.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique/groundings.tsv @@ -1,200 +1,203 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result -ATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ATL', 'full_name': 'ascending thin limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE -Adaptive / Maladaptive / Repairing Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Fibroblast', 'full_name': 'adaptive (successful or maladaptive repair) fibroblast', 'paper_synonyms': 'aFIB; aStr', 'tissue_context': ''} CL:0000057 fibroblast FALSE -Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell', 'full_name': 'adaptive (successful or maladaptive tubular repair) proximal tubule epithelial cell', 'paper_synonyms': 'aPT; adaptive epithelial (aEpi)', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE -Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell', 'full_name': 'adaptive/maladaptive repairing thick ascending limb epithelial cell', 'paper_synonyms': 'aTAL; adaptive TAL; adaptive epithelial (aEpi)', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE -Afferent / Efferent Arteriole Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Afferent / Efferent Arteriole Endothelial Cell', 'full_name': 'endothelial cell of the afferent/efferent arterioles', 'paper_synonyms': 'EC-AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole FALSE -Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Thin Limb Cell', 'full_name': 'ascending thin limb (ATL) cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE -Ascending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Vasa Recta Endothelial Cell', 'full_name': 'Ascending Vasa Recta Endothelial Cell', 'paper_synonyms': 'EC-AVR', 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell FALSE +ATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ATL', 'full_name': 'ascending thin limbs', 'paper_synonyms': '', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +Adaptive / Maladaptive / Repairing Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Fibroblast', 'full_name': 'adaptive fibroblast', 'paper_synonyms': 'aFIB', 'tissue_context': ''} CL:0000057 fibroblast FALSE +Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Proximal Tubule Epithelial Cell', 'full_name': 'adaptive proximal tubule (aPT) epithelial cell (successful or maladaptive tubular repair)', 'paper_synonyms': 'aPT; aEpi; Ad/Mal; PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Adaptive / Maladaptive / Repairing Thick Ascending Limb Cell', 'full_name': 'adaptive (successful or maladaptive repair) thick ascending limb cell (aTAL)', 'paper_synonyms': 'aTAL; adaptive epithelial (aEpi); Ad/Mal', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +Afferent / Efferent Arteriole Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Afferent / Efferent Arteriole Endothelial Cell', 'full_name': 'endothelial cell of the afferent/efferent arterioles (EC-AEA)', 'paper_synonyms': 'EC-AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole FALSE +Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Thin Limb Cell', 'full_name': 'ascending thin limb cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +Ascending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Ascending Vasa Recta Endothelial Cell', 'full_name': 'ascending vasa recta endothelial cell', 'paper_synonyms': 'EC-AVR', 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell FALSE B CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'B', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell TRUE B Cell CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'B Cell', 'full_name': 'B cell', 'paper_synonyms': 'B', 'tissue_context': ''} CL:0000236 B cell TRUE -C-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-IC-A', 'full_name': 'cortical intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE -C-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-PC', 'full_name': 'cortical principal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE +C-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-IC-A', 'full_name': 'cortical intercalated cell A', 'paper_synonyms': '', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell FALSE +C-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-PC', 'full_name': 'cortical principal cell', 'paper_synonyms': 'PC; principal cells', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE C-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'C-TAL', 'full_name': 'cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE -CCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-IC-A', 'full_name': 'cortical collecting duct intercalated cells', 'paper_synonyms': 'CCD; C-CD; IC; intercalated cells', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE -CCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-PC', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; CCD; cortical collecting duct', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE +CCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-IC-A', 'full_name': 'cortical collecting duct intercalated cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE +CCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CCD-PC', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'CCD; cortical collecting duct; PC; principal cells', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE CNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT', 'full_name': 'connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE -CNT-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-IC-A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC; CNT', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell FALSE -CNT-PC CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-PC', 'full_name': 'connecting tubule principal cell', 'paper_synonyms': 'PC; principal cells; CNT; connecting tubules', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell FALSE -Classical Dendritic Cell CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Classical Dendritic Cell', 'full_name': 'Classical Dendritic Cell', 'paper_synonyms': 'cDC', 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE +CNT-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-IC-A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell FALSE +CNT-PC CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'CNT-PC', 'full_name': 'connecting tubule principal cell', 'paper_synonyms': 'CNT; connecting tubule; PC; principal cells', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell FALSE +Classical Dendritic Cell CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Classical Dendritic Cell', 'full_name': 'classical dendritic cell', 'paper_synonyms': 'cDC', 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Cell', 'full_name': 'connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE -Connecting Tubule Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Intercalated Cell Type A', 'full_name': 'connecting tubule intercalated cell', 'paper_synonyms': 'CNT-IC; IC', 'tissue_context': ''} CL:4030019 kidney connecting tubule intercalated cell FALSE -Connecting Tubule Principal Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Principal Cell', 'full_name': 'Connecting tubule principal cell', 'paper_synonyms': 'CNT-PC', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell FALSE -Cortical Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Intercalated Cell Type A', 'full_name': 'cortical collecting duct intercalated cell', 'paper_synonyms': 'CCD; IC; C-CD', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE -Cortical Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Principal Cell', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'PC; C-PC', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE -Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Thick Ascending Limb Cell', 'full_name': 'Cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; cortical TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE -Cycling Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Connecting Tubule Cell', 'full_name': 'cycling connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE -Cycling Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Distal Convoluted Tubule Cell', 'full_name': 'cycling distal convoluted tubule cell', 'paper_synonyms': 'DCT; Cyc', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE -Cycling Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Endothelial Cell', 'full_name': 'Cycling Endothelial Cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Connecting Tubule Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Intercalated Cell Type A', 'full_name': 'Connecting Tubule Intercalated Cell Type A', 'paper_synonyms': 'CNT-IC; IC; CNT', 'tissue_context': ''} CL:4030020 kidney connecting tubule alpha-intercalated cell FALSE +Connecting Tubule Principal Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Connecting Tubule Principal Cell', 'full_name': 'Connecting tubule principal cell', 'paper_synonyms': 'CNT-PC; CNT; PC', 'tissue_context': ''} CL:4030018 kidney connecting tubule principal cell FALSE +Cortical Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Intercalated Cell Type A', 'full_name': 'Cortical collecting duct intercalated cell type A', 'paper_synonyms': 'CCD; IC; collecting duct (CD)', 'tissue_context': ''} CL:4030015 kidney collecting duct alpha-intercalated cell FALSE +Cortical Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Collecting Duct Principal Cell', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'C-PC; PC', 'tissue_context': ''} CL:1000714 kidney cortex collecting duct principal cell FALSE +Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cortical Thick Ascending Limb Cell', 'full_name': 'cortical thick ascending limb (C-TAL) cell', 'paper_synonyms': 'C-TAL; TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE +Cycling Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Connecting Tubule Cell', 'full_name': 'cycling connecting tubule cell', 'paper_synonyms': 'CNT; Cyc', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +Cycling Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Distal Convoluted Tubule Cell', 'full_name': 'Cycling Distal Convoluted Tubule Cell', 'paper_synonyms': 'DCT; DCT1; DCT2', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +Cycling Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Endothelial Cell', 'full_name': 'endothelial cell, cycling state', 'paper_synonyms': '', 'tissue_context': ''} CL:0000115 endothelial cell TRUE Cycling Mononuclear Phagocyte CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Mononuclear Phagocyte', 'full_name': 'cycling mononuclear phagocyte', 'paper_synonyms': 'cycMNP', 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte FALSE -Cycling Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Myofibroblast', 'full_name': 'Cycling myofibroblast', 'paper_synonyms': 'cycMyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE -Cycling Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Cycling natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:4033071 cycling natural killer cell FALSE -Cycling Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Proximal Tubule Epithelial Cell', 'full_name': 'Cycling Proximal Tubule Epithelial Cell', 'paper_synonyms': 'PT; Cyc', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Cycling Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Myofibroblast', 'full_name': 'cycling myofibroblast', 'paper_synonyms': 'cycMyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE +Cycling Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Natural Killer Cell / Natural Killer T Cell', 'full_name': 'cycling natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:4033071 cycling natural killer cell FALSE +Cycling Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Cycling Proximal Tubule Epithelial Cell', 'full_name': 'cycling proximal tubule epithelial cell', 'paper_synonyms': 'PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE DCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT', 'full_name': 'distal convoluted tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE -DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT1', 'full_name': 'distal convoluted tubule cell (type 1)', 'paper_synonyms': 'DCT; distal convoluted tubule', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule FALSE +DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT1', 'full_name': 'distal convoluted tubule cell 1', 'paper_synonyms': 'DCT; distal convoluted tubule', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule FALSE DCT2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DCT2', 'full_name': 'distal convoluted tubule cell 2', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule FALSE DTL CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL', 'full_name': 'descending thin limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE -DTL1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL1', 'full_name': 'descending thin limb cell type 1', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE -DTL2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL2', 'full_name': 'descending thin limb 2', 'paper_synonyms': 'DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE -DTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL3', 'full_name': 'descending thin limb 3', 'paper_synonyms': 'descending thin limb; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE -Degenerative Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Ascending Thin Limb Cell', 'full_name': 'degenerative ascending thin limb (ATL) cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE -Degenerative Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Connecting Tubule Cell', 'full_name': 'degenerative connecting tubule (CNT) cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE -Degenerative Cortical Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Intercalated Cell Type A', 'full_name': 'degenerative cortical intercalated cell type A', 'paper_synonyms': 'IC; intercalated cells', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell FALSE -Degenerative Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Thick Ascending Limb Cell', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE -Degenerative Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Descending Thin Limb Cell Type 3', 'full_name': 'Degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE -Degenerative Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Distal Convoluted Tubule Cell', 'full_name': 'Degenerative distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE -Degenerative Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Endothelial Cell', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell TRUE +DTL1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL1', 'full_name': 'descending thin limb 1', 'paper_synonyms': 'DTL; descending thin limb', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +DTL2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL2', 'full_name': 'descending thin limb cell type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +DTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'DTL3', 'full_name': 'descending thin limb 3', 'paper_synonyms': 'DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Degenerative Ascending Thin Limb Cell CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Ascending Thin Limb Cell', 'full_name': 'Degenerative ascending thin limb cell', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +Degenerative Connecting Tubule Cell CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Connecting Tubule Cell', 'full_name': 'Degenerative connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +Degenerative Cortical Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Intercalated Cell Type A', 'full_name': 'Degenerative cortical intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE +Degenerative Cortical Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Cortical Thick Ascending Limb Cell', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; cortical TAL', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE +Degenerative Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Descending Thin Limb Cell Type 3', 'full_name': 'degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Degenerative Distal Convoluted Tubule Cell CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Distal Convoluted Tubule Cell', 'full_name': 'degenerative distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +Degenerative Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Endothelial Cell', 'full_name': 'Degenerative Endothelial Cell', 'paper_synonyms': 'EC-AEA; EC-GC; EC-LYM; EC-AVR; EC-DVR; EC', 'tissue_context': ''} CL:0000115 endothelial cell TRUE Degenerative Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Fibroblast', 'full_name': 'degenerative fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast FALSE -Degenerative Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Inner Medullary Collecting Duct Cell', 'full_name': 'degenerative inner medullary collecting duct cell', 'paper_synonyms': 'IMCD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE -Degenerative Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Fibroblast', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE -Degenerative Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Thick Ascending Limb Cell', 'full_name': 'Degenerative Medullary Thick Ascending Limb Cell', 'paper_synonyms': 'M-TAL; TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE -Degenerative Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'degenerative medullary principal cells; dM-PCs', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE +Degenerative Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Inner Medullary Collecting Duct Cell', 'full_name': 'degenerative inner medullary collecting duct cell', 'paper_synonyms': 'IMCD; CD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE +Degenerative Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Fibroblast', 'full_name': 'Degenerative Medullary Fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE +Degenerative Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Medullary Thick Ascending Limb Cell', 'full_name': 'degenerative medullary thick ascending limb cell', 'paper_synonyms': 'TAL; M-TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE +Degenerative Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'paper_synonyms': 'OMCD; PC; degenerative medullary principal cells; dM-PCs', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE Degenerative Peritubular Capilary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell FALSE -Degenerative Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Podocyte', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'POD', 'tissue_context': ''} CL:0000653 podocyte TRUE -Degenerative Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Proximal Tubule Epithelial Cell', 'full_name': 'degenerative proximal tubule epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Degenerative Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Podocyte', 'full_name': 'Degenerative Podocyte', 'paper_synonyms': 'POD; PODs', 'tissue_context': ''} CL:0000653 podocyte TRUE +Degenerative Proximal Tubule Epithelial Cell CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Proximal Tubule Epithelial Cell', 'full_name': 'degenerative proximal tubule (PT) epithelial cell', 'paper_synonyms': 'PT; degen', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE Degenerative Vascular Smooth Muscle Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Degenerative Vascular Smooth Muscle Cell', 'full_name': 'degenerative vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE -Descending Thin Limb Cell Type 1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 1', 'full_name': 'descending thin limb cell type 1 (DTL1)', 'paper_synonyms': 'DTL1', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE -Descending Thin Limb Cell Type 2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 2', 'full_name': 'descending thin limb cell type 2', 'paper_synonyms': 'DTL2', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Descending Thin Limb Cell Type 1 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 1', 'full_name': 'descending thin limb cell type 1', 'paper_synonyms': 'DTL1', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +Descending Thin Limb Cell Type 2 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 2', 'full_name': 'descending thin limb cell type 2 (DTL2)', 'paper_synonyms': 'DTL2', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE Descending Thin Limb Cell Type 3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Thin Limb Cell Type 3', 'full_name': 'descending thin limb cell type 3', 'paper_synonyms': 'DTL3; DTL', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE -Descending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Vasa Recta Endothelial Cell', 'full_name': 'endothelial cell of the descending vasa recta', 'paper_synonyms': 'EC-DVR', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE +Descending Vasa Recta Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Descending Vasa Recta Endothelial Cell', 'full_name': 'Descending vasa recta endothelial cell', 'paper_synonyms': 'EC-DVR', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE Distal Convoluted Tubule Cell Type 1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 1', 'full_name': 'Distal convoluted tubule cell type 1', 'paper_synonyms': 'DCT1', 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule FALSE -Distal Convoluted Tubule Cell Type 2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 2', 'full_name': 'Distal Convoluted Tubule Cell Type 2', 'paper_synonyms': 'DCT2', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule FALSE +Distal Convoluted Tubule Cell Type 2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Distal Convoluted Tubule Cell Type 2', 'full_name': 'distal convoluted tubule cell 2', 'paper_synonyms': 'DCT2', 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule FALSE EC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE -EC-AEA CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AEA', 'full_name': 'endothelial cells of the afferent/efferent arterioles', 'paper_synonyms': 'AEA', 'tissue_context': ''} CL:1000412 endothelial cell of arteriole FALSE -EC-AVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AVR', 'full_name': 'endothelial cell, vasa recta', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE -EC-DVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-DVR', 'full_name': 'endothelial cells of the vasa recta', 'paper_synonyms': None, 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE -EC-GC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-GC', 'full_name': 'glomerular capillary endothelial cell', 'paper_synonyms': 'glomerular capillaries; EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell FALSE +EC-AEA CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AEA', 'full_name': 'endothelial cells of the afferent/efferent arterioles', 'paper_synonyms': None, 'tissue_context': ''} CL:1000412 endothelial cell of arteriole FALSE +EC-AVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-AVR', 'full_name': 'endothelial cell of the vasa recta', 'paper_synonyms': '', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE +EC-DVR CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-DVR', 'full_name': 'endothelial cells of the vasa recta', 'paper_synonyms': '', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE +EC-GC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-GC', 'full_name': 'glomerular capillaries', 'paper_synonyms': '', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell FALSE EC-LYM CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-LYM', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': None, 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel FALSE -EC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-PTC', 'full_name': 'endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE -FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'FIB', 'full_name': 'fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast FALSE -Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': 'Fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast FALSE -Glomerular Capillary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Glomerular Capillary Endothelial Cell', 'full_name': 'glomerular capillaries', 'paper_synonyms': 'EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell FALSE +EC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'EC-PTC', 'full_name': 'endothelial cell PTC', 'paper_synonyms': 'EC; endothelial cells', 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell FALSE +FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'FIB', 'full_name': 'fibroblast', 'paper_synonyms': 'fibroblast (FIB)', 'tissue_context': ''} CL:0000057 fibroblast FALSE +Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': 'fibroblast (FIB)', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast FALSE +Glomerular Capillary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Glomerular Capillary Endothelial Cell', 'full_name': 'glomerular capillary endothelial cell', 'paper_synonyms': 'EC-GC', 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell FALSE IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC', 'full_name': 'intercalated cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE -IC-B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC-B', 'full_name': 'intercalated cells B', 'paper_synonyms': 'IC; intercalated cells', 'tissue_context': ''} CL:0002201 renal beta-intercalated cell FALSE -IMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMCD', 'full_name': 'inner medullary collecting duct', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE -IMM CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -IMM CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IC-B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IC-B', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC; CNT-IC', 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE +IMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMCD', 'full_name': 'inner medullary collecting duct cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE +IMM CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE +IMM CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'IMM', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes; immune cells', 'tissue_context': ''} CL:0000738 leukocyte FALSE Inner Medullary Collecting Duct Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Inner Medullary Collecting Duct Cell', 'full_name': 'inner medullary collecting duct cell', 'paper_synonyms': 'IMCD', 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE Intercalated Cell Type B CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Intercalated Cell Type B', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE -Lymphatic Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Lymphatic Endothelial Cell', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': 'EC-LYM', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel FALSE -M-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-FIB', 'full_name': 'medullary fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE -M-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-IC-A', 'full_name': 'intercalated cells', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE -M-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-PC', 'full_name': 'medullary principal cell', 'paper_synonyms': 'principal cells (PC)', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell TRUE -M-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-TAL', 'full_name': 'medullary thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE -M2 Macrophage CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M2 Macrophage', 'full_name': 'M2 Macrophage', 'paper_synonyms': 'MAC-M2', 'tissue_context': ''} CL:0000890 alternatively activated macrophage FALSE +Lymphatic Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Lymphatic Endothelial Cell', 'full_name': 'endothelial cell of the lymphatics', 'paper_synonyms': 'EC-LYM', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel FALSE +M-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-FIB', 'full_name': 'medullary fibroblast', 'paper_synonyms': '', 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE +M-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-IC-A', 'full_name': 'medullary intercalated cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1001432 kidney collecting duct intercalated cell TRUE +M-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-PC', 'full_name': 'medullary principal cell', 'paper_synonyms': 'principal cell (PC)', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell TRUE +M-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M-TAL', 'full_name': 'medullary thick ascending limb', 'paper_synonyms': '', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE +M2 Macrophage CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'M2 Macrophage', 'full_name': 'M2 macrophage', 'paper_synonyms': 'MAC-M2', 'tissue_context': ''} CL:0000890 alternatively activated macrophage FALSE MAC-M2 CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MAC-M2', 'full_name': 'M2 macrophage', 'paper_synonyms': 'M2 macrophages', 'tissue_context': ''} CL:0000890 alternatively activated macrophage FALSE MAST CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MAST', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell TRUE MC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell FALSE -MD CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MD', 'full_name': 'macula densa cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000850 macula densa epithelial cell FALSE -MDC CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MDC', 'full_name': 'monocyte-derived cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0011031 monocyte-derived dendritic cell FALSE -MYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MYOF', 'full_name': 'myofibroblast', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE +MD CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MD', 'full_name': 'macula densa cells', 'paper_synonyms': 'Macula Densa', 'tissue_context': ''} CL:1000850 macula densa epithelial cell FALSE +MDC CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MDC', 'full_name': 'monocyte-derived cells', 'paper_synonyms': 'MDCs', 'tissue_context': ''} CL:0000782 myeloid dendritic cell FALSE +MYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'MYOF', 'full_name': 'myofibroblasts', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE Macula Densa Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Macula Densa Cell', 'full_name': 'macula densa cell', 'paper_synonyms': 'MD', 'tissue_context': ''} CL:1000850 macula densa epithelial cell FALSE Mast Cell CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Mast Cell', 'full_name': 'mast cell', 'paper_synonyms': 'MAST', 'tissue_context': ''} CL:0000097 mast cell TRUE -Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Fibroblast', 'full_name': 'medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE -Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Thick Ascending Limb Cell', 'full_name': 'medullary thick ascending limb cell', 'paper_synonyms': 'M-TAL; TAL', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE +Medullary Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Fibroblast', 'full_name': 'medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:0000057 fibroblast FALSE +Medullary Thick Ascending Limb Cell CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Medullary Thick Ascending Limb Cell', 'full_name': 'Medullary thick ascending limb cell', 'paper_synonyms': 'M-TAL; thick ascending limb (TAL)', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE Mesangial Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Mesangial Cell', 'full_name': 'mesangial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000650 mesangial cell FALSE Monocyte-derived Cell CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Monocyte-derived Cell', 'full_name': 'monocyte-derived cell', 'paper_synonyms': 'MDCs', 'tissue_context': ''} NO MATCH found FALSE Myofibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Myofibroblast', 'full_name': 'myofibroblast', 'paper_synonyms': 'MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE N CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'N', 'full_name': 'neutrophils', 'paper_synonyms': 'MPO+ cells', 'tissue_context': ''} CL:0000775 neutrophil TRUE -NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NEU', 'full_name': 'neuronal cell', 'paper_synonyms': 'Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0000540 neuron FALSE -NKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NKC/T', 'full_name': 'T cells', 'paper_synonyms': 'T', 'tissue_context': ''} CL:0000084 T cell FALSE -Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Natural Killer Cell / Natural Killer T Cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:0000623 natural killer cell FALSE -Neutrophil CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'neutrophil', 'paper_synonyms': 'N; MPO+ (N)', 'tissue_context': ''} CL:0000775 neutrophil TRUE -Non-classical Monocyte CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Non-classical Monocyte', 'full_name': 'Non-classical Monocyte', 'paper_synonyms': 'ncMON', 'tissue_context': ''} CL:0000875 non-classical monocyte TRUE -OMCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-IC-A', 'full_name': 'outer medullary collecting duct intercalated cells', 'paper_synonyms': 'OMCD; IC; intercalated cells', 'tissue_context': ''} CL:1000717 kidney outer medulla collecting duct intercalated cell FALSE -OMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-PC', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; OMCD; outer medullary collecting duct', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE -Outer Medullary Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'full_name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'paper_synonyms': 'OMCD; IC', 'tissue_context': ''} CL:4030015 kidney collecting duct alpha-intercalated cell FALSE -Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; principal cells (PC); medullary principal cell (M-PC)', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE -PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PC', 'full_name': 'principal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell FALSE -PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PEC', 'full_name': 'parietal epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell TRUE -PT-S1/2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S1/2', 'full_name': 'proximal tubule S1/S2', 'paper_synonyms': 'PT-S1/PT-S2', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NEU', 'full_name': 'neuronal cell', 'paper_synonyms': 'SCI/NEU; Schwann/neuronal', 'tissue_context': ''} CL:0000540 neuron FALSE +NKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'NKC/T', 'full_name': 'T cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000084 T cell FALSE +Natural Killer Cell / Natural Killer T Cell CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:0000623 natural killer cell FALSE +Neutrophil CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'neutrophil', 'paper_synonyms': 'N', 'tissue_context': ''} CL:0000775 neutrophil TRUE +Non-classical Monocyte CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Non-classical Monocyte', 'full_name': 'non-classical monocyte', 'paper_synonyms': 'ncMON', 'tissue_context': ''} CL:0000875 non-classical monocyte TRUE +OMCD-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-IC-A', 'full_name': 'outer medullary collecting duct intercalated cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000717 kidney outer medulla collecting duct intercalated cell FALSE +OMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'OMCD-PC', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; outer medullary collecting duct; PC; principal cells', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE +Outer Medullary Collecting Duct Intercalated Cell Type A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'full_name': 'outer medullary collecting duct intercalated cell', 'paper_synonyms': 'OMCD; IC', 'tissue_context': ''} CL:1000717 kidney outer medulla collecting duct intercalated cell FALSE +Outer Medullary Collecting Duct Principal Cell CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Outer Medullary Collecting Duct Principal Cell', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; PC; M-PC', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE +PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PC', 'full_name': 'principal cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0005009 renal principal cell FALSE +PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PEC', 'full_name': 'parietal epithelial cells', 'paper_synonyms': '', 'tissue_context': ''} CL:1000452 parietal epithelial cell TRUE +PL CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PL', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +POD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'POD', 'full_name': 'podocytes', 'paper_synonyms': 'PODs', 'tissue_context': ''} CL:0000653 podocyte TRUE +PT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT', 'full_name': 'proximal tubule', 'paper_synonyms': 'proximal tubule (PT)', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +PT-S1/2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S1/2', 'full_name': 'proximal tubule S1/2', 'paper_synonyms': 'PT; proximal tubule', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE PT-S3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PT-S3', 'full_name': 'proximal tubule S3', 'paper_synonyms': 'proximal tubule (PT)', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 FALSE -PapE CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PapE', 'full_name': 'papillary tip epithelial cells abutting the calyx', 'paper_synonyms': None, 'tissue_context': ''} CL:0000731 urothelial cell FALSE -Papillary Tip Epithelial Cell CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Papillary Tip Epithelial Cell', 'full_name': 'Papillary tip epithelial cell', 'paper_synonyms': 'PapE', 'tissue_context': ''} CL:0000731 urothelial cell FALSE +PapE CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'PapE', 'full_name': 'papillary tip epithelial cells abutting the calyx', 'paper_synonyms': '', 'tissue_context': ''} CL:1000597 papillary tips cell TRUE +Papillary Tip Epithelial Cell CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Papillary Tip Epithelial Cell', 'full_name': 'papillary tip epithelial cell', 'paper_synonyms': 'PapE', 'tissue_context': ''} CL:1000597 papillary tips cell TRUE Parietal Epithelial Cell CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Parietal Epithelial Cell', 'full_name': 'parietal epithelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell TRUE Peritubular Capilary Endothelial Cell CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell FALSE Plasma Cell CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasma Cell', 'full_name': 'Plasma cell', 'paper_synonyms': 'PL', 'tissue_context': ''} CL:0000786 plasma cell TRUE -Plasmacytoid Dendritic Cell CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasmacytoid Dendritic Cell', 'full_name': 'Plasmacytoid dendritic cell', 'paper_synonyms': 'pDC', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell FALSE -Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Podocyte', 'full_name': 'podocyte', 'paper_synonyms': 'PODs', 'tissue_context': ''} CL:0000653 podocyte TRUE -Proximal Tubule Epithelial Cell Segment 1 / Segment 2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 1 / Segment 2', 'full_name': 'Proximal tubule epithelial cell, segments 1 and 2', 'paper_synonyms': 'PT-S1; PT-S2; PT-S1/PT-S2; PT', 'tissue_context': ''} CL:1000838 kidney proximal convoluted tubule epithelial cell FALSE -Proximal Tubule Epithelial Cell Segment 3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 3', 'full_name': 'Proximal tubule epithelial cell, segment 3', 'paper_synonyms': 'PT-S3; PT', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 FALSE -REN CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'REN', 'full_name': 'juxtaglomerular renin-producing granular cells', 'paper_synonyms': 'renin-producing granular cells', 'tissue_context': ''} CL:0000648 kidney granular cell FALSE -Renin-positive Juxtaglomerular Granular Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Renin-positive Juxtaglomerular Granular Cell', 'full_name': 'juxtaglomerular renin-producing granular (REN) cell', 'paper_synonyms': 'renin-producing granular (REN) cells; REN; juxtaglomerular renin-producing granular cells (REN)', 'tissue_context': ''} CL:0000648 kidney granular cell FALSE +Plasmacytoid Dendritic Cell CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Plasmacytoid Dendritic Cell', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': 'pDC', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell FALSE +Podocyte CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Podocyte', 'full_name': 'podocyte (POD)', 'paper_synonyms': 'POD; PODs', 'tissue_context': ''} CL:0000653 podocyte TRUE +Proximal Tubule Epithelial Cell Segment 1 / Segment 2 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 1 / Segment 2', 'full_name': 'proximal tubule (PT) epithelial cell, segment 1/segment 2', 'paper_synonyms': 'PT-S1; PT-S2; PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Proximal Tubule Epithelial Cell Segment 3 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Proximal Tubule Epithelial Cell Segment 3', 'full_name': 'proximal tubule (PT) epithelial cell, segment 3', 'paper_synonyms': 'PT-S3; PT', 'tissue_context': ''} CL:4030011 epithelial cell of proximal tubule segment 3 FALSE +REN CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'REN', 'full_name': 'juxtaglomerular renin-producing granular cell', 'paper_synonyms': 'renin-producing granular cells; juxtaglomerular renin-producing granular cells', 'tissue_context': ''} CL:0000648 kidney granular cell FALSE +Renin-positive Juxtaglomerular Granular Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Renin-positive Juxtaglomerular Granular Cell', 'full_name': 'Juxtaglomerular renin-producing granular cell', 'paper_synonyms': 'REN; renin-producing granular cell', 'tissue_context': ''} CL:0000648 kidney granular cell FALSE SC/NEU CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'SC/NEU', 'full_name': 'Schwann/neuronal', 'paper_synonyms': 'SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell TRUE -Schwann Cell / Neural CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Schwann Cell / Neural', 'full_name': 'Schwann/neuronal cell', 'paper_synonyms': 'SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell TRUE -T CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T', 'full_name': 'T cell', 'paper_synonyms': 'CD3+ cells; lymphoid or T cells', 'tissue_context': ''} CL:0000084 T cell TRUE -T Cell CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T Cell', 'full_name': 'T cell', 'paper_synonyms': 'T; CD3+ cells', 'tissue_context': ''} CL:0000084 T cell TRUE -TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'C-TAL; M-TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE -Transitional Principal-Intercalated Cell CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Transitional Principal-Intercalated Cell', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE -VSM/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSM/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'vascular smooth muscle cell; pericyte; VSMC', 'tissue_context': ''} NO MATCH found FALSE -VSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSM/P; pericyte', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE -VSMC/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC; vascular smooth muscle cell; pericyte', 'tissue_context': ''} CL:0008034 mural cell FALSE +Schwann Cell / Neural CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Schwann Cell / Neural', 'full_name': 'Schwann/neuronal cell', 'paper_synonyms': 'Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell TRUE +T CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T', 'full_name': 'T cell', 'paper_synonyms': 'CD3+ cells', 'tissue_context': ''} CL:0000084 T cell TRUE +T Cell CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'T Cell', 'full_name': 'T cell', 'paper_synonyms': 'CD3+ cells; lymphoid (T) cells; T', 'tissue_context': ''} CL:0000084 T cell TRUE +TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +Transitional Principal-Intercalated Cell CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Transitional Principal-Intercalated Cell', 'full_name': 'Transitional principal–intercalated cell', 'paper_synonyms': 'transitioning principal and intercalated cells; PC, principal cells; IC, intercalated cells', 'tissue_context': ''} CL:1001225 kidney collecting duct cell FALSE +VSM/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSM/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSMC', 'tissue_context': ''} CL:0008034 mural cell FALSE +VSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE +VSMC/P CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'VSMC/P', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P', 'tissue_context': ''} CL:4033054 perivascular cell FALSE Vascular Smooth Muscle Cell CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE -Vascular Smooth Muscle Cell / Pericyte CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell / Pericyte', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC', 'tissue_context': ''} CL:0008034 mural cell FALSE +Vascular Smooth Muscle Cell / Pericyte CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'Vascular Smooth Muscle Cell / Pericyte', 'full_name': 'vascular smooth muscle cell or pericyte', 'paper_synonyms': 'VSM/P; VSMC', 'tissue_context': ''} CL:4033054 perivascular cell FALSE aFIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aFIB', 'full_name': 'adaptive fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast FALSE -aPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aPT', 'full_name': 'adaptive proximal tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE -aTAL1 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL1', 'full_name': 'adaptive thick ascending limb 1', 'paper_synonyms': 'aTAL; aEpi', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE -aTAL2 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL2', 'full_name': 'adaptive thick ascending limb 2', 'paper_synonyms': 'adaptive TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +aPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aPT', 'full_name': 'adaptive proximal tubule', 'paper_synonyms': '', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +aTAL1 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL1', 'full_name': 'adaptive thick ascending limb 1', 'paper_synonyms': 'aTAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +aTAL2 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'aTAL2', 'full_name': 'adaptive thick ascending limb cell 2', 'paper_synonyms': 'adaptive TAL; aTAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE cDC CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE -cycCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycCNT', 'full_name': 'cycling connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE -cycDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycDCT', 'full_name': 'cycling distal convoluted tubule cell', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE -cycEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycEC', 'full_name': 'cycling endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE -cycMNP CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMNP', 'full_name': 'cycling', 'paper_synonyms': None, 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte FALSE -cycMYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMYOF', 'full_name': 'cycling myofibroblasts', 'paper_synonyms': 'MyoF; cycMyoF; myofibroblasts', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE -cycNKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycNKC/T', 'full_name': 'cycling T cells', 'paper_synonyms': 'T; T cells', 'tissue_context': ''} CL:4033069 cycling T cell FALSE -cycPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycPT', 'full_name': 'cycling proximal tubule cell', 'paper_synonyms': 'PT; cycling', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE -dATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dATL', 'full_name': 'degenerative ascending thin limb', 'paper_synonyms': 'ATL; ascending thin limbs', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE -dC-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-IC-A', 'full_name': 'degenerative cortical intercalated cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000715 kidney cortex collecting duct intercalated cell FALSE -dC-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-TAL', 'full_name': 'degenerative cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL); cortical thick ascending limb (C-TAL)', 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE -dCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dCNT', 'full_name': 'degenerative connecting tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE -dDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDCT', 'full_name': 'degenerative distal convoluted tubule cells', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE -dDTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDTL3', 'full_name': 'degenerative descending thin limb cell type 3', 'paper_synonyms': 'DTL3', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE -dEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE -dEC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC-PTC', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +cycCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycCNT', 'full_name': 'cycling connecting tubule cell', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +cycDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycDCT', 'full_name': 'cycling distal convoluted tubule', 'paper_synonyms': 'DCT', 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +cycEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycEC', 'full_name': 'cycling endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +cycMNP CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMNP', 'full_name': 'cycling MNP', 'paper_synonyms': '', 'tissue_context': ''} CL:4033078 cycling mononuclear phagocyte FALSE +cycMYOF CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycMYOF', 'full_name': 'cycling myofibroblasts', 'paper_synonyms': 'cycMyoF; cycling MyoF', 'tissue_context': ''} CL:0000186 myofibroblast cell FALSE +cycNKC/T CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycNKC/T', 'full_name': 'cycling T cells', 'paper_synonyms': None, 'tissue_context': ''} CL:4033069 cycling T cell FALSE +cycPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'cycPT', 'full_name': 'cycling proximal tubule cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +dATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dATL', 'full_name': 'ascending thin limb', 'paper_synonyms': 'ATL', 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +dC-IC-A CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-IC-A', 'full_name': 'degenerative cortical intercalated cell A', 'paper_synonyms': 'intercalated cells (IC)', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell FALSE +dC-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dC-TAL', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1001109 kidney loop of Henle cortical thick ascending limb epithelial cell FALSE +dCNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dCNT', 'full_name': 'degenerative connecting tubule cell', 'paper_synonyms': 'connecting tubules (CNT)', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +dDCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDCT', 'full_name': 'degenerative distal convoluted tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +dDTL3 CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dDTL3', 'full_name': 'degenerative descending thin limb 3', 'paper_synonyms': 'DTL3; DTL; descending thin limb', 'tissue_context': ''} CL:1001111 kidney loop of Henle thin descending limb epithelial cell TRUE +dEC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell TRUE +dEC-PTC CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dEC-PTC', 'full_name': 'degenerative endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE dFIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dFIB', 'full_name': 'degenerative fibroblast', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast FALSE -dIMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dIMCD', 'full_name': 'degenerative inner medullary collecting duct', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE +dIMCD CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dIMCD', 'full_name': 'degenerative inner medullary collecting duct cell', 'paper_synonyms': None, 'tissue_context': ''} CL:1000547 kidney inner medulla collecting duct epithelial cell FALSE dM-FIB CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-FIB', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''} CL:4030022 renal medullary fibroblast FALSE -dM-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-PC', 'full_name': 'degenerative medullary principal cell', 'paper_synonyms': 'dM-PCs', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell TRUE -dM-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': 'TAL', 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE -dOMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dOMCD-PC', 'full_name': 'degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'PC; principal cells; OMCD; outer medullary collecting duct; degenerative medullary principal cells (dM-PCs)', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE -dPOD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPOD', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'POD', 'tissue_context': ''} CL:0000653 podocyte TRUE -dPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPT', 'full_name': 'degenerative proximal tubule cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE -dVSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dVSMC', 'full_name': 'degenerative vascular smooth muscle cell', 'paper_synonyms': 'VSMC; vascular smooth muscle cell; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE -endothelial cells CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'endothelial cells', 'full_name': 'endothelial cells', 'paper_synonyms': 'EC', 'tissue_context': ''} CL:0000115 endothelial cell TRUE +dM-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-PC', 'full_name': 'degenerative medullary principal cell', 'paper_synonyms': 'degenerative medullary principal cells (dM-PCs)', 'tissue_context': ''} CL:1001431 kidney collecting duct principal cell TRUE +dM-TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dM-TAL', 'full_name': 'degenerative medullary thick ascending limb cell', 'paper_synonyms': '', 'tissue_context': ''} CL:1001108 kidney loop of Henle medullary thick ascending limb epithelial cell FALSE +dOMCD-PC CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dOMCD-PC', 'full_name': 'degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; outer medullary collecting duct; PC; principal cells', 'tissue_context': ''} CL:1000716 kidney outer medulla collecting duct principal cell FALSE +dPOD CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPOD', 'full_name': 'degenerative podocyte', 'paper_synonyms': 'PODs', 'tissue_context': ''} CL:0000653 podocyte TRUE +dPT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dPT', 'full_name': 'degenerative proximal tubule', 'paper_synonyms': '', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +dVSMC CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'dVSMC', 'full_name': 'vascular smooth muscle cell', 'paper_synonyms': 'VSMC; VSM/P', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell FALSE +endothelial cells CL:0000115 endothelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'endothelial cells', 'full_name': 'endothelial cells', 'paper_synonyms': 'EC; endothelium', 'tissue_context': ''} CL:0000115 endothelial cell TRUE epithelial cells CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -epithelial cells CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000066 epithelial cell FALSE -immune cells CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'IMM', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE -immune cells CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte FALSE +epithelial cells CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001111 kidney loop of Henle thin descending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:0000653 podocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1001431 kidney collecting duct principal cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1000452 parietal epithelial cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +epithelial cells CL:1000597 papillary tips cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'epithelial cells', 'full_name': 'epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell FALSE +immune cells CL:0000542 lymphocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:1000695 kidney interstitial alternatively activated macrophage DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000775 neutrophil DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000113 mononuclear phagocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000084 T cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000786 plasma cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000990 conventional dendritic cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000097 mast cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0000236 B cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE +immune cells CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'immune cells', 'full_name': 'immune cells', 'paper_synonyms': 'leukocytes', 'tissue_context': ''} CL:0000738 leukocyte FALSE ncMON CL:0000875 non-classical monocyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'ncMON', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000875 non-classical monocyte TRUE -neural cells CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'neural cells', 'full_name': 'neural cell types', 'paper_synonyms': 'neuronal; Schwann/neuronal; SCI/NEU', 'tissue_context': ''} CL:0002319 neural cell TRUE -pDC CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'pDC', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell FALSE -stroma cells CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': 'stromal cells', 'paper_synonyms': 'stroma; STR', 'tissue_context': ''} CL:0000499 stromal cell FALSE -stroma cells CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000499 stromal cell FALSE -tPC-IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'tPC-IC', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': 'principal cells (PC); intercalated cells (IC)', 'tissue_context': ''} CL:1001225 kidney collecting duct cell FALSE +neural cells CL:0002319 neural cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'neural cells', 'full_name': 'neural cells', 'paper_synonyms': 'NEU; SCI/NEU; Schwann/neuronal', 'tissue_context': ''} CL:0002319 neural cell TRUE +pDC CL:0001058 plasmacytoid dendritic cell, human DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'pDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell FALSE +stroma cells CL:1000692 kidney interstitial fibroblast DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': 'stromal cells', 'paper_synonyms': 'STR; stroma', 'tissue_context': ''} CL:0000499 stromal cell FALSE +stroma cells CL:1001318 renal interstitial pericyte DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'stroma cells', 'full_name': 'stromal cells', 'paper_synonyms': 'STR; stroma', 'tissue_context': ''} CL:0000499 stromal cell FALSE +tPC-IC CL:1001432 kidney collecting duct intercalated cell DOI:10.1038/s41586-023-05769-3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique {'name': 'tPC-IC', 'full_name': 'transitioning principal and intercalated cells', 'paper_synonyms': '', 'tissue_context': ''} CL:1001225 kidney collecting duct cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv index 65cb3d5..634f8a9 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -1,20 +1,20 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label -Astrocytes CL:0000127 astrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Astrocytes', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} -Endo/Pericytes CL:0000115 endothelial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Endo/Pericytes', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} -Excitatory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_1', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Astrocytes CL:0000127 astrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Astrocytes', 'full_name': 'astrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000127 astrocyte +Endo/Pericytes CL:0000115 endothelial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Endo/Pericytes', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Excitatory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_1', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron Excitatory_10 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_10', 'full_name': 'excitatory neuron 10', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron Excitatory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_2', 'full_name': 'excitatory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron -Excitatory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_3', 'full_name': 'excitatory neuron 3', 'paper_synonyms': 'excitatory neurons', 'tissue_context': ''} CL:0000679 glutamatergic neuron -Excitatory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_4', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron -Excitatory_5 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_5', 'full_name': 'excitatory neuron 5', 'paper_synonyms': 'excitatory neurons', 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_3', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_4', 'full_name': 'excitatory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_5 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_5', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron Excitatory_6 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_6', 'full_name': 'excitatory neuron 6', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron -Excitatory_7 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_7', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron -Excitatory_8 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_8', 'full_name': 'excitatory neuron 8', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_7 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_7', 'full_name': 'excitatory neuron 7', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron +Excitatory_8 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_8', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron Excitatory_9 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_9', 'full_name': 'excitatory neuron 9', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron -Inhibitory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_1', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron -Inhibitory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_2', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron -Inhibitory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_3', 'full_name': 'inhibitory neuron 3', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron +Inhibitory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_1', 'full_name': 'inhibitory neuron', 'paper_synonyms': '', 'tissue_context': ''} CL:0000617 GABAergic neuron +Inhibitory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_2', 'full_name': 'inhibitory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000617 GABAergic neuron +Inhibitory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_3', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron Inhibitory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_4', 'full_name': 'inhibitory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000617 GABAergic neuron -Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Microglia', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000129 microglial cell -OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'OPCs', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell -Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte +Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Microglia', 'full_name': 'microglia', 'paper_synonyms': None, 'tissue_context': ''} CL:0000129 microglial cell +OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'OPCs', 'full_name': 'oligodendrocyte progenitor cells', 'paper_synonyms': 'OPCs', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell +Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': 'oligodendrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/groundings.tsv index 7255207..f4c1a13 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/groundings.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique/groundings.tsv @@ -1,18 +1,20 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result -Excitatory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_1', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Astrocytes CL:0000127 astrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Astrocytes', 'full_name': 'astrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000127 astrocyte TRUE +Endo/Pericytes CL:0000115 endothelial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Endo/Pericytes', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Excitatory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_1', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE Excitatory_10 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_10', 'full_name': 'excitatory neuron 10', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE Excitatory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_2', 'full_name': 'excitatory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE -Excitatory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_3', 'full_name': 'excitatory neuron 3', 'paper_synonyms': 'excitatory neurons', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE -Excitatory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_4', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE -Excitatory_5 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_5', 'full_name': 'excitatory neuron 5', 'paper_synonyms': 'excitatory neurons', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_3', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_4', 'full_name': 'excitatory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_5 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_5', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE Excitatory_6 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_6', 'full_name': 'excitatory neuron 6', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE -Excitatory_7 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_7', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE -Excitatory_8 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_8', 'full_name': 'excitatory neuron 8', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_7 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_7', 'full_name': 'excitatory neuron 7', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE +Excitatory_8 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_8', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE Excitatory_9 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Excitatory_9', 'full_name': 'excitatory neuron 9', 'paper_synonyms': None, 'tissue_context': ''} CL:0000679 glutamatergic neuron FALSE -Inhibitory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_1', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE -Inhibitory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_2', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE -Inhibitory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_3', 'full_name': 'inhibitory neuron 3', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE +Inhibitory_1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_1', 'full_name': 'inhibitory neuron', 'paper_synonyms': '', 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE +Inhibitory_2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_2', 'full_name': 'inhibitory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE +Inhibitory_3 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_3', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE Inhibitory_4 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Inhibitory_4', 'full_name': 'inhibitory neurons', 'paper_synonyms': '', 'tissue_context': ''} CL:0000617 GABAergic neuron FALSE -Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Microglia', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000129 microglial cell TRUE -OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'OPCs', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell TRUE -Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte TRUE +Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Microglia', 'full_name': 'microglia', 'paper_synonyms': None, 'tissue_context': ''} CL:0000129 microglial cell TRUE +OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'OPCs', 'full_name': 'oligodendrocyte progenitor cells', 'paper_synonyms': 'OPCs', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell TRUE +Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': 'oligodendrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv index 18cf7c6..aa56852 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -1,17 +1,17 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label -Activated fibroblasts CCL19 ADAMADEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Activated fibroblasts CCL19 ADAMADEC1', 'full_name': 'ADAMDEC+ Fibroblast clusters', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Activated fibroblasts CCL19 ADAMADEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Activated fibroblasts CCL19 ADAMADEC1', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast Endothelial cells CA4 CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CA4 CD36', 'full_name': 'Endothelial cells CA4+ CD36+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell -Endothelial cells CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CD36', 'full_name': 'CD36+ endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell -Endothelial cells DARC CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells DARC', 'full_name': 'DARC+ endothelial cells', 'paper_synonyms': 'ACKR1', 'tissue_context': ''} CL:0000115 endothelial cell +Endothelial cells CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CD36', 'full_name': 'CD36+ endothelial cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000115 endothelial cell +Endothelial cells DARC CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells DARC', 'full_name': 'DARC/ACKR1+ endothelial cells', 'paper_synonyms': 'ACKR1; DARC/ACKR1', 'tissue_context': ''} CL:0000115 endothelial cell Endothelial cells LTC4S SEMA3G CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells LTC4S SEMA3G', 'full_name': 'Endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell -Fibroblasts ADAMDEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts ADAMDEC1', 'full_name': 'ADAMDEC1+ fibroblasts', 'paper_synonyms': 'ADAMDEC+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast +Fibroblasts ADAMDEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts ADAMDEC1', 'full_name': 'ADAMDEC1+ fibroblasts', 'paper_synonyms': 'ADAMDEC+ Fibroblast clusters', 'tissue_context': ''} CL:0000057 fibroblast Fibroblasts KCNN3 LY6H CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts KCNN3 LY6H', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast Fibroblasts NPY SLITRK6 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts NPY SLITRK6', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast Fibroblasts SFRP2 SLPI CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SFRP2 SLPI', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast -Fibroblasts SMOC2 PTGIS CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SMOC2 PTGIS', 'full_name': 'SMOC2+ PTGIS+ fibroblasts', 'paper_synonyms': 'SMOC2+ PTGIS+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast +Fibroblasts SMOC2 PTGIS CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SMOC2 PTGIS', 'full_name': 'SMOC2+ PTGIS+ fibroblasts', 'paper_synonyms': 'SMOC2+ PTGIS+ Fibroblast clusters', 'tissue_context': ''} CL:0000057 fibroblast Glial cells CL:0000125 glial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Glial cells', 'full_name': 'Glial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000125 glial cell Lymphatics CL:0000542 lymphocyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Lymphatics', 'full_name': 'Lymphatics', 'paper_synonyms': 'lymphatic endothelial cells', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel Myofibroblasts GREM1 GREM2 CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts GREM1 GREM2', 'full_name': 'GREM1+ GREM2+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell -Myofibroblasts HHIP NPNT CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts HHIP NPNT', 'full_name': 'HHIP+ NPNT+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell -Pericytes HIGD1B STEAP4 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes HIGD1B STEAP4', 'full_name': 'Pericytes HIGD1B+ STEAP4+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte -Pericytes RERGL NTRK2 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes RERGL NTRK2', 'full_name': 'Pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte +Myofibroblasts HHIP NPNT CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts HHIP NPNT', 'full_name': 'HHIP+ NPNT+ myofibroblasts', 'paper_synonyms': 'Myofibroblasts HHIP+ NPNT+', 'tissue_context': ''} CL:0000186 myofibroblast cell +Pericytes HIGD1B STEAP4 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes HIGD1B STEAP4', 'full_name': 'Pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte +Pericytes RERGL NTRK2 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes RERGL NTRK2', 'full_name': 'Pericytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000669 pericyte diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/groundings.tsv index 5ef9a7f..1787e6d 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/groundings.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique/groundings.tsv @@ -1,17 +1,17 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result -Activated fibroblasts CCL19 ADAMADEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Activated fibroblasts CCL19 ADAMADEC1', 'full_name': 'ADAMDEC+ Fibroblast clusters', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +Activated fibroblasts CCL19 ADAMADEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Activated fibroblasts CCL19 ADAMADEC1', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE Endothelial cells CA4 CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CA4 CD36', 'full_name': 'Endothelial cells CA4+ CD36+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE -Endothelial cells CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CD36', 'full_name': 'CD36+ endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE -Endothelial cells DARC CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells DARC', 'full_name': 'DARC+ endothelial cells', 'paper_synonyms': 'ACKR1', 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Endothelial cells CD36 CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells CD36', 'full_name': 'CD36+ endothelial cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Endothelial cells DARC CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells DARC', 'full_name': 'DARC/ACKR1+ endothelial cells', 'paper_synonyms': 'ACKR1; DARC/ACKR1', 'tissue_context': ''} CL:0000115 endothelial cell TRUE Endothelial cells LTC4S SEMA3G CL:0000115 endothelial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Endothelial cells LTC4S SEMA3G', 'full_name': 'Endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE -Fibroblasts ADAMDEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts ADAMDEC1', 'full_name': 'ADAMDEC1+ fibroblasts', 'paper_synonyms': 'ADAMDEC+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Fibroblasts ADAMDEC1 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts ADAMDEC1', 'full_name': 'ADAMDEC1+ fibroblasts', 'paper_synonyms': 'ADAMDEC+ Fibroblast clusters', 'tissue_context': ''} CL:0000057 fibroblast TRUE Fibroblasts KCNN3 LY6H CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts KCNN3 LY6H', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE Fibroblasts NPY SLITRK6 CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts NPY SLITRK6', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE Fibroblasts SFRP2 SLPI CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SFRP2 SLPI', 'full_name': 'Fibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE -Fibroblasts SMOC2 PTGIS CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SMOC2 PTGIS', 'full_name': 'SMOC2+ PTGIS+ fibroblasts', 'paper_synonyms': 'SMOC2+ PTGIS+ fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Fibroblasts SMOC2 PTGIS CL:0000057 fibroblast DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Fibroblasts SMOC2 PTGIS', 'full_name': 'SMOC2+ PTGIS+ fibroblasts', 'paper_synonyms': 'SMOC2+ PTGIS+ Fibroblast clusters', 'tissue_context': ''} CL:0000057 fibroblast TRUE Glial cells CL:0000125 glial cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Glial cells', 'full_name': 'Glial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000125 glial cell TRUE Lymphatics CL:0000542 lymphocyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Lymphatics', 'full_name': 'Lymphatics', 'paper_synonyms': 'lymphatic endothelial cells', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel FALSE Myofibroblasts GREM1 GREM2 CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts GREM1 GREM2', 'full_name': 'GREM1+ GREM2+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE -Myofibroblasts HHIP NPNT CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts HHIP NPNT', 'full_name': 'HHIP+ NPNT+ myofibroblasts', 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE -Pericytes HIGD1B STEAP4 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes HIGD1B STEAP4', 'full_name': 'Pericytes HIGD1B+ STEAP4+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE -Pericytes RERGL NTRK2 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes RERGL NTRK2', 'full_name': 'Pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE +Myofibroblasts HHIP NPNT CL:0000186 myofibroblast cell DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Myofibroblasts HHIP NPNT', 'full_name': 'HHIP+ NPNT+ myofibroblasts', 'paper_synonyms': 'Myofibroblasts HHIP+ NPNT+', 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE +Pericytes HIGD1B STEAP4 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes HIGD1B STEAP4', 'full_name': 'Pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE +Pericytes RERGL NTRK2 CL:0000669 pericyte DOI:10.1016/j.immuni.2023.01.002 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique {'name': 'Pericytes RERGL NTRK2', 'full_name': 'Pericytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000669 pericyte TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..72f85b5 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,14 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Diff. Keratinocytes CL:0000312 keratinocyte DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Diff. Keratinocytes', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000312 keratinocyte +EpSC and undiff. progenitors CL:1000428 stem cell of epidermis DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'EpSC and undiff. progenitors', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0011026 progenitor cell +Erythrocytes CL:0000232 erythrocyte DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Erythrocytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte +Lymphatic EC CL:0002138 endothelial cell of lymphatic vessel DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Lymphatic EC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel +Macrophages+DC CL:0000235 macrophage DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Macrophages+DC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000113 mononuclear phagocyte +Melanocytes CL:0000148 melanocyte DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Melanocytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000148 melanocyte +Mesenchymal fibroblasts CL:0002620 skin fibroblast DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Mesenchymal fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Pericytes CL:0000669 pericyte DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Pericytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte +Pro-inflammatory fibroblasts CL:0002620 skin fibroblast DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Pro-inflammatory fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Secretory-papillary fibroblasts CL:0002620 skin fibroblast DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Secretory-papillary fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000302 fibroblast of papillary layer of dermis +Secretory-reticular fibroblasts CL:0002620 skin fibroblast DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Secretory-reticular fibroblasts', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:2000096 fibroblast of the reticular layer of dermis +T cells CL:0000084 T cell DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'T cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000084 T cell +Vascular EC CL:0002139 endothelial cell of vascular tree DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Vascular EC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000071 blood vessel endothelial cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..78cc768 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/124744b8-4681-474a-9894-683896122708_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,14 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Diff. Keratinocytes CL:0000312 keratinocyte DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Diff. Keratinocytes', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000312 keratinocyte TRUE +EpSC and undiff. progenitors CL:1000428 stem cell of epidermis DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'EpSC and undiff. progenitors', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0011026 progenitor cell FALSE +Erythrocytes CL:0000232 erythrocyte DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Erythrocytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte TRUE +Lymphatic EC CL:0002138 endothelial cell of lymphatic vessel DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Lymphatic EC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel TRUE +Macrophages+DC CL:0000235 macrophage DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Macrophages+DC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000113 mononuclear phagocyte FALSE +Melanocytes CL:0000148 melanocyte DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Melanocytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000148 melanocyte TRUE +Mesenchymal fibroblasts CL:0002620 skin fibroblast DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Mesenchymal fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast FALSE +Pericytes CL:0000669 pericyte DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Pericytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE +Pro-inflammatory fibroblasts CL:0002620 skin fibroblast DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Pro-inflammatory fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast FALSE +Secretory-papillary fibroblasts CL:0002620 skin fibroblast DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Secretory-papillary fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000302 fibroblast of papillary layer of dermis FALSE +Secretory-reticular fibroblasts CL:0002620 skin fibroblast DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Secretory-reticular fibroblasts', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:2000096 fibroblast of the reticular layer of dermis FALSE +T cells CL:0000084 T cell DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'T cells', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000084 T cell TRUE +Vascular EC CL:0002139 endothelial cell of vascular tree DOI:10.1038/s42003-020-0922-4 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique {'name': 'Vascular EC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000071 blood vessel endothelial cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..f287937 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,31 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +AntiB CL:0000786 plasma cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'AntiB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000946 antibody secreting cell +C-Hepato CL:0019029 centrilobular region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'C-Hepato', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte +C-Hepato2 CL:0019029 centrilobular region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'C-Hepato2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte +CD4T CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'CD4T', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +Chol CL:0002538 intrahepatic cholangiocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Chol', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000488 cholangiocyte +Chol--Kupffer-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Chol--Kupffer-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000488 cholangiocyte +Chol--Stellate-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Chol--Stellate-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000488 cholangiocyte +Chol-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Chol-Doublet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1000488 cholangiocyte +CholMucus CL:0002538 intrahepatic cholangiocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'CholMucus', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000488 cholangiocyte +Fibroblast CL:0000057 fibroblast DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Hepato-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Hepato-Doublet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +I-Hepato CL:0019028 midzonal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'I-Hepato', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte +Kupffer CL:0000091 Kupffer cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Kupffer', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000091 Kupffer cell +Kupffer-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Kupffer-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000091 Kupffer cell +Monocyte CL:0000576 monocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte +P-Hepato CL:0019026 periportal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'P-Hepato', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte +P-Hepato2 CL:0019026 periportal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'P-Hepato2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte +Prolif unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Prolif', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +Prolif-Mac CL:0000235 macrophage DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Prolif-Mac', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033076 cycling macrophage +Stellate CL:0000632 hepatic stellate cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Stellate', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000632 hepatic stellate cell +Stellate-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Stellate-Doublet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000632 hepatic stellate cell +Tcell-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Tcell-Doublet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000084 T cell +VSMC CL:0000359 vascular associated smooth muscle cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'VSMC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell +aStellate CL:0000632 hepatic stellate cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'aStellate', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000632 hepatic stellate cell +cvEndo CL:0002543 vein endothelial cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'cvEndo', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +cvLSEC CL:0019022 endothelial cell of pericentral hepatic sinusoid DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'cvLSEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000398 endothelial cell of hepatic sinusoid +cvLSEC--Mac unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'cvLSEC--Mac', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000235 macrophage +cvLSEC-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'cvLSEC-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000398 endothelial cell of hepatic sinusoid +lrNK CL:2000054 hepatic pit cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'lrNK', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:4047101 liver-resident natural killer cell +ppLSEC CL:0019021 endothelial cell of periportal hepatic sinusoid DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'ppLSEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0019021 endothelial cell of periportal hepatic sinusoid diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..463a1f9 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,31 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +AntiB CL:0000786 plasma cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'AntiB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000946 antibody secreting cell FALSE +C-Hepato CL:0019029 centrilobular region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'C-Hepato', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte FALSE +C-Hepato2 CL:0019029 centrilobular region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'C-Hepato2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte FALSE +CD4T CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'CD4T', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell TRUE +Chol CL:0002538 intrahepatic cholangiocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Chol', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000488 cholangiocyte FALSE +Chol--Kupffer-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Chol--Kupffer-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000488 cholangiocyte FALSE +Chol--Stellate-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Chol--Stellate-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000488 cholangiocyte FALSE +Chol-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Chol-Doublet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:1000488 cholangiocyte FALSE +CholMucus CL:0002538 intrahepatic cholangiocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'CholMucus', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000488 cholangiocyte FALSE +Fibroblast CL:0000057 fibroblast DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +Hepato-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Hepato-Doublet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +I-Hepato CL:0019028 midzonal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'I-Hepato', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte FALSE +Kupffer CL:0000091 Kupffer cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Kupffer', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000091 Kupffer cell TRUE +Kupffer-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Kupffer-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000091 Kupffer cell FALSE +Monocyte CL:0000576 monocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte TRUE +P-Hepato CL:0019026 periportal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'P-Hepato', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000182 hepatocyte FALSE +P-Hepato2 CL:0019026 periportal region hepatocyte DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'P-Hepato2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000182 hepatocyte FALSE +Prolif unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Prolif', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +Prolif-Mac CL:0000235 macrophage DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Prolif-Mac', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033076 cycling macrophage FALSE +Stellate CL:0000632 hepatic stellate cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Stellate', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000632 hepatic stellate cell TRUE +Stellate-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Stellate-Doublet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000632 hepatic stellate cell FALSE +Tcell-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'Tcell-Doublet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000084 T cell FALSE +VSMC CL:0000359 vascular associated smooth muscle cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'VSMC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell TRUE +aStellate CL:0000632 hepatic stellate cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'aStellate', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000632 hepatic stellate cell TRUE +cvEndo CL:0002543 vein endothelial cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'cvEndo', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell FALSE +cvLSEC CL:0019022 endothelial cell of pericentral hepatic sinusoid DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'cvLSEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000398 endothelial cell of hepatic sinusoid FALSE +cvLSEC--Mac unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'cvLSEC--Mac', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000235 macrophage FALSE +cvLSEC-Doublet unknown unknown DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'cvLSEC-Doublet', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000398 endothelial cell of hepatic sinusoid FALSE +lrNK CL:2000054 hepatic pit cell DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'lrNK', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:4047101 liver-resident natural killer cell FALSE +ppLSEC CL:0019021 endothelial cell of periportal hepatic sinusoid DOI:10.1016/j.jhep.2023.12.023 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique {'name': 'ppLSEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0019021 endothelial cell of periportal hepatic sinusoid TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..0192774 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,15 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +CD4-positive, alpha-beta memory T cell CL:0000897 CD4-positive, alpha-beta memory T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'CD4-positive, alpha-beta memory T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000897 CD4-positive, alpha-beta memory T cell +CD8-positive, alpha-beta memory T cell CL:0000909 CD8-positive, alpha-beta memory T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'CD8-positive, alpha-beta memory T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000909 CD8-positive, alpha-beta memory T cell +TCRVbeta13.1pos CL:0000084 T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'TCRVbeta13.1pos', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +TissueResMemT CL:0000813 memory T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'TissueResMemT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000813 memory T cell +double negative T cell (DNT) CL:0002489 double negative thymocyte DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'double negative T cell (DNT)', 'full_name': 'double negative T cell', 'paper_synonyms': 'DNT', 'tissue_context': ''} CL:0002489 double negative thymocyte +double-positive T cell (DPT) CL:0000809 double-positive, alpha-beta thymocyte DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'double-positive T cell (DPT)', 'full_name': 'double-positive T cell', 'paper_synonyms': 'DPT', 'tissue_context': ''} CL:0000809 double-positive, alpha-beta thymocyte +gamma-delta T cell CL:0000798 gamma-delta T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'gamma-delta T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000798 gamma-delta T cell +memory B cell CL:0000787 memory B cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'memory B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000787 memory B cell +mucosal invariant T cell (MAIT) CL:0000940 mucosal invariant T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'mucosal invariant T cell (MAIT)', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000940 mucosal-associated invariant T cell +naive B cell CL:0000788 naive B cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'naive B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000788 naive B cell +naive CD4+ T cell CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'naive CD4+ T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell +naive CD8+ T cell CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'naive CD8+ T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell +plasmablast CL:0000980 plasmablast DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'plasmablast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000980 plasmablast +regulatory T cell CL:0000815 regulatory T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'regulatory T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000815 regulatory T cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..aa1c0f4 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,15 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +CD4-positive, alpha-beta memory T cell CL:0000897 CD4-positive, alpha-beta memory T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'CD4-positive, alpha-beta memory T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000897 CD4-positive, alpha-beta memory T cell TRUE +CD8-positive, alpha-beta memory T cell CL:0000909 CD8-positive, alpha-beta memory T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'CD8-positive, alpha-beta memory T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000909 CD8-positive, alpha-beta memory T cell TRUE +TCRVbeta13.1pos CL:0000084 T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'TCRVbeta13.1pos', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +TissueResMemT CL:0000813 memory T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'TissueResMemT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000813 memory T cell TRUE +double negative T cell (DNT) CL:0002489 double negative thymocyte DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'double negative T cell (DNT)', 'full_name': 'double negative T cell', 'paper_synonyms': 'DNT', 'tissue_context': ''} CL:0002489 double negative thymocyte TRUE +double-positive T cell (DPT) CL:0000809 double-positive, alpha-beta thymocyte DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'double-positive T cell (DPT)', 'full_name': 'double-positive T cell', 'paper_synonyms': 'DPT', 'tissue_context': ''} CL:0000809 double-positive, alpha-beta thymocyte TRUE +gamma-delta T cell CL:0000798 gamma-delta T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'gamma-delta T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000798 gamma-delta T cell TRUE +memory B cell CL:0000787 memory B cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'memory B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000787 memory B cell TRUE +mucosal invariant T cell (MAIT) CL:0000940 mucosal invariant T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'mucosal invariant T cell (MAIT)', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000940 mucosal-associated invariant T cell TRUE +naive B cell CL:0000788 naive B cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'naive B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000788 naive B cell TRUE +naive CD4+ T cell CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'naive CD4+ T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell TRUE +naive CD8+ T cell CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'naive CD8+ T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell TRUE +plasmablast CL:0000980 plasmablast DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'plasmablast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000980 plasmablast TRUE +regulatory T cell CL:0000815 regulatory T cell DOI:10.1016/j.cell.2021.02.018 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique {'name': 'regulatory T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000815 regulatory T cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..821d3c7 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,26 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Activated CD4 T CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Activated CD4 T', 'full_name': 'CD4+ T cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +B cell IgA Plasma CL:0000987 IgA plasma cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'B cell IgA Plasma', 'full_name': 'IgA+ plasma cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000987 IgA plasma cell +B cell IgG Plasma CL:0000236 B cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'B cell IgG Plasma', 'full_name': 'IgG+ plasma cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000985 IgG plasma cell +B cell cycling CL:0000236 B cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'B cell cycling', 'full_name': 'MKI67+ cycling B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:4033068 cycling B cell +B cell memory CL:0000787 memory B cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'B cell memory', 'full_name': 'memory B cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000787 memory B cell +CD8 T CL:0000625 CD8-positive, alpha-beta T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'CD8 T', 'full_name': 'CD8+ T cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell +Follicular B cell CL:0000843 follicular B cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Follicular B cell', 'full_name': 'follicular B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000843 follicular B cell +ILC CL:0001065 innate lymphoid cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'ILC', 'full_name': 'innate lymphoid cells', 'paper_synonyms': 'ILCs', 'tissue_context': ''} CL:0001065 innate lymphoid cell +LYVE1 Macrophage CL:0000235 macrophage DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'LYVE1 Macrophage', 'full_name': 'LYVE1+ macrophages', 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage +Lymphoid DC CL:0000451 dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Lymphoid DC', 'full_name': 'dendritic cells', 'paper_synonyms': 'DCs', 'tissue_context': ''} CL:0000451 dendritic cell +Macrophage CL:0009038 colon macrophage DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Macrophage', 'full_name': 'macrophage', 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage +Mast CL:0000097 mast cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Mast', 'full_name': 'mast cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000097 mast cell +Monocyte CL:0000576 monocyte DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Monocyte', 'full_name': 'monocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte +NK CL:0000623 natural killer cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'NK', 'full_name': 'natural killer cells', 'paper_synonyms': 'NK cells', 'tissue_context': ''} CL:0000623 natural killer cell +Tcm CL:0000813 memory T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Tcm', 'full_name': 'central memory T cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000813 memory T cell +Tfh CL:0002038 T follicular helper cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Tfh', 'full_name': 'follicular helper cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002038 T follicular helper cell +Th1 CL:0000545 T-helper 1 cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Th1', 'full_name': 'T helper 1 cells', 'paper_synonyms': 'TH1 cells', 'tissue_context': ''} CL:0000545 T-helper 1 cell +Th17 CL:0000899 T-helper 17 cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Th17', 'full_name': 'T helper 17 cell', 'paper_synonyms': 'TH17; T helper (TH) 17 cells', 'tissue_context': ''} CL:0000899 T-helper 17 cell +Treg CL:0000815 regulatory T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Treg', 'full_name': 'T regulatory cells', 'paper_synonyms': 'Treg cells; regulatory T cells', 'tissue_context': ''} CL:0000815 regulatory T cell +cDC1 CL:0000990 conventional dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'cDC1', 'full_name': 'conventional dendritic cell 1', 'paper_synonyms': None, 'tissue_context': ''} CL:0000990 conventional dendritic cell +cDC2 CL:0000990 conventional dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'cDC2', 'full_name': 'conventional dendritic cell 2', 'paper_synonyms': None, 'tissue_context': ''} CL:0000990 conventional dendritic cell +cycling DCs CL:0000451 dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'cycling DCs', 'full_name': 'cycling dendritic cells', 'paper_synonyms': None, 'tissue_context': ''} CL:4033070 cycling dendritic cell +cycling gd T CL:0000798 gamma-delta T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'cycling gd T', 'full_name': 'cycling gammadelta T cell', 'paper_synonyms': 'gammadelta T cells', 'tissue_context': ''} CL:4033072 cycling gamma-delta T cell +gd T CL:0000798 gamma-delta T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'gd T', 'full_name': 'gammadelta T cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000798 gamma-delta T cell +pDC CL:0000784 plasmacytoid dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'pDC', 'full_name': 'plasmacytoid DCs', 'paper_synonyms': 'pDCs', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..55824c5 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,26 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Activated CD4 T CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Activated CD4 T', 'full_name': 'CD4+ T cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell TRUE +B cell IgA Plasma CL:0000987 IgA plasma cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'B cell IgA Plasma', 'full_name': 'IgA+ plasma cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000987 IgA plasma cell TRUE +B cell IgG Plasma CL:0000236 B cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'B cell IgG Plasma', 'full_name': 'IgG+ plasma cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000985 IgG plasma cell FALSE +B cell cycling CL:0000236 B cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'B cell cycling', 'full_name': 'MKI67+ cycling B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:4033068 cycling B cell FALSE +B cell memory CL:0000787 memory B cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'B cell memory', 'full_name': 'memory B cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000787 memory B cell TRUE +CD8 T CL:0000625 CD8-positive, alpha-beta T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'CD8 T', 'full_name': 'CD8+ T cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell TRUE +Follicular B cell CL:0000843 follicular B cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Follicular B cell', 'full_name': 'follicular B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000843 follicular B cell TRUE +ILC CL:0001065 innate lymphoid cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'ILC', 'full_name': 'innate lymphoid cells', 'paper_synonyms': 'ILCs', 'tissue_context': ''} CL:0001065 innate lymphoid cell TRUE +LYVE1 Macrophage CL:0000235 macrophage DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'LYVE1 Macrophage', 'full_name': 'LYVE1+ macrophages', 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage TRUE +Lymphoid DC CL:0000451 dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Lymphoid DC', 'full_name': 'dendritic cells', 'paper_synonyms': 'DCs', 'tissue_context': ''} CL:0000451 dendritic cell TRUE +Macrophage CL:0009038 colon macrophage DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Macrophage', 'full_name': 'macrophage', 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage FALSE +Mast CL:0000097 mast cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Mast', 'full_name': 'mast cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000097 mast cell TRUE +Monocyte CL:0000576 monocyte DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Monocyte', 'full_name': 'monocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte TRUE +NK CL:0000623 natural killer cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'NK', 'full_name': 'natural killer cells', 'paper_synonyms': 'NK cells', 'tissue_context': ''} CL:0000623 natural killer cell TRUE +Tcm CL:0000813 memory T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Tcm', 'full_name': 'central memory T cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000813 memory T cell TRUE +Tfh CL:0002038 T follicular helper cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Tfh', 'full_name': 'follicular helper cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002038 T follicular helper cell TRUE +Th1 CL:0000545 T-helper 1 cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Th1', 'full_name': 'T helper 1 cells', 'paper_synonyms': 'TH1 cells', 'tissue_context': ''} CL:0000545 T-helper 1 cell TRUE +Th17 CL:0000899 T-helper 17 cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Th17', 'full_name': 'T helper 17 cell', 'paper_synonyms': 'TH17; T helper (TH) 17 cells', 'tissue_context': ''} CL:0000899 T-helper 17 cell TRUE +Treg CL:0000815 regulatory T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'Treg', 'full_name': 'T regulatory cells', 'paper_synonyms': 'Treg cells; regulatory T cells', 'tissue_context': ''} CL:0000815 regulatory T cell TRUE +cDC1 CL:0000990 conventional dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'cDC1', 'full_name': 'conventional dendritic cell 1', 'paper_synonyms': None, 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE +cDC2 CL:0000990 conventional dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'cDC2', 'full_name': 'conventional dendritic cell 2', 'paper_synonyms': None, 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE +cycling DCs CL:0000451 dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'cycling DCs', 'full_name': 'cycling dendritic cells', 'paper_synonyms': None, 'tissue_context': ''} CL:4033070 cycling dendritic cell FALSE +cycling gd T CL:0000798 gamma-delta T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'cycling gd T', 'full_name': 'cycling gammadelta T cell', 'paper_synonyms': 'gammadelta T cells', 'tissue_context': ''} CL:4033072 cycling gamma-delta T cell FALSE +gd T CL:0000798 gamma-delta T cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'gd T', 'full_name': 'gammadelta T cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000798 gamma-delta T cell TRUE +pDC CL:0000784 plasmacytoid dendritic cell DOI:10.1038/s41590-020-0602-z 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique {'name': 'pDC', 'full_name': 'plasmacytoid DCs', 'paper_synonyms': 'pDCs', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv index e337b26..ca18413 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -1,3 +1,3 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label -H1 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H1', 'full_name': 'H1 horizontal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0004217 H1 horizontal cell -H2 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H2', 'full_name': 'H2 horizontal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0004218 H2 horizontal cell +H1 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H1', 'full_name': 'horizontal cell type H1', 'paper_synonyms': None, 'tissue_context': ''} CL:0004217 H1 horizontal cell +H2 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H2', 'full_name': 'horizontal cell type H2', 'paper_synonyms': None, 'tissue_context': ''} CL:0004218 H2 horizontal cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/groundings.tsv index c887a9e..e282b5e 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/groundings.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique/groundings.tsv @@ -1,3 +1,3 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result -H1 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H1', 'full_name': 'H1 horizontal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0004217 H1 horizontal cell FALSE -H2 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H2', 'full_name': 'H2 horizontal cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0004218 H2 horizontal cell FALSE +H1 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H1', 'full_name': 'horizontal cell type H1', 'paper_synonyms': None, 'tissue_context': ''} CL:0004217 H1 horizontal cell FALSE +H2 CL:0000745 retina horizontal cell DOI:10.1038/s41598-020-66092-9 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique {'name': 'H2', 'full_name': 'horizontal cell type H2', 'paper_synonyms': None, 'tissue_context': ''} CL:0004218 H2 horizontal cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..72d6f92 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,11 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +NK_CD16hi CL:0000939 CD16-positive, CD56-dim natural killer cell, human DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'NK_CD16hi', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000939 CD16-positive, CD56-dim natural killer cell, human +NK_CD56hiCD16lo CL:0000938 CD16-negative, CD56-bright natural killer cell, human DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'NK_CD56hiCD16lo', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000938 CD16-negative, CD56-bright natural killer cell, human +NK_CD56loCD16lo CL:0000623 natural killer cell DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'NK_CD56loCD16lo', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell +classical monocyte CL:0000860 classical monocyte DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'classical monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000860 classical monocyte +conventional dendritic cell CL:0000990 conventional dendritic cell DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'conventional dendritic cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000990 conventional dendritic cell +granulocyte CL:0000094 granulocyte DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'granulocyte', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000094 granulocyte +intermediate monocyte CL:0002393 intermediate monocyte DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'intermediate monocyte', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002393 intermediate monocyte +non-classical monocyte CL:0000875 non-classical monocyte DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'non-classical monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000875 non-classical monocyte +plasmacytoid dendritic cell CL:0000784 plasmacytoid dendritic cell DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'plasmacytoid dendritic cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell +platelet CL:0000233 platelet DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'platelet', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..8ca2e77 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,10 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +NK_CD16hi CL:0000939 CD16-positive, CD56-dim natural killer cell, human DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'NK_CD16hi', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000939 CD16-positive, CD56-dim natural killer cell, human TRUE +NK_CD56hiCD16lo CL:0000938 CD16-negative, CD56-bright natural killer cell, human DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'NK_CD56hiCD16lo', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000938 CD16-negative, CD56-bright natural killer cell, human TRUE +NK_CD56loCD16lo CL:0000623 natural killer cell DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'NK_CD56loCD16lo', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell TRUE +classical monocyte CL:0000860 classical monocyte DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'classical monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000860 classical monocyte TRUE +conventional dendritic cell CL:0000990 conventional dendritic cell DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'conventional dendritic cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE +granulocyte CL:0000094 granulocyte DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'granulocyte', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000094 granulocyte TRUE +intermediate monocyte CL:0002393 intermediate monocyte DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'intermediate monocyte', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002393 intermediate monocyte TRUE +non-classical monocyte CL:0000875 non-classical monocyte DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'non-classical monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000875 non-classical monocyte TRUE +plasmacytoid dendritic cell CL:0000784 plasmacytoid dendritic cell DOI:10.1016/j.cell.2021.02.018 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique {'name': 'plasmacytoid dendritic cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..632e360 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,43 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +ACKR1+ endothelium CL:0000115 endothelial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'ACKR1+ endothelium', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +B cells CL:0000236 B cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'B cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +CD36+ endothelium CL:0000115 endothelial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'CD36+ endothelium', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Cycling unknown unknown DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Cycling', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +Doublets unknown unknown DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Doublets', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +Endothelium CL:0000115 endothelial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Endothelium', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Endothelium CL:0002138 endothelial cell of lymphatic vessel DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Endothelium', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +Enterocytes CL:1000342 enterocyte of epithelium proper of ileum DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Enterocytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte +Enteroendocrines CL:0009006 enteroendocrine cell of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Enteroendocrines', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell +Fibs CL:0000057 fibroblast DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Fibs', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +Glial cells CL:0000125 glial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Glial cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000125 glial cell +Goblets CL:1000326 ileal goblet cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Goblets', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell +ILC CL:0001065 innate lymphoid cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'ILC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0001065 innate lymphoid cell +Immune cells CL:0000786 plasma cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +Immune cells CL:0000789 alpha-beta T cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +Immune cells CL:0001065 innate lymphoid cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +Immune cells CL:0000236 B cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +Immune cells CL:0000113 mononuclear phagocyte DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +Immune cells CL:0000097 mast cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +Lymphatics CL:0002138 endothelial cell of lymphatic vessel DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Lymphatics', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel +MNP CL:0000113 mononuclear phagocyte DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'MNP', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000113 mononuclear phagocyte +Mast cells CL:0000097 mast cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Mast cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell +Paneth cells CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Paneth cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000510 paneth cell +Pericytes CL:0000669 pericyte DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Pericytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte +Plasma cells CL:0000786 plasma cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Plasma cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000786 plasma cell +Progenitors CL:0011026 progenitor cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Progenitors', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0011026 progenitor cell +SM CL:1000278 smooth muscle fiber of ileum DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'SM', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000192 smooth muscle cell +Stroma CL:0011026 progenitor cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:0000115 endothelial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma unknown unknown DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:1000342 enterocyte of epithelium proper of ileum DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:1000278 smooth muscle fiber of ileum DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:1000326 ileal goblet cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:0000057 fibroblast DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:0000669 pericyte DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:0009006 enteroendocrine cell of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:0000125 glial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +Stroma CL:0002138 endothelial cell of lymphatic vessel DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell +T cells CL:0000789 alpha-beta T cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'T cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000084 T cell +TA CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'TA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009010 transit amplifying cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..518eda2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,43 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +ACKR1+ endothelium CL:0000115 endothelial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'ACKR1+ endothelium', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +B cells CL:0000236 B cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'B cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell TRUE +CD36+ endothelium CL:0000115 endothelial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'CD36+ endothelium', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Cycling unknown unknown DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Cycling', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +Doublets unknown unknown DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Doublets', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +Endothelium CL:0000115 endothelial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Endothelium', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +Endothelium CL:0002138 endothelial cell of lymphatic vessel DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Endothelium', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell FALSE +Enterocytes CL:1000342 enterocyte of epithelium proper of ileum DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Enterocytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte FALSE +Enteroendocrines CL:0009006 enteroendocrine cell of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Enteroendocrines', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell FALSE +Fibs CL:0000057 fibroblast DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Fibs', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +Glial cells CL:0000125 glial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Glial cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000125 glial cell TRUE +Goblets CL:1000326 ileal goblet cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Goblets', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell FALSE +ILC CL:0001065 innate lymphoid cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'ILC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0001065 innate lymphoid cell TRUE +Immune cells CL:0000786 plasma cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE +Immune cells CL:0000789 alpha-beta T cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE +Immune cells CL:0001065 innate lymphoid cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE +Immune cells CL:0000236 B cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE +Immune cells CL:0000113 mononuclear phagocyte DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE +Immune cells CL:0000097 mast cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Immune cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE +Lymphatics CL:0002138 endothelial cell of lymphatic vessel DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Lymphatics', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel TRUE +MNP CL:0000113 mononuclear phagocyte DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'MNP', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000113 mononuclear phagocyte TRUE +Mast cells CL:0000097 mast cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Mast cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell TRUE +Paneth cells CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Paneth cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000510 paneth cell FALSE +Pericytes CL:0000669 pericyte DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Pericytes', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE +Plasma cells CL:0000786 plasma cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Plasma cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000786 plasma cell TRUE +Progenitors CL:0011026 progenitor cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Progenitors', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0011026 progenitor cell TRUE +SM CL:1000278 smooth muscle fiber of ileum DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'SM', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000192 smooth muscle cell FALSE +Stroma CL:0011026 progenitor cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:0000115 endothelial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma unknown unknown DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:1000342 enterocyte of epithelium proper of ileum DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:1000278 smooth muscle fiber of ileum DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:1000326 ileal goblet cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:0000057 fibroblast DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:0000669 pericyte DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:0009006 enteroendocrine cell of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:1000343 paneth cell of epithelium of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:0000125 glial cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +Stroma CL:0002138 endothelial cell of lymphatic vessel DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'Stroma', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000499 stromal cell FALSE +T cells CL:0000789 alpha-beta T cell DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'T cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000084 T cell FALSE +TA CL:0009012 transit amplifying cell of small intestine DOI:10.1016/j.cell.2019.08.008 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique {'name': 'TA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009010 transit amplifying cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..a59be2d --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,15 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +acinar CL:0002064 pancreatic acinar cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'acinar', 'full_name': 'acinar cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000622 acinar cell +acinar_minor_mhcclassII CL:0002064 pancreatic acinar cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'acinar_minor_mhcclassII', 'full_name': 'MHC Class II acinar cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000622 acinar cell +alpha CL:0000171 pancreatic A cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'alpha', 'full_name': 'alpha cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000171 pancreatic A cell +beta_major CL:0000169 type B pancreatic cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'beta_major', 'full_name': 'beta cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000169 type B pancreatic cell +beta_minor CL:0000169 type B pancreatic cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'beta_minor', 'full_name': 'beta cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000169 type B pancreatic cell +delta CL:0000173 pancreatic D cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'delta', 'full_name': 'delta cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000173 pancreatic D cell +duct_acinar_related CL:0002079 pancreatic ductal cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'duct_acinar_related', 'full_name': 'ductal-acinar related cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002080 pancreatic centro-acinar cell +duct_major CL:0002079 pancreatic ductal cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'duct_major', 'full_name': 'ductal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002079 pancreatic ductal cell +endothelial CL:0000115 endothelial cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'endothelial', 'full_name': 'endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +epsilon CL:0005019 pancreatic epsilon cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'epsilon', 'full_name': 'epsilon cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005019 pancreatic epsilon cell +hybrid unknown unknown DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'hybrid', 'full_name': 'Hybrid cells', 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +immune_stellates unknown unknown DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'immune_stellates', 'full_name': 'immune and stellate cells', 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +pp CL:0000696 PP cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'pp', 'full_name': 'PP cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000696 PP cell +stellates CL:0002410 pancreatic stellate cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'stellates', 'full_name': 'stellate cell', 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..43c77bd --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,15 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +acinar CL:0002064 pancreatic acinar cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'acinar', 'full_name': 'acinar cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000622 acinar cell FALSE +acinar_minor_mhcclassII CL:0002064 pancreatic acinar cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'acinar_minor_mhcclassII', 'full_name': 'MHC Class II acinar cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000622 acinar cell FALSE +alpha CL:0000171 pancreatic A cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'alpha', 'full_name': 'alpha cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000171 pancreatic A cell TRUE +beta_major CL:0000169 type B pancreatic cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'beta_major', 'full_name': 'beta cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000169 type B pancreatic cell TRUE +beta_minor CL:0000169 type B pancreatic cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'beta_minor', 'full_name': 'beta cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000169 type B pancreatic cell TRUE +delta CL:0000173 pancreatic D cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'delta', 'full_name': 'delta cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000173 pancreatic D cell TRUE +duct_acinar_related CL:0002079 pancreatic ductal cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'duct_acinar_related', 'full_name': 'ductal-acinar related cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002080 pancreatic centro-acinar cell FALSE +duct_major CL:0002079 pancreatic ductal cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'duct_major', 'full_name': 'ductal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002079 pancreatic ductal cell TRUE +endothelial CL:0000115 endothelial cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'endothelial', 'full_name': 'endothelial cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +epsilon CL:0005019 pancreatic epsilon cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'epsilon', 'full_name': 'epsilon cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005019 pancreatic epsilon cell TRUE +hybrid unknown unknown DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'hybrid', 'full_name': 'Hybrid cells', 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +immune_stellates unknown unknown DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'immune_stellates', 'full_name': 'immune and stellate cells', 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +pp CL:0000696 PP cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'pp', 'full_name': 'PP cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000696 PP cell TRUE +stellates CL:0002410 pancreatic stellate cell DOI:10.1038/s42255-022-00531-x 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique {'name': 'stellates', 'full_name': 'stellate cell', 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv index 3c4800c..651e1bc 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -1,18 +1,18 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label -Astrocytes CL:0000127 astrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Astrocytes', 'full_name': 'astrocytes', 'paper_synonyms': None, 'tissue_context': ''} -Endothelial CL:0000115 endothelial cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Endothelial', 'full_name': 'endothelial cells', 'paper_synonyms': '', 'tissue_context': ''} -L2-L3 Intratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L2-L3 Intratelencephalic', 'full_name': 'L2-L3 intratelencephalic', 'paper_synonyms': None, 'tissue_context': ''} CL:4030059 L2/3 intratelencephalic projecting glutamatergic neuron +Astrocytes CL:0000127 astrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Astrocytes', 'full_name': 'Astrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000127 astrocyte +Endothelial CL:0000115 endothelial cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Endothelial', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +L2-L3 Intratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L2-L3 Intratelencephalic', 'full_name': 'L2-L3 intratelencephalic', 'paper_synonyms': '', 'tissue_context': ''} CL:4030059 L2/3 intratelencephalic projecting glutamatergic neuron L3-L5 Intratelencephalic Type 1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 1', 'full_name': 'L3-L5 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron -L3-L5 Intratelencephalic Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 2', 'full_name': 'L3-L5 intratelencephalic type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron -L5 Extratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5 Extratelencephalic', 'full_name': 'L5 Extratelencephalic', 'paper_synonyms': None, 'tissue_context': ''} CL:4023041 L5 extratelencephalic projecting glutamatergic cortical neuron -L5-L6 Near Projecting CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5-L6 Near Projecting', 'full_name': 'L5-L6 near projecting', 'paper_synonyms': None, 'tissue_context': ''} CL:4030067 L5/6 near-projecting glutamatergic neuron +L3-L5 Intratelencephalic Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 2', 'full_name': 'L3-L5 intratelencephalic type 2', 'paper_synonyms': '', 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron +L5 Extratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5 Extratelencephalic', 'full_name': 'L5 extratelencephalic neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023041 L5 extratelencephalic projecting glutamatergic cortical neuron +L5-L6 Near Projecting CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5-L6 Near Projecting', 'full_name': 'L5-L6 near projecting neuronal cluster', 'paper_synonyms': None, 'tissue_context': ''} CL:4030067 L5/6 near-projecting glutamatergic neuron L6 Corticothalamic / L6B CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Corticothalamic / L6B', 'full_name': 'L6 corticothalamic / L6B', 'paper_synonyms': 'L6 corticothalamic; L6B', 'tissue_context': ''} CL:4023042 L6 corticothalamic-projecting glutamatergic cortical neuron L6 Intratelencephalic - Type 1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 1', 'full_name': 'L6 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron -L6 Intratelencephalic - Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 2', 'full_name': 'L6 intratelencephalic type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron +L6 Intratelencephalic - Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 2', 'full_name': 'L6 intratelencephalic type 2', 'paper_synonyms': '', 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Microglia', 'full_name': 'microglia', 'paper_synonyms': None, 'tissue_context': ''} CL:0000129 microglial cell -OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'OPCs', 'full_name': 'oligodendrocyte progenitor cells', 'paper_synonyms': 'oligodendrocyte progenitor cells; OPCs', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell -Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': 'oligodendrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte -Parvalbumin interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Parvalbumin interneurons', 'full_name': 'parvalbumin interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023018 pvalb GABAergic interneuron -SV2C LAMP5 Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'SV2C LAMP5 Interneurons', 'full_name': 'SV2C LAMP5 Interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023011 lamp5 GABAergic interneuron -Somatostatin Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Somatostatin Interneurons', 'full_name': 'Somatostatin Interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023017 sst GABAergic interneuron -VIP Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'VIP Interneurons', 'full_name': 'VIP Interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023016 VIP GABAergic interneuron +OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'OPCs', 'full_name': 'oligodendrocyte progenitor cells', 'paper_synonyms': 'oligodendrocyte progenitor cells', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell +Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': 'Oligodendrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte +Parvalbumin interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Parvalbumin interneurons', 'full_name': 'Parvalbumin interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023018 pvalb GABAergic interneuron +SV2C LAMP5 Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'SV2C LAMP5 Interneurons', 'full_name': 'SV2C LAMP5 interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023011 lamp5 GABAergic interneuron +Somatostatin Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Somatostatin Interneurons', 'full_name': 'somatostatin interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023017 sst GABAergic interneuron +VIP Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'VIP Interneurons', 'full_name': 'VIP interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023016 VIP GABAergic interneuron diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/groundings.tsv index bc4b120..59dbd00 100644 --- a/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/groundings.tsv +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique/groundings.tsv @@ -1,16 +1,18 @@ annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result -L2-L3 Intratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L2-L3 Intratelencephalic', 'full_name': 'L2-L3 intratelencephalic', 'paper_synonyms': None, 'tissue_context': ''} CL:4030059 L2/3 intratelencephalic projecting glutamatergic neuron FALSE +Astrocytes CL:0000127 astrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Astrocytes', 'full_name': 'Astrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000127 astrocyte TRUE +Endothelial CL:0000115 endothelial cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Endothelial', 'full_name': 'endothelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +L2-L3 Intratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L2-L3 Intratelencephalic', 'full_name': 'L2-L3 intratelencephalic', 'paper_synonyms': '', 'tissue_context': ''} CL:4030059 L2/3 intratelencephalic projecting glutamatergic neuron FALSE L3-L5 Intratelencephalic Type 1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 1', 'full_name': 'L3-L5 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron FALSE -L3-L5 Intratelencephalic Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 2', 'full_name': 'L3-L5 intratelencephalic type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron FALSE -L5 Extratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5 Extratelencephalic', 'full_name': 'L5 Extratelencephalic', 'paper_synonyms': None, 'tissue_context': ''} CL:4023041 L5 extratelencephalic projecting glutamatergic cortical neuron FALSE -L5-L6 Near Projecting CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5-L6 Near Projecting', 'full_name': 'L5-L6 near projecting', 'paper_synonyms': None, 'tissue_context': ''} CL:4030067 L5/6 near-projecting glutamatergic neuron FALSE +L3-L5 Intratelencephalic Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L3-L5 Intratelencephalic Type 2', 'full_name': 'L3-L5 intratelencephalic type 2', 'paper_synonyms': '', 'tissue_context': ''} CL:4023008 intratelencephalic-projecting glutamatergic cortical neuron FALSE +L5 Extratelencephalic CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5 Extratelencephalic', 'full_name': 'L5 extratelencephalic neurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023041 L5 extratelencephalic projecting glutamatergic cortical neuron FALSE +L5-L6 Near Projecting CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L5-L6 Near Projecting', 'full_name': 'L5-L6 near projecting neuronal cluster', 'paper_synonyms': None, 'tissue_context': ''} CL:4030067 L5/6 near-projecting glutamatergic neuron FALSE L6 Corticothalamic / L6B CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Corticothalamic / L6B', 'full_name': 'L6 corticothalamic / L6B', 'paper_synonyms': 'L6 corticothalamic; L6B', 'tissue_context': ''} CL:4023042 L6 corticothalamic-projecting glutamatergic cortical neuron FALSE L6 Intratelencephalic - Type 1 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 1', 'full_name': 'L6 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron FALSE -L6 Intratelencephalic - Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 2', 'full_name': 'L6 intratelencephalic type 2', 'paper_synonyms': None, 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron FALSE +L6 Intratelencephalic - Type 2 CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'L6 Intratelencephalic - Type 2', 'full_name': 'L6 intratelencephalic type 2', 'paper_synonyms': '', 'tissue_context': ''} CL:4030065 L6 intratelencephalic projecting glutamatergic neuron FALSE Microglia CL:0000129 microglial cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Microglia', 'full_name': 'microglia', 'paper_synonyms': None, 'tissue_context': ''} CL:0000129 microglial cell TRUE -OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'OPCs', 'full_name': 'oligodendrocyte progenitor cells', 'paper_synonyms': 'oligodendrocyte progenitor cells; OPCs', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell TRUE -Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': 'oligodendrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte TRUE -Parvalbumin interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Parvalbumin interneurons', 'full_name': 'parvalbumin interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023018 pvalb GABAergic interneuron FALSE -SV2C LAMP5 Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'SV2C LAMP5 Interneurons', 'full_name': 'SV2C LAMP5 Interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023011 lamp5 GABAergic interneuron FALSE -Somatostatin Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Somatostatin Interneurons', 'full_name': 'Somatostatin Interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023017 sst GABAergic interneuron FALSE -VIP Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'VIP Interneurons', 'full_name': 'VIP Interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023016 VIP GABAergic interneuron FALSE +OPCs CL:0002453 oligodendrocyte precursor cell DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'OPCs', 'full_name': 'oligodendrocyte progenitor cells', 'paper_synonyms': 'oligodendrocyte progenitor cells', 'tissue_context': ''} CL:0002453 oligodendrocyte precursor cell TRUE +Oligodendrocytes CL:0000128 oligodendrocyte DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Oligodendrocytes', 'full_name': 'Oligodendrocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000128 oligodendrocyte TRUE +Parvalbumin interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Parvalbumin interneurons', 'full_name': 'Parvalbumin interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023018 pvalb GABAergic interneuron FALSE +SV2C LAMP5 Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'SV2C LAMP5 Interneurons', 'full_name': 'SV2C LAMP5 interneurons', 'paper_synonyms': '', 'tissue_context': ''} CL:4023011 lamp5 GABAergic interneuron FALSE +Somatostatin Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'Somatostatin Interneurons', 'full_name': 'somatostatin interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023017 sst GABAergic interneuron FALSE +VIP Interneurons CL:0000540 neuron DOI:10.1007/s00401-023-02599-5 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique {'name': 'VIP Interneurons', 'full_name': 'VIP interneurons', 'paper_synonyms': None, 'tissue_context': ''} CL:4023016 VIP GABAergic interneuron FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..7e9d19e --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,70 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +B cell CL:0000787 memory B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B cell', 'full_name': 'B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +B cell CL:0000788 naive B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B cell', 'full_name': 'B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +B cell CL:0000818 transitional stage B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B cell', 'full_name': 'B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +B intermediate CL:0000818 transitional stage B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B intermediate', 'full_name': 'intermediate B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000818 transitional stage B cell +B memory CL:0000787 memory B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B memory', 'full_name': 'memory B cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000787 memory B cell +B naive CL:0000788 naive B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B naive', 'full_name': 'naive B cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000788 naive B cell +CD14+ Monocyte CL:0000860 classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD14+ Monocyte', 'full_name': 'CD14+ monocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0001054 CD14-positive monocyte +CD16+ Monocyte CL:0000875 non-classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD16+ Monocyte', 'full_name': 'monocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000576 monocyte +CD4+ CTL CL:0000934 CD4-positive, alpha-beta cytotoxic T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ CTL', 'full_name': 'CD4+ Cytotoxic T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000934 CD4-positive, alpha-beta cytotoxic T cell +CD4+ T activated CL:0001043 activated CD4-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T activated', 'full_name': 'CD4+ activated T cell', 'paper_synonyms': 'activated T cells', 'tissue_context': ''} CL:0000896 activated CD4-positive, alpha-beta T cell +CD4+ T cell CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD4+ T cell CL:0000934 CD4-positive, alpha-beta cytotoxic T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD4+ T cell CL:0000904 central memory CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD4+ T cell CL:0000815 regulatory T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD4+ T cell CL:0001043 activated CD4-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD4+ T naive CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T naive', 'full_name': 'CD4+ Naive T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell +CD4+ Tcm CL:0000904 central memory CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ Tcm', 'full_name': 'CD4+ Central Memory T cell', 'paper_synonyms': 'CD4+ TCM; CD4+ Central Memory T cell', 'tissue_context': ''} CL:0000904 central memory CD4-positive, alpha-beta T cell +CD8+ T activated CL:0001049 activated CD8-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T activated', 'full_name': 'Activated CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000906 activated CD8-positive, alpha-beta T cell +CD8+ T cell CL:0000913 effector memory CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T cell', 'full_name': 'CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell +CD8+ T cell CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T cell', 'full_name': 'CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell +CD8+ T cell CL:0001049 activated CD8-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T cell', 'full_name': 'CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell +CD8+ T cell CL:0000940 mucosal invariant T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T cell', 'full_name': 'CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell +CD8+ T naive CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T naive', 'full_name': 'CD8+ Naive T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell +CD8+ Tem CL:0000913 effector memory CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ Tem', 'full_name': 'CD8+ Effector Memory cell', 'paper_synonyms': 'CD8+ TEM', 'tissue_context': ''} CL:0000913 effector memory CD8-positive, alpha-beta T cell +Classical Monocyte CL:0000860 classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Classical Monocyte', 'full_name': 'classical monocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000860 classical monocyte +HSPC CL:0000037 hematopoietic stem cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'HSPC', 'full_name': 'Hematopoietic stem and progenitor cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0008001 hematopoietic precursor cell +Hematopoietic_Mega CL:0000233 platelet DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Hematopoietic_Mega', 'full_name': 'hematopoietic megakaryocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000556 megakaryocyte +Hematopoietic_R CL:0000232 erythrocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Hematopoietic_R', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000988 hematopoietic cell +Hematopoietic_SC CL:0000037 hematopoietic stem cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Hematopoietic_SC', 'full_name': 'hematopoietic stem and progenitor cells', 'paper_synonyms': 'HSPC', 'tissue_context': ''} CL:0008001 hematopoietic precursor cell +Lymphoid_B CL:0000787 memory B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_B', 'full_name': 'B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +Lymphoid_B CL:0000788 naive B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_B', 'full_name': 'B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +Lymphoid_B CL:0000818 transitional stage B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_B', 'full_name': 'B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +Lymphoid_P CL:0000980 plasmablast DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_P', 'full_name': 'lymphoid', 'paper_synonyms': None, 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000623 natural killer cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000913 effector memory CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000934 CD4-positive, alpha-beta cytotoxic T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000542 lymphocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000938 CD16-negative, CD56-bright natural killer cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000904 central memory CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000815 regulatory T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0001043 activated CD4-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0001049 activated CD8-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +Lymphoid_T/NK CL:0000940 mucosal invariant T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte +MAIT CL:0000940 mucosal invariant T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'MAIT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000940 mucosal-associated invariant T cell +Myeloid CL:0000990 conventional dendritic cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid', 'full_name': 'myeloid cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000763 myeloid cell +Myeloid CL:0000860 classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid', 'full_name': 'myeloid cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000763 myeloid cell +Myeloid CL:0001058 plasmacytoid dendritic cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid', 'full_name': 'myeloid cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000763 myeloid cell +Myeloid CL:0000875 non-classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid', 'full_name': 'myeloid cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000763 myeloid cell +Myeloid_G CL:0000775 neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid_G', 'full_name': 'myeloid cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000763 myeloid cell +Myeloid_G CL:0000776 immature neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid_G', 'full_name': 'myeloid cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000763 myeloid cell +NK CL:0000623 natural killer cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK', 'full_name': 'NK cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000623 natural killer cell +NK CD56bright CL:0000938 CD16-negative, CD56-bright natural killer cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK CD56bright', 'full_name': 'CD56-bright NK cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000938 CD16-negative, CD56-bright natural killer cell, human +NK activated CL:0000623 natural killer cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK activated', 'full_name': 'Activated NK cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell +NK cell CL:0000623 natural killer cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK cell', 'full_name': 'NK cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell +NK cell CL:0000938 CD16-negative, CD56-bright natural killer cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK cell', 'full_name': 'NK cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell +Neutrophil CL:0000775 neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'Neutrophil', 'paper_synonyms': None, 'tissue_context': ''} CL:0000775 neutrophil +Neutrophil CL:0000776 immature neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'Neutrophil', 'paper_synonyms': None, 'tissue_context': ''} CL:0000775 neutrophil +Non-classical Monocyte CL:0000875 non-classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Non-classical Monocyte', 'full_name': 'non-classical monocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000875 non-classical monocyte +Other T CL:0000542 lymphocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Other T', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000084 T cell +Plasmablast CL:0000980 plasmablast DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Plasmablast', 'full_name': 'plasmablast', 'paper_synonyms': '', 'tissue_context': ''} CL:0000980 plasmablast +Platelet CL:0000233 platelet DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Platelet', 'full_name': 'platelet', 'paper_synonyms': None, 'tissue_context': ''} CL:0000233 platelet +RBC CL:0000232 erythrocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'RBC', 'full_name': 'red blood cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte +T/NK proliferative CL:0000542 lymphocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'T/NK proliferative', 'full_name': 'proliferative T and NK cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000542 lymphocyte +Treg CL:0000815 regulatory T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Treg', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000815 regulatory T cell +cDC CL:0000990 conventional dendritic cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'cDC', 'full_name': 'conventional dendritic cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000990 conventional dendritic cell +immature Neutrophil CL:0000776 immature neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'immature Neutrophil', 'full_name': 'immature neutrophil', 'paper_synonyms': None, 'tissue_context': ''} CL:0000776 immature neutrophil +pDC CL:0001058 plasmacytoid dendritic cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'pDC', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': 'plasmacytoid dendritic cells', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..5dd63da --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,70 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +B cell CL:0000787 memory B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B cell', 'full_name': 'B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell FALSE +B cell CL:0000788 naive B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B cell', 'full_name': 'B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell FALSE +B cell CL:0000818 transitional stage B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B cell', 'full_name': 'B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell FALSE +B intermediate CL:0000818 transitional stage B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B intermediate', 'full_name': 'intermediate B cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000818 transitional stage B cell TRUE +B memory CL:0000787 memory B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B memory', 'full_name': 'memory B cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000787 memory B cell TRUE +B naive CL:0000788 naive B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'B naive', 'full_name': 'naive B cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000788 naive B cell TRUE +CD14+ Monocyte CL:0000860 classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD14+ Monocyte', 'full_name': 'CD14+ monocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0001054 CD14-positive monocyte FALSE +CD16+ Monocyte CL:0000875 non-classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD16+ Monocyte', 'full_name': 'monocytes', 'paper_synonyms': '', 'tissue_context': ''} CL:0000576 monocyte FALSE +CD4+ CTL CL:0000934 CD4-positive, alpha-beta cytotoxic T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ CTL', 'full_name': 'CD4+ Cytotoxic T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000934 CD4-positive, alpha-beta cytotoxic T cell TRUE +CD4+ T activated CL:0001043 activated CD4-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T activated', 'full_name': 'CD4+ activated T cell', 'paper_synonyms': 'activated T cells', 'tissue_context': ''} CL:0000896 activated CD4-positive, alpha-beta T cell FALSE +CD4+ T cell CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell FALSE +CD4+ T cell CL:0000934 CD4-positive, alpha-beta cytotoxic T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell FALSE +CD4+ T cell CL:0000904 central memory CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell FALSE +CD4+ T cell CL:0000815 regulatory T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell FALSE +CD4+ T cell CL:0001043 activated CD4-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T cell', 'full_name': 'CD4+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell FALSE +CD4+ T naive CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ T naive', 'full_name': 'CD4+ Naive T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell TRUE +CD4+ Tcm CL:0000904 central memory CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD4+ Tcm', 'full_name': 'CD4+ Central Memory T cell', 'paper_synonyms': 'CD4+ TCM; CD4+ Central Memory T cell', 'tissue_context': ''} CL:0000904 central memory CD4-positive, alpha-beta T cell TRUE +CD8+ T activated CL:0001049 activated CD8-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T activated', 'full_name': 'Activated CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000906 activated CD8-positive, alpha-beta T cell FALSE +CD8+ T cell CL:0000913 effector memory CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T cell', 'full_name': 'CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell FALSE +CD8+ T cell CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T cell', 'full_name': 'CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell FALSE +CD8+ T cell CL:0001049 activated CD8-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T cell', 'full_name': 'CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell FALSE +CD8+ T cell CL:0000940 mucosal invariant T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T cell', 'full_name': 'CD8+ T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell FALSE +CD8+ T naive CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ T naive', 'full_name': 'CD8+ Naive T cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell TRUE +CD8+ Tem CL:0000913 effector memory CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'CD8+ Tem', 'full_name': 'CD8+ Effector Memory cell', 'paper_synonyms': 'CD8+ TEM', 'tissue_context': ''} CL:0000913 effector memory CD8-positive, alpha-beta T cell TRUE +Classical Monocyte CL:0000860 classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Classical Monocyte', 'full_name': 'classical monocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000860 classical monocyte TRUE +HSPC CL:0000037 hematopoietic stem cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'HSPC', 'full_name': 'Hematopoietic stem and progenitor cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0008001 hematopoietic precursor cell FALSE +Hematopoietic_Mega CL:0000233 platelet DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Hematopoietic_Mega', 'full_name': 'hematopoietic megakaryocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000556 megakaryocyte FALSE +Hematopoietic_R CL:0000232 erythrocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Hematopoietic_R', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000988 hematopoietic cell FALSE +Hematopoietic_SC CL:0000037 hematopoietic stem cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Hematopoietic_SC', 'full_name': 'hematopoietic stem and progenitor cells', 'paper_synonyms': 'HSPC', 'tissue_context': ''} CL:0008001 hematopoietic precursor cell FALSE +Lymphoid_B CL:0000787 memory B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_B', 'full_name': 'B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell FALSE +Lymphoid_B CL:0000788 naive B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_B', 'full_name': 'B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell FALSE +Lymphoid_B CL:0000818 transitional stage B cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_B', 'full_name': 'B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell FALSE +Lymphoid_P CL:0000980 plasmablast DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_P', 'full_name': 'lymphoid', 'paper_synonyms': None, 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000623 natural killer cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000895 naive thymus-derived CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000913 effector memory CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000934 CD4-positive, alpha-beta cytotoxic T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000542 lymphocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte TRUE +Lymphoid_T/NK CL:0000938 CD16-negative, CD56-bright natural killer cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000904 central memory CD4-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000815 regulatory T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0001043 activated CD4-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000900 naive thymus-derived CD8-positive, alpha-beta T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0001049 activated CD8-positive, alpha-beta T cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +Lymphoid_T/NK CL:0000940 mucosal invariant T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Lymphoid_T/NK', 'full_name': 'Lymphoid T and NK cells', 'paper_synonyms': 'T cell; NK cell', 'tissue_context': ''} CL:0000542 lymphocyte FALSE +MAIT CL:0000940 mucosal invariant T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'MAIT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000940 mucosal-associated invariant T cell TRUE +Myeloid CL:0000990 conventional dendritic cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid', 'full_name': 'myeloid cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000763 myeloid cell FALSE +Myeloid CL:0000860 classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid', 'full_name': 'myeloid cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000763 myeloid cell FALSE +Myeloid CL:0001058 plasmacytoid dendritic cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid', 'full_name': 'myeloid cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000763 myeloid cell FALSE +Myeloid CL:0000875 non-classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid', 'full_name': 'myeloid cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000763 myeloid cell FALSE +Myeloid_G CL:0000775 neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid_G', 'full_name': 'myeloid cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000763 myeloid cell FALSE +Myeloid_G CL:0000776 immature neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Myeloid_G', 'full_name': 'myeloid cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000763 myeloid cell FALSE +NK CL:0000623 natural killer cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK', 'full_name': 'NK cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000623 natural killer cell TRUE +NK CD56bright CL:0000938 CD16-negative, CD56-bright natural killer cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK CD56bright', 'full_name': 'CD56-bright NK cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000938 CD16-negative, CD56-bright natural killer cell, human TRUE +NK activated CL:0000623 natural killer cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK activated', 'full_name': 'Activated NK cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell TRUE +NK cell CL:0000623 natural killer cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK cell', 'full_name': 'NK cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell TRUE +NK cell CL:0000938 CD16-negative, CD56-bright natural killer cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'NK cell', 'full_name': 'NK cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000623 natural killer cell FALSE +Neutrophil CL:0000775 neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'Neutrophil', 'paper_synonyms': None, 'tissue_context': ''} CL:0000775 neutrophil TRUE +Neutrophil CL:0000776 immature neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'Neutrophil', 'paper_synonyms': None, 'tissue_context': ''} CL:0000775 neutrophil FALSE +Non-classical Monocyte CL:0000875 non-classical monocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Non-classical Monocyte', 'full_name': 'non-classical monocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000875 non-classical monocyte TRUE +Other T CL:0000542 lymphocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Other T', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000084 T cell FALSE +Plasmablast CL:0000980 plasmablast DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Plasmablast', 'full_name': 'plasmablast', 'paper_synonyms': '', 'tissue_context': ''} CL:0000980 plasmablast TRUE +Platelet CL:0000233 platelet DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Platelet', 'full_name': 'platelet', 'paper_synonyms': None, 'tissue_context': ''} CL:0000233 platelet TRUE +RBC CL:0000232 erythrocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'RBC', 'full_name': 'red blood cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte TRUE +T/NK proliferative CL:0000542 lymphocyte DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'T/NK proliferative', 'full_name': 'proliferative T and NK cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000542 lymphocyte TRUE +Treg CL:0000815 regulatory T cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'Treg', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000815 regulatory T cell TRUE +cDC CL:0000990 conventional dendritic cell DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'cDC', 'full_name': 'conventional dendritic cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000990 conventional dendritic cell TRUE +immature Neutrophil CL:0000776 immature neutrophil DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'immature Neutrophil', 'full_name': 'immature neutrophil', 'paper_synonyms': None, 'tissue_context': ''} CL:0000776 immature neutrophil TRUE +pDC CL:0001058 plasmacytoid dendritic cell, human DOI:10.1016/j.isci.2021.103115 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique {'name': 'pDC', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': 'plasmacytoid dendritic cells', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..c68c033 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,52 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +37- unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': '37-', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +38- unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': '38-', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +5- unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': '5-', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_01 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_01', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +AC_B_02 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_02', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_04 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_04', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +AC_B_05 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_05', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_06 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_06', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_07 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_07', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_08 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_08', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_09 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_09', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +AC_B_10 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_10', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_11 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_11', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +AC_B_12 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_12', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_13 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_13', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_15 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_15', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +AC_B_16 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_16', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_B_17 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_17', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +AC_B_18 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_18', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_Y_01 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_Y_01', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +AC_Y_03 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_Y_03', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000561 amacrine cell +Ast CL:0000127 astrocyte DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'Ast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000127 astrocyte +CdBC_01 CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_01', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron +CdBC_02 CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_02', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron +CdBC_03 CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_03', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +CdBC_04 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_04', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +CdBC_05 CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_05', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +ChBC_01 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'ChBC_01', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +ChBC_02 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'ChBC_02', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +ChBC_03 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'ChBC_03', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +ChBC_04 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'ChBC_04', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found +HC_02 CL:0000745 retina horizontal cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'HC_02', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000745 retina horizontal cell +L/M cone CL:0000573 retinal cone cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'L/M cone', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000573 retinal cone cell +MC_01 CL:0000636 Mueller cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'MC_01', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +MC_02 CL:0000636 Mueller cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'MC_02', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +MC_03 CL:0000636 Mueller cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'MC_03', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +RBC CL:0000751 rod bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'RBC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte +RPE CL:0002586 retinal pigment epithelial cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'RPE', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002586 retinal pigment epithelial cell +S cone CL:0000573 retinal cone cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'S cone', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0003050 S cone cell +amacrine CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'amacrine', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000561 amacrine cell +amacrine unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'amacrine', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000561 amacrine cell +bipolar CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'bipolar', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000103 bipolar neuron +bipolar CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'bipolar', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000103 bipolar neuron +bipolar CL:0000751 rod bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'bipolar', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000103 bipolar neuron +cone CL:0000573 retinal cone cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'cone', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000573 retinal cone cell +horizontal CL:0000745 retina horizontal cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'horizontal', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000745 retina horizontal cell +horizontal unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'horizontal', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000745 retina horizontal cell +macroglia CL:0000636 Mueller cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'macroglia', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000126 macroglial cell +macroglia CL:0000127 astrocyte DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'macroglia', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000126 macroglial cell +pigmented CL:0002586 retinal pigment epithelial cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'pigmented', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000147 pigment cell +rod CL:0000604 retinal rod cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'rod', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..54ebcac --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,52 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +37- unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': '37-', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +38- unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': '38-', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +5- unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': '5-', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_01 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_01', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +AC_B_02 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_02', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_04 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_04', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +AC_B_05 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_05', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_06 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_06', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_07 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_07', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_08 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_08', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_09 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_09', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +AC_B_10 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_10', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_11 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_11', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +AC_B_12 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_12', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_13 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_13', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_15 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_15', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +AC_B_16 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_16', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_B_17 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_17', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +AC_B_18 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_B_18', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_Y_01 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_Y_01', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +AC_Y_03 CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'AC_Y_03', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000561 amacrine cell TRUE +Ast CL:0000127 astrocyte DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'Ast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000127 astrocyte TRUE +CdBC_01 CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_01', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron FALSE +CdBC_02 CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_02', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron FALSE +CdBC_03 CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_03', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +CdBC_04 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_04', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +CdBC_05 CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'CdBC_05', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +ChBC_01 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'ChBC_01', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +ChBC_02 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'ChBC_02', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +ChBC_03 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'ChBC_03', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +ChBC_04 CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'ChBC_04', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} NO MATCH found FALSE +HC_02 CL:0000745 retina horizontal cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'HC_02', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000745 retina horizontal cell TRUE +L/M cone CL:0000573 retinal cone cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'L/M cone', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000573 retinal cone cell TRUE +MC_01 CL:0000636 Mueller cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'MC_01', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +MC_02 CL:0000636 Mueller cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'MC_02', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +MC_03 CL:0000636 Mueller cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'MC_03', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +RBC CL:0000751 rod bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'RBC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000232 erythrocyte FALSE +RPE CL:0002586 retinal pigment epithelial cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'RPE', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0002586 retinal pigment epithelial cell TRUE +S cone CL:0000573 retinal cone cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'S cone', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0003050 S cone cell FALSE +amacrine CL:0000561 amacrine cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'amacrine', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000561 amacrine cell TRUE +amacrine unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'amacrine', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000561 amacrine cell FALSE +bipolar CL:0000749 ON-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'bipolar', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000103 bipolar neuron FALSE +bipolar CL:0000750 OFF-bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'bipolar', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000103 bipolar neuron FALSE +bipolar CL:0000751 rod bipolar cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'bipolar', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000103 bipolar neuron FALSE +cone CL:0000573 retinal cone cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'cone', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000573 retinal cone cell TRUE +horizontal CL:0000745 retina horizontal cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'horizontal', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000745 retina horizontal cell TRUE +horizontal unknown unknown DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'horizontal', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000745 retina horizontal cell FALSE +macroglia CL:0000636 Mueller cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'macroglia', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000126 macroglial cell FALSE +macroglia CL:0000127 astrocyte DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'macroglia', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000126 macroglial cell FALSE +pigmented CL:0002586 retinal pigment epithelial cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'pigmented', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000147 pigment cell FALSE +rod CL:0000604 retinal rod cell DOI:10.1016/j.cell.2020.08.013 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique {'name': 'rod', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..c296f4b --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,13 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +BB+GB* CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'BB+GB*', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +DB1 CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB1', 'full_name': 'bipolar type DB1', 'paper_synonyms': None, 'tissue_context': ''} CL:4033027 diffuse bipolar 1 cell +DB2 CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB2', 'full_name': 'bipolar type DB2', 'paper_synonyms': None, 'tissue_context': ''} CL:4033028 diffuse bipolar 2 cell +DB3a CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB3a', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033029 diffuse bipolar 3a cell +DB3b CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB3b', 'full_name': 'bipolar type DB3b', 'paper_synonyms': None, 'tissue_context': ''} CL:4033030 diffuse bipolar 3b cell +DB4 CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB4', 'full_name': 'bipolar type DB4', 'paper_synonyms': None, 'tissue_context': ''} CL:4033031 diffuse bipolar 4 cell +DB5* CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB5*', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033085 diffuse bipolar 5 cell +DB6 CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB6', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033032 diffuse bipolar 6 cell +FMB CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'FMB', 'full_name': 'bipolar type FMB', 'paper_synonyms': None, 'tissue_context': ''} CL:4033033 flat midget bipolar cell +IMB CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'IMB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033034 invaginating midget bipolar cell +OFFx CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'OFFx', 'full_name': 'OFFx type', 'paper_synonyms': None, 'tissue_context': ''} CL:4033036 OFFx cell +RB1 CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'RB1', 'full_name': None, 'paper_synonyms': 'rod bipolar cells; rod BCs', 'tissue_context': ''} CL:0000751 rod bipolar cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..e0f03a1 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,13 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +BB+GB* CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'BB+GB*', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +DB1 CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB1', 'full_name': 'bipolar type DB1', 'paper_synonyms': None, 'tissue_context': ''} CL:4033027 diffuse bipolar 1 cell FALSE +DB2 CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB2', 'full_name': 'bipolar type DB2', 'paper_synonyms': None, 'tissue_context': ''} CL:4033028 diffuse bipolar 2 cell FALSE +DB3a CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB3a', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033029 diffuse bipolar 3a cell FALSE +DB3b CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB3b', 'full_name': 'bipolar type DB3b', 'paper_synonyms': None, 'tissue_context': ''} CL:4033030 diffuse bipolar 3b cell FALSE +DB4 CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB4', 'full_name': 'bipolar type DB4', 'paper_synonyms': None, 'tissue_context': ''} CL:4033031 diffuse bipolar 4 cell FALSE +DB5* CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB5*', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033085 diffuse bipolar 5 cell FALSE +DB6 CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'DB6', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033032 diffuse bipolar 6 cell FALSE +FMB CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'FMB', 'full_name': 'bipolar type FMB', 'paper_synonyms': None, 'tissue_context': ''} CL:4033033 flat midget bipolar cell FALSE +IMB CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'IMB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033034 invaginating midget bipolar cell FALSE +OFFx CL:0000750 OFF-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'OFFx', 'full_name': 'OFFx type', 'paper_synonyms': None, 'tissue_context': ''} CL:4033036 OFFx cell FALSE +RB1 CL:0000749 ON-bipolar cell DOI:10.1038/s41598-020-66092-9 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique {'name': 'RB1', 'full_name': None, 'paper_synonyms': 'rod bipolar cells; rod BCs', 'tissue_context': ''} CL:0000751 rod bipolar cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..e040730 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,42 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Activated B cell CL:0000236 B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Activated B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +Activated T CL:0001043 activated CD4-positive, alpha-beta T cell, human DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Activated T', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000084 T cell +Arterial endothelial cell CL:1000413 endothelial cell of artery DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Arterial endothelial cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000413 endothelial cell of artery +B cell CL:0000236 B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +BEST4 enterocyte CL:0000584 enterocyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'BEST4 enterocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030026 BEST4+ enterocyte +CD4 T cell CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'CD4 T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD8 T cell CL:0000625 CD8-positive, alpha-beta T cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'CD8 T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell +Cycling B cell CL:0000236 B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Cycling B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033068 cycling B cell +Cycling myeloid cells CL:0000763 myeloid cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Cycling myeloid cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033081 cycling myeloid cell +Cycling plasma cell CL:0000786 plasma cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Cycling plasma cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047003 cycling plasma cell +FCER2 B cell CL:0000236 B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'FCER2 B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +Glial cell CL:0000125 glial cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Glial cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000125 glial cell +Goblet cell CL:0019031 intestine goblet cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Goblet cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell +IL2RG+ enterocyte (M cell) CL:0000682 M cell of gut DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'IL2RG+ enterocyte (M cell)', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000682 M cell of gut +IgA plasma cell CL:0000987 IgA plasma cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'IgA plasma cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000987 IgA plasma cell +IgG plasma cell CL:0000985 IgG plasma cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'IgG plasma cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000985 IgG plasma cell +Lymphatic endothelial cell CL:0002138 endothelial cell of lymphatic vessel DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Lymphatic endothelial cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel +Macrophage CL:0000125 glial cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Macrophage', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage +Memory B cell CL:0000787 memory B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Memory B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000787 memory B cell +Monocyte CL:0000576 monocyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte +Paneth cell CL:0000125 glial cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Paneth cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000510 paneth cell +S1 fibroblasts CL:0000057 fibroblast DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'S1 fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +S2 fibroblasts CL:0000057 fibroblast DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'S2 fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +S4 fibroblasts CL:0000057 fibroblast DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'S4 fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +TA CL:0009010 transit amplifying cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'TA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009010 transit amplifying cell +Tfh CL:0002038 T follicular helper cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Tfh', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002038 T follicular helper cell +Treg CL:0000815 regulatory T cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Treg', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000815 regulatory T cell +Tuft CL:0019032 intestinal tuft cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell +Venous endothelial cell CL:0002543 vein endothelial cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Venous endothelial cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002543 vein endothelial cell +activated DC CL:0001056 dendritic cell, human DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'activated DC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000451 dendritic cell +cDC1 CL:0000990 conventional dendritic cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'cDC1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002394 CD141-positive myeloid dendritic cell +cDC2 CL:0000990 conventional dendritic cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'cDC2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002399 CD1c-positive myeloid dendritic cell +crypt CL:0002250 intestinal crypt stem cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'crypt', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002250 intestinal crypt stem cell +early enterocyte CL:0000584 enterocyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'early enterocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047019 early enterocyte +enterocyte CL:0000584 enterocyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'enterocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte +enteroendocrine CL:0000164 enteroendocrine cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'enteroendocrine', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell +gd T/NK cell CL:0000798 gamma-delta T cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'gd T/NK cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000798 gamma-delta T cell +mast cells CL:0000097 mast cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'mast cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell +myofibroblast CL:0000186 myofibroblast cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'myofibroblast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell +pDC CL:0000784 plasmacytoid dendritic cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'pDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell +pericyte CL:0000669 pericyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'pericyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..22502d9 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,42 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Activated B cell CL:0000236 B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Activated B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell TRUE +Activated T CL:0001043 activated CD4-positive, alpha-beta T cell, human DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Activated T', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000084 T cell FALSE +Arterial endothelial cell CL:1000413 endothelial cell of artery DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Arterial endothelial cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000413 endothelial cell of artery TRUE +B cell CL:0000236 B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell TRUE +BEST4 enterocyte CL:0000584 enterocyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'BEST4 enterocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030026 BEST4+ enterocyte FALSE +CD4 T cell CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'CD4 T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell TRUE +CD8 T cell CL:0000625 CD8-positive, alpha-beta T cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'CD8 T cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell TRUE +Cycling B cell CL:0000236 B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Cycling B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033068 cycling B cell FALSE +Cycling myeloid cells CL:0000763 myeloid cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Cycling myeloid cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4033081 cycling myeloid cell FALSE +Cycling plasma cell CL:0000786 plasma cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Cycling plasma cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047003 cycling plasma cell FALSE +FCER2 B cell CL:0000236 B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'FCER2 B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell TRUE +Glial cell CL:0000125 glial cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Glial cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000125 glial cell TRUE +Goblet cell CL:0019031 intestine goblet cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Goblet cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell FALSE +IL2RG+ enterocyte (M cell) CL:0000682 M cell of gut DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'IL2RG+ enterocyte (M cell)', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000682 M cell of gut TRUE +IgA plasma cell CL:0000987 IgA plasma cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'IgA plasma cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000987 IgA plasma cell TRUE +IgG plasma cell CL:0000985 IgG plasma cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'IgG plasma cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000985 IgG plasma cell TRUE +Lymphatic endothelial cell CL:0002138 endothelial cell of lymphatic vessel DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Lymphatic endothelial cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel TRUE +Macrophage CL:0000125 glial cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Macrophage', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage FALSE +Memory B cell CL:0000787 memory B cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Memory B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000787 memory B cell TRUE +Monocyte CL:0000576 monocyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Monocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte TRUE +Paneth cell CL:0000125 glial cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Paneth cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000510 paneth cell FALSE +S1 fibroblasts CL:0000057 fibroblast DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'S1 fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +S2 fibroblasts CL:0000057 fibroblast DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'S2 fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +S4 fibroblasts CL:0000057 fibroblast DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'S4 fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +TA CL:0009010 transit amplifying cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'TA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0009010 transit amplifying cell TRUE +Tfh CL:0002038 T follicular helper cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Tfh', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002038 T follicular helper cell TRUE +Treg CL:0000815 regulatory T cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Treg', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000815 regulatory T cell TRUE +Tuft CL:0019032 intestinal tuft cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell FALSE +Venous endothelial cell CL:0002543 vein endothelial cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'Venous endothelial cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002543 vein endothelial cell TRUE +activated DC CL:0001056 dendritic cell, human DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'activated DC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000451 dendritic cell FALSE +cDC1 CL:0000990 conventional dendritic cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'cDC1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002394 CD141-positive myeloid dendritic cell FALSE +cDC2 CL:0000990 conventional dendritic cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'cDC2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002399 CD1c-positive myeloid dendritic cell FALSE +crypt CL:0002250 intestinal crypt stem cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'crypt', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002250 intestinal crypt stem cell TRUE +early enterocyte CL:0000584 enterocyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'early enterocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4047019 early enterocyte FALSE +enterocyte CL:0000584 enterocyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'enterocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte TRUE +enteroendocrine CL:0000164 enteroendocrine cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'enteroendocrine', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000164 enteroendocrine cell TRUE +gd T/NK cell CL:0000798 gamma-delta T cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'gd T/NK cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000798 gamma-delta T cell TRUE +mast cells CL:0000097 mast cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'mast cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000097 mast cell TRUE +myofibroblast CL:0000186 myofibroblast cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'myofibroblast', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE +pDC CL:0000784 plasmacytoid dendritic cell DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'pDC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell TRUE +pericyte CL:0000669 pericyte DOI:10.1016/j.devcel.2020.11.010 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique {'name': 'pericyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..2a18afe --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,10 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Iris_APE CL:0000529 pigmented epithelial cell DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Iris_APE', 'full_name': 'iris anterior pigmented epithelium', 'paper_synonyms': 'anterior pigmented epithelium; APE; iris dilator muscle', 'tissue_context': ''} CL:0002565 iris pigment epithelial cell +Iris_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Iris_Fibro', 'full_name': 'iris stromal fibroblast', 'paper_synonyms': 'iris fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast +Iris_PPE CL:0000529 pigmented epithelial cell DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Iris_PPE', 'full_name': 'iris posterior pigmented epithelium', 'paper_synonyms': 'posterior pigmented epithelium; PPE', 'tissue_context': ''} CL:0002565 iris pigment epithelial cell +Iris_Sphincter CL:0002243 smooth muscle cell of sphincter of pupil DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Iris_Sphincter', 'full_name': 'iris sphincter muscle cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0002243 smooth muscle cell of sphincter of pupil +Lymphocyte CL:0000542 lymphocyte DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Lymphocyte', 'full_name': 'lymphocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000542 lymphocyte +Macrophage CL:0000235 macrophage DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Macrophage', 'full_name': 'macrophage', 'paper_synonyms': 'clump cells', 'tissue_context': ''} CL:0000235 macrophage +Schwann CL:0002573 Schwann cell DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Schwann', 'full_name': 'Schwann cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0002573 Schwann cell +Uveal_Melanocyte CL:0000148 melanocyte DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Uveal_Melanocyte', 'full_name': 'uveal melanocyte', 'paper_synonyms': '', 'tissue_context': ''} CL:0000148 melanocyte +Vasc_Endo CL:0000071 blood vessel endothelial cell DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Vasc_Endo', 'full_name': 'vascular endothelium', 'paper_synonyms': None, 'tissue_context': ''} CL:0000071 blood vessel endothelial cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..fa8e0b3 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,10 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Iris_APE CL:0000529 pigmented epithelial cell DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Iris_APE', 'full_name': 'iris anterior pigmented epithelium', 'paper_synonyms': 'anterior pigmented epithelium; APE; iris dilator muscle', 'tissue_context': ''} CL:0002565 iris pigment epithelial cell FALSE +Iris_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Iris_Fibro', 'full_name': 'iris stromal fibroblast', 'paper_synonyms': 'iris fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Iris_PPE CL:0000529 pigmented epithelial cell DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Iris_PPE', 'full_name': 'iris posterior pigmented epithelium', 'paper_synonyms': 'posterior pigmented epithelium; PPE', 'tissue_context': ''} CL:0002565 iris pigment epithelial cell FALSE +Iris_Sphincter CL:0002243 smooth muscle cell of sphincter of pupil DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Iris_Sphincter', 'full_name': 'iris sphincter muscle cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0002243 smooth muscle cell of sphincter of pupil TRUE +Lymphocyte CL:0000542 lymphocyte DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Lymphocyte', 'full_name': 'lymphocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000542 lymphocyte TRUE +Macrophage CL:0000235 macrophage DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Macrophage', 'full_name': 'macrophage', 'paper_synonyms': 'clump cells', 'tissue_context': ''} CL:0000235 macrophage TRUE +Schwann CL:0002573 Schwann cell DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Schwann', 'full_name': 'Schwann cell', 'paper_synonyms': None, 'tissue_context': ''} CL:0002573 Schwann cell TRUE +Uveal_Melanocyte CL:0000148 melanocyte DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Uveal_Melanocyte', 'full_name': 'uveal melanocyte', 'paper_synonyms': '', 'tissue_context': ''} CL:0000148 melanocyte TRUE +Vasc_Endo CL:0000071 blood vessel endothelial cell DOI:10.1073/pnas.2200914119 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique {'name': 'Vasc_Endo', 'full_name': 'vascular endothelium', 'paper_synonyms': None, 'tissue_context': ''} CL:0000071 blood vessel endothelial cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..96e52ff --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,16 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +CNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'CNT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'DCT1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule +DCT2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'DCT2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule +ENDO CL:1000892 kidney capillary endothelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'ENDO', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +FIB CL:0000057 fibroblast DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'FIB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +ICA CL:0005011 renal alpha-intercalated cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'ICA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell +ICB CL:0002201 renal beta-intercalated cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'ICB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002201 renal beta-intercalated cell +LEUK CL:0000738 leukocyte DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'LEUK', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +MES CL:0000650 mesangial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'MES', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000650 mesangial cell +PC CL:0005009 renal principal cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell +PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell +PODO CL:0000653 podocyte DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PODO', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000653 podocyte +PT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +PT_VCAM1 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PT_VCAM1', 'full_name': 'VCAM1', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'TAL', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..233b46b --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,16 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +CNT CL:1000768 kidney connecting tubule epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'CNT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'DCT1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule FALSE +DCT2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'DCT2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule FALSE +ENDO CL:1000892 kidney capillary endothelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'ENDO', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell FALSE +FIB CL:0000057 fibroblast DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'FIB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +ICA CL:0005011 renal alpha-intercalated cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'ICA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell TRUE +ICB CL:0002201 renal beta-intercalated cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'ICB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002201 renal beta-intercalated cell TRUE +LEUK CL:0000738 leukocyte DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'LEUK', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte TRUE +MES CL:0000650 mesangial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'MES', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000650 mesangial cell TRUE +PC CL:0005009 renal principal cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell TRUE +PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell TRUE +PODO CL:0000653 podocyte DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PODO', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000653 podocyte TRUE +PT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +PT_VCAM1 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'PT_VCAM1', 'full_name': 'VCAM1', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41467-021-22368-w 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique {'name': 'TAL', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..bf2c0dd --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,38 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Ascending vasa recta endothelium CL:1001131 vasa recta ascending limb cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Ascending vasa recta endothelium', 'full_name': 'ascending vasa recta endothelium', 'paper_synonyms': 'AVRE', 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell +B cell CL:0000236 B cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'B cell', 'full_name': 'B cell', 'paper_synonyms': 'B', 'tissue_context': ''} CL:0000236 B cell +CD4 T cell CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'CD4 T cell', 'full_name': 'CD4 T cell', 'paper_synonyms': 'CD4 T', 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell +CD8 T cell CL:0000625 CD8-positive, alpha-beta T cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'CD8 T cell', 'full_name': 'CD8 T cell', 'paper_synonyms': 'CD8 T', 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell +Connecting tubule CL:1000768 kidney connecting tubule epithelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Connecting tubule', 'full_name': 'connecting nephron tubule', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +Descending vasa recta endothelium CL:1001285 vasa recta descending limb cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Descending vasa recta endothelium', 'full_name': 'descending vasa recta endothelium', 'paper_synonyms': 'DVRE', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell +Distinct proximal tubule 1 CL:0002306 epithelial cell of proximal tubule DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Distinct proximal tubule 1', 'full_name': 'distinct proximal tubule 1', 'paper_synonyms': 'dPT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Distinct proximal tubule 2 CL:0002306 epithelial cell of proximal tubule DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Distinct proximal tubule 2', 'full_name': 'distinct proximal tubule 2', 'paper_synonyms': 'dPT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Epithelial progenitor cell CL:0011026 progenitor cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Epithelial progenitor cell', 'full_name': 'epithelial progenitor cell', 'paper_synonyms': 'EPC', 'tissue_context': ''} CL:0011026 progenitor cell +Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': 'fibroblast', 'paper_synonyms': 'Fib', 'tissue_context': ''} CL:0000057 fibroblast +Glomerular endothelium CL:0002188 glomerular endothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Glomerular endothelium', 'full_name': 'glomerular endothelium', 'paper_synonyms': 'GE; glomerular endothelial cells (GE)', 'tissue_context': ''} CL:0002188 glomerular endothelial cell +Indistinct intercalated cell CL:0005010 renal intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Indistinct intercalated cell', 'full_name': 'intercalated cell', 'paper_synonyms': 'intercalated cells (IC); IC', 'tissue_context': ''} CL:0005010 renal intercalated cell +Intercalated cell CL:0005011 renal alpha-intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Intercalated cell', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell +Intercalated cell CL:0005010 renal intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Intercalated cell', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell +Intercalated cell CL:0002201 renal beta-intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Intercalated cell', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell +MNP-a/classical monocyte derived CL:0000860 classical monocyte DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'MNP-a/classical monocyte derived', 'full_name': 'mononuclear phagocyte a/classical monocyte-derived', 'paper_synonyms': 'MNPa', 'tissue_context': ''} CL:0000113 mononuclear phagocyte +MNP-b/non-classical monocyte derived CL:0000875 non-classical monocyte DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'MNP-b/non-classical monocyte derived', 'full_name': 'mononuclear phagocyte b/non-classical monocyte derived', 'paper_synonyms': 'MNPb', 'tissue_context': ''} CL:0000875 non-classical monocyte +MNP-c/dendritic cell CL:0000451 dendritic cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'MNP-c/dendritic cell', 'full_name': 'mononuclear phagocyte c (dendritic cell)', 'paper_synonyms': 'MNPc; classical myeloid DC; cDC', 'tissue_context': ''} CL:0000451 dendritic cell +MNP-d/Tissue macrophage CL:1000698 kidney resident macrophage DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'MNP-d/Tissue macrophage', 'full_name': 'mononuclear phagocyte d/Tissue macrophage', 'paper_synonyms': 'MNPd', 'tissue_context': ''} CL:0000864 tissue-resident macrophage +Mast cell CL:0000097 mast cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Mast cell', 'full_name': 'mast cell', 'paper_synonyms': 'Mast', 'tissue_context': ''} CL:0000097 mast cell +Myofibroblast CL:0000186 myofibroblast cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Myofibroblast', 'full_name': 'myofibroblast', 'paper_synonyms': 'MFib', 'tissue_context': ''} CL:0000186 myofibroblast cell +NK cell CL:0000623 natural killer cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'NK cell', 'full_name': 'natural killer cell', 'paper_synonyms': 'NK', 'tissue_context': ''} CL:0000623 natural killer cell +NKT cell CL:0000814 mature NK T cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'NKT cell', 'full_name': 'natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:0000814 mature NK T cell +Neutrophil CL:0000775 neutrophil DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'neutrophil', 'paper_synonyms': 'NO', 'tissue_context': ''} CL:0000775 neutrophil +Pelvic epithelium CL:0002518 kidney epithelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Pelvic epithelium', 'full_name': 'pelvic epithelium', 'paper_synonyms': 'PE', 'tissue_context': ''} CL:0000731 urothelial cell +Peritubular capillary endothelium CL:0002144 capillary endothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Peritubular capillary endothelium', 'full_name': 'peritubular capillary endothelium', 'paper_synonyms': 'PCE', 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell +Peritubular capillary endothelium 1 CL:0002144 capillary endothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Peritubular capillary endothelium 1', 'full_name': 'peritubular capillary endothelium', 'paper_synonyms': 'peritubular capillaries (PCap); PCE', 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell +Peritubular capillary endothelium 2 CL:0002144 capillary endothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Peritubular capillary endothelium 2', 'full_name': 'peritubular capillary endothelium 2', 'paper_synonyms': 'PCE; peritubular capillaries; PCap', 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell +Plasmacytoid dendritic cell CL:0000784 plasmacytoid dendritic cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Plasmacytoid dendritic cell', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': 'pDC', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell +Podocyte CL:0000653 podocyte DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Podocyte', 'full_name': 'podocyte', 'paper_synonyms': 'Podo', 'tissue_context': ''} CL:0000653 podocyte +Principal cell CL:0005009 renal principal cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Principal cell', 'full_name': 'principal cell', 'paper_synonyms': 'PC', 'tissue_context': ''} CL:0005009 renal principal cell +Proliferating Proximal Tubule CL:0002306 epithelial cell of proximal tubule DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Proliferating Proximal Tubule', 'full_name': 'proximal tubule', 'paper_synonyms': 'PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Proximal tubule CL:0002306 epithelial cell of proximal tubule DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Proximal tubule', 'full_name': 'proximal tubule', 'paper_synonyms': 'PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Thick ascending limb of Loop of Henle CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Thick ascending limb of Loop of Henle', 'full_name': 'loop of Henle', 'paper_synonyms': 'LOH; loop of Henle', 'tissue_context': ''} CL:1000909 kidney loop of Henle epithelial cell +Transitional urothelium CL:0000731 urothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Transitional urothelium', 'full_name': 'transitional epithelium of ureter', 'paper_synonyms': 'TE; transitional epithelium; transitional epithelium of ureter', 'tissue_context': ''} CL:1000706 ureter urothelial cell +Type A intercalated cell CL:0005011 renal alpha-intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Type A intercalated cell', 'full_name': 'type A intercalated cell', 'paper_synonyms': 'intercalated cells (IC); IC (A+B); IC', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell +Type B intercalated cell CL:0002201 renal beta-intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Type B intercalated cell', 'full_name': 'type B intercalated cell', 'paper_synonyms': 'IC; intercalated cells; IC (A+B)', 'tissue_context': ''} CL:0002201 renal beta-intercalated cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..58edacb --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,38 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Ascending vasa recta endothelium CL:1001131 vasa recta ascending limb cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Ascending vasa recta endothelium', 'full_name': 'ascending vasa recta endothelium', 'paper_synonyms': 'AVRE', 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell TRUE +B cell CL:0000236 B cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'B cell', 'full_name': 'B cell', 'paper_synonyms': 'B', 'tissue_context': ''} CL:0000236 B cell TRUE +CD4 T cell CL:0000624 CD4-positive, alpha-beta T cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'CD4 T cell', 'full_name': 'CD4 T cell', 'paper_synonyms': 'CD4 T', 'tissue_context': ''} CL:0000624 CD4-positive, alpha-beta T cell TRUE +CD8 T cell CL:0000625 CD8-positive, alpha-beta T cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'CD8 T cell', 'full_name': 'CD8 T cell', 'paper_synonyms': 'CD8 T', 'tissue_context': ''} CL:0000625 CD8-positive, alpha-beta T cell TRUE +Connecting tubule CL:1000768 kidney connecting tubule epithelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Connecting tubule', 'full_name': 'connecting nephron tubule', 'paper_synonyms': 'CNT', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell TRUE +Descending vasa recta endothelium CL:1001285 vasa recta descending limb cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Descending vasa recta endothelium', 'full_name': 'descending vasa recta endothelium', 'paper_synonyms': 'DVRE', 'tissue_context': ''} CL:1000892 kidney capillary endothelial cell FALSE +Distinct proximal tubule 1 CL:0002306 epithelial cell of proximal tubule DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Distinct proximal tubule 1', 'full_name': 'distinct proximal tubule 1', 'paper_synonyms': 'dPT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Distinct proximal tubule 2 CL:0002306 epithelial cell of proximal tubule DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Distinct proximal tubule 2', 'full_name': 'distinct proximal tubule 2', 'paper_synonyms': 'dPT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Epithelial progenitor cell CL:0011026 progenitor cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Epithelial progenitor cell', 'full_name': 'epithelial progenitor cell', 'paper_synonyms': 'EPC', 'tissue_context': ''} CL:0011026 progenitor cell TRUE +Fibroblast CL:1000692 kidney interstitial fibroblast DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Fibroblast', 'full_name': 'fibroblast', 'paper_synonyms': 'Fib', 'tissue_context': ''} CL:0000057 fibroblast FALSE +Glomerular endothelium CL:0002188 glomerular endothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Glomerular endothelium', 'full_name': 'glomerular endothelium', 'paper_synonyms': 'GE; glomerular endothelial cells (GE)', 'tissue_context': ''} CL:0002188 glomerular endothelial cell TRUE +Indistinct intercalated cell CL:0005010 renal intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Indistinct intercalated cell', 'full_name': 'intercalated cell', 'paper_synonyms': 'intercalated cells (IC); IC', 'tissue_context': ''} CL:0005010 renal intercalated cell TRUE +Intercalated cell CL:0005011 renal alpha-intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Intercalated cell', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE +Intercalated cell CL:0005010 renal intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Intercalated cell', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell TRUE +Intercalated cell CL:0002201 renal beta-intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Intercalated cell', 'full_name': 'intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE +MNP-a/classical monocyte derived CL:0000860 classical monocyte DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'MNP-a/classical monocyte derived', 'full_name': 'mononuclear phagocyte a/classical monocyte-derived', 'paper_synonyms': 'MNPa', 'tissue_context': ''} CL:0000113 mononuclear phagocyte FALSE +MNP-b/non-classical monocyte derived CL:0000875 non-classical monocyte DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'MNP-b/non-classical monocyte derived', 'full_name': 'mononuclear phagocyte b/non-classical monocyte derived', 'paper_synonyms': 'MNPb', 'tissue_context': ''} CL:0000875 non-classical monocyte TRUE +MNP-c/dendritic cell CL:0000451 dendritic cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'MNP-c/dendritic cell', 'full_name': 'mononuclear phagocyte c (dendritic cell)', 'paper_synonyms': 'MNPc; classical myeloid DC; cDC', 'tissue_context': ''} CL:0000451 dendritic cell TRUE +MNP-d/Tissue macrophage CL:1000698 kidney resident macrophage DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'MNP-d/Tissue macrophage', 'full_name': 'mononuclear phagocyte d/Tissue macrophage', 'paper_synonyms': 'MNPd', 'tissue_context': ''} CL:0000864 tissue-resident macrophage FALSE +Mast cell CL:0000097 mast cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Mast cell', 'full_name': 'mast cell', 'paper_synonyms': 'Mast', 'tissue_context': ''} CL:0000097 mast cell TRUE +Myofibroblast CL:0000186 myofibroblast cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Myofibroblast', 'full_name': 'myofibroblast', 'paper_synonyms': 'MFib', 'tissue_context': ''} CL:0000186 myofibroblast cell TRUE +NK cell CL:0000623 natural killer cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'NK cell', 'full_name': 'natural killer cell', 'paper_synonyms': 'NK', 'tissue_context': ''} CL:0000623 natural killer cell TRUE +NKT cell CL:0000814 mature NK T cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'NKT cell', 'full_name': 'natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''} CL:0000814 mature NK T cell TRUE +Neutrophil CL:0000775 neutrophil DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Neutrophil', 'full_name': 'neutrophil', 'paper_synonyms': 'NO', 'tissue_context': ''} CL:0000775 neutrophil TRUE +Pelvic epithelium CL:0002518 kidney epithelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Pelvic epithelium', 'full_name': 'pelvic epithelium', 'paper_synonyms': 'PE', 'tissue_context': ''} CL:0000731 urothelial cell FALSE +Peritubular capillary endothelium CL:0002144 capillary endothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Peritubular capillary endothelium', 'full_name': 'peritubular capillary endothelium', 'paper_synonyms': 'PCE', 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell FALSE +Peritubular capillary endothelium 1 CL:0002144 capillary endothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Peritubular capillary endothelium 1', 'full_name': 'peritubular capillary endothelium', 'paper_synonyms': 'peritubular capillaries (PCap); PCE', 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell FALSE +Peritubular capillary endothelium 2 CL:0002144 capillary endothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Peritubular capillary endothelium 2', 'full_name': 'peritubular capillary endothelium 2', 'paper_synonyms': 'PCE; peritubular capillaries; PCap', 'tissue_context': ''} CL:1001033 peritubular capillary endothelial cell FALSE +Plasmacytoid dendritic cell CL:0000784 plasmacytoid dendritic cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Plasmacytoid dendritic cell', 'full_name': 'plasmacytoid dendritic cell', 'paper_synonyms': 'pDC', 'tissue_context': ''} CL:0000784 plasmacytoid dendritic cell TRUE +Podocyte CL:0000653 podocyte DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Podocyte', 'full_name': 'podocyte', 'paper_synonyms': 'Podo', 'tissue_context': ''} CL:0000653 podocyte TRUE +Principal cell CL:0005009 renal principal cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Principal cell', 'full_name': 'principal cell', 'paper_synonyms': 'PC', 'tissue_context': ''} CL:0005009 renal principal cell TRUE +Proliferating Proximal Tubule CL:0002306 epithelial cell of proximal tubule DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Proliferating Proximal Tubule', 'full_name': 'proximal tubule', 'paper_synonyms': 'PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Proximal tubule CL:0002306 epithelial cell of proximal tubule DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Proximal tubule', 'full_name': 'proximal tubule', 'paper_synonyms': 'PT', 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Thick ascending limb of Loop of Henle CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Thick ascending limb of Loop of Henle', 'full_name': 'loop of Henle', 'paper_synonyms': 'LOH; loop of Henle', 'tissue_context': ''} CL:1000909 kidney loop of Henle epithelial cell FALSE +Transitional urothelium CL:0000731 urothelial cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Transitional urothelium', 'full_name': 'transitional epithelium of ureter', 'paper_synonyms': 'TE; transitional epithelium; transitional epithelium of ureter', 'tissue_context': ''} CL:1000706 ureter urothelial cell FALSE +Type A intercalated cell CL:0005011 renal alpha-intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Type A intercalated cell', 'full_name': 'type A intercalated cell', 'paper_synonyms': 'intercalated cells (IC); IC (A+B); IC', 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell TRUE +Type B intercalated cell CL:0002201 renal beta-intercalated cell DOI:10.1126/science.aat5031 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique {'name': 'Type B intercalated cell', 'full_name': 'type B intercalated cell', 'paper_synonyms': 'IC; intercalated cells; IC (A+B)', 'tissue_context': ''} CL:0002201 renal beta-intercalated cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..4814ddc --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,8 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Immune CL:0000738 leukocyte DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'Immune', 'full_name': 'immune cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte +K_Endo CL:0000132 corneal endothelial cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Endo', 'full_name': 'corneal endothelium', 'paper_synonyms': 'corneal endothelial cells', 'tissue_context': ''} CL:0000132 corneal endothelial cell +K_Epi-Basal CL:0000646 basal cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Epi-Basal', 'full_name': 'corneal basal epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000575 corneal epithelial cell +K_Epi-Superficial CL:0000575 corneal epithelial cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Epi-Superficial', 'full_name': 'corneal epithelium superficial cells', 'paper_synonyms': 'superficial-most squamous epithelial cells', 'tissue_context': ''} CL:0000575 corneal epithelial cell +K_Epi-TA CL:0009010 transit amplifying cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Epi-TA', 'full_name': 'corneal epithelium transit amplifying cell', 'paper_synonyms': 'TA; transit amplifying cells', 'tissue_context': ''} CL:0009010 transit amplifying cell +K_Epi-Wing CL:0000575 corneal epithelial cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Epi-Wing', 'full_name': 'corneal epithelium wing cells', 'paper_synonyms': 'polygonal suprabasal cells', 'tissue_context': ''} CL:0000575 corneal epithelial cell +K_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Fibro', 'full_name': 'corneal fibroblasts', 'paper_synonyms': 'stromal keratocytes; corneal stromal keratocytes', 'tissue_context': ''} CL:0002363 keratocyte diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..4c03789 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,8 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Immune CL:0000738 leukocyte DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'Immune', 'full_name': 'immune cells', 'paper_synonyms': '', 'tissue_context': ''} CL:0000738 leukocyte TRUE +K_Endo CL:0000132 corneal endothelial cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Endo', 'full_name': 'corneal endothelium', 'paper_synonyms': 'corneal endothelial cells', 'tissue_context': ''} CL:0000132 corneal endothelial cell TRUE +K_Epi-Basal CL:0000646 basal cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Epi-Basal', 'full_name': 'corneal basal epithelial cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000575 corneal epithelial cell FALSE +K_Epi-Superficial CL:0000575 corneal epithelial cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Epi-Superficial', 'full_name': 'corneal epithelium superficial cells', 'paper_synonyms': 'superficial-most squamous epithelial cells', 'tissue_context': ''} CL:0000575 corneal epithelial cell TRUE +K_Epi-TA CL:0009010 transit amplifying cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Epi-TA', 'full_name': 'corneal epithelium transit amplifying cell', 'paper_synonyms': 'TA; transit amplifying cells', 'tissue_context': ''} CL:0009010 transit amplifying cell TRUE +K_Epi-Wing CL:0000575 corneal epithelial cell DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Epi-Wing', 'full_name': 'corneal epithelium wing cells', 'paper_synonyms': 'polygonal suprabasal cells', 'tissue_context': ''} CL:0000575 corneal epithelial cell TRUE +K_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique {'name': 'K_Fibro', 'full_name': 'corneal fibroblasts', 'paper_synonyms': 'stromal keratocytes; corneal stromal keratocytes', 'tissue_context': ''} CL:0002363 keratocyte FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..ca1246c --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,15 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Muller cell CL:0000636 Mueller cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'Muller cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000636 Mueller cell +amacrine cell CL:0000561 amacrine cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'amacrine cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000561 amacrine cell +microglial cell CL:0000129 microglial cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'microglial cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000129 microglial cell +retinal bipolar neuron type A CL:0000749 ON-bipolar cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal bipolar neuron type A', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron +retinal bipolar neuron type B CL:0000750 OFF-bipolar cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal bipolar neuron type B', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000748 retinal bipolar neuron +retinal bipolar neuron type C CL:0000749 ON-bipolar cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal bipolar neuron type C', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron +retinal bipolar neuron type D CL:0000749 ON-bipolar cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal bipolar neuron type D', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron +retinal cone cell CL:0000573 retinal cone cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal cone cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000573 retinal cone cell +retinal ganglion cell CL:0000740 retinal ganglion cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal ganglion cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000740 retinal ganglion cell +retinal rod cell type A CL:0000604 retinal rod cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal rod cell type A', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell +retinal rod cell type B CL:0000604 retinal rod cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal rod cell type B', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell +retinal rod cell type C CL:0000604 retinal rod cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal rod cell type C', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell +unannotated unknown unknown DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'unannotated', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +unspecified unknown unknown DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'unspecified', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000000 cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..b6553cb --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,15 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Muller cell CL:0000636 Mueller cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'Muller cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000636 Mueller cell TRUE +amacrine cell CL:0000561 amacrine cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'amacrine cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000561 amacrine cell TRUE +microglial cell CL:0000129 microglial cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'microglial cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000129 microglial cell TRUE +retinal bipolar neuron type A CL:0000749 ON-bipolar cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal bipolar neuron type A', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron FALSE +retinal bipolar neuron type B CL:0000750 OFF-bipolar cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal bipolar neuron type B', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000748 retinal bipolar neuron FALSE +retinal bipolar neuron type C CL:0000749 ON-bipolar cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal bipolar neuron type C', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron FALSE +retinal bipolar neuron type D CL:0000749 ON-bipolar cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal bipolar neuron type D', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000748 retinal bipolar neuron FALSE +retinal cone cell CL:0000573 retinal cone cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal cone cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000573 retinal cone cell TRUE +retinal ganglion cell CL:0000740 retinal ganglion cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal ganglion cell', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000740 retinal ganglion cell TRUE +retinal rod cell type A CL:0000604 retinal rod cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal rod cell type A', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell TRUE +retinal rod cell type B CL:0000604 retinal rod cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal rod cell type B', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell TRUE +retinal rod cell type C CL:0000604 retinal rod cell DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'retinal rod cell type C', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell TRUE +unannotated unknown unknown DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'unannotated', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +unspecified unknown unknown DOI:10.15252/embj.2018100811 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique {'name': 'unspecified', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000000 cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..7d6ec41 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,29 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +CC_VenEndo CL:0000115 endothelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'CC_VenEndo', 'full_name': 'Collector Channel/Venous Endothelium', 'paper_synonyms': None, 'tissue_context': ''} CL:0002543 vein endothelial cell +Ciliary_Muscle CL:1000443 ciliary muscle cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Ciliary_Muscle', 'full_name': 'ciliary muscle cells', 'paper_synonyms': 'CM', 'tissue_context': ''} CL:1000443 ciliary muscle cell +Conj_Epi-Basal CL:1000432 conjunctival epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Conj_Epi-Basal', 'full_name': 'conjunctival basal epithelial cell', 'paper_synonyms': '', 'tissue_context': ''} CL:1000432 conjunctival epithelial cell +Conj_Epi-Superficial CL:1000432 conjunctival epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Conj_Epi-Superficial', 'full_name': 'conjunctival superficial epithelial cell', 'paper_synonyms': '', 'tissue_context': ''} CL:1000432 conjunctival epithelial cell +Conj_Epi-Wing CL:1000432 conjunctival epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Conj_Epi-Wing', 'full_name': 'conjunctival epithelial wing cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000432 conjunctival epithelial cell +Conj_Melanocyte CL:0000148 melanocyte DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Conj_Melanocyte', 'full_name': 'conjunctival melanocyte', 'paper_synonyms': '', 'tissue_context': ''} CL:0000148 melanocyte +FibroX CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'FibroX', 'full_name': 'FibroX', 'paper_synonyms': '', 'tissue_context': ''} CL:0000057 fibroblast +Goblet CL:0000160 goblet cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Goblet', 'full_name': 'goblet cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell +K_Endo CL:0000132 corneal endothelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Endo', 'full_name': 'corneal endothelium', 'paper_synonyms': 'corneal endothelial cells', 'tissue_context': ''} CL:0000132 corneal endothelial cell +K_Epi-Basal CL:0000646 basal cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Epi-Basal', 'full_name': 'corneal epithelial basal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000575 corneal epithelial cell +K_Epi-Superficial CL:0000575 corneal epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Epi-Superficial', 'full_name': 'corneal superficial epithelial cell', 'paper_synonyms': 'superficial-most squamous epithelial cells', 'tissue_context': ''} CL:0000575 corneal epithelial cell +K_Epi-Wing CL:0000575 corneal epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Epi-Wing', 'full_name': 'corneal epithelial wing cell', 'paper_synonyms': 'wing cells; polygonal suprabasal cells', 'tissue_context': ''} CL:0000575 corneal epithelial cell +K_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Fibro', 'full_name': 'corneal fibroblasts', 'paper_synonyms': 'K_Fibro; stromal keratocytes', 'tissue_context': ''} CL:0002363 keratocyte +Limbal_Epi-Basal CL:0000646 basal cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Limbal_Epi-Basal', 'full_name': 'limbal epithelial basal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000575 corneal epithelial cell +Limbal_Epi-Superficial CL:0000646 basal cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Limbal_Epi-Superficial', 'full_name': 'limbal superficial epithelial cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000575 corneal epithelial cell +Limbal_Epi-Wing CL:0000646 basal cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Limbal_Epi-Wing', 'full_name': 'limbal epithelial wing cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000575 corneal epithelial cell +Lymphatic_Endo CL:0002138 endothelial cell of lymphatic vessel DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Lymphatic_Endo', 'full_name': 'lymphatic endothelium', 'paper_synonyms': 'conjunctival lymphatic endothelium', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel +Lymphocyte CL:0000542 lymphocyte DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Lymphocyte', 'full_name': 'lymphocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000542 lymphocyte +Macrophage CL:0000235 macrophage DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Macrophage', 'full_name': 'macrophage', 'paper_synonyms': 'Mo; clump cells', 'tissue_context': ''} CL:0000235 macrophage +Mast CL:0000097 mast cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Mast', 'full_name': 'mast cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000097 mast cell +Pericyte CL:0000669 pericyte DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Pericyte', 'full_name': 'pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte +Schlemm_Endo CL:0000115 endothelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Schlemm_Endo', 'full_name': 'Schlemm canal endothelium', 'paper_synonyms': 'SC endothelium', 'tissue_context': ''} CL:4033097 Schlemm's canal endothelial cell +Schwann CL:0002573 Schwann cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Schwann', 'full_name': 'Schwann cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0002573 Schwann cell +Sclera_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Sclera_Fibro', 'full_name': 'scleral fibroblast', 'paper_synonyms': '', 'tissue_context': ''} CL:0000057 fibroblast +TM_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'TM_Fibro', 'full_name': 'trabecular meshwork fibroblasts', 'paper_synonyms': 'TM fibroblasts', 'tissue_context': ''} CL:0002367 trabecular meshwork cell +Uveal_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Uveal_Fibro', 'full_name': 'uveal fibroblast', 'paper_synonyms': 'ciliary fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast +Uveal_Melanocyte CL:0000148 melanocyte DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Uveal_Melanocyte', 'full_name': 'uveal melanocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000148 melanocyte +VascEndo CL:0000071 blood vessel endothelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'VascEndo', 'full_name': 'vascular endothelium', 'paper_synonyms': 'Vasc_Endo', 'tissue_context': ''} CL:0000071 blood vessel endothelial cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..69b9416 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,29 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +CC_VenEndo CL:0000115 endothelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'CC_VenEndo', 'full_name': 'Collector Channel/Venous Endothelium', 'paper_synonyms': None, 'tissue_context': ''} CL:0002543 vein endothelial cell FALSE +Ciliary_Muscle CL:1000443 ciliary muscle cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Ciliary_Muscle', 'full_name': 'ciliary muscle cells', 'paper_synonyms': 'CM', 'tissue_context': ''} CL:1000443 ciliary muscle cell TRUE +Conj_Epi-Basal CL:1000432 conjunctival epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Conj_Epi-Basal', 'full_name': 'conjunctival basal epithelial cell', 'paper_synonyms': '', 'tissue_context': ''} CL:1000432 conjunctival epithelial cell TRUE +Conj_Epi-Superficial CL:1000432 conjunctival epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Conj_Epi-Superficial', 'full_name': 'conjunctival superficial epithelial cell', 'paper_synonyms': '', 'tissue_context': ''} CL:1000432 conjunctival epithelial cell TRUE +Conj_Epi-Wing CL:1000432 conjunctival epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Conj_Epi-Wing', 'full_name': 'conjunctival epithelial wing cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1000432 conjunctival epithelial cell TRUE +Conj_Melanocyte CL:0000148 melanocyte DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Conj_Melanocyte', 'full_name': 'conjunctival melanocyte', 'paper_synonyms': '', 'tissue_context': ''} CL:0000148 melanocyte TRUE +FibroX CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'FibroX', 'full_name': 'FibroX', 'paper_synonyms': '', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Goblet CL:0000160 goblet cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Goblet', 'full_name': 'goblet cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell TRUE +K_Endo CL:0000132 corneal endothelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Endo', 'full_name': 'corneal endothelium', 'paper_synonyms': 'corneal endothelial cells', 'tissue_context': ''} CL:0000132 corneal endothelial cell TRUE +K_Epi-Basal CL:0000646 basal cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Epi-Basal', 'full_name': 'corneal epithelial basal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000575 corneal epithelial cell FALSE +K_Epi-Superficial CL:0000575 corneal epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Epi-Superficial', 'full_name': 'corneal superficial epithelial cell', 'paper_synonyms': 'superficial-most squamous epithelial cells', 'tissue_context': ''} CL:0000575 corneal epithelial cell TRUE +K_Epi-Wing CL:0000575 corneal epithelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Epi-Wing', 'full_name': 'corneal epithelial wing cell', 'paper_synonyms': 'wing cells; polygonal suprabasal cells', 'tissue_context': ''} CL:0000575 corneal epithelial cell TRUE +K_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'K_Fibro', 'full_name': 'corneal fibroblasts', 'paper_synonyms': 'K_Fibro; stromal keratocytes', 'tissue_context': ''} CL:0002363 keratocyte FALSE +Limbal_Epi-Basal CL:0000646 basal cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Limbal_Epi-Basal', 'full_name': 'limbal epithelial basal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000575 corneal epithelial cell FALSE +Limbal_Epi-Superficial CL:0000646 basal cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Limbal_Epi-Superficial', 'full_name': 'limbal superficial epithelial cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000575 corneal epithelial cell FALSE +Limbal_Epi-Wing CL:0000646 basal cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Limbal_Epi-Wing', 'full_name': 'limbal epithelial wing cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000575 corneal epithelial cell FALSE +Lymphatic_Endo CL:0002138 endothelial cell of lymphatic vessel DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Lymphatic_Endo', 'full_name': 'lymphatic endothelium', 'paper_synonyms': 'conjunctival lymphatic endothelium', 'tissue_context': ''} CL:0002138 endothelial cell of lymphatic vessel TRUE +Lymphocyte CL:0000542 lymphocyte DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Lymphocyte', 'full_name': 'lymphocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000542 lymphocyte TRUE +Macrophage CL:0000235 macrophage DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Macrophage', 'full_name': 'macrophage', 'paper_synonyms': 'Mo; clump cells', 'tissue_context': ''} CL:0000235 macrophage TRUE +Mast CL:0000097 mast cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Mast', 'full_name': 'mast cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000097 mast cell TRUE +Pericyte CL:0000669 pericyte DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Pericyte', 'full_name': 'pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE +Schlemm_Endo CL:0000115 endothelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Schlemm_Endo', 'full_name': 'Schlemm canal endothelium', 'paper_synonyms': 'SC endothelium', 'tissue_context': ''} CL:4033097 Schlemm's canal endothelial cell FALSE +Schwann CL:0002573 Schwann cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Schwann', 'full_name': 'Schwann cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0002573 Schwann cell TRUE +Sclera_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Sclera_Fibro', 'full_name': 'scleral fibroblast', 'paper_synonyms': '', 'tissue_context': ''} CL:0000057 fibroblast TRUE +TM_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'TM_Fibro', 'full_name': 'trabecular meshwork fibroblasts', 'paper_synonyms': 'TM fibroblasts', 'tissue_context': ''} CL:0002367 trabecular meshwork cell FALSE +Uveal_Fibro CL:0000057 fibroblast DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Uveal_Fibro', 'full_name': 'uveal fibroblast', 'paper_synonyms': 'ciliary fibroblasts', 'tissue_context': ''} CL:0000057 fibroblast TRUE +Uveal_Melanocyte CL:0000148 melanocyte DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'Uveal_Melanocyte', 'full_name': 'uveal melanocyte', 'paper_synonyms': None, 'tissue_context': ''} CL:0000148 melanocyte TRUE +VascEndo CL:0000071 blood vessel endothelial cell DOI:10.1073/pnas.2200914119 d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique {'name': 'VascEndo', 'full_name': 'vascular endothelium', 'paper_synonyms': 'Vasc_Endo', 'tissue_context': ''} CL:0000071 blood vessel endothelial cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..0c4c6fa --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,17 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +ATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'ATL', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'DCT1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule +DCT2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'DCT2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule +ENDO CL:0000115 endothelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'ENDO', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +FIB CL:0000057 fibroblast DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'FIB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast +ICA CL:0005011 renal alpha-intercalated cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'ICA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell +ICB CL:0002201 renal beta-intercalated cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'ICB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002201 renal beta-intercalated cell +LEUK CL:0000738 leukocyte DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'LEUK', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +MES CL:0000650 mesangial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'MES', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000650 mesangial cell +PC CL:0005009 renal principal cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell +PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell +PODO CL:0000653 podocyte DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PODO', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000653 podocyte +PT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4023041 L5 extratelencephalic projecting glutamatergic cortical neuron +PTVCAM1 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PTVCAM1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +TAL1 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'TAL1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +TAL2 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'TAL2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..3c60068 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,17 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +ATL CL:1001107 kidney loop of Henle thin ascending limb epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'ATL', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell TRUE +DCT1 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'DCT1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030016 epithelial cell of early distal convoluted tubule FALSE +DCT2 CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'DCT2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4030017 epithelial cell of late distal convoluted tubule FALSE +ENDO CL:0000115 endothelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'ENDO', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell TRUE +FIB CL:0000057 fibroblast DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'FIB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000057 fibroblast TRUE +ICA CL:0005011 renal alpha-intercalated cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'ICA', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell TRUE +ICB CL:0002201 renal beta-intercalated cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'ICB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002201 renal beta-intercalated cell TRUE +LEUK CL:0000738 leukocyte DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'LEUK', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte TRUE +MES CL:0000650 mesangial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'MES', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000650 mesangial cell TRUE +PC CL:0005009 renal principal cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell TRUE +PEC CL:1000452 parietal epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PEC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1000452 parietal epithelial cell TRUE +PODO CL:0000653 podocyte DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PODO', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000653 podocyte TRUE +PT CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PT', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4023041 L5 extratelencephalic projecting glutamatergic cortical neuron FALSE +PTVCAM1 CL:0002306 epithelial cell of proximal tubule DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'PTVCAM1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +TAL1 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'TAL1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +TAL2 CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1038/s41467-022-32972-z e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique {'name': 'TAL2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..e0363d6 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,11 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +WNT1 CL:0000312 keratinocyte DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'WNT1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +basal1 CL:0002187 basal cell of epidermis DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'basal1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000646 basal cell +basal2 CL:0002187 basal cell of epidermis DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'basal2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000646 basal cell +channel CL:0000242 Merkel cell DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'channel', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +folicular CL:2000092 hair follicular keratinocyte DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'folicular', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000500 follicular epithelial cell +granular CL:0002189 granular cell of epidermis DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'granular', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000094 granulocyte +immune CL:0000988 hematopoietic cell DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'immune', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte +melanocyte CL:0000148 melanocyte DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'melanocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000148 melanocyte +mitotic CL:0000312 keratinocyte DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'mitotic', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +spinous CL:0000649 spinous cell of epidermis DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'spinous', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4052060 spinous cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..8f91799 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,11 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +WNT1 CL:0000312 keratinocyte DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'WNT1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +basal1 CL:0002187 basal cell of epidermis DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'basal1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000646 basal cell FALSE +basal2 CL:0002187 basal cell of epidermis DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'basal2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000646 basal cell FALSE +channel CL:0000242 Merkel cell DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'channel', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +folicular CL:2000092 hair follicular keratinocyte DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'folicular', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000500 follicular epithelial cell FALSE +granular CL:0002189 granular cell of epidermis DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'granular', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000094 granulocyte FALSE +immune CL:0000988 hematopoietic cell DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'immune', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000738 leukocyte FALSE +melanocyte CL:0000148 melanocyte DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'melanocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000148 melanocyte TRUE +mitotic CL:0000312 keratinocyte DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'mitotic', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +spinous CL:0000649 spinous cell of epidermis DOI:10.1016/j.celrep.2018.09.006 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique {'name': 'spinous', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:4052060 spinous cell FALSE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..2e0f461 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,15 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Muller CL:0000636 Mueller cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'Muller', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000636 Mueller cell +RGC CL:0000740 retinal ganglion cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'RGC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000740 retinal ganglion cell +amacrine CL:0000561 amacrine cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'amacrine', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000561 amacrine cell +astrocyte CL:4033015 retinal astrocyte DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'astrocyte', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000127 astrocyte +cone CL:0000573 retinal cone cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'cone', 'full_name': 'cone photoreceptor cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000573 retinal cone cell +cone-off-BC CL:0000752 cone retinal bipolar cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'cone-off-BC', 'full_name': 'cone photoreceptor cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000573 retinal cone cell +cone-off-BC-BC3A CL:0000752 cone retinal bipolar cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'cone-off-BC-BC3A', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0004213 type 3a cone bipolar cell +cone-on-BC CL:0000752 cone retinal bipolar cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'cone-on-BC', 'full_name': 'cone photoreceptor cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000573 retinal cone cell +endothelial CL:0002585 retinal blood vessel endothelial cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'endothelial', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell +horizontal CL:0000745 retina horizontal cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'horizontal', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000745 retina horizontal cell +microglia CL:0000129 microglial cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'microglia', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000129 microglial cell +pericyte CL:0000669 pericyte DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'pericyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte +rod CL:0000604 retinal rod cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'rod', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell +rod-BC CL:0000751 rod bipolar cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'rod-BC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000751 rod bipolar cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..92ec7be --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,15 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Muller CL:0000636 Mueller cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'Muller', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000636 Mueller cell TRUE +RGC CL:0000740 retinal ganglion cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'RGC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000740 retinal ganglion cell TRUE +amacrine CL:0000561 amacrine cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'amacrine', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000561 amacrine cell TRUE +astrocyte CL:4033015 retinal astrocyte DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'astrocyte', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000127 astrocyte FALSE +cone CL:0000573 retinal cone cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'cone', 'full_name': 'cone photoreceptor cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000573 retinal cone cell TRUE +cone-off-BC CL:0000752 cone retinal bipolar cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'cone-off-BC', 'full_name': 'cone photoreceptor cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000573 retinal cone cell FALSE +cone-off-BC-BC3A CL:0000752 cone retinal bipolar cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'cone-off-BC-BC3A', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0004213 type 3a cone bipolar cell FALSE +cone-on-BC CL:0000752 cone retinal bipolar cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'cone-on-BC', 'full_name': 'cone photoreceptor cell', 'paper_synonyms': '', 'tissue_context': ''} CL:0000573 retinal cone cell FALSE +endothelial CL:0002585 retinal blood vessel endothelial cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'endothelial', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000115 endothelial cell FALSE +horizontal CL:0000745 retina horizontal cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'horizontal', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000745 retina horizontal cell TRUE +microglia CL:0000129 microglial cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'microglia', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000129 microglial cell TRUE +pericyte CL:0000669 pericyte DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'pericyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE +rod CL:0000604 retinal rod cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'rod', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''} CL:0000604 retinal rod cell TRUE +rod-BC CL:0000751 rod bipolar cell DOI:10.1093/hmg/ddab140 f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique {'name': 'rod-BC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''} CL:0000751 rod bipolar cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..3b8d9fc --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,26 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +AEA-DVR CL:1001285 vasa recta descending limb cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'AEA-DVR', 'full_name': 'afferent/efferent arterioles/descending vasa recta', 'paper_synonyms': 'AEA/DVR', 'tissue_context': ''} CL:0000071 blood vessel endothelial cell +AVR CL:1001131 vasa recta ascending limb cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'AVR', 'full_name': 'ascending vasa recta', 'paper_synonyms': None, 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell +Bcell CL:0000236 B cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Bcell', 'full_name': 'B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell +CNT CL:1001225 kidney collecting duct cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'CNT', 'full_name': 'connecting tubule', 'paper_synonyms': 'connecting duct', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell +DCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'DCT', 'full_name': 'distal convoluted tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell +DL CL:1001285 vasa recta descending limb cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'DL', 'full_name': 'descending limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001021 kidney loop of Henle descending limb epithelial cell +GC CL:1001005 glomerular capillary endothelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'GC', 'full_name': 'glomerular capillaries', 'paper_synonyms': None, 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell +IC-A CL:0005011 renal alpha-intercalated cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'IC-A', 'full_name': 'intercalated cell A', 'paper_synonyms': None, 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell +IC-B CL:0002201 renal beta-intercalated cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'IC-B', 'full_name': 'intercalated cell B', 'paper_synonyms': None, 'tissue_context': ''} CL:0002201 renal beta-intercalated cell +IC-PC CL:0000075 columnar/cuboidal epithelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'IC-PC', 'full_name': 'intercalated cell–principal cell', 'paper_synonyms': 'transitional cell type between PC and IC cells', 'tissue_context': ''} CL:0005010 renal intercalated cell +Macro CL:0000235 macrophage DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Macro', 'full_name': 'macrophages', 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage +Mesangial CL:0000650 mesangial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Mesangial', 'full_name': 'mesangial cells', 'paper_synonyms': 'Mesa', 'tissue_context': ''} CL:0000650 mesangial cell +Mono CL:0000576 monocyte DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Mono', 'full_name': 'monocytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte +NKcell CL:0000623 natural killer cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'NKcell', 'full_name': 'natural killer cells', 'paper_synonyms': 'NK', 'tissue_context': ''} CL:0000623 natural killer cell +PC CL:0005009 renal principal cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'PC', 'full_name': 'principal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell +PT-A CL:0002306 epithelial cell of proximal tubule DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'PT-A', 'full_name': 'proximal tubule cell A', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +PT-B CL:0002306 epithelial cell of proximal tubule DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'PT-B', 'full_name': 'proximal tubule B', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +PT-C CL:0002306 epithelial cell of proximal tubule DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'PT-C', 'full_name': 'proximal tubule C', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule +Peri CL:0000669 pericyte DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Peri', 'full_name': 'pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte +Podo CL:0000653 podocyte DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Podo', 'full_name': 'podocytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000653 podocyte +TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell +Tcell CL:0000084 T cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Tcell', 'full_name': 'T cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000084 T cell +UC unknown unknown DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'UC', 'full_name': 'uncharacterized', 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found +tAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'tAL', 'full_name': 'thin ascending limb cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell +vSMC CL:0000359 vascular associated smooth muscle cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'vSMC', 'full_name': 'vascular smooth muscle cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..c934d54 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,26 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +AEA-DVR CL:1001285 vasa recta descending limb cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'AEA-DVR', 'full_name': 'afferent/efferent arterioles/descending vasa recta', 'paper_synonyms': 'AEA/DVR', 'tissue_context': ''} CL:0000071 blood vessel endothelial cell FALSE +AVR CL:1001131 vasa recta ascending limb cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'AVR', 'full_name': 'ascending vasa recta', 'paper_synonyms': None, 'tissue_context': ''} CL:1001131 vasa recta ascending limb cell TRUE +Bcell CL:0000236 B cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Bcell', 'full_name': 'B cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000236 B cell TRUE +CNT CL:1001225 kidney collecting duct cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'CNT', 'full_name': 'connecting tubule', 'paper_synonyms': 'connecting duct', 'tissue_context': ''} CL:1000768 kidney connecting tubule epithelial cell FALSE +DCT CL:1000849 kidney distal convoluted tubule epithelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'DCT', 'full_name': 'distal convoluted tubule', 'paper_synonyms': None, 'tissue_context': ''} CL:1000849 kidney distal convoluted tubule epithelial cell TRUE +DL CL:1001285 vasa recta descending limb cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'DL', 'full_name': 'descending limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001021 kidney loop of Henle descending limb epithelial cell FALSE +GC CL:1001005 glomerular capillary endothelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'GC', 'full_name': 'glomerular capillaries', 'paper_synonyms': None, 'tissue_context': ''} CL:1001005 glomerular capillary endothelial cell TRUE +IC-A CL:0005011 renal alpha-intercalated cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'IC-A', 'full_name': 'intercalated cell A', 'paper_synonyms': None, 'tissue_context': ''} CL:0005011 renal alpha-intercalated cell TRUE +IC-B CL:0002201 renal beta-intercalated cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'IC-B', 'full_name': 'intercalated cell B', 'paper_synonyms': None, 'tissue_context': ''} CL:0002201 renal beta-intercalated cell TRUE +IC-PC CL:0000075 columnar/cuboidal epithelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'IC-PC', 'full_name': 'intercalated cell–principal cell', 'paper_synonyms': 'transitional cell type between PC and IC cells', 'tissue_context': ''} CL:0005010 renal intercalated cell FALSE +Macro CL:0000235 macrophage DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Macro', 'full_name': 'macrophages', 'paper_synonyms': None, 'tissue_context': ''} CL:0000235 macrophage TRUE +Mesangial CL:0000650 mesangial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Mesangial', 'full_name': 'mesangial cells', 'paper_synonyms': 'Mesa', 'tissue_context': ''} CL:0000650 mesangial cell TRUE +Mono CL:0000576 monocyte DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Mono', 'full_name': 'monocytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000576 monocyte TRUE +NKcell CL:0000623 natural killer cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'NKcell', 'full_name': 'natural killer cells', 'paper_synonyms': 'NK', 'tissue_context': ''} CL:0000623 natural killer cell TRUE +PC CL:0005009 renal principal cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'PC', 'full_name': 'principal cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0005009 renal principal cell TRUE +PT-A CL:0002306 epithelial cell of proximal tubule DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'PT-A', 'full_name': 'proximal tubule cell A', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +PT-B CL:0002306 epithelial cell of proximal tubule DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'PT-B', 'full_name': 'proximal tubule B', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +PT-C CL:0002306 epithelial cell of proximal tubule DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'PT-C', 'full_name': 'proximal tubule C', 'paper_synonyms': None, 'tissue_context': ''} CL:0002306 epithelial cell of proximal tubule TRUE +Peri CL:0000669 pericyte DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Peri', 'full_name': 'pericytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000669 pericyte TRUE +Podo CL:0000653 podocyte DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Podo', 'full_name': 'podocytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000653 podocyte TRUE +TAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'TAL', 'full_name': 'thick ascending limb', 'paper_synonyms': None, 'tissue_context': ''} CL:1001106 kidney loop of Henle thick ascending limb epithelial cell TRUE +Tcell CL:0000084 T cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'Tcell', 'full_name': 'T cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000084 T cell TRUE +UC unknown unknown DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'UC', 'full_name': 'uncharacterized', 'paper_synonyms': None, 'tissue_context': ''} NO MATCH found FALSE +tAL CL:1001106 kidney loop of Henle thick ascending limb epithelial cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'tAL', 'full_name': 'thin ascending limb cells', 'paper_synonyms': None, 'tissue_context': ''} CL:1001107 kidney loop of Henle thin ascending limb epithelial cell FALSE +vSMC CL:0000359 vascular associated smooth muscle cell DOI:10.1073/pnas.2103240118 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique {'name': 'vSMC', 'full_name': 'vascular smooth muscle cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000359 vascular associated smooth muscle cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv new file mode 100644 index 0000000..f526a98 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/cell_type_annotations_un_filtered.tsv @@ -0,0 +1,18 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label +Enterochromaffin cells CL:0000577 type EC enteroendocrine cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Enterochromaffin cells', 'full_name': 'Enterochromaffin cells', 'paper_synonyms': 'EC cells; EC', 'tissue_context': ''} CL:0000577 type EC enteroendocrine cell +Enterocytes BEST4 CL:0000584 enterocyte DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Enterocytes BEST4', 'full_name': 'Enterocytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte +Enterocytes TMIGD1 MEP1A CL:0000584 enterocyte DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Enterocytes TMIGD1 MEP1A', 'full_name': 'Enterocytes TMIGD1+ MEP1A+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte +Enterocytes TMIGD1 MEP1A GSTA1 CL:0000584 enterocyte DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Enterocytes TMIGD1 MEP1A GSTA1', 'full_name': 'Enterocytes TMIGD1+ MEP1A+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte +Epithelial Cycling cells CL:0000066 epithelial cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Epithelial Cycling cells', 'full_name': 'Cycling cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000000 cell +Epithelial HBB HBA CL:0000066 epithelial cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Epithelial HBB HBA', 'full_name': 'Epithelial cells HBB+ HBA+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +Epithelial cells METTL12 MAFB CL:0000066 epithelial cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Epithelial cells METTL12 MAFB', 'full_name': 'Epithelial cells METTL12+ MAFB+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell +Goblet cells MUC2 TFF1 CL:0000160 goblet cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Goblet cells MUC2 TFF1', 'full_name': 'Goblet cells', 'paper_synonyms': 'Goblets', 'tissue_context': ''} CL:0000160 goblet cell +Goblet cells MUC2 TFF1- CL:0000160 goblet cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Goblet cells MUC2 TFF1-', 'full_name': 'Goblet cells', 'paper_synonyms': 'Goblets', 'tissue_context': ''} CL:0000160 goblet cell +Goblet cells SPINK4 CL:0000160 goblet cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Goblet cells SPINK4', 'full_name': 'Goblet cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell +L cells CL:0002279 type L enteroendocrine cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'L cells', 'full_name': 'L-cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002279 type L enteroendocrine cell +Paneth cells CL:0000510 paneth cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Paneth cells', 'full_name': 'Paneth cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000510 paneth cell +Stem cells OLFM4 CL:0000034 stem cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Stem cells OLFM4', 'full_name': 'Stem cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000034 stem cell +Stem cells OLFM4 GSTA1 CL:0000034 stem cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Stem cells OLFM4 GSTA1', 'full_name': 'Stem cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000034 stem cell +Stem cells OLFM4 LGR5 CL:0000034 stem cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Stem cells OLFM4 LGR5', 'full_name': 'Stem cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000034 stem cell +Stem cells OLFM4 PCNA CL:0000034 stem cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Stem cells OLFM4 PCNA', 'full_name': 'Stem cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000034 stem cell +Tuft cells CL:0002204 tuft cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Tuft cells', 'full_name': 'Tuft cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/groundings.tsv b/cellsem_agent/graphs/cxg_annotate/resources/output/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/groundings.tsv new file mode 100644 index 0000000..405ae62 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique/groundings.tsv @@ -0,0 +1,18 @@ +annotation_text cl_id cl_label article_id_doi dataset_name enrichment grounding_cl_id grounding_cl_label result +Enterochromaffin cells CL:0000577 type EC enteroendocrine cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Enterochromaffin cells', 'full_name': 'Enterochromaffin cells', 'paper_synonyms': 'EC cells; EC', 'tissue_context': ''} CL:0000577 type EC enteroendocrine cell TRUE +Enterocytes BEST4 CL:0000584 enterocyte DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Enterocytes BEST4', 'full_name': 'Enterocytes', 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte TRUE +Enterocytes TMIGD1 MEP1A CL:0000584 enterocyte DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Enterocytes TMIGD1 MEP1A', 'full_name': 'Enterocytes TMIGD1+ MEP1A+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte TRUE +Enterocytes TMIGD1 MEP1A GSTA1 CL:0000584 enterocyte DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Enterocytes TMIGD1 MEP1A GSTA1', 'full_name': 'Enterocytes TMIGD1+ MEP1A+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000584 enterocyte TRUE +Epithelial Cycling cells CL:0000066 epithelial cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Epithelial Cycling cells', 'full_name': 'Cycling cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000000 cell FALSE +Epithelial HBB HBA CL:0000066 epithelial cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Epithelial HBB HBA', 'full_name': 'Epithelial cells HBB+ HBA+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell TRUE +Epithelial cells METTL12 MAFB CL:0000066 epithelial cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Epithelial cells METTL12 MAFB', 'full_name': 'Epithelial cells METTL12+ MAFB+', 'paper_synonyms': None, 'tissue_context': ''} CL:0000066 epithelial cell TRUE +Goblet cells MUC2 TFF1 CL:0000160 goblet cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Goblet cells MUC2 TFF1', 'full_name': 'Goblet cells', 'paper_synonyms': 'Goblets', 'tissue_context': ''} CL:0000160 goblet cell TRUE +Goblet cells MUC2 TFF1- CL:0000160 goblet cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Goblet cells MUC2 TFF1-', 'full_name': 'Goblet cells', 'paper_synonyms': 'Goblets', 'tissue_context': ''} CL:0000160 goblet cell TRUE +Goblet cells SPINK4 CL:0000160 goblet cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Goblet cells SPINK4', 'full_name': 'Goblet cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000160 goblet cell TRUE +L cells CL:0002279 type L enteroendocrine cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'L cells', 'full_name': 'L-cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002279 type L enteroendocrine cell TRUE +Paneth cells CL:0000510 paneth cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Paneth cells', 'full_name': 'Paneth cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000510 paneth cell TRUE +Stem cells OLFM4 CL:0000034 stem cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Stem cells OLFM4', 'full_name': 'Stem cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000034 stem cell TRUE +Stem cells OLFM4 GSTA1 CL:0000034 stem cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Stem cells OLFM4 GSTA1', 'full_name': 'Stem cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000034 stem cell TRUE +Stem cells OLFM4 LGR5 CL:0000034 stem cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Stem cells OLFM4 LGR5', 'full_name': 'Stem cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000034 stem cell TRUE +Stem cells OLFM4 PCNA CL:0000034 stem cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Stem cells OLFM4 PCNA', 'full_name': 'Stem cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0000034 stem cell TRUE +Tuft cells CL:0002204 tuft cell DOI:10.1016/j.immuni.2023.01.002 fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique {'name': 'Tuft cells', 'full_name': 'Tuft cells', 'paper_synonyms': None, 'tissue_context': ''} CL:0002204 tuft cell TRUE diff --git a/cellsem_agent/graphs/cxg_annotate/resources/output/granularity_report.md b/cellsem_agent/graphs/cxg_annotate/resources/output/granularity_report.md new file mode 100644 index 0000000..779afc4 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/output/granularity_report.md @@ -0,0 +1,858 @@ +# Annotation Granularity Report + +## Dataset: d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique +Found 0 instances of improved granularity. + +## Dataset: 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique +Found 1 instances of improved granularity. + +## Dataset: f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique +Found 1 instances of improved granularity. + +## Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +Found 53 instances of improved granularity. + +## Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +Found 12 instances of improved granularity. + +## Dataset: d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique +Found 3 instances of improved granularity. + +## Dataset: fe4b89d5-461e-440c-a5a8-621b37b122c0_cxg_dataset_unique +Found 0 instances of improved granularity. + +## Dataset: 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique +Found 4 instances of improved granularity. + +## Dataset: 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique +Found 2 instances of improved granularity. + +## Dataset: 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique +Found 1 instances of improved granularity. + +## Dataset: 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique +Found 7 instances of improved granularity. + +## Dataset: 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique +Found 3 instances of improved granularity. + +## Dataset: f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique +Found 1 instances of improved granularity. + +## Dataset: 9a281de7-cee5-4e80-8584-1929f46f152f_cxg_dataset_unique +Found 0 instances of improved granularity. + +## Dataset: 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique +Found 1 instances of improved granularity. + +## Dataset: a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique +Found 1 instances of improved granularity. + +## Dataset: 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique +Found 1 instances of improved granularity. + +## Dataset: 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique +Found 2 instances of improved granularity. + +## Dataset: e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique +Found 2 instances of improved granularity. + +## Dataset: 30cd5311-6c09-46c9-94f1-71fe4b91813c_cxg_dataset_unique +Found 0 instances of improved granularity. + +## Dataset: 36c867a7-be10-4e69-9b39-5de12b0af6da_cxg_dataset_unique +Found 0 instances of improved granularity. + +## Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +Found 14 instances of improved granularity. + +## Dataset: 21d3e683-80a4-4d9b-bc89-ebb2df513dde_cxg_dataset_unique +Found 0 instances of improved granularity. + +## Dataset: 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique +Found 4 instances of improved granularity. + +## Dataset: f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique +Found 1 instances of improved granularity. + +## Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +Found 11 instances of improved granularity. + +## Dataset: 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique +Found 3 instances of improved granularity. + +## Dataset: 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique +Found 0 instances of improved granularity. + +## Dataset: 0f4865d5-8000-4f68-8ac7-f5efea9e5e70_cxg_dataset_unique +Found 0 instances of improved granularity. + +# Good Examples of Improved Granularity + +### Dataset: 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique +- **Annotation Text:** Prolif-Mac +- **Author's Mapping:** macrophage (CL:0000235) +- **Agent's Mapping:** cycling macrophage (CL:4033076) +- **Enrichment Info:** `{'name': 'Prolif-Mac', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique +- **Annotation Text:** IC-PC +- **Author's Mapping:** columnar/cuboidal epithelial cell (CL:0000075) +- **Agent's Mapping:** renal intercalated cell (CL:0005010) +- **Enrichment Info:** `{'name': 'IC-PC', 'full_name': 'intercalated cell–principal cell', 'paper_synonyms': 'transitional cell type between PC and IC cells', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Afferent / Efferent Arteriole Endothelial Cell +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** endothelial cell of arteriole (CL:1000412) +- **Enrichment Info:** `{'name': 'Afferent / Efferent Arteriole Endothelial Cell', 'full_name': 'endothelial cell of the afferent/efferent arterioles (EC-AEA)', 'paper_synonyms': 'EC-AEA', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Ascending Vasa Recta Endothelial Cell +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** vasa recta ascending limb cell (CL:1001131) +- **Enrichment Info:** `{'name': 'Ascending Vasa Recta Endothelial Cell', 'full_name': 'ascending vasa recta endothelial cell', 'paper_synonyms': 'EC-AVR', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** C-PC +- **Author's Mapping:** kidney collecting duct principal cell (CL:1001431) +- **Agent's Mapping:** kidney cortex collecting duct principal cell (CL:1000714) +- **Enrichment Info:** `{'name': 'C-PC', 'full_name': 'cortical principal cell', 'paper_synonyms': 'PC; principal cells', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** C-TAL +- **Author's Mapping:** kidney loop of Henle thick ascending limb epithelial cell (CL:1001106) +- **Agent's Mapping:** kidney loop of Henle cortical thick ascending limb epithelial cell (CL:1001109) +- **Enrichment Info:** `{'name': 'C-TAL', 'full_name': 'cortical thick ascending limb', 'paper_synonyms': 'thick ascending limb (TAL)', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** CCD-IC-A +- **Author's Mapping:** kidney collecting duct intercalated cell (CL:1001432) +- **Agent's Mapping:** kidney cortex collecting duct intercalated cell (CL:1000715) +- **Enrichment Info:** `{'name': 'CCD-IC-A', 'full_name': 'cortical collecting duct intercalated cells', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** CCD-PC +- **Author's Mapping:** kidney collecting duct principal cell (CL:1001431) +- **Agent's Mapping:** kidney cortex collecting duct principal cell (CL:1000714) +- **Enrichment Info:** `{'name': 'CCD-PC', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'CCD; cortical collecting duct; PC; principal cells', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** CNT-PC +- **Author's Mapping:** kidney connecting tubule epithelial cell (CL:1000768) +- **Agent's Mapping:** kidney connecting tubule principal cell (CL:4030018) +- **Enrichment Info:** `{'name': 'CNT-PC', 'full_name': 'connecting tubule principal cell', 'paper_synonyms': 'CNT; connecting tubule; PC; principal cells', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Connecting Tubule Principal Cell +- **Author's Mapping:** kidney connecting tubule epithelial cell (CL:1000768) +- **Agent's Mapping:** kidney connecting tubule principal cell (CL:4030018) +- **Enrichment Info:** `{'name': 'Connecting Tubule Principal Cell', 'full_name': 'Connecting tubule principal cell', 'paper_synonyms': 'CNT-PC; CNT; PC', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Cortical Collecting Duct Intercalated Cell Type A +- **Author's Mapping:** kidney collecting duct intercalated cell (CL:1001432) +- **Agent's Mapping:** kidney collecting duct alpha-intercalated cell (CL:4030015) +- **Enrichment Info:** `{'name': 'Cortical Collecting Duct Intercalated Cell Type A', 'full_name': 'Cortical collecting duct intercalated cell type A', 'paper_synonyms': 'CCD; IC; collecting duct (CD)', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Cortical Collecting Duct Principal Cell +- **Author's Mapping:** kidney collecting duct principal cell (CL:1001431) +- **Agent's Mapping:** kidney cortex collecting duct principal cell (CL:1000714) +- **Enrichment Info:** `{'name': 'Cortical Collecting Duct Principal Cell', 'full_name': 'cortical collecting duct principal cell', 'paper_synonyms': 'C-PC; PC', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Cortical Thick Ascending Limb Cell +- **Author's Mapping:** kidney loop of Henle thick ascending limb epithelial cell (CL:1001106) +- **Agent's Mapping:** kidney loop of Henle cortical thick ascending limb epithelial cell (CL:1001109) +- **Enrichment Info:** `{'name': 'Cortical Thick Ascending Limb Cell', 'full_name': 'cortical thick ascending limb (C-TAL) cell', 'paper_synonyms': 'C-TAL; TAL', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Cycling Mononuclear Phagocyte +- **Author's Mapping:** mononuclear phagocyte (CL:0000113) +- **Agent's Mapping:** cycling mononuclear phagocyte (CL:4033078) +- **Enrichment Info:** `{'name': 'Cycling Mononuclear Phagocyte', 'full_name': 'cycling mononuclear phagocyte', 'paper_synonyms': 'cycMNP', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Cycling Natural Killer Cell / Natural Killer T Cell +- **Author's Mapping:** lymphocyte (CL:0000542) +- **Agent's Mapping:** cycling natural killer cell (CL:4033071) +- **Enrichment Info:** `{'name': 'Cycling Natural Killer Cell / Natural Killer T Cell', 'full_name': 'cycling natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** DCT1 +- **Author's Mapping:** kidney distal convoluted tubule epithelial cell (CL:1000849) +- **Agent's Mapping:** epithelial cell of early distal convoluted tubule (CL:4030016) +- **Enrichment Info:** `{'name': 'DCT1', 'full_name': 'distal convoluted tubule cell 1', 'paper_synonyms': 'DCT; distal convoluted tubule', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** DCT2 +- **Author's Mapping:** kidney distal convoluted tubule epithelial cell (CL:1000849) +- **Agent's Mapping:** epithelial cell of late distal convoluted tubule (CL:4030017) +- **Enrichment Info:** `{'name': 'DCT2', 'full_name': 'distal convoluted tubule cell 2', 'paper_synonyms': 'DCT', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Degenerative Cortical Intercalated Cell Type A +- **Author's Mapping:** kidney collecting duct intercalated cell (CL:1001432) +- **Agent's Mapping:** kidney cortex collecting duct intercalated cell (CL:1000715) +- **Enrichment Info:** `{'name': 'Degenerative Cortical Intercalated Cell Type A', 'full_name': 'Degenerative cortical intercalated cell', 'paper_synonyms': 'IC', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Degenerative Cortical Thick Ascending Limb Cell +- **Author's Mapping:** kidney loop of Henle thick ascending limb epithelial cell (CL:1001106) +- **Agent's Mapping:** kidney loop of Henle cortical thick ascending limb epithelial cell (CL:1001109) +- **Enrichment Info:** `{'name': 'Degenerative Cortical Thick Ascending Limb Cell', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': 'C-TAL; cortical TAL', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Degenerative Medullary Fibroblast +- **Author's Mapping:** kidney interstitial fibroblast (CL:1000692) +- **Agent's Mapping:** renal medullary fibroblast (CL:4030022) +- **Enrichment Info:** `{'name': 'Degenerative Medullary Fibroblast', 'full_name': 'Degenerative Medullary Fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Degenerative Medullary Thick Ascending Limb Cell +- **Author's Mapping:** kidney loop of Henle thick ascending limb epithelial cell (CL:1001106) +- **Agent's Mapping:** kidney loop of Henle medullary thick ascending limb epithelial cell (CL:1001108) +- **Enrichment Info:** `{'name': 'Degenerative Medullary Thick Ascending Limb Cell', 'full_name': 'degenerative medullary thick ascending limb cell', 'paper_synonyms': 'TAL; M-TAL', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Degenerative Outer Medullary Collecting Duct Principal Cell +- **Author's Mapping:** kidney collecting duct principal cell (CL:1001431) +- **Agent's Mapping:** kidney outer medulla collecting duct principal cell (CL:1000716) +- **Enrichment Info:** `{'name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'full_name': 'Degenerative Outer Medullary Collecting Duct Principal Cell', 'paper_synonyms': 'OMCD; PC; degenerative medullary principal cells; dM-PCs', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Degenerative Peritubular Capilary Endothelial Cell +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** peritubular capillary endothelial cell (CL:1001033) +- **Enrichment Info:** `{'name': 'Degenerative Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Descending Vasa Recta Endothelial Cell +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** kidney capillary endothelial cell (CL:1000892) +- **Enrichment Info:** `{'name': 'Descending Vasa Recta Endothelial Cell', 'full_name': 'Descending vasa recta endothelial cell', 'paper_synonyms': 'EC-DVR', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Distal Convoluted Tubule Cell Type 1 +- **Author's Mapping:** kidney distal convoluted tubule epithelial cell (CL:1000849) +- **Agent's Mapping:** epithelial cell of early distal convoluted tubule (CL:4030016) +- **Enrichment Info:** `{'name': 'Distal Convoluted Tubule Cell Type 1', 'full_name': 'Distal convoluted tubule cell type 1', 'paper_synonyms': 'DCT1', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Distal Convoluted Tubule Cell Type 2 +- **Author's Mapping:** kidney distal convoluted tubule epithelial cell (CL:1000849) +- **Agent's Mapping:** epithelial cell of late distal convoluted tubule (CL:4030017) +- **Enrichment Info:** `{'name': 'Distal Convoluted Tubule Cell Type 2', 'full_name': 'distal convoluted tubule cell 2', 'paper_synonyms': 'DCT2', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** EC-AEA +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** endothelial cell of arteriole (CL:1000412) +- **Enrichment Info:** `{'name': 'EC-AEA', 'full_name': 'endothelial cells of the afferent/efferent arterioles', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** EC-AVR +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** kidney capillary endothelial cell (CL:1000892) +- **Enrichment Info:** `{'name': 'EC-AVR', 'full_name': 'endothelial cell of the vasa recta', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** EC-DVR +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** kidney capillary endothelial cell (CL:1000892) +- **Enrichment Info:** `{'name': 'EC-DVR', 'full_name': 'endothelial cells of the vasa recta', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** EC-GC +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** glomerular capillary endothelial cell (CL:1001005) +- **Enrichment Info:** `{'name': 'EC-GC', 'full_name': 'glomerular capillaries', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** EC-LYM +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** endothelial cell of lymphatic vessel (CL:0002138) +- **Enrichment Info:** `{'name': 'EC-LYM', 'full_name': 'endothelial cells of the lymphatics', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** EC-PTC +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** peritubular capillary endothelial cell (CL:1001033) +- **Enrichment Info:** `{'name': 'EC-PTC', 'full_name': 'endothelial cell PTC', 'paper_synonyms': 'EC; endothelial cells', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Glomerular Capillary Endothelial Cell +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** glomerular capillary endothelial cell (CL:1001005) +- **Enrichment Info:** `{'name': 'Glomerular Capillary Endothelial Cell', 'full_name': 'glomerular capillary endothelial cell', 'paper_synonyms': 'EC-GC', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Lymphatic Endothelial Cell +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** endothelial cell of lymphatic vessel (CL:0002138) +- **Enrichment Info:** `{'name': 'Lymphatic Endothelial Cell', 'full_name': 'endothelial cell of the lymphatics', 'paper_synonyms': 'EC-LYM', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** M-FIB +- **Author's Mapping:** kidney interstitial fibroblast (CL:1000692) +- **Agent's Mapping:** renal medullary fibroblast (CL:4030022) +- **Enrichment Info:** `{'name': 'M-FIB', 'full_name': 'medullary fibroblast', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** M-TAL +- **Author's Mapping:** kidney loop of Henle thick ascending limb epithelial cell (CL:1001106) +- **Agent's Mapping:** kidney loop of Henle medullary thick ascending limb epithelial cell (CL:1001108) +- **Enrichment Info:** `{'name': 'M-TAL', 'full_name': 'medullary thick ascending limb', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** MDC +- **Author's Mapping:** mononuclear phagocyte (CL:0000113) +- **Agent's Mapping:** myeloid dendritic cell (CL:0000782) +- **Enrichment Info:** `{'name': 'MDC', 'full_name': 'monocyte-derived cells', 'paper_synonyms': 'MDCs', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Medullary Thick Ascending Limb Cell +- **Author's Mapping:** kidney loop of Henle thick ascending limb epithelial cell (CL:1001106) +- **Agent's Mapping:** kidney loop of Henle medullary thick ascending limb epithelial cell (CL:1001108) +- **Enrichment Info:** `{'name': 'Medullary Thick Ascending Limb Cell', 'full_name': 'Medullary thick ascending limb cell', 'paper_synonyms': 'M-TAL; thick ascending limb (TAL)', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Mesangial Cell +- **Author's Mapping:** renal interstitial pericyte (CL:1001318) +- **Agent's Mapping:** mesangial cell (CL:0000650) +- **Enrichment Info:** `{'name': 'Mesangial Cell', 'full_name': 'mesangial cell', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** NEU +- **Author's Mapping:** neural cell (CL:0002319) +- **Agent's Mapping:** neuron (CL:0000540) +- **Enrichment Info:** `{'name': 'NEU', 'full_name': 'neuronal cell', 'paper_synonyms': 'SCI/NEU; Schwann/neuronal', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** NKC/T +- **Author's Mapping:** lymphocyte (CL:0000542) +- **Agent's Mapping:** T cell (CL:0000084) +- **Enrichment Info:** `{'name': 'NKC/T', 'full_name': 'T cells', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Natural Killer Cell / Natural Killer T Cell +- **Author's Mapping:** lymphocyte (CL:0000542) +- **Agent's Mapping:** natural killer cell (CL:0000623) +- **Enrichment Info:** `{'name': 'Natural Killer Cell / Natural Killer T Cell', 'full_name': 'Natural killer cell / natural killer T cell', 'paper_synonyms': 'NKT', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** OMCD-IC-A +- **Author's Mapping:** kidney collecting duct intercalated cell (CL:1001432) +- **Agent's Mapping:** kidney outer medulla collecting duct intercalated cell (CL:1000717) +- **Enrichment Info:** `{'name': 'OMCD-IC-A', 'full_name': 'outer medullary collecting duct intercalated cells', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** OMCD-PC +- **Author's Mapping:** kidney collecting duct principal cell (CL:1001431) +- **Agent's Mapping:** kidney outer medulla collecting duct principal cell (CL:1000716) +- **Enrichment Info:** `{'name': 'OMCD-PC', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; outer medullary collecting duct; PC; principal cells', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Outer Medullary Collecting Duct Intercalated Cell Type A +- **Author's Mapping:** kidney collecting duct intercalated cell (CL:1001432) +- **Agent's Mapping:** kidney outer medulla collecting duct intercalated cell (CL:1000717) +- **Enrichment Info:** `{'name': 'Outer Medullary Collecting Duct Intercalated Cell Type A', 'full_name': 'outer medullary collecting duct intercalated cell', 'paper_synonyms': 'OMCD; IC', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Outer Medullary Collecting Duct Principal Cell +- **Author's Mapping:** kidney collecting duct principal cell (CL:1001431) +- **Agent's Mapping:** kidney outer medulla collecting duct principal cell (CL:1000716) +- **Enrichment Info:** `{'name': 'Outer Medullary Collecting Duct Principal Cell', 'full_name': 'outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; PC; M-PC', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** PT-S3 +- **Author's Mapping:** epithelial cell of proximal tubule (CL:0002306) +- **Agent's Mapping:** epithelial cell of proximal tubule segment 3 (CL:4030011) +- **Enrichment Info:** `{'name': 'PT-S3', 'full_name': 'proximal tubule S3', 'paper_synonyms': 'proximal tubule (PT)', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Peritubular Capilary Endothelial Cell +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** peritubular capillary endothelial cell (CL:1001033) +- **Enrichment Info:** `{'name': 'Peritubular Capilary Endothelial Cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** Proximal Tubule Epithelial Cell Segment 3 +- **Author's Mapping:** epithelial cell of proximal tubule (CL:0002306) +- **Agent's Mapping:** epithelial cell of proximal tubule segment 3 (CL:4030011) +- **Enrichment Info:** `{'name': 'Proximal Tubule Epithelial Cell Segment 3', 'full_name': 'proximal tubule (PT) epithelial cell, segment 3', 'paper_synonyms': 'PT-S3; PT', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** cycMNP +- **Author's Mapping:** mononuclear phagocyte (CL:0000113) +- **Agent's Mapping:** cycling mononuclear phagocyte (CL:4033078) +- **Enrichment Info:** `{'name': 'cycMNP', 'full_name': 'cycling MNP', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** cycNKC/T +- **Author's Mapping:** lymphocyte (CL:0000542) +- **Agent's Mapping:** cycling T cell (CL:4033069) +- **Enrichment Info:** `{'name': 'cycNKC/T', 'full_name': 'cycling T cells', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** dC-TAL +- **Author's Mapping:** kidney loop of Henle thick ascending limb epithelial cell (CL:1001106) +- **Agent's Mapping:** kidney loop of Henle cortical thick ascending limb epithelial cell (CL:1001109) +- **Enrichment Info:** `{'name': 'dC-TAL', 'full_name': 'degenerative cortical thick ascending limb cell', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** dM-FIB +- **Author's Mapping:** kidney interstitial fibroblast (CL:1000692) +- **Agent's Mapping:** renal medullary fibroblast (CL:4030022) +- **Enrichment Info:** `{'name': 'dM-FIB', 'full_name': 'degenerative medullary fibroblast', 'paper_synonyms': 'FIB', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** dM-TAL +- **Author's Mapping:** kidney loop of Henle thick ascending limb epithelial cell (CL:1001106) +- **Agent's Mapping:** kidney loop of Henle medullary thick ascending limb epithelial cell (CL:1001108) +- **Enrichment Info:** `{'name': 'dM-TAL', 'full_name': 'degenerative medullary thick ascending limb cell', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique +- **Annotation Text:** dOMCD-PC +- **Author's Mapping:** kidney collecting duct principal cell (CL:1001431) +- **Agent's Mapping:** kidney outer medulla collecting duct principal cell (CL:1000716) +- **Enrichment Info:** `{'name': 'dOMCD-PC', 'full_name': 'degenerative outer medullary collecting duct principal cell', 'paper_synonyms': 'OMCD; outer medullary collecting duct; PC; principal cells', 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** L2-L3 Intratelencephalic +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** L2/3 intratelencephalic projecting glutamatergic neuron (CL:4030059) +- **Enrichment Info:** `{'name': 'L2-L3 Intratelencephalic', 'full_name': 'L2-L3 intratelencephalic', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** L3-L5 Intratelencephalic Type 1 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** intratelencephalic-projecting glutamatergic cortical neuron (CL:4023008) +- **Enrichment Info:** `{'name': 'L3-L5 Intratelencephalic Type 1', 'full_name': 'L3-L5 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** L3-L5 Intratelencephalic Type 2 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** intratelencephalic-projecting glutamatergic cortical neuron (CL:4023008) +- **Enrichment Info:** `{'name': 'L3-L5 Intratelencephalic Type 2', 'full_name': 'L3-L5 intratelencephalic type 2', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** L5 Extratelencephalic +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** L5 extratelencephalic projecting glutamatergic cortical neuron (CL:4023041) +- **Enrichment Info:** `{'name': 'L5 Extratelencephalic', 'full_name': 'L5 extratelencephalic neurons', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** L5-L6 Near Projecting +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** L5/6 near-projecting glutamatergic neuron (CL:4030067) +- **Enrichment Info:** `{'name': 'L5-L6 Near Projecting', 'full_name': 'L5-L6 near projecting neuronal cluster', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** L6 Corticothalamic / L6B +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** L6 corticothalamic-projecting glutamatergic cortical neuron (CL:4023042) +- **Enrichment Info:** `{'name': 'L6 Corticothalamic / L6B', 'full_name': 'L6 corticothalamic / L6B', 'paper_synonyms': 'L6 corticothalamic; L6B', 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** L6 Intratelencephalic - Type 1 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** L6 intratelencephalic projecting glutamatergic neuron (CL:4030065) +- **Enrichment Info:** `{'name': 'L6 Intratelencephalic - Type 1', 'full_name': 'L6 intratelencephalic type 1', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** L6 Intratelencephalic - Type 2 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** L6 intratelencephalic projecting glutamatergic neuron (CL:4030065) +- **Enrichment Info:** `{'name': 'L6 Intratelencephalic - Type 2', 'full_name': 'L6 intratelencephalic type 2', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** Parvalbumin interneurons +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** pvalb GABAergic interneuron (CL:4023018) +- **Enrichment Info:** `{'name': 'Parvalbumin interneurons', 'full_name': 'Parvalbumin interneurons', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** SV2C LAMP5 Interneurons +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** lamp5 GABAergic interneuron (CL:4023011) +- **Enrichment Info:** `{'name': 'SV2C LAMP5 Interneurons', 'full_name': 'SV2C LAMP5 interneurons', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** Somatostatin Interneurons +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** sst GABAergic interneuron (CL:4023017) +- **Enrichment Info:** `{'name': 'Somatostatin Interneurons', 'full_name': 'somatostatin interneurons', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 3b8b5de4-3aa1-4ac6-8890-8d03c8219981_cxg_dataset_unique +- **Annotation Text:** VIP Interneurons +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** VIP GABAergic interneuron (CL:4023016) +- **Enrichment Info:** `{'name': 'VIP Interneurons', 'full_name': 'VIP interneurons', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique +- **Annotation Text:** CC_VenEndo +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** vein endothelial cell (CL:0002543) +- **Enrichment Info:** `{'name': 'CC_VenEndo', 'full_name': 'Collector Channel/Venous Endothelium', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique +- **Annotation Text:** K_Fibro +- **Author's Mapping:** fibroblast (CL:0000057) +- **Agent's Mapping:** keratocyte (CL:0002363) +- **Enrichment Info:** `{'name': 'K_Fibro', 'full_name': 'corneal fibroblasts', 'paper_synonyms': 'K_Fibro; stromal keratocytes', 'tissue_context': ''}` + +### Dataset: d967b47c-a9e6-4337-b2f4-977f690cb67f_cxg_dataset_unique +- **Annotation Text:** Schlemm_Endo +- **Author's Mapping:** endothelial cell (CL:0000115) +- **Agent's Mapping:** Schlemm's canal endothelial cell (CL:4033097) +- **Enrichment Info:** `{'name': 'Schlemm_Endo', 'full_name': 'Schlemm canal endothelium', 'paper_synonyms': 'SC endothelium', 'tissue_context': ''}` + +### Dataset: 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique +- **Annotation Text:** Peritubular capillary endothelium +- **Author's Mapping:** capillary endothelial cell (CL:0002144) +- **Agent's Mapping:** peritubular capillary endothelial cell (CL:1001033) +- **Enrichment Info:** `{'name': 'Peritubular capillary endothelium', 'full_name': 'peritubular capillary endothelium', 'paper_synonyms': 'PCE', 'tissue_context': ''}` + +### Dataset: 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique +- **Annotation Text:** Peritubular capillary endothelium 1 +- **Author's Mapping:** capillary endothelial cell (CL:0002144) +- **Agent's Mapping:** peritubular capillary endothelial cell (CL:1001033) +- **Enrichment Info:** `{'name': 'Peritubular capillary endothelium 1', 'full_name': 'peritubular capillary endothelium', 'paper_synonyms': 'peritubular capillaries (PCap); PCE', 'tissue_context': ''}` + +### Dataset: 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique +- **Annotation Text:** Peritubular capillary endothelium 2 +- **Author's Mapping:** capillary endothelial cell (CL:0002144) +- **Agent's Mapping:** peritubular capillary endothelial cell (CL:1001033) +- **Enrichment Info:** `{'name': 'Peritubular capillary endothelium 2', 'full_name': 'peritubular capillary endothelium 2', 'paper_synonyms': 'PCE; peritubular capillaries; PCap', 'tissue_context': ''}` + +### Dataset: 9ea768a2-87ab-46b6-a73d-c4e915f25af3_cxg_dataset_unique +- **Annotation Text:** Transitional urothelium +- **Author's Mapping:** urothelial cell (CL:0000731) +- **Agent's Mapping:** ureter urothelial cell (CL:1000706) +- **Enrichment Info:** `{'name': 'Transitional urothelium', 'full_name': 'transitional epithelium of ureter', 'paper_synonyms': 'TE; transitional epithelium; transitional epithelium of ureter', 'tissue_context': ''}` + +### Dataset: 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique +- **Annotation Text:** H1 +- **Author's Mapping:** retina horizontal cell (CL:0000745) +- **Agent's Mapping:** H1 horizontal cell (CL:0004217) +- **Enrichment Info:** `{'name': 'H1', 'full_name': 'horizontal cell type H1', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 2f6a20f1-173d-4b8d-860b-c47ffea120fa_cxg_dataset_unique +- **Annotation Text:** H2 +- **Author's Mapping:** retina horizontal cell (CL:0000745) +- **Agent's Mapping:** H2 horizontal cell (CL:0004218) +- **Enrichment Info:** `{'name': 'H2', 'full_name': 'horizontal cell type H2', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 7e7f63c5-d964-40be-83de-ecbcccafd233_cxg_dataset_unique +- **Annotation Text:** S cone +- **Author's Mapping:** retinal cone cell (CL:0000573) +- **Agent's Mapping:** S cone cell (CL:0003050) +- **Enrichment Info:** `{'name': 'S cone', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique +- **Annotation Text:** BEST4 enterocyte +- **Author's Mapping:** enterocyte (CL:0000584) +- **Agent's Mapping:** BEST4+ enterocyte (CL:4030026) +- **Enrichment Info:** `{'name': 'BEST4 enterocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique +- **Annotation Text:** Cycling B cell +- **Author's Mapping:** B cell (CL:0000236) +- **Agent's Mapping:** cycling B cell (CL:4033068) +- **Enrichment Info:** `{'name': 'Cycling B cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique +- **Annotation Text:** Cycling myeloid cells +- **Author's Mapping:** myeloid cell (CL:0000763) +- **Agent's Mapping:** cycling myeloid cell (CL:4033081) +- **Enrichment Info:** `{'name': 'Cycling myeloid cells', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique +- **Annotation Text:** Cycling plasma cell +- **Author's Mapping:** plasma cell (CL:0000786) +- **Agent's Mapping:** cycling plasma cell (CL:4047003) +- **Enrichment Info:** `{'name': 'Cycling plasma cell', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique +- **Annotation Text:** cDC1 +- **Author's Mapping:** conventional dendritic cell (CL:0000990) +- **Agent's Mapping:** CD141-positive myeloid dendritic cell (CL:0002394) +- **Enrichment Info:** `{'name': 'cDC1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique +- **Annotation Text:** cDC2 +- **Author's Mapping:** conventional dendritic cell (CL:0000990) +- **Agent's Mapping:** CD1c-positive myeloid dendritic cell (CL:0002399) +- **Enrichment Info:** `{'name': 'cDC2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8e47ed12-c658-4252-b126-381df8d52a3d_cxg_dataset_unique +- **Annotation Text:** early enterocyte +- **Author's Mapping:** enterocyte (CL:0000584) +- **Agent's Mapping:** early enterocyte (CL:4047019) +- **Enrichment Info:** `{'name': 'early enterocyte', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique +- **Annotation Text:** Secretory-papillary fibroblasts +- **Author's Mapping:** skin fibroblast (CL:0002620) +- **Agent's Mapping:** fibroblast of papillary layer of dermis (CL:1000302) +- **Enrichment Info:** `{'name': 'Secretory-papillary fibroblasts', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique +- **Annotation Text:** Secretory-reticular fibroblasts +- **Author's Mapping:** skin fibroblast (CL:0002620) +- **Agent's Mapping:** fibroblast of the reticular layer of dermis (CL:2000096) +- **Enrichment Info:** `{'name': 'Secretory-reticular fibroblasts', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 124744b8-4681-474a-9894-683896122708_cxg_dataset_unique +- **Annotation Text:** Vascular EC +- **Author's Mapping:** endothelial cell of vascular tree (CL:0002139) +- **Agent's Mapping:** blood vessel endothelial cell (CL:0000071) +- **Enrichment Info:** `{'name': 'Vascular EC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: f5be9ed2-5d41-4a52-91e4-4ff24ff84900_cxg_dataset_unique +- **Annotation Text:** cone-off-BC-BC3A +- **Author's Mapping:** cone retinal bipolar cell (CL:0000752) +- **Agent's Mapping:** type 3a cone bipolar cell (CL:0004213) +- **Enrichment Info:** `{'name': 'cone-off-BC-BC3A', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 02792605-4760-4023-82ad-40fc4458a5db_cxg_dataset_unique +- **Annotation Text:** LAM-like +- **Author's Mapping:** macrophage (CL:0000235) +- **Agent's Mapping:** lipid-associated macrophage (CL:4033086) +- **Enrichment Info:** `{'name': 'LAM-like', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: a4157949-6f2a-40e2-b960-63f6e2bde918_cxg_dataset_unique +- **Annotation Text:** K_Fibro +- **Author's Mapping:** fibroblast (CL:0000057) +- **Agent's Mapping:** keratocyte (CL:0002363) +- **Enrichment Info:** `{'name': 'K_Fibro', 'full_name': 'corneal fibroblasts', 'paper_synonyms': 'stromal keratocytes; corneal stromal keratocytes', 'tissue_context': ''}` + +### Dataset: 59b69042-47c2-47fd-ad03-d21beb99818f_cxg_dataset_unique +- **Annotation Text:** Other T +- **Author's Mapping:** lymphocyte (CL:0000542) +- **Agent's Mapping:** T cell (CL:0000084) +- **Enrichment Info:** `{'name': 'Other T', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique +- **Annotation Text:** DCT1 +- **Author's Mapping:** kidney distal convoluted tubule epithelial cell (CL:1000849) +- **Agent's Mapping:** epithelial cell of early distal convoluted tubule (CL:4030016) +- **Enrichment Info:** `{'name': 'DCT1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 9df60c57-fdf3-4e93-828e-fe9303f20438_cxg_dataset_unique +- **Annotation Text:** DCT2 +- **Author's Mapping:** kidney distal convoluted tubule epithelial cell (CL:1000849) +- **Agent's Mapping:** epithelial cell of late distal convoluted tubule (CL:4030017) +- **Enrichment Info:** `{'name': 'DCT2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique +- **Annotation Text:** DCT1 +- **Author's Mapping:** kidney distal convoluted tubule epithelial cell (CL:1000849) +- **Agent's Mapping:** epithelial cell of early distal convoluted tubule (CL:4030016) +- **Enrichment Info:** `{'name': 'DCT1', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: e067e5ca-e53e-485f-aa8e-efd5435229c8_cxg_dataset_unique +- **Annotation Text:** DCT2 +- **Author's Mapping:** kidney distal convoluted tubule epithelial cell (CL:1000849) +- **Agent's Mapping:** epithelial cell of late distal convoluted tubule (CL:4030017) +- **Enrichment Info:** `{'name': 'DCT2', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_1 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_1', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_10 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_10', 'full_name': 'excitatory neuron 10', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_2 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_2', 'full_name': 'excitatory neurons', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_3 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_3', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_4 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_4', 'full_name': 'excitatory neurons', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_5 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_5', 'full_name': 'excitatory neuron', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_6 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_6', 'full_name': 'excitatory neuron 6', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_7 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_7', 'full_name': 'excitatory neuron 7', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_8 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_8', 'full_name': 'excitatory neurons', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Excitatory_9 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** glutamatergic neuron (CL:0000679) +- **Enrichment Info:** `{'name': 'Excitatory_9', 'full_name': 'excitatory neuron 9', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Inhibitory_1 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** GABAergic neuron (CL:0000617) +- **Enrichment Info:** `{'name': 'Inhibitory_1', 'full_name': 'inhibitory neuron', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Inhibitory_2 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** GABAergic neuron (CL:0000617) +- **Enrichment Info:** `{'name': 'Inhibitory_2', 'full_name': 'inhibitory neurons', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Inhibitory_3 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** GABAergic neuron (CL:0000617) +- **Enrichment Info:** `{'name': 'Inhibitory_3', 'full_name': 'inhibitory neurons', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 0bc7235a-ae5a-479d-a487-510435377e55_cxg_dataset_unique +- **Annotation Text:** Inhibitory_4 +- **Author's Mapping:** neuron (CL:0000540) +- **Agent's Mapping:** GABAergic neuron (CL:0000617) +- **Enrichment Info:** `{'name': 'Inhibitory_4', 'full_name': 'inhibitory neurons', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique +- **Annotation Text:** C_earlyACC +- **Author's Mapping:** enterocyte of epithelium of large intestine (CL:0002071) +- **Agent's Mapping:** early colonocyte (CL:4047018) +- **Enrichment Info:** `{'name': 'C_earlyACC', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique +- **Annotation Text:** C_earlyCC +- **Author's Mapping:** enterocyte of epithelium of large intestine (CL:0002071) +- **Agent's Mapping:** early colonocyte (CL:4047018) +- **Enrichment Info:** `{'name': 'C_earlyCC', 'full_name': '', 'paper_synonyms': '', 'tissue_context': ''}` + +### Dataset: 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique +- **Annotation Text:** SI_BEST4 +- **Author's Mapping:** epithelial cell of small intestine (CL:0002254) +- **Agent's Mapping:** small intestine BEST4+ enterocyte (CL:4047051) +- **Enrichment Info:** `{'name': 'SI_BEST4', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 019c7af2-c827-4454-9970-44d5e39ce068_cxg_dataset_unique +- **Annotation Text:** SI_tuft +- **Author's Mapping:** intestinal tuft cell (CL:0019032) +- **Agent's Mapping:** tuft cell of small intestine (CL:0009080) +- **Enrichment Info:** `{'name': 'SI_tuft', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique +- **Annotation Text:** immune +- **Author's Mapping:** hematopoietic cell (CL:0000988) +- **Agent's Mapping:** leukocyte (CL:0000738) +- **Enrichment Info:** `{'name': 'immune', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** DB1 +- **Author's Mapping:** OFF-bipolar cell (CL:0000750) +- **Agent's Mapping:** diffuse bipolar 1 cell (CL:4033027) +- **Enrichment Info:** `{'name': 'DB1', 'full_name': 'bipolar type DB1', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** DB2 +- **Author's Mapping:** OFF-bipolar cell (CL:0000750) +- **Agent's Mapping:** diffuse bipolar 2 cell (CL:4033028) +- **Enrichment Info:** `{'name': 'DB2', 'full_name': 'bipolar type DB2', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** DB3a +- **Author's Mapping:** OFF-bipolar cell (CL:0000750) +- **Agent's Mapping:** diffuse bipolar 3a cell (CL:4033029) +- **Enrichment Info:** `{'name': 'DB3a', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** DB3b +- **Author's Mapping:** OFF-bipolar cell (CL:0000750) +- **Agent's Mapping:** diffuse bipolar 3b cell (CL:4033030) +- **Enrichment Info:** `{'name': 'DB3b', 'full_name': 'bipolar type DB3b', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** DB4 +- **Author's Mapping:** ON-bipolar cell (CL:0000749) +- **Agent's Mapping:** diffuse bipolar 4 cell (CL:4033031) +- **Enrichment Info:** `{'name': 'DB4', 'full_name': 'bipolar type DB4', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** DB5* +- **Author's Mapping:** ON-bipolar cell (CL:0000749) +- **Agent's Mapping:** diffuse bipolar 5 cell (CL:4033085) +- **Enrichment Info:** `{'name': 'DB5*', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** DB6 +- **Author's Mapping:** ON-bipolar cell (CL:0000749) +- **Agent's Mapping:** diffuse bipolar 6 cell (CL:4033032) +- **Enrichment Info:** `{'name': 'DB6', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** FMB +- **Author's Mapping:** OFF-bipolar cell (CL:0000750) +- **Agent's Mapping:** flat midget bipolar cell (CL:4033033) +- **Enrichment Info:** `{'name': 'FMB', 'full_name': 'bipolar type FMB', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** IMB +- **Author's Mapping:** ON-bipolar cell (CL:0000749) +- **Agent's Mapping:** invaginating midget bipolar cell (CL:4033034) +- **Enrichment Info:** `{'name': 'IMB', 'full_name': None, 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** OFFx +- **Author's Mapping:** OFF-bipolar cell (CL:0000750) +- **Agent's Mapping:** OFFx cell (CL:4033036) +- **Enrichment Info:** `{'name': 'OFFx', 'full_name': 'OFFx type', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique +- **Annotation Text:** RB1 +- **Author's Mapping:** ON-bipolar cell (CL:0000749) +- **Agent's Mapping:** rod bipolar cell (CL:0000751) +- **Enrichment Info:** `{'name': 'RB1', 'full_name': None, 'paper_synonyms': 'rod bipolar cells; rod BCs', 'tissue_context': ''}` + +### Dataset: 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique +- **Annotation Text:** B cell cycling +- **Author's Mapping:** B cell (CL:0000236) +- **Agent's Mapping:** cycling B cell (CL:4033068) +- **Enrichment Info:** `{'name': 'B cell cycling', 'full_name': 'MKI67+ cycling B cells', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique +- **Annotation Text:** cycling DCs +- **Author's Mapping:** dendritic cell (CL:0000451) +- **Agent's Mapping:** cycling dendritic cell (CL:4033070) +- **Enrichment Info:** `{'name': 'cycling DCs', 'full_name': 'cycling dendritic cells', 'paper_synonyms': None, 'tissue_context': ''}` + +### Dataset: 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique +- **Annotation Text:** cycling gd T +- **Author's Mapping:** gamma-delta T cell (CL:0000798) +- **Agent's Mapping:** cycling gamma-delta T cell (CL:4033072) +- **Enrichment Info:** `{'name': 'cycling gd T', 'full_name': 'cycling gammadelta T cell', 'paper_synonyms': 'gammadelta T cells', 'tissue_context': ''}` \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_cell_2019_08_008.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_cell_2019_08_008.txt new file mode 100644 index 0000000..8aaedc2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_cell_2019_08_008.txt @@ -0,0 +1 @@ +FULL TEXT NOT AVAILABLE \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_cell_2020_08_013.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_cell_2020_08_013.txt new file mode 100644 index 0000000..8aaedc2 --- /dev/null +++ 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end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_devcel_2020_11_010.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_devcel_2020_11_010.txt new file mode 100644 index 0000000..8aaedc2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_devcel_2020_11_010.txt @@ -0,0 +1 @@ +FULL TEXT NOT AVAILABLE \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_isci_2021_103115.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_isci_2021_103115.txt new file mode 100644 index 0000000..b96c783 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_isci_2021_103115.txt @@ -0,0 +1,349 @@ +An interactive single cell web portal identifies gene and cell networks in COVID-19 host responses +Summary +Numerous studies have provided single-cell transcriptome profiles of host responses to SARS-CoV-2 infection. Critically lacking however is a data mine that allows users to compare and explore cell profiles to gain insights and develop new hypotheses. To accomplish this, we harmonized datasets from COVID-19 and other control condition blood, bronchoalveolar lavage, and tissue samples, and derived a compendium of gene signature modules per cell type, subtype, clinical condition, and compartment. We demonstrate approaches to interacting with, exploring, and functional evaluating these modules via a new interactive web portal ToppCell (http://toppcell.cchmc.org/). As examples, we develop three hypotheses: (1) alternatively-differentiated monocyte-derived macrophages form a multicelllar signaling cascade that drives T cell recruitment and activation; (2) COVID-19-generated platelet subtypes exhibit dramatically altered potential to adhere, coagulate, and thrombose; and (3) extrafollicular B maturation is driven by a multilineage cell activation network that expresses an ensemble of genes strongly associated with risk for developing post-viral autoimmunity. +Graphical abstract +Highlights +Topp-toolkit creates the first COVID-19 immune signature atlas +Monocytic cell subtypes form a signaling cascade capable of creating the cytokine storm of severe COVID-19 +Activated platelets display dysregulated expression of adhesion, activation, and coagulation genes with upregulated heparanase +Autoantibody generation outside of germinal centers is promoted by multilineage multicellular activator network of autoimmunity-associated genes +Virology; Systems Immunobiology; Omics; Transcriptomics, AI/ML Bionetworks +Introduction +COVID-19 clinical outcomes are variable. The poorer outcomes due to this infection are highly associated with immunological and inflammatory responses to SARS-Cov-2 infection and many recent single cell expression profiling studies have characterized patterns of immunoinflammatory responses among individuals, mostly during acute infection phases. Different studies have revealed a spectrum of responses that range from lymphopenia, cytokine storms, differential interferon responses and emergency myelopoiesis. However, a variety of obstacles limit the ability of the research and medical communities to explore and compare these studies to pursue additional questions and gain additional insights that could improve our understanding of cell type specific responses to SARS-Cov-2 infection and their impact on clinical outcome. +Although many studies have focused on the peripheral blood mononuclear cells (PBMC) (; ) because of ease of procurement, other studies have profiled airway locations via bronchoalveolar lavage (BAL) (; ), nasopharyngeal swabs, and bronchial brushes . Additional sampling sites that could also be infected or affected have also been approached in autopsy-derived materials from the central nervous system (; ), and other sites. Moreover, as major COVID-19 consortiums working on the collection and integration of each of their individual studies and interpreting important features of these individual datasets as downloadable datasets or browsable versions, such as single cell portal (https://singlecell.broadinstitute.org/single_cell/covid19) and COVID-19 Cell Atlas (https://www.covid19cellatlas.org/), using these data beyond markers, cell types, and individual signatures is either not possible or not accomplishable across-datasets. Thus, a well-organized and systematic study of immune cells across tissues for in-depth biological explorations is an unmet need for a deeper understanding of the underlying basis of the breadth of COVID-19 host defense and pathobiology. +Here we harmonized and analyzed eight high quality publicly available single-cell RNA-seq datasets from COVID-19 and immunologically-related studies that in total covered more than 480,000 cells isolated from peripheral blood, bronchial alveolar lavage and lung parenchyma samples, and assembled an integrated COVID-19 atlas (https://toppcell.cchmc.org/). We established a framework for deriving, characterizing, and establishing reference gene expression signatures from these harmonized datasets using modular and hierarchical approaches based on signatures per class, subclass, and signaling/activation and clinical status per each sample group. Leveraging these gene expression signature modules, we demonstrate data mining approaches that allow for the identification of a series of fundamental disease processes: (1) an intercellular monocytic activation cascade capable of mediating the emergence of hyperinflammatory monocyte-derived alveolar macrophages in severe COVID-19 patients; (2) the generation of several alternatively differentiated platelet subtypes with dramatically different expression of sets of genes associated with critical platelet tasks capable of altering vascular and tissue responses to infectious agents; and (3) a multilineage and multi cell type cooperative signaling network with the potential to drive extrafollicular B maturation at a lesion site, but do so with high risk for the development of B cell-associated immunity. Additionally, immune hallmarks of COVID-19 patients were compared with other immune-mediated diseases using single-cell data from patients with influenza, sepsis, or multiple sclerosis. Consistent and varied compositional and gene patterns were identified across these implicating striking COVID-19 effects in some individuals. +Results +Creating the first COVID-19 signature atlas using ToppCell portal +Creating a COVID-19 signature atlas +(A) Representative aggregation of multiple +single-cell RNA-sequencing datasets from COVID-19 and related studies. The +present study is derived from a total of 231,800 peripheral blood mononuclear +cells (PBMCs), 101,800 bronchoalveolar lavage (BAL) cells and 146,361 lung +parenchyma cells from 43 healthy; 22 mild, 42 severe, and 2 convalescent +patients. Data was collated from eight public datasets (right). +(B) Data analysis pipeline of the study using +Topp-toolkit. It includes three phases: (1) clustering and annotation; (2) +downstream analysis using Topp-toolkit; (3) biological exploration. Output +includes the evaluation of abundance of cell populations, cell type (cluster) +specific gene modules, functional associations of disease-associated cell +classes and clusters, inference of cell-cell interactions, as well as +comparative analysis across diseases, including influenza, sepsis, and multiple +sclerosis. Additional newer datasets not included in this manuscript are present +and will continue to be added to ToppCell (http://toppcell.cchmc.org). See also Table S1. +To have a comprehensive coverage of cells, we collated single-cell data of COVID-19 patients from eight public datasets, which in total contains 231,800 PBMCs, 101,800 BAL cells, and 146,361 lung parenchyma cells from donors: 43 healthy; 22 mild; 42 severe; and 2 convalescent patients (Figure 1A, Table S1). +To assemble an integrated atlas of human cell responses to COVID-19, we sought to harmonize metadata encompassing clinical information, sampling compartments, and cell and gene expression module designations. Doing so provides a rich framework for detecting perturbations of cell repertoire and differentiative state adaptations. We first integrated single cell RNA-seq data in Seurat and annotated cell types using canonical markers (Table S2). Further annotations of B cell and T cell subtypes were completed using the reference-based labeling tool Azimuth . Sub-clustering was applied for some cell types, such as neutrophils and platelets, to interrogate finer resolutions of disease-specific sub-populations (Figure 1B). Using the ToppCell toolkit (https://toppcell.cchmc.org/), we created an atlas of more than 3,000 hierarchically organized gene modules corresponding to differentially expressed genes (DEGs) derived from all cell classes and sub-classes among all compartments and clinical subgroups such as disease severity levels (Table S1). These modules were then used to infer gene networks within celltypes and subtypes as well as cell-cell interactions that could be further combined for functional comparative enrichment, interactions, and fuzzy network AI-based analyses using ToppCluster and ToppGene (Figure 1B), such as differential functional enrichments of sub-clusters of platelets. Integration of ToppCluster output of cells from multiple compartments and disease conditions built pathogenic heatmaps and networks, highlighted by the coagulation map of COVID-19 (Figure S12). In addition, perturbation of cell abundance was evaluated either in one cell population, or in multiple populations across diseases. Taken together, we investigated cell abundance changes, severity-associated signatures, mechanisms of COVID-19 specific symptoms and unique features of COVID-19 as an immune-mediated disease (Figure 1B). +Dynamic changes and balance of COVID-19 immune repository in blood and lung +Modularized representation of cell type specific +gene signatures and dynamic changes of cell abundance +(A) Uniform Manifold Approximation and Projection +(UMAP) of 28 distinct cell types identified in the integrated peripheral blood +mononuclear cell (PBMC) data. +(B) Comparative analysis of cell abundance effects +of COVID-19. Reproducible multi-study data present high impact effects on 5 cell +types in PBMC. Percentages of selected cell types in each sample are shown +(where Vent: Ventilated patients; Non Vent: Non-ventilated patients). +Significance between two conditions was measured by the Mann-Whitney rank-sum +test (Wilcoxon, paired = False), which was also used in following significance +tests of cell abundance changes in this study. *: p <= 0.05; **: p <= 0.01; ***: p +<= 0.001; ****: p <= 0.0001. Boxplot figures: the lower and upper hinges +correspond to the 25th and 75th percentiles; the upper whisker extends from the +hinge to the largest value no further than 1.5 x inter-quartile range (IQR); the +lower whisker extends from the hinge to the smallest value at most 1.5 x IQR of +the hinge. The line within the box corresponds to the median. +(C) UMAP of 24 distinct cell types identified in the +integrated BAL data. +(D) Dynamic changes of cell abundances for cell +types in two bronchoalveolar lavage (BAL) single-cell datasets. Statistical +methods are same with (B). +(E) ToppCell allows for gene signatures to be +hierarchically organized by lineage, cell type, subtype, and disease condition. +The global heatmap shows gene modules with top 50 upregulated genes (student t +test) for each cell type in a specific disease condition and compartment. Gene +modules from control donors and severe COVID-19 patients were included in the +figure. See also Figures +S1-S4 and Table +S2. +After the aforementioned cell annotation procedure, we identified 28 and 24 distinct cell types in PBMC and BAL, respectively (Figures 2A and 2C; Table S2). Shifts of Uniform Manifold Approximation and Projection (UMAP) of cell type distributions were observed in both compartments of mild and severe patients (Figures 2A, 2C, S1A and 3A). In PBMC, conventional dendritic cells (cDC), plasmacytoid dendritic cells (pDC), and non-classical monocytes displayed a prominent reduction in severe patients (Figures 2B and S1C), consistent with prior reports. In contrast, severe patients demonstrated dramatic expansion of neutrophils, especially immature stages (Figures S1C and S2). Integration with evoked pathways in the following analysis implicated that neutrophil expansion was likely the consequence of emergency myelopoiesis. In addition, a general down-regulation of T cell and NK cell was observed, consistent with lymphopenia reported in clinical practices (Figures S1C and S2). However, the trend of T cell subtypes varies across studies and individuals, apart from proliferative T cells which have a dramatic increase in mild and severe patients (Figure S2). Notably, plasmablasts substantially increased in COVID-19 patients, and especially so in severe patients, suggesting upregulated antibody production (Figures 2B and S1C). Expansion of platelets is another significant change observed in severe patients, possibly leading to immunothrombosis in the lung, which could be closely associated with the severity of the disease (; ) (Figures 2B and S1C). +In samples obtained from patients' lungs, we observed the depletion of FABP4high tissue-resident alveolar macrophages (TRAM) and dramatic expansion of FCN1high monocyte-derived alveolar macrophages (MoAM) in severe patients (Figures 2C, 2D, and S3D). Mild patients exhibited a moderate reduction of tissue-resident macrophages, but no evidence of aggregation of monocyte-derived macrophages (Figures 2C, 2D, S3A, and S3D). Dynamic changes of these two subtypes suggest increased tissue chemoattraction and potential damage of patients' lungs . In addition, neutrophils were only identified in severe patients in the integrated BAL data (Figures 2C and S3A), which might be related with neutrophil extracellular traps (NETs) in the lung . However, more samples are required to draw a solid conclusion. We also noted that conventional dendritic cells decreased in the severe patients, which is consistent with the trend of the counterpart in PBMC data. Opposite to the change in PBMC, an expansion of plasmacytoid dendritic cells is observed in both mild and severe patients (Figure 2D). Other cell types, including T cell and NK cell in the BAL, also have converse changes of their counterparts in PBMC, which could be attracted by lung macrophages or epithelial cells after infection or damages (Figures 2D and S3D). These changes were consistently observed in lung parenchyma samples from severe COVID-19 patients (Figure S4). With cells well-annotated in the integrated COVID-19 atlas, we drew a global heatmap for cells in both blood and lung using ToppCell gene modules (top 50 DEG in each module) of all identified cell classes. While there was conservation of gene patterns involved in healthy donors and severe COVID-19 patients, there were substantial differences most notably in myeloid cells (Figure 2E). Such hierarchically ordered ToppCell gene modules were broadly used in visualization, large-scale comparisons and fine-resolution investigations in the following analyses. +Myeloid cell atlas: functionally distinct neutrophils at different levels of maturation and derailed macrophages in the lung +Functional analysis of compartment-specific immature +and subtype-differentiated neutrophils and monocytic macrophages in COVID-19 +patients +(A) Five sub-clusters and three cell groups were +identified after the integration of neutrophils in peripheral blood mononuclear +cells (PBMC) and bronchoalveolar lavage (BAL) (Left). The distribution of +compartments is shown on the right. +(B) Sub-clusters (Left) and COVID-19 conditions +(Right) of monocyte-derived macrophages and tissue-resident macrophages were +identified after integration of BAL datasets. +(C) Heatmap of gene modules from ToppCell with top +200 upregulated genes for each neutrophil sub-cluster. Important +neutrophil-associated genes and inferred roles of sub-clusters were shown on two +sides. +(D) Heatmap of associations between subclusters of +neutrophils and macrophages and myeloid-cell-associated pathways (Gene +Ontology). Gene modules with 200 upregulated genes for sub-clusters were used +for enrichment in ToppCluster. Additionally, enrichment of top 200 +differentially expressed genes (DEGs) for comparisons in Figures S5D and S6B were appended on the right. +Gene enrichment scores, defined as -log10(adjusted p value), +were calculated as the strength of associations. Pie charts showed the +proportions of COVID-19 conditions in each cluster. +(E) Gene interaction network in the BAL of severe +patients. Highly expressed ligands and receptors of each cell type in the BAL of +severe patients were selected based on Figure S8. Among them, genes with unique and +distinct expression patterns in each cell type were chosen, for example, CCL17 +for cDCs and CXCR1 for neutrophils. Interaction was inferred using both CellChat +database and embedded cell interaction database in ToppCell. The molecular +interaction between cell types is represented as a flow of arrows in such a way: +secreting cells ligands receptors receptor cells, where rectangles +represent various cell types and hexagons represent ligands and receptors. The +color for a gene is consistent with the cell type with the highest expression +level. See also Figures +S5-S11 and Table +S3. +Dysregulated myeloid cells have been reported as an important marker of severe COVID-19 patients (; ). To gain a deeper and comprehensive understanding of these cells, we applied the sub-clustering strategy on the integrated data of key cell types, such as neutrophils and macrophages, and then generated gene modules for comparative functional analysis and interactome inference. We successfully identified 5 neutrophil sub-clusters after the integration of PBMC and BAL data, including 3 FCGR3B+ mature sub-clusters and 2 FCGR3B- immature sub-clusters (Figures 3A and S5B; Table S3). They're mainly from severe patients and their gene modules were generated and subjected to comparative functional enrichment using ToppCell and ToppCluster (Figures 3C, 3D and S5A). We identified proliferative neutrophils (referred to as pro-neutrophils and Neu4) and MMP8high precursor immature neutrophils (referred to as pre-neutrophils and Neu2) (Figures 3A and S5B) consistent with prior studies . Although immune response genes and pathways were barely activated in the immature neutrophils, they displayed upregulation of granule formation pathways and NETosis-associated proteins, including ELANE, DEFA4, and MPO, especially in Neu4 (; ) (Figures 3C, S5B, and S5C). Upregulated myeloid leukocyte mediated immunity in Neu2 suggests involvement of this cell type in antiviral function (Figure S5D). Yet, the absence of cytokine and interferon response pathways suggests the lack of mature immune responses (Figure 3D). Notably, compared to mature neutrophils (Neu0 and Neu1) in the blood, the extravasated hyperinflammatory sub-cluster (Neu3) from BAL of severe patients shows extraordinarily high expression of interferon-stimulated genes, as well as prominent upregulation of productions and responses to cytokines and interferons (Figures 3C, 3D, S5B and S5D). +MoAM and TRAM were two main macrophage types in the BAL (Figure 2C); both are known to have distinct roles in immune responses in the lung . As described above, five sub-clusters among the expanded COVID-19 patient-specific MoAM (Figure 3B; Table S3) were found, where the loss of HLA class II genes and elevation of interferon-stimulated genes (ISGs) were consistently observed (Figures 3F and S6A). Relative to MoAM3,4, MoAM1,2,5 displayed an upregulation of interferon responses and cytokine production (Figures 3D and S6B; Table S3), indicating their pro-inflammatory characteristics. Notably, MoAM5 shows dramatic upregulation of IL-6 secretion and cytokine receptor binding activities (Figures S7A-S7D). However, cells in this sub-cluster were mainly from one severe patient (Figure S3C). We still need more data to fully understand such dramatic upregulation of IL-6 secretion in some severe patients. Similar to MoAM, we also identified two distinct groups of TRAM in BAL (Figures 3B and S6B), including quiescent TRAM (TRAM1 and TRAM2) and activated TRAM (TRAM3). The quiescent group was mainly from healthy donors with enriched pathways of ATP metabolism (Figure 3D), while the activated group from mild and severe patients displays upregulation of ISGs and cytokine signaling pathways (Figure S6B; Table S3). However, the magnitude of activation and inflammatory responses in TRAM3 is smaller than MoAM1,2,5. Not surprisingly, stronger antigen processing and presentation activities were observed in TRAM3 relative to MoAM1,2,5 (Figures 3D and S6B; Table S3). Collectively, we concluded that tissue-resident macrophages were greatly depleted in severe patients as the front-line innate immune responders in the lung. Pro-inflammatory monocyte-derived macrophages infiltrate into the lung, leading to the cytokine storm and damage of the lung. Large amounts of infiltration of MoAM were not observed in mild COVID-19 patients, probably due to the controlled infection, which could explain milder lung damages in those patients. +To develop insights into molecular and cellular interaction networks active in the lung microenvironment of COVID-19 patients , we focused on signaling ligands, receptors and pathways using ToppCell and CellChat (Figures 3E, S8A and S8B). Notably, basal cells, MoAMs, neutrophils and T cells all contributed to the cytokine, chemokine, and interleukin signaling networks. Strikingly, severe patient specific MoAM2 shows the broadest upregulation of signaling ligands, including CCL2, CCL3, CCL7, CCL8, CXCL9, CXCL9, CXCL10, CXCL11, IL6, IL15, and IL27, suggesting its role as a signaling network hub that is distinct from the other major signaling ligand-expressing cells of BAL such as epithelial and other myeloid cell types such as TRAM3 and proliferating myeloid cells (Figure S8A). Among the MoAM2 top signaling molecules, attractants CXCL8, CXCL9, and CXCL10 are known to target CXCR3 on T cells, suggesting their role is to stimulate migration of T cells to the epithelial interface and into BAL fluid (Figure 3E) . In addition, many of MoAM2's ligands have the potential to cause autocrine signaling activation via IL6-IL6R, IL1RN-ILR2, CCL7-CCR1, CCL2-CCR1, and CCL4-CCR1, indicating its active roles in self-stimulation and development, which further amplify the attraction and migration of T cells and other immune cells. Notably, CCR1 was also expressed in activated TRAM3, but with a lower level. Although IL6 expression level is relatively low compared to other ligands in BAL data, substantial expression of IL6R was observed in MoAMs. The CCL and CXCL signaling pathways of neutrophils are less strong than MoAMs (Figure S8B), but they displayed high expression levels of CXCR1 and CXCR2, which binds with a large number of the chemokines from MoAM and epithelial cells (Figure 3E). In addition, neutrophils exhibit an extraordinarily high level of IL1B, which could potentially in turn activate macrophages (Figures S8A and S8B). TRAM3 also displayed a unique pattern of signaling molecules, with a substantial level of CCL23 which could potentially attract MoAM by the interaction with CCR1. Secretion of CXCL3 and CXCL5 in TRAM3 toward CXCR2 could be a potential chemoattraction pathway for neutrophils. In turn, neutrophils could activate TRAM3 by secreting IL1B, which binds with IL1RAP. In addition, CD4+ T cells could also activate TRAM3 by IL10-IL10RB interaction (Figures 3E, S8A, and S8B). +In addition to substantial alterations of neutrophils and macrophages, the upregulation of ISGs was observed in classical monocytes of both mild and severe patients (cMono4, cMono3), whereas the reduction of the MHC class II cell surface receptor HLA-DR genes was only observed in severe patients (cMono3) (Figure S9). In cDCs, polarization of interleukin secretion was observed in mild-patient and severe-patient specific clusters (Figure S10F). Collectively, dynamic changes of marker genes, transcriptional profiles, signaling molecules, and biological activities reveal the heterogeneity of myeloid cell sub-clusters across disease severity (Figure S11C). Pro-inflammatory gene expression was found in all major myeloid cell types, including cMono4, DC1, and DC9 in PBMC and Neu3, DC10, MoAM1, MoAM2, and MoAM5 in BAL of COVID-19 patients. The reduction of MHC class II (HLA-II) genes is a common feature of classical monocytes and macrophages in COVID-19 patients and implies impaired capacity to activate T cell adaptive immunity. +COVID-19 coagulation and immunothrombosis map +COVID-19 driven reprogramming of platelets leads to +drastically altered expression of genes associated with platelet adhesion, +activation, coagulation and thrombosis +(A and B) Uniform Manifold Approximation and +Projections (UMAPs) show distributions of sub-clusters (A) and COVID-19 +conditions (B) of platelets after the integration of PBMC +datasets. +(C) Severity-associated coagulation genes were +selected and shown on the heatmap, with disease and sub-cluster specific gene +patterns identified and labeled. Their functional associations with coagulation +pathways were retrieved from ToppGene and shown on the right. +(D) Functional and phenotypical associations of +coagulation-association genes in each gene pattern from (C). Associations were +retrieved from ToppGene enrichment output and eight enrichment categories are +represented by rectangles with unique colors. Thicken colored edges represent +associations of highlighted functions, such as cell-matrix binding and +regulation of coagulation. Hypothesized pathway cascade that potentially drives +thromboembolism in COVID-19 patients is drawn in red dotted line. See also +Figures S12, S13 +and Table +S4. +Individuals severely affected during acute phase COVID-19 infection, and in particular those with significantly elevated risk of death, frequently demonstrate striking dysregulation of coagulation and thrombosis characterized by hypercoagulability and microvascular thrombosis (endothelial aggregations of platelets and fibrin) and highly elevated D-dimer levels. Yet, COVID-19 does not lead to wide scale consumption of fibrinogen and clotting factors (; ). At present, we lack a molecular or cellular explanation of the underlying basis of this pathobiology. To evaluate candidate effectors of this pathobiology, we used a list of genes associated with abnormal thrombosis from mouse and human gene mutation phenotypes and identified parenchymal lung sample endothelial cells and platelets in PBMC as cell types highly enriched with respect to genes responsible for the regulation of hemostasis (Figure S12). Because platelet counts were greatly elevated in severe versus mild individuals, we further examined platelet gene expression signatures and cell type differentiation and identified six distinct platelet sub-clusters shared across all datasets after data integration (Figures 4A and 4B). Severe-patient-specific PLT0 is an interesting sub-cluster with elevated integrin genes, including ITGA2B, ITGB1, ITGB3, and ITGB5, as well as thrombosis-related genes, such as SELP, HPSE, ANO6, and PF4V1. Antibodies against the latter are associated with thrombosis including adverse reactions to recent COVID-19 vaccine ChAdOx1 nCoV-19. In addition, upregulated pathways of hemostasis, wound healing and blood coagulation were also observed in PLT0 (Figure S13A; Table S4). Importantly, PLT2 is an inflammatory sub-cluster with an upregulation of ISGs and interferon signaling pathways, whereas PLT4 is highlighted by upregulated post-transcriptional RNA splicing activities (Figure S13A and S13C). +Severity-associated gene patterns were also identified by selecting coagulation-associated genes modules (Figure 4C; Table S4), indicating distinct coagulation activities across platelets. Apart from pan-platelet genes, we found dramatic upregulation of genes involved in platelet activation, fibrinogen binding and blood coagulation in platelets of severe COVID-19, including procoagulant heparanase (HPSE), Anoctamin-6 (ANO6), and selectin P (SELP) (Figures 4C and 4D). Heparanase is an endoglycosidase that cleaves heparan sulfate constituents, a major component of anti-coagulation glycocalyx on the surface of vascular endothelium. Upregulated heparanase was related to upregulation of cell-matrix adhesion and coagulation (Figure 4D). Thrombotic vascular damages could be caused by the degradation function of heparinase enriched in platelets of severe patients. Elevation of ANO6 is known to trigger phospholipid scrambling in platelets, resulting in phosphatidylserine exposure which is essential for activation of the clotting system. In addition, other upregulated genes involved in coagulation-associated activities were also observed, including wound healing, fibrinolysis, platelet aggregation and activation (Figure 4D), which likely collectively contribute to the clotting issue of severe COVID-19 patients. +Emergence of developing plasmablasts and B cell association with autoimmunity +Implicating a multi-lineage cell network capable of +driving extrafollicular B cell maturation and the emergence of humoral +autoimmunity in COVID-19 patients +(A) Uniform Manifold Approximation and Projections +(UMAPs) of sub-clusters (Left) and COVID-19 conditions (Right) of B cells after +integration of peripheral blood mononuclear cells (PBMC) and bronchoalveolar +lavage (BAL) datasets. +(B) UMAPs of subtypes (Left) and COVID-19 conditions +(Right) of plasmablasts after integration of PBMC and BAL +datasets. +(C) Volcano plot depicts differentially expressed +genes between plasmablasts and developing plasmablasts. Student t-tests were +applied and p values were adjusted by the Benjamini-Hochberg procedure. +Thresholds of adjusted p values and log fold changes in the volcano plot are +1.0*10-6 and 1.0, respectively. +(D) Workflow of discovering and prioritizing +candidate genes related to a disease-specific phenotype with limited +understanding. +(E) The heatmap shows the normalized expression +levels of candidate ligands and receptors for COVID-19 autoimmunity in multiple +compartments in healthy donors and COVID-19 patients. Binding ligands of +receptor genes were shown in parentheses on the right. Hot spots of expression +are highlighted. +(F) Network analysis of autoimmunity-associated gene +expression by COVID-19 cell types. Prior knowledge associated gene associations +include GWAS, OMIM, mouse knockout phenotype, and additional recent manuscripts +were selected from ToppGene enrichment results of differentially expressed +ligands and receptors and shown on the network. Orange arrows present the +interaction directions from ligands (green) to receptors (pink) on B cells. +Annotations for these genes, including single-cell co-expression (blue), mouse +phenotype (light blue), transcription factor binding site (purple) and signaling +pathways (green) are shown. See also Figures S14, S15 and Table S5. +Autoimmune disorders in COVID-19 patients such as immune thrombocytopenic purpura (ITP) is now recognized as a known disease complication (; ). However, little is known about the molecular and cellular mechanism behind it. To examine this further, we integrated B cells and plasmablasts from both PBMC and BAL and conducted systematic analysis (Figures 5A, 5B, S14A, and S14B). Several COVID-19 specific sub-clusters were identified in B cells, such as ISGhigh activated B cells (cluster 7) (Figure 5A). Importantly, activated B cells showed dramatic upregulation of interferon signaling pathways and cytokine productions (Figure S15A), indicating its anti-virus characteristics. Notably, plasmablasts were mainly observed in severe COVID-19 patients, where a group of proliferative cells was identified and labeled as developing plasmablasts (Figure 5B). In contrast, non-dividing plasmablasts displayed upregulation of immunoglobulin genes (IGHA1, IGHA2, and IGKC), B cell markers (CD79A), interleukin receptors (IL2RG) and type II HLA complex (HLA-DOB) (Figure 5C; Table S5). In addition, non-dividing plasmablasts showed unique isotypes of immunoglobulin (Ig) in sub-regions of UMAP, whereas developing plasmablasts displayed obscure Ig types (Figure S14E and S14F). Antibody production activities were upregulated in non-dividing plasmablasts based on gene enrichment analysis (Figure S15A; Table S5). Collectively, we inferred that non-dividing plasmablasts had definite immunoglobulin isotypes and were actively involved in immune responses toward COVID infection, whereas developing plasmablasts were less mature but highly proliferative to replenish the repertoire of plasma cells. +Because there are few clues of gene associations of autoimmunity in COVID-19, we brought up a hypothesis-driven, prior knowledge-based approach to discover and prioritize genes for the specific phenotype (Figure 5D). First, gene modules of B cells and other cells in severe patients were collected and subjected to ToppGene for enrichment analysis. Then we queried autoimmunity-associated terms in the enriched output and identified associated genes. After that, we retrieved interaction pairs using the ToppCluster and CellChat database. In the end, we identified genes that are not only involved in autoimmunity, but have a mediator role in the immune signaling network. Using this approach, we observed several candidate pairs of genes, including TNFSF13B-TNSRSF13, IL10-IL10RA, IL21-IL21RA, IL6-IL6R, CXCL13-CXCR5, CXCL12-CXCR4, CCL21-CCR7, CCL19-CCR7, and CCL20-CCR6 in severe patients, which were enriched for autoimmune diseases, such as autoimmune thyroid diseases, lupus nephritis, and autoimmune encephalomyelitis (; ). Candidate cytokine and chemokine ligand genes were expressed in various cell types in PBMC and BAL, including IL21 and CXCL13 from exhausted T cells of BAL, CXCL12 from mesenchymal cells, IL6, and CCL21 from endothelial cells, CCL19 from cDC and CCL20, TNFSF13B, and TNFSF13 from lung macrophages (Figure 5E; Table S5). These interaction pairs have been linked with auto-immunity (; ). In addition, we analyzed single-cell studies of rheumatoid arthritis and lupus nephritis patients and found that high expression levels of the candidate receptors in B cells and ligands in other cells were also observed, such as CXCL13 in helper T cells and CXCR5 in B cells in both studies (Figures S15C and S15D). However, more evidence is still required to infer the association between these interactions and autoimmunity in COVID-19 patients. Supported by the evidence above, we drew a network for potential mediator interactions of B cells and their associations with autoimmune disorders, where linkages with diseases, such as rheumatoid arthritis, systemic lupus erythematosus, were highlighted, as well as linkages with mouse phenotypes, such as abnormal immune tolerance and increased susceptibility to autoimmune disorder (Figure 5F). As a caveat, although using prior knowledge to prioritize gene and cell-associated functions and interactions may introduce biases, such approaches also have the potential to highlight key similarities and differences between different disease causes and clinical responses and improve our understanding of the molecular and cellular mechanisms at work. +Functional map and immune cell interplay landscape in COVID-19 +Comparative analysis of cell type specific gene +signatures associated with lineage, class, subclass, compartment, and disease +state in the COVID-19 atlas +(A) Enrichment scores of gene modules for all cell +types across different compartments and COVID-19 conditions were generated by +ToppCluster and shown on the heatmap. ToppCluster enriched functions from Gene +Ontology, Human Phenotype, Mouse Phenotype, Pathway and Interaction databases +were used to generate a feature matrix (cell types by features) and were +hierarchically clustered. Hot spots of the disease-specific enrichments were +highlighted and details were shown on the left. More details can be found in +STAR +Methods. +(B) Summarizing predicted functions and interplay of +immune cells in COVID-19 blood and lung. Aforementioned key observations in this +study were shown in peripheral blood mononuclear cells (PBMC) and +bronchoalveolar lavage (BAL) in healthy donors, mild and severe COVID-19 +patients, including changes of cell abundance, specific marker genes, +upregulated secretion, cell development and cell-cell interactions. See also +Table +S6. +As above, where highly significant enrichments of unique functions and pathways could be identified in the subtypes of multiple cell classes, such as neutrophils, platelets and B cells, we sought to get a more holistic understanding of COVID-19 specific cell class and subclass-level signatures, including T cell subtypes (Figures S16 and S17), we built an integrative functional map of all cell types in three compartments across multiple disease conditions using a highly integrated gene module set (Figure 6A; Table S6). All enriched functional associations in ToppCluster for gene modules of cell types and sub-clusters were depicted. They were grouped by disease conditions and compartments to show heterogeneity of cellular functions in different circumstances. +In the heatmap (Figure 6A), most enrichments were consistently observed across cells of healthy donors and COVID-19 patients. However, some unique patterns were also identified. For example, T cells, and NK cells in healthy donors show enrichments of mitochondrial transport and ATP metabolic process, whereas activated T cells in mild patients show upregulation of type I interferon production and cytokine signaling. Enrichments of macrophage differentiation and neutrophil migration regulation were uniquely found in MoAM1 in severe patients (Figure 6A). The function map provides a high-level approach to investigate functional variations of cells across disease conditions and compartments. The predicted interplay of immune cells across multiple compartments and disease conditions is displayed in Figure 6B. Cell proportion changes, sub-cluster specific signatures, and cell-cell interaction are also depicted. +Similarity and heterogeneity between COVID-19 and other immune-mediated diseases +Comparative analysis of differentially-expressed +immunoregulatory genes between COVID-19 and other immune-mediated +diseases +(A) Uniform Manifold Approximation and Projection +(UMAP) shows the distributions of cell types (Left) and diseases (Top right) +after the integration of datasets in multiple studies. MS: multiple sclerosis; +IIH: idiopathic intracranial hypertension. IIH patients were recruited as +controls in the multiple sclerosis study. +(B) Dynamic changes of immune cell types in +different immune-mediated diseases compared to healthy controls. Log2(ratio) was +calculated to show the levels of changes. *, p < 0.05, **, p < 0.01, ***, +p < 0.001. Statistical models can be found in the STAR Methods. Leuk-UTI: sepsis patients that +enrolled into UTI with leukocytosis (blood WBC >=12,000 per mm3) but no organ +dysfunction. Box features are same with Figure 2B. +(C) Normalized expression values of key genes +involved in immune signaling and responses are shown for cell types across +multiple diseases. Lowly expressed genes (maximal average expression level +across all cell types in the heatmap is less than 0.5 after +Log2CPM normalization) were removed. See also Figure S18 and Table S7. +To further analyze COVID-19 specific immune signatures, we compared immune cells from COVID-19 patients with cells in other immune-mediated diseases, including severe influenza, sepsis , and multiple sclerosis. 404,125 cells were included after the integration of PBMC single-cell datasets (Figures 7A and S18; Table S7). Dynamic changes of cell abundance were compared in diseases versus healthy donors. Similar to COVID-19 patients, severe influenza patients also exhibited the reduction of non-classical monocytes, pDC, cDC, and CD4+ TCM, but the effect of the former two types was smaller in magnitude (Figure 7B). However, the reduction of non-classical monocytes is more significant in severe COVID-19 patients than severe influenza or mild COVID-19 patients (Figure 7B). Notably, NK cell reduction is associated with COVID-19 severity, whereas T cell depletion is a more dramatic perturbation in severe influenza. Within these comparisons, the expansion of plasmablasts is consistently observed, whereas the accumulation of platelets is unique to SARS-CoV-2 and in particular, to severe COVID-19 clinical status (Figure 7B). +In addition to dynamic changes of cell ratios, we also investigated the regulation of immune mediator genes across various diseases (Figure 7C; Table S7). IL-6 is an important factor of cytokine storms in COVID-19. As shown in the heatmap, naive B cells are the main sources of IL-6 in COVID-19 patients while CD14+ monocytes show the highest expression levels in severe influenza patients (Figure 7C). Specific ligands, including CXCL2, CXCL3, and CCL20 were upregulated in both severe COVID-19 patients and severe influenza patients. CCR4 and IL2RA are uniquely high in CD4+ T cells of COVID-19 patients. Interestingly, most PBMC myeloid cell types displayed upregulated levels of interferon-stimulated genes in both COVID-19 and influenza, especially in COVID-19, where highest levels of ISGs in CD14+ Monocytes, cDC and pDC were observed. +Discussion +In this work, we have constructed an innovative immune signature atlas of the blood and lung of COVID-19 patients using the integrated single cell RNA-sequencing data and Topp-toolkit. By virtue of systemic analysis of large sample size from multiple sampling sites, consistent immunopathology-associated changes of cell abundance and transcriptional profiles were observed in the circulating and lung immune repertoire of COVID-19 patients. The established single cell atlas and the provided public portal (https://toppcell.cchmc.org/) enables the query of candidate molecules and pathways in each of these processes. +Leveraging this approach, we identified three major candidate mechanisms capable of driving COVID-19 severity: (1) a cascade-like network of proinflammatory autocrine and paracrine ligand receptor interactions among subtypes of differentiating mononuclear, lymphoid, as well as other cell types; (2) the production of emergency platelets whose gene expression signatures implicate significantly elevated potential for adhesion, thrombosis, attenuated fibrinolysis, and potential to enhance the release of heparin-bound cytokines as well as further influence the activation of neutrophils causing further inflammatory cell recruitment and neutrophil netosis; and (3) the extrafollicular activation of naive and immature B cells via a multilineage network that includes monocytic subtypes and exhausted T cells of cytokines and interleukins with the potential to generate local antigen specific response to virus infected targets and collateral autoimmunity. More details will be discussed below. +We identified dramatically expanded macrophages which were marked by the loss of HLA class II genes and upregulation of interferon-stimulated genes. It implicates a key role for these activated macrophages involved in signaling networks and less so in activation of adaptive T cell immunity. Among them, MoAM2 displayed hyperinflammatory responses and extraordinary high levels of signaling molecules, which are involved in both autocrine (e.g., IL-6, CCL2, CCL4, and CCL8) and paracrine (e.g., CXCL2, CXCL9, CXCL10, and CXCL11) signaling pathways. The former pathway contributed to the self-stimulation and development, which amplified the paracrine pathway for T cell and neutrophil chemoattraction. The latter two cell types in turn activated MoAMs with cytokines genes (CCL5, IL10 of T cells and IL1B of neutrophils, respectively). Based on the intercellular and multifactor complexity of the signaling cascade we have outlined, to effectively control a malignant inflammatory cascade, it may be essential to consider simultaneously targeting multiple nodes of this network of cytokines and interleukins. In addition, HLA-DRlow monocytes, likely reflecting dysfunctional cells, were observed in severe infection. This, along with evidence of emergency myelopoiesis with immature circulating neutrophils into the circulation was detected in severe COVID-19. These neutrophils had transcriptional programs suggestive of dysfunction and immunosuppression not seen in patients with mild COVID-19. As such, we have presented evidence for the contribution of defective monocyte activation and dysregulated myelopoiesis to severe COVID. +Platelet expansion is uniquely observed in COVID-19 versus other immune-mediated diseases. Strikingly, these activated platelets were highlighted with abnormal thrombosis and upregulated heparanase, a procoagulant endoglycosidase that cleaves anti-coagulation heparan sulfate constituents on endothelial cells and potentially causes thrombotic vascular damages. In addition, heparanase-cleaved heparan sulfate (HS) fragments were capable of stimulating the release of pro-inflammatory cytokines, such as IL1B, IL6, IL8, IL10, and TNF through the TLR-4 pathway in PBMC, further contributing to the hyperinflammatory environment in COVID-19 patients. Because heparanase is recognized as a hallmark in tumor progression and metastasis, we hypothesize COVID-19 infection could be associated with higher occurrence of lung tumor metastasis. However, more data is required to support it. Pro-neutrophil secreted proteins (e.g., ELANE, DEF4) of neutrophil extracellular trap (NET), which have been reported to be associated with higher risk of morbid thrombotic events. Approaches for combating NETs could be a potential anticoagulation treatment. +We propose a signaling network which potentially shapes the differentiation of B cells toward the formation of autoantibodies. Proliferation and activation of inflammatory myeloid cells and the formation of exhausted CD4+ T helper around an area of direct or indirect viral tissue injury leads to the production of a set of interleukins and cytokines known to have both direct cell activating and maturing effects on naive and immature B cells. Previous report had revealed the exaggerated extrafollicular B cell response, which is part of a mechanism that stimulates somatic mutation and maturation of B cells to produce plasma cells with specificity for antigens present in the vicinity of tissue damage sites. In the absence of macrophages or dendritic cells to restrict self vs non-self, the presence of IL-10, IL-21, CXCL13 CXC10, IL-6, and others acting on receptors present in naive and immature B cells leads to the selection and maturation of self-reactive maturation of B cells clones with formation of autoantibodies. Many of these COVID-19-activated genes (e.g., CXCL13, CCL19, CCL20, and TNFRSF13) are known to be genetically associated with rheumatoid arthritis, lupus, and risk of developing autoimmune disease in humans and mouse models. The development of different patterns of autoimmunity may be a hallmark of "Long Haul" Covid disease and could explain why some individuals develop different autoantibodies and suffer different forms of clinical consequences depending on which antigens drive the B-cell maturation. Thus, an additional prediction that could be made based on these findings and our network model is that among individuals treated with corticosteroids at the time these auto-immunogenic processes are activated, there should be a protective effect and lower likelihood of developing post acute sequela of Covid. +Consistent and varied compositional changes and gene patterns of immune cells were identified in COVID-19, influenza and sepsis. Expansion of plasmablasts, as well as the reduction of non-classical monocytes, are more significant changes in severe COVID-19 patients, whereas the depletion of T cells is more dramatic in severe influenza patients. The accumulation is a unique immune hallmark of COVID-19 within the selected diseases, which contributes to the coagulation abnormalities and thrombosis, a key cause of fatality in COVID-19 patients. Different signaling gene patterns were identified across immune-mediated diseases, with CCR4 only highly expressed in CD4+ T cells of COVID-19 patients, which might be related with extravasation of these cells . Upregulated interferon-stimulated genes of myeloid cells in PBMC revealed the inflammatory environment of COVID-19. +Collectively, using the COVID-19 single cell atlas data exploration environment, we have illustrated is that researchers are now enabled to systematically explore, learn, and formulate new hypotheses within and between compartments, cell types, and biological processes, and provided access to these reprocessed datasets through a suite of explorative and evaluative tools. Moreover, we have shown different hypotheses can be developed and explored using the approaches that we have outlined and the database that we have provided. Certainly additional critical information will also be obtained using approaches that include in situ spatial, temporal data as well as those of viral products and viral and inflammatory-process affected complexes. Next steps for improving its ability to be mined more deeply will be based on additional statistical methods that extend the current ToppCell/ToppGene Suite based on fuzzy measure similarity, Page-Rank, and cell-cell signaling approaches. +Limitations of study +There are several limitations in our study. Different studies used various standards of COVID-19 severity definition. To generalize conclusions, we simplified disease conditions into several universal groups. Prospectively, a standardized definition of disease stages will assist in the accuracy of future studies. In additionn, the timing of sample collection was not considered as a variable in this study, rather disease stages were used to consolidate data across samples. We lack follow-up data of patients with sequela, which will be helpful for understanding the long-haul effects of the disease. +STAR Methods +Key resources table +REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data scRNA-seq for COVID-19 PBMC GSE155673 scRNA-seq for COVID-19 PBMC GSE150861 scRNA-seq for COVID-19 PBMC GSE149689 scRNA-seq for COVID-19 PBMC EGAS00001004571 scRNA-seq for COVID-19 PBMC GSE150728 scRNA-seq for COVID-19 BAL GSE145926 scRNA-seq for COVID-19 BAL GSE155249 scRNA-seq for COVID-19 Lung Biopsy GSE158127 scRNA-seq for Sepsis PBMC SCP548, SCP550 scRNA-seq for Multiple-Sclerosis PBMC GSE138266 Integrated COVID-19 scRNA-seq data This paper Mendeley Data: https://doi.org/10.17632/vrxdg7mm6x.1 Integrated COVID-19 scRNA-seq interface This paper cellxgene: https://cellxgene.cziscience.com/collections/b9fc3d70-5a72-4479-a046-c2cc1ab19efc Software and Algorithms Seurat (v4.0) https://satijalab.org/seurat/ R (v3.6.1) N/A https://www.r-project.org/ Scanpy (v1.7.2) https://scanpy-tutorials.readthedocs.io/en/latest/index.html Python (v3.7.0) N/A https://www.python.org/ CellChat (v1.0.0) http://www.cellchat.org/ ToppGene https://toppgene.cchmc.org/ ToppCluster https://toppcluster.cchmc.org/ ToppCell this paper https://toppcell.cchmc.org/ EnhancedVolcano (v1.4.0) https://bioconductor.org/packages/release/bioc/html/EnhancedVolcano.html Cellxgene N/A https://github.com/chanzuckerberg/cellxgene +Resource availability +Lead contact +Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Bruce Aronow (bruce.aronow@cchmc.org). +Materials availability +This study did not generate new unique reagents. +Method details +Single-cell RNA-seq data source +To have a comprehensive understanding of immune cells in different repertoires, we collected 8 public COVID-19 single-cell RNA-seq datasets of multiple compartments, including peripheral blood mononuclear cells, bronchoalveolar lavage and lung biopsy, which in total covered over 43 healthy donors, 22 mild/moderate, 42 severe and 2 convalescent COVID-19 patients. More details can be found in Figure 1A and Table S1 and Data Availability. Lung biopsy samples were taken from the explanted lung or postmortem lungs of COVID-19 patients. Various criteria were used in these publications to describe COVID-19 severity. For example, we found asymptomatic, mild, moderate and floor COVID-19 patients under the definition of non-severe COVID-19 patients in our data sources. A recent paper used the WHO score of COVID-19 severity to categorize disease conditions of patients, which is a more standardized and robust approach for the description of disease stages. However, in order to address the issue of missing information for disease stratification and to simplify the comparison, we grouped disease conditions into three groups, including healthy donors, mild COVID-19 patients and severe COVID-19 patients. Convalescent patients were excluded in some of our analysis for simplification. Sequencing data of healthy donors in Guo et al. was excluded since it was not from the same institute. +We also collected PBMC single-cell RNA-seq data from 29 sepsis patients and 4 multiple sclerosis patients for comparative analysis of immune-mediated diseases (Figure 1A; Table S1). Data sources can be found in Data Availability. +Data preprocessing and normalization +For datasets with raw UMI counts, we first removed cells with less than 300 detected genes or less than 600 UMI counts. Then cells with more than 15% counts of mitochondrial genes were filtered out. Genes expressed in less than 5 cells were removed. +To remove doublets and low-quality cells which might pass the filtering, we further interrogated cell qualities in individual datasets. Distributions of UMI counts or number of genes per cell were used as criteria of clusters with low sequencing depth, which is one of important features of low-quality cells. Doublets usually express marker genes of multiple cell types, which is an important evidence that we used for doublet cluster detection. Before we integrated cells, we first investigated cell qualities and filter low-quality cells in each individual dataset. Original data without quality control can be found in Data Availability. +Cluster 14 in Arunchalam et al.'s data was removed due to its low-sequencing depth; Cluster 13 in Guo et al.'s data and cluster 9 in Schulte-Schrepping et al.'s data were removed for the same reason; the bottom region of cluster 2 on the UMAP, which is close to platelets but expresses high levels of T cell markers, was defined as T cell - platelet doublets and removed using cellxgene. +For some datasets that only provide processed and normalized h5ad or rds files, we checked their preprocessing procedures in the original publications and confirmed that stringent quality control procedures were used. +After quality control, we finally harvested 483,765 high-quality cells from 8 studies (Table S1). Both original and processed data can be found in Data Availability. We normalized the total UMI counts per gene to 1 million (CPM) and applied log2(CPM+1) transformation for heatmap visualization and downstream differential gene expression analysis. Steps above were done in Scanpy. +Integration of PBMC datasets and BAL datasets using reciprocal PCA in seurat +We input raw count files of 5 preprocessed PBMC datasets into Seurat and created a list of Seurat objects. Reciprocal PCA procedure (https://satijalab.org/seurat/v3.2/integration.html#reciprocal-pca) was used for data integration. First, normalization and variable feature detection were applied for each dataset in the list. Then we used SelectIntegrationFeatures to select features for downstream integration. Next, we scaled data and ran the principal component analysis with selected features using ScaleData and RunPCA. Then we found integration anchors and integrated data using FindIntegrationAchnors and IntegrateData. RPCA was used as the reduction method. After integration, we scaled data and ran PCA on integrated expression values. UMAP was generated using the top 30 reduced dimensions with RunUMAP. The same approach was also used in BAL data integration and multi-disease integration. We also used it for the integration of specific cell types across multiple datasets, for example, the integration of neutrophils from PBMC and BAL datasets. Compared with standard workflow and SCTransform (https://satijalab.org/seurat/v3.2/integration.html) in Seurat, we found Reciprocal PCA is much less computation-intensive and time-consuming, making the integration of multiple large single-cell datasets feasible. +Cell annotations using canonical markers after unsupervised clustering +Cell annotations were assigned in each dataset and then mapped to the integrated data. For some datasets without available cell annotations, we first used unsupervised clustering in Scanpy. Detailed steps include (1) detecting top 3,000 highly variable genes using pp.highly_variable_genes; (2) scaling each gene to unit variance on highly variable genes using pp.scale; (3) running PCA using arpack approach in tl.pca; (4) finding neighbors using pp.neighbors; (5) running leiden clustering with resolution of 1 using tl.leiden (resolutions were determined swiftly based on the size and complexity of data). More details can be found in the code (point to it). For datasets with available annotations, we checked their validity and corrected wrong annotations. For example, hematopoietic stem and progenitor cells (HSPC) were mistakenly annotated as "SC&Eosinophil" in the original paper and were corrected in our annotation. +After unsupervised clustering, well recognized immune cell markers were used to annotate clusters, including CD4+ T cell markers such as TRAC, CD3D, CD3E, CD3G, CD4; CD8+ T cell markers such as CD8A, CD8B, NKG7; NK cell markers such as NKG7, GNLY, KLRD1; B cell markers such as CD19, MS4A1, CD79A; plasmablast markers such as MZB1, XBP1; monocyte markers such as S100A8, S100A9, CST3, CD14; conventional dendritic cell markers such as XCR1, plasmacytoid dendritic cell markers such as TCF4; megakaryocyte/platelet marker PPBP; red blood cell markers HBA1, HBA2; HSPC marker CD34. Exhaustion-associated markers, including PDCD1, HAVCR2, CTLA4 and LAG3 were used to identify exhausted T cells. +Additionally, other markers were used for annotations of lung-specific cells, including AGER, MSLN for AT1 cells; SFTPC, SFTPB for AT2 cells; SCGB3A2, SCGB1A1 for Club cells; TPPP3, FOXJ1 for Ciliated cells; KRT5 for Basal cells; CFTR for Ionocytes; FABP4, CD68 for tissue-resident macrophages; FCN1 for monocyte-derived macrophages, TPSB2 for Mast cells. More details can be found in Table S2. +Cell annotations using Azimuth +To better annotate T cells in our study, we applied Azimuth (https://satijalab.org/azimuth/), a tool for reference-based single-cell analysis developed in Seurat version 4.0. High-quality PBMC single-cell data in Azimuth was used as the reference for label projection. After removing annotations with low prediction scores or low mapping scores, we got a collection of well-annotated T cell subtypes, including CD4+ Cytotoxic T cell, CD4+ Naive T cell, CD4+ Central Memory T cell, CD8+ Naive T cell, CD8+ Effector Memory cell, gamma-delta T cell, double-negative T cell. CD4+ Effector Memory T (CD4+ TEM) cell and CD8+ Central Memory T (CD8+ TCM) cell were found by Azimuth. However, we manually re-annotated them into other T cell subtypes, such as CD4+ TCM and CD8+ TEM cells, based on expression levels of marker genes. There're several reasons for the re-annotation: (1) There's no clear cluster or boundary for these two predicted cell types on reduced dimension (UMAP), which indicates that they may have no distinct transcriptomic pattern compared with other T cell subtypes; (2) Many marker genes didn't show significant expression patterns in these two cell types. For example, CD4+ TEM markes from Azimuth, such as TMSB10 and ITGB1, didn't show higher expression levels in Azimuth-predicted CD4+ TEM cells in Arunchalam et al.'s data; (3) Although developers and researchers of Azimuth spent a lot of time annotating cells, the existence of CD4 TEM and CD8 TCM in single-cell data was not widely observed by other groups. In addition, we didn't use CITE-seq data like the reference dataset of Azimuth. Thus, the power of detecting fine resolution of CD4 and CD8 T cell subtypes might be relatively low. +Apart from annotations of T cell subtypes, we also found CD56-bright NK cell, intermediate B cell and memory B cell using Azimuth. +Sub-clustering for specific cell types +Sub-clustering was used for the discovery of subtypes or distinct stages of a specific cell type. In our work, we applied sub-cluster for various immune cell types, including classical monocytes, neutrophils, conventional dendritic, B cells and platelets. First, all cells in the specific cell type were integrated using the same procedure as PBMC data integration. Then Louvain clustering (resolution = 0.5, except for sub-clustering of classical monocytes where resolution = 0.3) was applied to detect sub-clusters of those cells. Importantly, neutrophils, cDCs and B cells were retrieved from both PBMC and BAL, whereas classical monocytes and platelets were only retrieved from PBMC. +Generation of ToppCell gene modules +ToppCell (https://toppcell.cchmc.org/) was designed to parallelly analyze transcriptional profiles of single-cell datasets by organizing differential expressed gene modules hierarchically as a function of sample types and compartments, clinical subgroups, and cell lineage, class, and subclass designations that emerge from post-hoc cell type evaluations. All the cells were grouped into specific hierarchical categories. For example, "PBMC_severe COVID-19_myeloid cells_classical-monocytes_cMono1" represents cells belonging to cMono1 (a sub-cluster of classical monocytes) in PBMC of severe COVID-19 patients. Within hierarchically ordered cell annotations, we calculated corresponding DEGs in a hierarchical way as well and consistently. We defined customized ranges for comparisons and applied t-test based on normalized expression values. More details can be seen on ToppCell website. Usually, the top 200 most differentially genes in each comparison were picked up as the gene modules for the selected cell group, which are the starting point of downstream analysis, including gene enrichment in ToppGene and interaction inference in ToppCluster. All gene modules in our study were curated in COVID-19 Atlas (https://toppcell.cchmc.org/biosystems/go/index3/COVID-19 Atlas) and ImmuneMap (https://toppcell.cchmc.org/biosystems/go/index3/ImmuneMap) on the ToppCell website. +Gene enrichment analysis using ToppGene +Gene modules generated with ToppCell were individually analyzed using the ToppGene/ToppFun, and comparatively analyzed using ToppCluster. Principle annotation categorizations used were GO-Molecular Function, GO-Biological Process, GO-Cellular Component, Mouse and Human Mutation Phenotypes, as well as ImmGen reference signatures, MSigDB Pathways, NCBI Pub2Gene and Protein Interactions, and DisGeNet Curated sources. P values of enrichment results were adjusted using the Benjamini-Hochberg procedure. +Generation of functional association heatmap using ToppCluster +Genes in gene modules of selected cell types or sub-clusters were sent to ToppCluster (https://toppcluster.cchmc.org/). Then multi-group functional enrichment was drawn for input gene modules and -log10(adjusted p-value) was used as the gene enrichment score to represent the strength of association between gene modules and pathways. Scores greater than 10 were trimmed to 10. Pathways from Gene Ontologies, including Molecular Functions, Biological Process and Cellular Component in the option list were used for the enrichment of gene modules in myeloid cells, B cells and platelets. In order to gain a broader knowledge of immunothrombosis-related pathways, "Pathway" and "Mouse Phenotype'' in the option list were also selected for enrichment. Morpheus was used for visualization of the heatmap (https://software.broadinstitute.org/morpheus/). +Cell interaction inference in immunothrombosis activities and cytokine signaling pathways +CellChat was used to infer the signaling network in the BAL of severe patients (Figure S8B). All 3 categories of interactions were used in the database CellChatDB.human. Over-expressed ligands or receptors in each cell type were first identified for further identification of over-expressed interaction pairs. Then cytokine, chemokine and IL signaling probability between multiple cell types was inferred using computeCommunProb and computeCommunProbPathway. +ToppCell was used to infer interactions in immunothrombosis. We first selected genes related to coagulation or immunothrombosis pathways from subtypes of endothelial cells, platelets, neutrophils, classical monocytes and monocyte-derived macrophages by filtering the output of ToppCluster (Figure S12A). Then we used CellChatDB as the knowledge base to find the subset of genes participating in cell-cell interaction, including genes involved in signaling via secretion, cell-cell contact and extracellular matrix interaction. These genes in each cluster were sent to ToppCluster to infer the interaction network using protein-protein interactions (PPI) between those genes. +Generation of volcano plots +We first calculated differential expressed genes using tl.rank_genes_groups in Scanpy. Adjusted p values and log fold changes in the output were used as the input of volcano plots. R package EnhancedVolcano was used to draw figures. +Construction of COVID-19 functional enrichment map +In order to characterize functional properties of cell types and subtypes observed in BAL, PBMC, and lung parenchymal samples from control, mild, and severe COVID-19 patient samples, we used the library of gene expression signatures ("Gene Module Report" from ToppCell) as an input to the ToppCluster enrichment analyzer web server. Using categories of Gene Ontology, Human Phenotype, Mouse Phenotype, Pathway and Protein Interaction, a matrix was constructed using minus log P enrichment values for each celltype gene list and then all cells and enriched features could be clustered and ordered based on their shared or distinct properties that could then be associated with lineage, cell subclass, tissue compartment, and disease state. +Quantification and statistical analysis +Cell proportion differences between disease groups for specific types and subtypes (Figures 2, S2-S4) shown on box plots were measured by Mann-Whitney test (Wilcoxon, paired = False). Significance between two disease conditions were shown on the top of figures. More details can be found in figure legends. +To investigate the dynamic changes of cell proportions across various immune-mediated diseases, we followed the approach in recent literature (Figure 7B). For each disease condition, we computed the relative ratio of each cell type in individual disease samples divided by individual healthy samples. Log2 transformed values were shown in the box plot. Then we calculated relative ratios of each cell type between all sample pairs of healthy donors as a control. To compute the significance, we used a two-sided Kolmogorov-Smirnov (KS) test using relative ratios in diseases and those values in healthy donors. More details can be found in figure legends. +References +Vascular disease and thrombosis in SARS-CoV-2-infected rhesus macaques +The immune cell landscape in kidneys of patients with lupus nephritis +Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans +The role of CXCR5 and its ligand CXCL13 in the compartmentalization of lymphocytes in thyroids affected by autoimmune thyroid diseases +Targeting potential drivers of COVID-19: neutrophil extracellular traps +Lung transplantation for patients with severe COVID-19 +Imbalanced host response to SARS-CoV-2 drives development of COVID-19 +EnhancedVolcano: publication-ready volcano plots with enhanced colouring and labeling. R package version 1.10.0 +COVID-19: immunopathology and its implications for therapy +ToppGene suite for gene list enrichment analysis and candidate gene prioritization +COVID-19 severity correlates with airway epithelium-immune cell interactions identified by single-cell analysis +Expansion of plasmablasts and loss of memory B cells in peripheral blood from COVID-19 patients with pneumonia +COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets +DoubletDecon: deconvoluting doublets from single-cell RNA-sequencing data +Systematic comparison of single-cell and single-nucleus RNA-sequencing methods +Role of endothelial heparanase in delayed-type hypersensitivity +Covid-19 and autoimmunity +Overlapping B cell pathways in severe COVID-19 and lupus +High levels of anti-SSA/Ro antibodies in COVID-19 patients with severe respiratory failure: a case-based review +A mechanistic model and therapeutic interventions for COVID-19 involving a RAS-mediated bradykinin storm +Soluble heparan sulfate fragments generated by heparanase trigger the release of pro-inflammatory cytokines through TLR-4 +Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia +Mapping systemic inflammation and antibody responses in multisystem inflammatory syndrome in children (MIS-C) +Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm +Impaired type I interferon activity and inflammatory responses in severe COVID-19 patients +Integrated analysis of multimodal single-cell data +The clinical course and its correlated immune status in COVID-19 pneumonia +Platelet-based coagulation: different populations, different functions +Neurological manifestations of COVID-19 feature T cell exhaustion and dedifferentiated monocytes in cerebrospinal fluid +Preferential recruitment of CCR6-expressing Th17 cells to inflamed joints via CCL20 in rheumatoid arthritis and its animal model +The unique characteristics of COVID-19 coagulopathy +The coagulopathy, endotheliopathy, and vasculitis of COVID-19 +Heparanase and the hallmarks of cancer +Inference and analysis of cell-cell communication using CellChat +ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems +CXCL13 antibody for the treatment of autoimmune disorders +CCR 7 ligands are required for development of experimental autoimmune encephalomyelitis through generating IL-23-dependent Th17 cells +Author Correction: a dynamic COVID-19 immune signature includes associations with poor prognosis +Serum BLC/CXCL13 concentrations and renal expression of CXCL13/CXCR5 in patients with systemic lupus erythematosus and lupus nephritis +Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19 +Coagulation abnormalities and thrombosis in patients with COVID-19 +Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19 +CD79a: a novel marker for B-cell neoplasms in routinely processed tissue samples +The role of cytokines including interleukin-6 in COVID-19 induced pneumonia and macrophage activation syndrome-like disease +COVID-19: consider cytokine storm syndromes and immunosuppression +Author Correction: pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages +Neutrophil extracellular traps contribute to immunothrombosis in COVID-19 acute respiratory distress syndrome +Immunothrombotic dysregulation in COVID-19 pneumonia is associated with respiratory failure and coagulopathy +Increased expression of heparanase in symptomatic carotid atherosclerosis +SARS-CoV-2: a storm is raging +Megakaryocytes and platelet-fibrin thrombi characterize multi-organ thrombosis at autopsy in COVID-19: a case series +An immune-cell signature of bacterial sepsis +Autoinflammatory and autoimmune conditions at the crossroad of COVID-19 +Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis +Severe COVID-19 is marked by a dysregulated myeloid cell compartment +Thrombosis and thrombocytopenia after ChAdOx1 nCoV-19 vaccination +COVID-19 infection: the perspectives on immune responses +Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19 +Interplay between coagulation and vascular inflammation in sickle cell disease +Upregulation of CCR4 in activated CD8+ T cells indicates enhanced lung homing in patients with severe acute SARS-CoV-2 infection +Analysis and classification of B-cell infiltrates in lupus and ANCA-associated nephritis +Comprehensive integration of single-cell data +Integrating platelet and coagulation activation in fibrin clot formation +The trinity of COVID-19: immunity, inflammation and intervention +Hematological findings and complications of COVID-19 +Neutrophil extracellular traps: villains and targets in arterial, venous, and cancer-associated thrombosis +Characteristics of peripheral lymphocyte subset alteration in COVID-19 pneumonia +A single-cell atlas of the peripheral immune response in patients with severe COVID-19 +Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19 +SCANPY: large-scale single-cell gene expression data analysis +Elevated production of B cell chemokine CXCL13 is correlated with systemic lupus erythematosus disease activity +Dysregulation of brain and choroid plexus cell types in severe COVID-19 +Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry +Cytokine storm and immunomodulatory therapy in COVID-19: role of chloroquine and anti-IL-6 monoclonal antibodies +Clinical and autoimmune characteristics of severe and critical cases of COVID-19 +Neutrophil extracellular traps and thrombosis in COVID-19 +Supplemental information +Data and code availability +Public single-cell RNA-seq datasets of PBMC in COVID-19 patients are available on NCBI Gene Expression Omnibus (GEO) and European Genome-phenome Archive, including GSE150728, GSE155673, GSE150861, GSE149689 and EGAS00001004571 (or Schulte-Schrepping_2020_COVID19_10x_PBMC under FastGenomics). BAL single-cell RNA-seq datasets of COVID-19 patients are available on GSE145926 and GSE155249. Lung Parenchyma single-cell RNA-seq data are available on GSE158127. Single-cell RNA-seq data of sepsis patients are available on the Single Cell Portal SCP548 and SCP550. Data of multiple sclerosis patients are available on GSE128266. Data of severe influenza patients are available on GSE149689. Accession numbers are also listed in the key resources table. Original and processed data can be downloaded on Mendeley Data (https://doi.org/10.17632/vrxdg7mm6x.1). Gene modules of all datasets analyzed using ToppCell web portal are available on COVID-19 Atlas in ToppCell (https://toppcell.cchmc.org), including gene modules from either a single dataset or an integrated dataset. Gene modules from the integration of specific cell types, such as B cells and neutrophils are also listed in ToppCell. More details are listed in Figure1A and Table S1. An interactive interface of integrated PBMC data and subclusters of immune cells will be public on cellxgene (https://cellxgene.cziscience.com/collections/b9fc3d70-5a72-4479-a046-c2cc1ab19efc). +Codes of preprocessing, normalization, clustering and plotting of single-cell datasets are available on github: https://github.com/KANG-BIOINFO/COVID-19-Atlas. +Any additional information required to reanalyze the data reported in the paper is available from the lead contact upon request. +Supplemental information can be found online at https://doi.org/10.1016/j.isci.2021.103115. \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_jcmgh_2022_02_007.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_jcmgh_2022_02_007.txt new file mode 100644 index 0000000..8aaedc2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_jcmgh_2022_02_007.txt @@ -0,0 +1 @@ +FULL TEXT NOT AVAILABLE \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_jhep_2023_12_023.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_jhep_2023_12_023.txt new file mode 100644 index 0000000..8aaedc2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1016_j_jhep_2023_12_023.txt @@ -0,0 +1 @@ +FULL TEXT NOT AVAILABLE \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41467-021-22368-w.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41467-021-22368-w.txt new file mode 100644 index 0000000..d595bae --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41467-021-22368-w.txt @@ -0,0 +1,3 @@ +AbstractThe integration of single cell transcriptome and chromatin accessibility datasets enables a deeper understanding of cell heterogeneity. We performed single nucleus ATAC (snATAC-seq) and RNA (snRNA-seq) sequencing to generate paired, cell-type-specific chromatin accessibility and transcriptional profiles of the adult human kidney. We demonstrate that snATAC-seq is comparable to snRNA-seq in the assignment of cell identity and can further refine our understanding of functional heterogeneity in the nephron. The majority of differentially accessible chromatin regions are localized to promoters and a significant proportion are closely associated with differentially expressed genes. Cell-type-specific enrichment of transcription factor binding motifs implicates the activation of NF-κB that promotes VCAM1 expression and drives transition between a subpopulation of proximal tubule epithelial cells. Our multi-omics approach improves the ability to detect unique cell states within the kidney and redefines cellular heterogeneity in the proximal tubule and thick ascending limb. + +FULL TEXT NOT AVAILABLE \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41467-022-32972-z.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41467-022-32972-z.txt new file mode 100644 index 0000000..1d6bdc3 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41467-022-32972-z.txt @@ -0,0 +1,3 @@ +AbstractThe proximal tubule is a key regulator of kidney function and glucose metabolism. Diabetic kidney disease leads to proximal tubule injury and changes in chromatin accessibility that modify the activity of transcription factors involved in glucose metabolism and inflammation. Here we use single nucleus RNA and ATAC sequencing to show that diabetic kidney disease leads to reduced accessibility of glucocorticoid receptor binding sites and an injury-associated expression signature in the proximal tubule. We hypothesize that chromatin accessibility is regulated by genetic background and closely-intertwined with metabolic memory, which pre-programs the proximal tubule to respond differently to external stimuli. Glucocorticoid excess has long been known to increase risk for type 2 diabetes, which raises the possibility that glucocorticoid receptor inhibition may mitigate the adverse metabolic effects of diabetic kidney disease. + +FULL TEXT NOT AVAILABLE \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41590-020-0602-z.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41590-020-0602-z.txt new file mode 100644 index 0000000..6c37fa4 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s41590-020-0602-z.txt @@ -0,0 +1,228 @@ +Distinct microbial and immune niches of the human colon +Gastrointestinal microbiota and immune cells interact closely and display regional specificity, but little is known about how these communities differ with location. Here, we simultaneously assess microbiota and single immune cells across the healthy, adult human colon, with paired characterization of immune cells in the mesenteric lymph nodes, to delineate colonic immune niches at steady-state. We describe distinct T helper cell activation and migration profiles along the colon and characterize the transcriptional adaptation trajectory of T regulatory cells between lymphoid tissue and colon. Finally, we show increasing B cell accumulation, clonal expansion and mutational frequency from cecum to sigmoid colon, and link this to the increasing number of reactive bacterial species. +The colon, as a barrier tissue, represents a unique immune environment where immune cells display tolerance towards a diverse community of microbes - collectively known as the microbiome. The microbiome is critical for many aspects of health and an imbalance of commensals and pathogenic microbes is linked with many disease states . Thus, understanding what constitutes a healthy, homeostatic relationship between host immune cells and the microbiome of the human colon is of critical importance. +The composition of the microbiota at any location in the intestines is determined by the availability of nutrients and oxygen, the transit rate of luminal content and compartmentalized host immune activity, and as such, is spatially distinct . Regional differences are most evident when comparing the small intestine and distal colon in humans or other mammals . Within the colon, increasing bacterial diversity from proximal (including the cecum, ascending and transverse colon) to distal regions (comprising the descending and sigmoid colon connecting to the rectum) has been reported . +The intestinal immune system has a symbiotic relationship with the microbiome and is central to the maintenance of epithelial barrier integrity. The lamina propria and associated lymphoid tissues contain one of the largest and most diverse communities of immune cells - including both lymphocytes and myeloid cells . There is marked regional variation in immune cells along the gastrointestinal tract, with T helper (TH) 17 cells decreasing in number from duodenum to colon, and T regulatory (Treg) cell numbers being highest in the colon . Immune cells can respond to environmental cues including the microbiota. Mouse studies have demonstrated that specific bacterial species can fine-tune intestinal immune responses, including TH17 , Treg , or TH1 and B cell activation . However, the extent to which there is regional variation in the mucosal microbiome within an individual, and how this might influence local immune cell niches along the colon, has not been investigated to date. +Here, we catalogued the mucosal microbiome in different regions of the human colon, a gastrointestinal organ with the most diverse and dense microbiome content and region-restricted disease states . In parallel, we applied single-cell RNA-seq (scRNA-seq) to make a census of steady-state immune cell populations in the adjacent tissue and in draining mesenteric lymph nodes (mLN), results of which are available at www.gutcellatlas.org. We demonstrate previously unappreciated changes in the proportions and activation status of T and B cells in distinct regions of the healthy human colon from proximal to distal, and relate these differences to the changing microbiota. +Results +Microbiome composition differs along distinct colon regions +To create a map of bacterial composition at the mucosal surface of the colon, we performed 16S ribosomal RNA (16S rRNA) sequencing of swabs from the mucosa surface of the cecum, transverse colon and sigmoid colon of twelve disease-free Caucasian deceased transplant donors (Methods, Figure 1a and Supplementary Table 1). The major gut phyla - Bacteroidetes, Firmicutes, Proteobacteria and Actinobacteria, were present throughout the gut of each donor (Figure 1b and Supplementary Table 2). While diversity of operational taxonomic units (OTUs) was consistent across the colon, and considerable variability existed between donor as previously reported (Supplementary Figure 1a), we did observe changes in the composition of the microbiome. Most notably at the level of phyla, Bacteroidetes was more prevalent in sigmoid colon (Figure 1c and Supplementary Figure 1b). This was mostly attributable to an increase in Bacteroides, which dominates the colonic microbiome of individuals on high protein and fat diets typical in Western countries (Figure 1c and Supplementary Figure 1c). Additionally, Enterococcus was more prevalent in the proximal colon, and Coprobacillus and Escherichia/Shigella were more abundant in the distal colon, although these proportions varied considerably between donors (Figure 1b,c and Supplementary Figure 1c). +Past studies characterizing the colonic microbiome typically rely on stool samples, which do not accurately recapitulate the composition of bacteria at the mucosal surface . Our catalog of mucosal bacteria throughout the colon demonstrates heterogeneity in the microbiome at the mucosal surface from proximal to distal colon, and reveals specific genera with preference for colonizing certain colon regions. +Immune cell heterogeneity in steady-state colon +Next, we sought to determine whether the heterogeneity we observed in the colonic microbiome was accompanied by differences in the adjacent host immune cells. To this end, we generated high-quality transcriptional data from over 41,000 single immune cells from the mesenteric lymph nodes (mLN) and lamina propria of cecum, transverse colon and sigmoid colon (Figure 1a). We acquired tissue biopsies from five deceased transplant donors (Methods and Supplementary Table 1). Samples were dissociated to release cells from the mLN and lamina propria of colonic tissue. Immune cells were enriched either by flow-sorting of CD4+ T cells (live CD45+CD3+CD4+) and other immune cells (live CD45+CD3-CD4-), or by CD45+ magnetic bead selection or Ficoll gradient (Methods and Supplementary Figure 2a). Each fraction was then subjected to scRNA-seq (Figure 1a). Despite the enrichment for immune cells, we captured epithelial cells and fibroblasts and these were computationally removed from further analysis (Methods). +Pooled analysis and visualization with Uniform Manifold Approximation and Projection (UMAP) of all four tissues from all donors revealed distinct clusters in the lymphoid and colonic tissues (Figure 2a and Supplementary Figure 2b). We identified 25 cell types and states in the intestinal lamina propria and mLN (Figure 2a, Supplementary Table 3 and Supplementary Table 4), consistent with the immune populations described in recent reports . Among these were follicular and memory B cells, IgA+ and IgG+ plasma cells, effector and memory CD4+ T cells, T regulatory (Treg) cells, CD8+ T cells, gammadelta T cells, innate lymphoid cells (ILCs), natural killer (NK) cells, mast cells and myeloid cells (Figure 2a). Sub-clustering of myeloid cells showed two distinct populations of conventional dendritic cells: cDC1 expressing XCR1, CADM1, CLEC9A, BATF3 and IDO1, and cDC2 expressing CLEC10A and CD86 (Figure 2a,b). In addition, we identified monocytes expressing CD14 and CD68, macrophages expressing FCGR3A (gene encoding CD16), LYVE1+ macrophages and plasmacytoid DCs (pDCs) expressing IRF4 and SELL. B cells, DCs and gammadelta T cells had MKI67+ cycling populations suggesting higher rates of proliferation compared to other colonic immune cell populations (Figure 2a,b). +To determine how immune cells differ along the colon from proximal to distal regions, we investigated the relative proportions of cell types within mLN and colon regions (Figure 2c,d). Since CD4+ T cells and all other immune cells were sorted separately during initial tissue processing, our analyses here were also kept separate. As noted from our visual inspection of the UMAP plot (Figure 2a), there were major differences in the cell types present between mLN and the colon. In particular, there were marked differences in the activation and memory status of T and B cells between the colon and lymph nodes, suggesting that these cell types are molded by their environment (Figure 2c,d). In the mLN, CD4+ T cells were typically CXCR5+, ICOShi follicular helper cells, and SELL+ (encodes CD62L), CCR7+ central memory cells (Figure 2b,c). In contrast, colonic CD4+ T cells had a more effector phenotype, expressing high levels of the tissue residency marker CD69 , falling into the TH17 (CCR6+, IL22+, CCL20+) or TH1 (CXCR3+, IFNG+) subtypes. There was an inverted gradient in the relative proportion of TH17 and TH1 cells, with the cecum dominated by TH17 cells that reduced in frequency in the transverse colon, and still further in the sigmoid colon, and TH1 cells following the opposite trend, being more abundant in the sigmoid colon (Figure 2c). This distinct distribution of colonic TH1 and TH17 cells is concordant with spatial variation of the microbiome, hinting at a relationship between the two populations. +B cells in the mLN were predominantly CD19+, MS4A1+ (encodes CD20), CD40+, TNFRSF13B+ (encodes TACI), CD38- memory or CXCR5+, TCL1A+, FCER2+ (encodes CD23) follicular B cells (Figure 2b,d). In contrast, in the three regions of the colon, the main population of B cells were SDC1+ (encodes CD138), CD38+, plasma cells. Plasma cells were enriched in the sigmoid colon relative to both the cecum and transverse colon, whereas the proportion of memory B cells was lower in the sigmoid colon (Figure 2d). This suggests that conditions in the sigmoid colon may favor the generation of plasma cells rather than memory B cells from germinal center responses in this region, or that the tissue contains more plasma cell niches. +T helper cells disseminate through the colon and adopt region-specific transcriptional profiles +We next investigated CD4+ T effector cells across the colon. These were annotated above based on expression of functional markers (IL17A, IL22 and CCL20 versus IFNG and CXCR3) (Supplementary Figure 3a) rather than transcriptional regulators (RORC and TBX21) that are lowly expressed. Correlation analysis of TH1 and TH17 cells between different colon tissues revealed high transcriptional similarity between these effector cell subtypes (Figure 3a), with mLN versus peripheral tissue signature accounting for the greatest amount of variability (Spearman's corr = 0.88). Within the effector T cells of the colon, transverse colon and cecum cells cluster by T helper subtype. TH1 and TH17 cells of the sigmoid colon did not cluster with their respective effector subtypes from other regions. Differential gene expression analysis between the effector cells in the sigmoid colon versus those in cecum and transverse colon revealed higher expression of activation-related molecules including TANK (TRAF family member associated NF-kB activator) (adjusted P < 10-10), CD83 (adjusted P < 10-10) and PIM3 (adjusted P < 10-8) (Figure 3b). Expression of CCL20, encoding the ligand for CCR6 that is expressed by epithelial and myeloid cells more highly in small intestines than colon , was also slightly increased by T helper cells of the proximal colon (Figure 3b). Although this is likely due to higher abundance of CCL20+ TH17 cells at this site (Figure 2c and Supplementary Figure 3a). Conversely, sigmoid colon effector T cells showed higher expression of KLF2 (adjusted P < 10-14) (Figure 3b) that encodes a transcriptional factor that transactivates the promoter for Sphingosine-1-phosphate receptor 1 (S1PR1) and is critical for T cell recirculation through peripheral lymphoid tissue , LMNA (adjusted P < 10-47) that encodes a molecule that is reported to promote TH1 differentiation and EEF1G (adjusted P < 10-13) that encodes a driver of protein synthesis. +Next, we looked into the clonal relationships between T helper cells. We performed plate-based Smartseq2 on TCRalpha/beta+ flow-sorted cells from colon regions and mLN of a sixth donor to capture paired gene expression and TCR sequences from individual T cells. Clonal groups were shared between TH1 and TH17 subtypes, supporting the notion that effector fate of CD4+ T cells is determined after their initial activation (Figure 3c,d) . Additionally, clonal expansion was observed by TH1 cells of the sigmoid colon (Figure 3d), in line with greater abundance in this tissue. Likewise, clonal expansion of TH17 cells was greatest in the cecum matching accumulation seen with the droplet-based scRNA-seq analysis (Figure 3d). Several TH1 and TH17 clonal sisters were shared between clonal sites (Figure 3d), evidence that T helper clones disseminate to distant regions of the colon. +Together these data demonstrate region-specific transcriptional differences relating to activation and tissue migration in TH1 and TH17 cells of the proximal and sigmoid colon. Identification of clonal sharing between these colon regions supports the idea that these observed transcriptional differences are due to cell-extrinsic rather than intrinsic factors. +Activation trajectory of colonic CD4+ T regulatory cells +Treg cells are known to play a role in balancing the immune activity of other CD4+ T cell subsets. Documented Treg cell activation by Clostridium spp. and previous descriptions of tissue-specific transcriptional profiles inspired us to interrogate these cells in greater detail. Firstly, we noted that the relative proportion of Treg cells did not change significantly from proximal to distal colon (Figure 2c). We then investigated whether the transcriptional profiles of Treg cells from different compartments indicated distinct activation states or functionalities. +As previously observed in the mouse , sub-clustering of Treg cells from the mLN revealed major populations of central Treg cells and effector Treg cells (Figure 4a). Central Treg cells were defined by highest expression of SELL, while effector Treg cells were characterized by genes associated with the TNFRSF-NF-kappaB pathway (TNFRSF9 and TNF) (Supplementary Figure 4a). In addition, we observed a population, previously termed non-lymphoid tissue-like Treg cells (NLT-like Treg). These have characteristics of non-lymphoid tissue Treg cells, including high expression of FOXP3, PRDM1, RORA, IL2RG, IL2RA and CTLA4 (Figure 4a & Supplementary Figure 4a). A fourth population, termed Treg-like cells, lacked the conventional markers of Treg cells - FOXP3 and IL2RA - but clustered more closely with Treg cells than conventional T cells (Figure 4a and Supplementary Figure 4b). These expressed the highest levels of PDCD1 (gene encoding PD-1) among Treg cell populations and, uniquely for this cell type, the transmembrane protein-encoding gene MS4A6A (Supplementary Figure 4a). This population of Treg-like cells could represent a Treg population that transiently loses FOXP3 expression . +In the colon, Treg cells clustered into three populations as previously described in mouse (Figure 4a) . KLRB1+ (also known as CD161) Treg cells were characterized by expression of LAG3, IL2RA, CTLA4, KLRB1, ICOS and FOXP3 (Supplementary Figure 4a) suggesting a robust regulatory function, analogous to CD161+ Treg cells described in human colon tissue . Non-lymphoid tissue Treg (NLT Treg) cells express IKZF2, GATA3 and DUSP4 (Supplementary Figure 4a) consistent with the profile of thymic-derived Treg cells . The third population exhibited a profile reminiscent of lymphoid tissue Treg cells with expression of SELL, CCR7, TCR7, CXCR5 and RGS2, and were termed Lymphoid Tissue-like Treg (LT-like Treg) cells (Supplementary Figure 4a). LT-like Treg cells are likely newly arrived in the colon tissue from mLN. A small number of Ki67+ Treg cells were also identified in the colon and mLN (Figure 4a and Supplementary Table 3). The proportions of these subsets varied by donor, but were mostly consistent between colon regions (Figure 4b). +The presence of NLT-like Treg cells in the mLN and LT-like Treg cells in the colon, both with profiles suggesting migration, led us to recreate this migration pathway in silico. We ordered all Treg cells along "pseudospace" using Monocle2. This gave rise to a smooth pseudospace trajectory from resting central Treg cells in the mLN to highly regulatory Treg cells in the colon (Figure 4c). As seen in the mouse , NLT-like Treg cells and LT-like Treg cells blended in the middle of the trajectory, in accordance with these cells representing transitioning and migratory populations between lymphoid and peripheral tissues. In order to understand which gene signatures drive the migration and tissue adaptation of Treg cells in human tissues, we determined the genes changing along the previously calculated pseudospace (Figure 4c). Genes expressed at the beginning of pseudospace included SELL, CCR7 and CXCR4, permitting entry into lymph nodes (Figure 4c). At the end of pseudospace, the most highly expressed genes were FOXP3, IL2RA, CTLA4, IL10 and LAG3. These genes encoding suppressive molecules were co-expressed with TNF receptor genes (TNFRSF4, TNFRSF18, TNFRSF1B) indicating a reliance on the TNFRSF-NF-kappaB axis. Chemokine receptor-encoding genes, CXCR3, CXCR6, CCR6 and CCR4, were also expressed by Treg cells in the periphery, matching previous reports of TH1- and TH17-like Treg cells in the colon (Figure 4c). +Together, these results highlight heterogeneity in Treg cell states in mLN and colon, and reveal a possible FOXP3-transiently absent population. We also infer a continuous activation trajectory of these Treg cell states between draining lymph nodes and colon, and highlight genes regulating Treg cell migration between tissues and their adoption of Th-like profiles. +B cells display a proximal-to-distal colon activation gradient +Following from our observations in CD4+ T cells across the colon, we next focused on humoral responses by performing a more in-depth analysis of B cells in different colon regions. We compared transcriptional profiles of plasma cells between different colonic regions. This analysis revealed CCL3 and CCL4 as highly enriched in cecal plasma cells (log fold change of 0.61 and 0.70 and adjusted P <10-28 and <10-9 respectively) (Figure 5a). These chemokine-encoding genes are expressed by B cells in response to BCR activation and result in the migration of CCR5-expressing cells such as T cells and monocytes to the tissue microenvironment. This suggests that BCR cross-linking and signaling may be more prominent in the proximal colon. Cecal plasma B cells were also enriched for CXCR4 (log fold change of 0.44, adjusted P < 10-10) (Figure 5a), which encodes a chemokine receptor highly expressed by germinal center (GC) B cells and important for the movement of plasmablasts to the GC-T zone interface post-GC responses . +Among the genes more highly expressed by plasma cells in the sigmoid colon was CD27 (Figure 5a; log fold change of 0.24; adjusted P <10-40), encoding a member of the TNF receptor family that is expressed by memory B cells and even more highly by plasma cells . We confirmed differential expression of CD27 protein and additionally observed a proximal-to-distal gradient of increasing CD27 expression by plasma cells in the colon (Figure 5b). Targeted homing of B cells from their site of activation in lymphoid tissues to the colonic lamina propria relies on signaling through CCR10 and its cognate ligand, CCL28, and integrin alpha4beta7 . CCR10 (log fold change of 0.24; adjusted P < 10-16), ITGA4 (log fold change of 0.58; adjusted P < 10-24) and ITGB4 (log fold change 0.57; adjusted P < 10-108) were also more highly expressed by sigmoid colon IgA+ plasma cells (Figure 5a). +To determine whether the B cell clonal repertoire changes across the colon, we took advantage of the paired single-cell VDJ-sequencing data available from two donors for which scRNA-seq libraries were generated using 10x Genomics 5' chemistry. We confirmed the expression of IgM and IgD isotypes by follicular B cells, IgG1 and IgG2 by IgG+ plasma cells, IgA1 and IgM expression by memory cells in the mLN and predominantly IgA2 expression by plasma cells of the colon (Figure 5c). The mutation frequency of the heavy chain variable region was greatest in the plasma cells followed by memory B cells, indicating more somatic hypermutation by these cell types compared to the naive follicular B cells (Figure 5d). Additionally, while mutational frequency was consistent across colon regions and mLN for memory and follicular B cells, it was significantly increased in IgA+ plasma cell of the sigmoid colon compared to the other colon regions (Figure 5e). IgG+ plasma cells also showed a trend towards increased mutational frequency in the sigmoid colon, however their numbers were limiting (Figure 5e). +We then identified clonally-related B cells to explore clonal expansion dynamics of different cellular populations throughout the gut. Clonal expansion was evident in memory B cells, IgA+ plasma cells and IgG+ plasma cells (Figure 5f). While the relative abundance of clonal groups did not differ across the colon regions of memory B cells and IgG+ plasma cells, again this was greatest for IgA+ plasma cells in the sigmoid colon (Figure 5f). This was supported by bootstrapped VDJ sequence diversity analysis of clonally-related IgA+ plasma cells, which showed that the diversity of BCR sequences was consistent between donors and that there was a trend for decreased diversity (consistent with higher rates of clonal expansion) of IgA+ plasma cells in the sigmoid colon (Supplementary Figure 5a,b). Although some clones were shared between B cell types (i.e. memory and IgA+ plasma), indicating that alternate B cell fates can derive from a single precursor cell, most expanded clones within the gut were of the same cell type (Supplementary Figure 5c). Finally, we found many examples of B cell clones shared between all three colonic regions, and to a lesser extent the mLN, for both donors (Figure 5g), indicating dissemination of B cells throughout the colon as previously reported . Our observation of B cell dissemination throughout the colon was replicated with bulk BCR sequencing from whole tissue (Figure 5h). Increased clonal variability of sigmoid colon B cells was reflected in a greater spread of BCR variable chains expressed compared with cecum and transverse colon (Supplementary Figure 5d), altogether suggesting a more active response in the distal versus proximal colon. +These data indicate a highly activated state of plasma cells in the distal colon compared with proximal colon plasma cells, characterized by greater accumulation, somatic hypermutation, clonal expansion and stronger homing to the colon mucosa. +IgA is responsive to a richer microbial community in the sigmoid colon +Previous reports have shown that IgA is secreted by plasma cells in response to the presence of specific bacteria species, rather than as a general response to the microbiota . In light of this, we examined whether the increased plasma cell activation we observed in the sigmoid colon was linked to the differences in bacteria species. To this end, we assessed IgA-opsonization of bacteria from the donor microbiota samples (Figure 6a). A greater proportion of bacteria from the sigmoid colon was positive for IgA-binding compared with bacteria of the cecum and transverse colon (Figure 6b). Furthermore, shotgun sequencing of IgA-opsonized bacteria revealed a richer community of species in the sigmoid colon (Figure 6c and Supplementary Table 5). Diversity of IgA-bound bacteria, which considers relative abundance of each species, was lower in the sigmoid colon compared to cecum (Figure 6d). +These data suggest that, compared with the cecum, IgA+ plasma cells of the sigmoid colon respond to a rich and unevenly represented community of bacterial species, likely contributing to their increased activation status, strong homing to the colon and trend towards greater clonal diversity (Supplementary Figure 6). +Discussion +In this study we performed the first simultaneous assessment of the colonic mucosal microbiome and immune cells in human donors at steady-state. This enabled us to compare lymphoid and peripheral tissue immunity and explore how immune cells and their neighboring microbiome change along the colon within the same individuals. In doing so, we highlight previously unappreciated regional differences in both cellular communities. Our unique annotated colon immune single-cell dataset is available at www.gutcellatlas.org, where users can visualize their genes of interest. +We describe a shift in the balance of T helper subsets, with a predominance of TH17 in the cecum and TH1 in the sigmoid colon. Decreasing abundance of TH17 cells has similarly been shown from proximal small intestine to colon of mice . Additionally, simultaneous increase of the genus Bacteroides and TH1 numbers in the sigmoid colon are in line with findings that polysaccharide from Bacteroides fragilis preferentially induces TH1 differentiation in the intestine of germ-free mice . An alternative or complementary explanation for skewed T helper proportions and variation in transcriptional profiles is offered in a study by Harbour et al., which demonstrated that TH17 cells can give rise to a IFNy+ 'TH1-like' cell in response to IL-23 production by innate cells . These findings demonstrate the complexity of external signals shaping colonic Th responses leading to regional changes in their numbers and differentiation. +In contrast to conventional T cells, Treg cells are evenly represented across the colon. Treg cell subpopulations within the mLN and colon tissue are analogous to those we have recently described in mouse . We also identify an additional population, termed Treg-like cells, reminiscent of a CD25-FOXP3loPD1hi Treg population in the peripheral blood, although the latter was described to also express Ki67 . This population could represent uncommitted Treg cells experiencing transient loss of FOXP3 while retaining regulatory potential or permanent loss of FOXP3 and adoption of a more pro-inflammatory phenotype after repeated stimulation . Our pseudospace analysis of Treg cells suggests a continuum of activation states from resting cells in the lymphoid tissue through to highly suppressive cells in the periphery. We identify genes underlying this transition including chemokine receptors that are strongly expressed on arrival to the intestine and enable interaction with, and suppression of, TH1 and TH17 cells . Similarly to mouse, pseudospace from lymphoid to peripheral tissue correlates with transcriptional markers of migratory potential and suppression of effector cells. +We find that IgA+ plasma cells are more abundant and have greater expression of colon-specific migration markers (CCR10, ITGA4 and ITGB7) in the sigmoid colon compared to the proximal colon. This adds finer resolution to previous reports describing increasing abundance of IgA+ plasma cells from the small intestine to the colon . Our B cell repertoire analysis demonstrates extensive clonal expansion within each colon region and to a lesser extent between regions, arguing for colonic dissemination of B cells from the same precursor pool, followed by local expansion. Furthermore, more clonal sharing existed between regions of the colon than with mLN, consistent with recent work showing that while mLN clones can also be detected in blood, the intestines are host to a unique B cell clonal network . Sigmoid colon plasma cells, in particular, exhibit greater mutational abundance and clonal expansion. This is consistent with a trend towards reduced plasma cell clonal diversity in the sigmoid colon. While this was not statistically significant, this may have been due to the relatively low number of VDJ sequences obtained from these samples, thus decreasing the power of this analysis. Previous work has shown the mutational frequency of B cells is consistently high between duodenum and colon and are primarily driven by dietary and microbiome antigens respectively . Thus, we suggest that enhanced plasma cell accumulation, mutation and expansion in the sigmoid colon is in response to continued stimulation from the local microbiome. This may happen through increased engagement of sigmoid colon plasma cells in T cell-mediated germinal center reactions in gut-associated lymphoid structures or in T cell-independent somatic hypermutation in local isolated lymphoid follicles followed by local expansion. Yet the exact mechanisms require further study. +Finally, we show greater IgA binding to the microbiota in the sigmoid colon compared to proximal sites. One possible explanation for this observation is the accumulation of upstream secreted IgA and IgA-bound bacteria in the sigmoid colon. However, our simultaneous observation of enhanced plasma cell responses in the sigmoid colon suggests that IgA is locally produced as a result of immune poising. Possible scenarios contributing to a richer immune-reactive microbiome in the sigmoid colon are bacteria derived externally via the rectum. Alternatively, environmental pressures (i.e. lower water and nutrient levels ) could restrict outgrowth of dominant gastrointestinal species of the proximal colon, providing space for smaller communities of opportunistic species. The IgA response in the colon is antigen-specific rather than a general response to the presence of bacteria . Thus, the overall increased number of unique species recognized by host IgA antibodies in the sigmoid colon is fitting with the enhanced clonal expansion and mutation of plasma cells at this site. +Together, our simultaneous analyses of microbiome and neighboring immune cells highlight the significance of environmental signals in shaping and maintaining regional adaptive immune cell composition and function in the intestine at steady-state. Dysregulation of T helper cells and plasma cells , has been implicated in susceptibility to inflammatory bowel disease. Observations of the linked compartmentalization of these immune cells and microbial species along the colon at steady-state may provide a platform for understanding the mechanisms underpinning the tropism of different intestinal diseases to specific regions of the gut, such as Crohn's disease and ulcerative colitis. +Methods +Colon and mesenteric lymph node tissue retrieval +Human tissue was obtained from deceased transplant organ donors after ethical approval (reference 15/EE/0152, East of England Cambridge South Research Ethics Committee) and informed consent from the donor family. Fresh mucosal tissue from the cecum, transverse colon and sigmoid colon, and lymph nodes from the intestine mesentery, were excised within 60 minutes of circulatory arrest and colon tissue preserved in University of Wisconsin (UW) organ preservation solution (Belzer UW Cold Storage Solution, Bridge to Life, USA) and mLN stored in saline at 4 C until processing. Tissue dissociation was conducted within 2 hours of tissue retrieval. Four individuals (287c, 296b, 403c and 411c) had received antibiotics in the two weeks prior to death (Supplementary Table 1). +Tissue dissociation for flow-sorting separation and MAC separation +Tissue pieces from donors 290b, 298c, 302c, 364b and 411c were manually diced and transferred into 5 mM EDTA (Thermo Fisher Scientific)/1 mM DTT (Sigma-Aldrich)/10 mM HEPES (Thermo Fisher Scientific)/2% FBS in RPMI and incubated in a shaker (~200 rpm) for 20 minutes at 37 C. Samples were briefly vortexed before the media renewed and incubation repeated. Tissue pieces were washed with 10 mM HEPES in PBS and transferred into 0.42 mg/ml Liberase TL (Roche)/0.125 KU DNase1 (Sigma)/10 mM HEPES in RPMI and incubation for 30 minutes at 37 C. The digested samples were passed through a 40 microm strainer and washed through with FBS/PBS. +Flow-sorting +Cells from donor 290b, 298c and 302c were pelleted and resuspended in 40% Percoll (GE Healthcare). This was underlayed with 80% Percoll and centrifuged at 600g for 20 minutes with minimal acceleration and break. Cells at the interface were collected and washed with PBS. Cells were stained for fluorescent cytometry using Zombie Aqua Fixable Viability Dye (Biolegend, cat. 423101; diluted 1:200), CD45-BV605 (clone HI30, Bioegend, cat. 304043; dilution 1:100), CD3-FITC (clone OKT3; Biolegend, cat. 317305; dilution 1:100), CD4-BV421 (clone SK3; Biolegend, cat. 344631; dilution 1:100), CD8-PE-Cy7 (clone SK1, Biolegend, cat. 344711; dilution 1:100), CD19-APC-Cy7 (clone HIB19, Biolegend, cat. 302217; dilution 1:100), IgD-PE Dazzle (clone IA6-2, Biolegend, cat. 348207; dilution 1:100), CD27-BV711 (clone M-T271, Biolegend, cat. 356429; dilution 1:100), HLA-DR- BV785 (clone L243, Biolegend, cat. 307641; dilution 1:100), CD14-APC (clone 63D3, Biolegend, cat. 367117; dilution 1:100) and CD11c-PE (clone 3.9; eBioscience, cat. 12-0116-42; dilution 1:100). Non-CD4+ T immune cells were sorted as live singlet CD45+, CD3- and CD4-. CD4+ T cells were sorted as live singlet CD45+, CD3+ and CD4+. Each faction was manually counted using 0.4% Trypan Blue (gibco) and a haemocytometer and diluted to 500 cells/microl in PBS. Sorting was carried out on a BD FACS ARIA Fusion. Analysis of flow-sorting data was conducted with FlowJo Software package (version 10.4). +MACS cell enrichment +Cells from donor 411c were pelleted for 5 minutes at 300g and resuspended in 80 microl of ice-cold MACS buffer (0.5% BSA (Sigma-Aldrich Co. Ltd), 2 mM EDTA (ThermoFisher) in DPBS (Gibco)) and 20 microl of CD45 Microbeads (Miltenyi Biotech). Cells were incubated for 15 minutes at 4 C before being washed with 2 ml of MACS buffer and centrifuged as above. Cells were resuspended in 500 microl of MACS buffer and passed through a pre-wetted MS column on QuadroMACS magnetic cell separator (Miltenyi). The column was washed 4 times with 500 microl of MACS buffer, allowing the full volume of each wash to pass through the column before the next wash. The column was removed from the magnet and the cells eluted with force with 1 ml of MACS buffer into a 15 ml tube. Cells were pelleted as above and cell number and viability were determined using a NucleoCounter NC-200 and Via1-Cassette (ChemoMetec). Cells were resuspended at 500 cells/microl in 0.04% BSA in PBS. +Tissue dissociation for Ficoll separation +Tissues from donor 390c were manually diced and <5.0 grams was added per Miltenyi C tube with 5mL tissue dissociation media (Liberase TL (0.13 U/mL; Roche) and DNase (10 U/mL Benzonase nuclease; Merck)) in 1% FCS and 20 mM HEPES in PBS (Lonza). Samples were dissociated with GentleMACS Octo for 30 minutes homogenizing/37 C cycle. Enzymatic digestion was stopped with the addition of 2 mM EDTA in tissue dissociation media. Digested samples were then passed through a 70 microM smart strainer (Miltenyi) before being washed with PBS and pelleted at 500g for 10 minutes. Cells were resuspended in PBS, layered onto FicollPaque Plus (GE Healthcare) and spun at RT 400g for 25 minutes. Mononuclear cells were retrieved from Ficoll layer and washed with PBS. Cells were filtered through 0.2 microM filter (FLowmi cell strainers, BelArt). Cells were manually counted using a hemocytometer and diluted to a concentration of 1000 cells/microl in 0.04% BSA in PBS. +10x Genomics Chromium GEX library preparation and sequencing +Cells were loaded according to the manufacturer's protocol for Chromium single cell 3' kit (version 2) or 5' gene expression (version 2) in order to attain between 2000-5000 cells/well. Library preparation was carried out according to the manufacturer's protocol. For samples from donors 290b, 298c and 302c, eight 10x Genomics Chromium 3' libraries were pooled sequenced on eight lanes of an Illumina Hiseq 4000. For samples from donors 390c and 417c, sixteen 10x Genomics Chromium 5' libraries were pooled and sequenced on 2 lanes of a S2 flowcell of Illumina Novaseq 6000 with 50 bp paired end reads. +10x Genomics Chromium VDJ library preparation and sequencing +10x Genomics VDJ libraries were generated from the 5' 10x Genomics Chromium cDNA libraries as detailed in the manufacturer's protocol. BCR libraries for each sample were pooled and sequenced on a single lane of Illumina HiSeq 4000 with 150 bp paired end reads. +Plate-based scRNA-seq +Plate-based scRNA-seq was performed with the NEBNext Single Cell/Low Input RNA Library Prep Kit for Illumina (New England Biolabs Inc, E6420L). Cells from donor 364b were snap frozen in 10% DMSO in 90% BSA. Cells were thawed rapidly in a 37 C water bath and diluted slowly with pre-warmed 2% FBS in D-PBS. Cells were pelleted for 5 minutes at 300g and washed with 500 microl of DPBS and pelleted as before. Cells were resuspended in 200 microl of CD25-PE (clone M-A251, Biolegend, cat. 356102; diluted 1:200), CD127-FITC (clone eBioRDR5, eBioscience, cat. 11-1278-42; diluted 1:200), CD4-BV421 (clone SK3, Biolegend, cat. 344632; diluted 1:200) and TCRalpha/beta-APC (clone 1p26, Biolegend, cat. 306718; diluted 1:200) and Zombie Aqua Fixable Viability Dye (diluted 1:400) and incubated for 30 minutes in the dark at room temperature. Cells were washed twice with 500 microl of 2% FBS in D-PBS before being filtered through a 100 microM filter. Single, live, TCRbeta+ cells were FACS sorted into a pre-prepared 384-well plate (Eppendorf, Cat. No. 0030128508) containing 2 microl of 1X NEBNext Cell Lysis Buffer. FACS sorting was performed with a BD Influx sorter with the indexing setting enabled. Plates were sealed and spun at 100g for 1 minute then immediately frozen on dry ice and stored at -80 C. +cDNA generation was then performed in an automated manner on the Agilent Bravo NGS workstation (Agilent Technologies). Briefly, 1.6 microl of Single Cell RT Primer Mix (New England Biolabs Inc) was added to each well and annealed on a PCR machine (MJ Research Peltier Thermal Cycler) at 70 C for 5 minutes. 4.4 microl of Reverse Transcription (RT) mix was added to the mixture and further incubated at 42 C for 90 minutes followed by 70 C for 10 minutes to generate cDNA. 22 microl of cDNA amplification mix containing NEBNext Single Cell cDNA PCR MasterMix and PCR primer was mixed with the cDNA, sealed and spun at 100g for 1 minute. cDNA amplification was then performed on a PCR machine (MJ Research Peltier Thermal Cycler) with 98 C 45 s, 20 cycles of [98 C 10 s, 62 C 15 s, 72 C 3 mins], 72 C 5 mins. The 384-well plate containing the amplified cDNA was purified with an AMPure XP workflow (Beckman Coulter, Cat No. A63880) and quantified with the Accuclear Ultra High Sensitivity dsDNA kit (Biotium, Cat. No. 31028). ~10 ng of cDNA was stamped into a fresh 384-well plate (Eppendorf, Cat. No. 0030128508) for sequencing library preparation. +Sequencing libraries were then generated on the Agilent Bravo NGS workstation (Agilent Technologies). Purified cDNA was fragmented by the addition of 0.8 microl of NEBNext Ultra II FS Enzyme Mix and 2.8 microl of NEBNext Ultra II FS Reaction buffer to each well and incubated on a PCR machine (MJ Research Peltier Thermal Cycler) for 72 C at 15 minutes and 65 C for 30 minutes. A ligation mixture was then prepared containing NEBNext Ultra II Ligation Master Mix, NEBNext Ligation Enhancer and 100 microM Illumina compatible adapters (Integrated DNA Technologies) and 13.4 microl added to each well of the 384-well plate. The ligation reaction was incubated on the Agilent workstation at 20 C for 15 minutes and then purified and size selected with an AMPure XP workflow (Beckman Coulter, Cat No. A63880). 20 microl of KAPA HiFi HS Ready Mix (Kapa Biosystems, Cat. No. 07958927001) was then added to a pre-prepared 384-well plate (Eppendorf, Cat. No. 0030128508) containing 100 microM i5 and i7 indexing primer mix (50 microM each) (Integrated DNA Technologies). The indexing primers pairs were unique to allow multiplexing of up to 384 single cells in one sequencing pool. The plate containing the PCR Master Mix and indexing primers was stamped onto the adapter ligated purified cDNA, sealed and spun at 100g for 1 minute. Amplification was performed on a PCR machine (MJ Research Peltier Thermal Cycler) with 95 C for 5 minutes, 8 cycles of [98 C 30 seconds, 65 C 30 seconds, 72 C 1 minute], 72 C 5 minutes. The PCR products were pooled in equal volume on the Microlab STAR automated liquid handler (Hamilton Robotics) and the pool purified and size selected with an AMPure XP workflow (Beckman Coulter, Cat No. A63880). The purified pool was quantified on an Agilent Bioanalyser (Agilent Technologies) and sequenced on one lane of an Illumina HiSeq 4000 instrument. +Single-cell RNA sequencing data alignment +10x Genomics gene expression raw sequencing data was processed using CellRanger software version 3.0.2 and 10X human transcriptome GRCh38-3.0.0 as the reference. 10x Genomics VDJ immunoglobulin heavy and light chain were processed using cellranger vdj (version 3.1.0) and the reference cellranger-vdj-GRCh38-alts-ensembl-3.1.0 with default settings. NEB sequencing data was processed using STAR 2.5.1b into HTSeq (version 0.11.2) and mapped to 10X human transcriptome GRCh38-1.2.0. +Single-cell RNA sequencing quality control +Single cell read counts from all samples were pooled and filtered considering number of UMIs - keeping genes expressed in minimum of 3 cells, keeping cells where genes detected are in a range 700-6000. Non-immune cells were excluded from the final analysis based on the absence of PTPRC and presence of markers such as EPCAM (epithelial cells) and COL1A1 (fibroblasts). +Cell type annotation +Cells were clustered using Scanpy (version 1.4) processing pipeline . In short, the counts were normalized to 10,000 reads per cell (sc.pp.normalise_per_cell) and log transformed (sc.pp.log1p) to be comparable amongst the cells. The number of UMIs and percentage of mitochondrial genes were regressed out (sc.pp.regress_out) and genes were scaled (sc.pp.scale) to unit variance. The normalized counts were used to detect highly variable genes (sc.pp.highly_variable_genes). Batch correction between the donors was performed using bbknn (version 1.3.6) on 50 PCs and trim parameter set to 100. Clusters were then identified using Leiden graph-based clustering (resolution set to 1). Cell identity was assigned using known markers shown in Figure 2b and the top differentially expressed genes identified using Wilcoxon rank-sum test (sc.tl.rank_genes_groups function, Supplementary Table 4). CD4 T cells, myeloid cells, B cells were subclustered for identification of subsets within each cluster. Treg cells annotated above were further subclustered using the "FindClusters" and "RunUMAP" functions from Seurat (version 3.0.1) (Figure 4). The number of PCs used for Treg cell clustering were estimated by the elbow of a PCA screen plot, in combination to manual exploration of the top genes from each PC. Clustering of mLN Treg cells was performed using 1-14 PCs and resolution of 0.3 and colonic Treg cells with 1-11 PCs and resolution of 0.3. +'Pseudospace' analysis +'Pseudospace' analysis of Treg cells was performed using Monocle (version 2.10.1) . Data was log normalized and cell ordered based on DDRTree reduction on highly variable genes with donor effect regression. The heatmap in Figure 4c was generated using the "plot_pseudotime_heatmap" function in Monocle (version 2.10.1) . Genes contributing to 'pseudospace' were first filtered for exclusion of mitochondria and immunoglobulin genes, expression in at least 15 cells and qval < 0.001. Gene expression was smoothed into 100 bins along pseudospace using a natural spline with 3 degrees of freedom. Matching column annotation for each bin was determined as the most prevalent tissue region origin of the cells within that bin. Genes were grouped into 2 clusters and ordered by hierarchical clustering. Cells were ordered through pseudotime. +Sampling of microbiome +Swabs were taken immediate of the mucosal surface of excised tissue using MWE Transwab Cary Blair (catalogue number: MW168). Swabs were maintained at 4 C. Working in a biosafety cabinet, swabs were washed in 500 microl of anaerobic PBS and mixed with anaerobic 50% glycerol before being snap frozen on dry ice and stored at -80 C until use. +Microbiota profiling and sequencing +The microbiota vials were defrosted on ice. Approximately 100 microl of each was transferred into new Eppendorf tubes. DNA was extracted from microbiome samples using the MP Biomedical FastDNA SPIN Kit for soil (catalogue number 116560200). 16S rRNA gene amplicon libraries were made by PCR amplification of variable regions 1 and 2 of the 16S rRNA gene using the Q5 High-Fidelity Polymerase Kit supplied by New England Biolabs as described in . Primers 27F AATGATACGGCGACCACCGAGATCTACAC (first part, Illumina adaptor) TATGGTAATT (second part, forward primer pad) CC (third part, forward primer linker) AGMGTTYGATYMTGGCTCAG (fourth part, forward primer) and 338R CAAGCAGAAGACGGCATACGAGAT (first part, reverse complement of 3' Illumina adaptor) ACGAGACTGATT (second part, golay barcode) AGTCAGTCAG (third part, reverse primer pad) AA (fourth part, reverse primer linker) GCTGCCTCCCGTAGGAGT (fifth part, reverse primer) were used. Four PCR amplification reactions per sample were carried out; products were pooled and combined in equimolar amounts for sequencing using the Illumina MiSeq platform, generating 150 bp reads. Analysis of partial 16S rRNA sequences was carried out using SILVA v132 and mothur MiSeq SOP v1.42.3 . The 16S rRNA gene alignments were used to determine a maximum likelihood phylogeny using FastTree (version 2.1.10) . Phylogenetic trees were visualized and edited using iTOL (version 5) . +T cell clonal sharing analysis +T cell receptor sequences generated using the Smartseq2 scRNA-seq protocol were reconstructed using the TraCeR software as previously described . +Bulk B cell receptor sequencing +Small portions of samples were taken from excised tissues and snap frozen in 1ml of RNAlater (Ambion). RNA extracted from tissue using the QIAshreddar and QIAGEN Mini Kit (50). RNA concentration was measured using a Bioanalyzer. B Cell Receptor (BCR) heavy chain sequences of all B lineage subsets present in the tissue were amplified as previously described . Briefly, RNA was reverse transcribed using a barcoded reverse primer set capturing all antibody (sub)classes. Targeted heavy-chain amplification was performed with a multiplex set of IGHV gene primers to FR1 and a universal reverse primer using HiFi qPCR KAPA Biosystems. After adapter filtering and trimming, BCR sequences were assembled and aligned using MiXCR (version 3.0.1) . It is worth noting that detected BCR sequences are biased towards those included in the reference database and while there is a continuous discovery of novel germline alleles, no database is currently a complete reflection of the human IGH locus diversity. Only in-frame and IGH sequences with at least 3 read counts were kept for the analysis. To calculate the CDR3 nucleotide shared repertoire, the tcR package (version 2.2.1) was used . +10x Genomics single-cell VDJ data processing, quality control and annotation +Poor quality VDJ contigs that either did not map to immunoglobulin chains or were assigned incomplete by cellranger vdj were discarded. For additional processing, all IgH sequence contigs per donor were combined together. We further filtered IgH contigs as to whether they had sufficient coverage of constant regions to ensure accurate isotype assignment between closely related subclasses using MaskPrimers.py (pRESTO version 0.5.10) . IgH sequences were then further annotated using IgBlast (version 1.12.0) and reassigned isotype classes using AssignGenes.py (pRESTO) prior to correction of ambiguous V gene assignments using TIgGER (version.03.1) . Clonally-related IgH sequences were identified using DefineClones.py (ChangeO version 0.4.5) with a nearest neighbor distance threshold of 0.2, as determined by visual inspection of the output of distToNearest (Shazam version 0.1.11) . CreateGermlines.py (ChangeO version 0.4.5) was then used to infer germline sequences for each clonal family and observedMutations (Shazam version 0.1.11) was used to calculate somatic hypermutation frequencies for each IgH contig. Estimated clonal abundances and IgH diversity analyses within each donor were performed using estimateAbundance, rarefyDiversity and testDiversity of Alakazam (version 0.2.11) with a bootstrap number of 500. Finally, the number of quality filtered and annotated IgH, IgK or IgL chains were determined per unique cell barcode. If more than one contig per chain was identified, metadata for that cell was ascribed as "Multi". The subsequent metadata table was then integrated with the single-cell RNA-seq gene expression objects for annotation of IgH contigs with B cell types and downstream analysis. Co-occurrence of expanded clone members between tissues and/or cell types was reported as a binary event for each clone that contained a member within two different tissues and/or cell types. +Quantifying IgA binding of bacteria +Microbiome frozen in 50% glycerol were defrosted on ice before being washed in PBS and pelleted for 3 minutes at 8,000 rpm. Bacteria were then stained on ice for 30 minutes with IgG-PE (Biolegend, clone HP6017, cat. 409304; dilution 1:100), IgA1/2 biotin (BD, clone G20-359, cat. 555884; dilution 1:50), followed by 20 minutes with streptavidin-APC (Biolegend, cat. 405207; dilution 1:100). For isotype controls, mouse IgG1 kappa Isotype- Biotin (BD, cat. 550615; dilution 1:50) or mouse IgG2a, kappa Isotype- PE (Biolegend, cat. 400212; dilution 1:100) were used. The bacteria were washed before and after DNA was stained with Hoechst33342 (Sigma-Aldrich). The stained bacteria were sorted as Hoechst+ and IgG+IgA+ or IgG-IgA+ into PBS using the BD Influx and then stored at -80 C until DNA extraction. +Genomic DNA Extraction for shotgun sequencing of IgA-bound bacteria +Bacteria in PBS was defrosted on ice before being pelleted at 3900 rpm for 5 minutes at 4 C. The supernatant was removed and the pellet resuspended in 2 ml of 25% sucrose in TE Buffer (10mM Tris pH8 and 1mM EDTA pH8). 50 microl of 100 mg/ml lysozyme in 0.25M Tris was added and incubated at 37 C for 1 hour. 100ul of Proteinase K (18 mg/ml),15 microl of RNAase A (20 mg/ml), 400 microl of 0.5 M EDTA (pH 8), and 250 microl of 10% Sarkosyl were then added. This was left on ice for 2 hours and then incubated at 50 C. The DNA was then mixed with 5 ml of Tris-EDTA buffer and purified using four cycles of addition of 5 ml Phenol: Chlorophorm: Isoamyl Alcohol in 15 ml MaXtract High Density Phase Lock Gel (PLG) tubes (QIAGEN), centrifuging at 2800 rpm for 5 min and retreiving the aqueous phase into a new PLG tube. DNA in the final aqueous phase was precipitated with 10 ml of 100% ethanol at -20 C overnight, centrifuged at 3900 rpm for 20 min at 4 C and washed with 10 ml of 70% ethanol, before being centrifuged at 4500 rpm for 10 min at 4 C and gently dried at 50 C overnight. DNA was then resuspended in 200 microl of Tris-EDTA buffer and isolated by running through a gel. Samples were quantitated by qbit and pooled. Pooled DNA was sequenced on the Illumina Hiseq2500 platform. Inverse Simpson (diversity) and chao (richness) of IgA-opsonized bacteria was determined by using R package "microbiome". +Statistical analysis +Sample sizes for each experimental group and statistical tests used are included in the relevant figure legends. Measurements were taken from distinct donors in all experiments. +Supplementary Material +Reporting summary +Further information on research design is available in the Nature Research Reporting Summary linked to this paper. +Data availability +Raw sequencing data files are available at ArrayExpress (accession numbers: E-MTAB-8007, E-MTAB-8474, E-MTAB-8476, E-MTAB-8484 & E-MTAB-8486). Sequencing data for the microbiome are available at MGnify (ERA numbers are listed in Supplementary Table 6). Processed single-cell RNA sequencing data is available for online visualization and download at gutcellatlas.org. +Author contributions +K. R. J. initiated this project, designed and performed scRNA-seq and microbiome experiments, analyzed data and wrote the manuscript. T. G. analyzed bulk BCR-seq data and contributed extensively to scRNA-seq data analysis. R. E. contributed to data interpretation and data analysis. N. K. and E. L. G. analyzed 16S ribosomal sequencing and metagenomics data. M. D. S assisted in microbiome related experiments. H. W. K. and L. K. J. analyzed the 10x Genomics VDJ datasets and contributed to generation of figures. B. R. B. and K. S. P. carried out tissue collection. J. R. F. designed flow-sorting panel and assisted in flow-sorting. V. P. assisted in bulk BCR library preparation and analysis. L. B. J., O. S., S. H. and J. L. J. dissociated tissues from donor 390c. K. P. carried out scRNA-seq read alignment and quality control. S. C. F., K. B. M. and M. R. C. designed experiments and interpreted data. T. D. L. and S. A. T. initiated and supervised the project and interpreted data. All authors edited the paper. +Competing interests +S.C.F. and T.D.L. are either employees of, or consultants to, Microbiotica Pty Ltd. +Dynamics of the human gut microbiome in inflammatory bowel disease +Gut biogeography of the bacterial microbiota +Molecular characterization of the microbial species that colonize human ileal and colonic mucosa by using 16S rDNA sequence analysis +Regional specialization within the intestinal immune system +Functional Specializations of Intestinal Dendritic Cell and Macrophage Subsets That Control Th17 and Regulatory T Cell Responses Are Dependent on the T Cell/APC Ratio, Source of Mouse Strain, and Regional Localization +Th17 Cell Induction by Adhesion of Microbes to Intestinal Epithelial Cells +Induction of intestinal Th17 cells by segmented filamentous bacteria +Induction of colonic regulatory T cells by indigenous Clostridium species +An immunomodulatory molecule of symbiotic bacteria directs maturation of the host immune system +Ectopic colonization of oral bacteria in the intestine drives TH1 cell induction and inflammation +Bifidobacterial surface-exopolysaccharide facilitates commensal-host interaction through immune modulation and pathogen protection +Structure, function and diversity of the healthy human microbiome +Linking long-term dietary patterns with gut microbial enterotypes +Inter-niche and inter-individual variation in gut microbial community assessment using stool, rectal swab, and mucosal samples +Intra- and inter-cellular rewiring of the human colon during ulcerative colitis +Two distinct interstitial macrophage populations coexist across tissues in specific subtissular niches +Human Tissue-Resident Memory T Cells Are Defined by Core Transcriptional and Functional Signatures in Lymphoid and Mucosal Sites +CCR6 mediates dendritic cell localization, lymphocyte homeostasis, and immune responses in mucosal tissue +Transcription factor KLF2 regulates the migration of naive T cells by restricting chemokine receptor expression patterns +Lamin A/C augments Th1 differentiation and response against vaccinia virus and Leishmania major +Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria +Single-Cell Transcriptomics of Regulatory T Cells Reveals Trajectories of Tissue Adaptation +Plasticity of Foxp3 T Cells Reflects Promiscuous Foxp3 Expression in Conventional T Cells but Not Reprogramming of Regulatory T Cells +Human retinoic acid-regulated CD161 regulatory T cells support wound repair in intestinal mucosa +The alarmin IL-33 promotes regulatory T-cell function in the intestine +Antigen receptor engagement selectively induces macrophage inflammatory protein-1 alpha (MIP-1 alpha) and MIP-1 beta chemokine production in human B cells +Highly specific blockade of CCR5 inhibits leukocyte trafficking and reduces mucosal inflammation in murine colitis +Plasma cell output from germinal centers is regulated by signals from Tfh and stromal cells +Circulating human B and plasma cells. Age-associated changes in counts and detailed characterization of circulating normal CD138- and CD138 plasma cells +Differentiation and homing of IgA-secreting cells +Characterization of the B Cell Receptor Repertoire in the Intestinal Mucosa and of Tumor-Infiltrating Lymphocytes in Colorectal Adenoma and Carcinoma +A Primitive T Cell-Independent Mechanism of Intestinal Mucosal IgA Responses to Commensal Bacteria +Th17 cells give rise to Th1 cells that are required for the pathogenesis of colitis +Cells with Treg-specific FOXP3 demethylation but low CD25 are prevalent in autoimmunity +Loss of FOXP3 expression in natural human CD4+CD25+ regulatory T cells upon repetitive in vitro stimulation +Function of mucosa-associated lymphoid tissue in antibody formation +An atlas of B-cell clonal distribution in the human body +Hypermutation, diversity and dissemination of human intestinal lamina propria plasma cells +Requirement for Lymphoid Tissue-Inducer Cells in Isolated Follicle Formation and T Cell-Independent Immunoglobulin A Generation in the Gut +Effector T Helper Cell Subsets in Inflammatory Bowel Diseases +Anti-commensal IgG drives intestinal inflammation and type 17 immunity in ulcerative colitis +SCANPY: large-scale single-cell gene expression data analysis +BBKNN: fast batch alignment of single cell transcriptomes +Comprehensive Integration of Single-Cell Data +The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells +Culturing of 'unculturable' human microbiota reveals novel taxa and extensive sporulation +Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform +FastTree 2 - Approximately Maximum-Likelihood Trees for Large Alignments +Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees +T cell fate and clonality inference from single-cell transcriptomes +Combined Influence of B-Cell Receptor Rearrangement and Somatic Hypermutation on B-Cell Class-Switch Fate in Health and in Chronic Lymphocytic Leukemia +MiXCR: software for comprehensive adaptive immunity profiling +tcR: an R package for T cell receptor repertoire advanced data analysis +pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires +IgBLAST: an immunoglobulin variable domain sequence analysis tool +Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data +Variation in the microbiome from proximal to distal colon. +a) Workflow for 16S ribosomal sequencing of matching mucosal microbiomes and +scRNA-seq profiling of immune cells from mesenteric lymph node (mLN), and lamina +propria of cecum, transverse colon and sigmoid colon. b) Phylogenetic tree +representing diversity and mean abundance of bacterial species in the cecum, +transverse colon and sigmoid colon. Mean abundance was calculated as the +percentage of operational taxonomic units (OTUs) for each species from total as +determined by 16S rRNA sequencing and averaged for twelve donors (black scale). +Unassigned OTUs are shown as black branches. Bacteria groups of interest are +highlighted. c) Relative abundances of OTUs at genus level of bacteria species +in colon regions as in (b). +Profiling immune cells along the steady-state colon. +a) UMAP illustration of pooled scRNA-seq data of immune cells of mLN, cecum, +transverse colon and sigmoid colon from five donors colored by cell type +annotation (left) and tissue of origin (right). b) Heatmap of mean expression of +marker genes used to annotate cell types in (a). Point size shows the fraction +of cells with nonzero expression. c) Relative percentages of CD4+ T +subtypes within all CD4+ T cells for each tissue as in (a). d) +Relative percentages of cell types within all non-CD4+ T immune cells +for each tissue as in (a), with B lineage cells shown in the left panel and all +other cell types in the right panel. +Dissemination of T helper cells in colon and region-determined +transcriptional profiles. +a) Correlation matrix of mean transcriptional profiles of TH1 and +TH17 cells from cecum, transverse colon, sigmoid colon and mLN +(n=5 donors). b) Mean expression level of differentially expressed genes of +pooled TH1 and TH17 between cecum, transverse colon and +sigmoid colon as in (a). Point size shows the fraction of cells with nonzero +expression. c) UMAP projection of Smartseq2 profiled flow-sorted T cells +annotated as TH1 and TH17 cells of the cecum, transverse +colon and sigmoid colon (n=1 donor). Colored lines connect cells sharing the +same CDR3 sequence. d) Heatmap of numbers of members within clonal families in +TH1/TH17 subsets (left) and colon region (right) as in +(c). +Treg activation pathway from lymphoid to peripheral +tissue. +a) UMAP visualization of Treg subtypes in mLN (left) and pooled from +cecum, transverse colon and sigmoid colon (right) (n=5 donors). b) Relative +proportions of Treg subsets within all Treg cells from mLN +and colon tissue regions as in (a). Bars show mean proportion across all donors +(circles). c) Density of Treg subclusters as in a across +'pseudospace' (top) and expression kinetics of genes contributing +to pseudospace smoothed into 100 bins (filtered by qval < 0.001 and +expression in >15 cells). Top bar shows the most represented tissue +within each bin. Various dynamically expressed immune-related molecules are +annotated, with key genes colored red. +B cells are more abundant, clonally expanded and mutated in the sigmoid +colon. +a) Mean expression of key differentially expressed genes by IgA+ +plasma cells in cecum, transverse colon and sigmoid colon (n=5 donors). Point +size shows the fraction of cells with nonzero expression. b) Proportion of +CD27+ B cells of total B cells from cecum, transverse colon and +sigmoid colon determined by flow cytometry (n=4 donors). Bar represents the mean +and connected points represents values of each donor. Analysis is a two-tailed +paired t test. c) UMAP visualization of B cells for which matched single-cell +VDJ libraries were derived using 10x Genomics 5' scRNA-seq (n=2 donors) +colored by cell type annotation (left), tissue (middle) and antibody isotype +(right). Bar plot of antibody isotype frequencies per annotated cell type (far +right). d) UMAP visualization of somatic hypermutation frequencies of IgH +sequences as in (c). e) Quantitation of somatic hypermutation frequencies of IgH +sequences from B cell types and gut regions as in (c). f) Estimated clonal +abundances per donor for members of expanded B cell clones in B cell types and +gut regions as in (c). g) Binary count of co-occurrence of expanded B cell +clones identified by single-cell VDJ analysis shared across gut regions as in +(c). h) Co-occurrence of expanded B cell clones identified by bulk B cell +receptor sequencing across gut regions (n=3 donors). Statistics in (e) and (f) +are calculated with two-sided Wilcoxon signed-rank tests. Rows and columns in +(g) and (H) are ordered by hierarchical clustering. * P <0.05; ** P +<0.01; *** P <0.001; **** P <0.0001 +Increasing number of microbiota species recognized by antibodies in the +sigmoid colon. +a) Experimental workflow for assessing Ig-opsonized colon bacterial species. b) +Representative histogram of IgA1/2-bound Hoechst+ bacteria and +summary plot of bound bacteria as a proportion of total bacteria (n=13 donors). +Positive binding is set against an isotype control. c) Richness of bacteria +species determined as the number of unique species and d) diversity of species +identified from shotgun sequencing of Ig-opsonized bacteria from (b) (n=6 +donors). P values were calculated using one-tailed paired t tests. * P +<0.05 \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s42003-020-0922-4.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s42003-020-0922-4.txt new file mode 100644 index 0000000..1239b22 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s42003-020-0922-4.txt @@ -0,0 +1,3 @@ +AbstractFibroblasts are an essential cell population for human skin architecture and function. While fibroblast heterogeneity is well established, this phenomenon has not been analyzed systematically yet. We have used single-cell RNA sequencing to analyze the transcriptomes of more than 5,000 fibroblasts from a sun-protected area in healthy human donors. Our results define four main subpopulations that can be spatially localized and show differential secretory, mesenchymal and pro-inflammatory functional annotations. Importantly, we found that this fibroblast ‘priming’ becomes reduced with age. We also show that aging causes a substantial reduction in the predicted interactions between dermal fibroblasts and other skin cells, including undifferentiated keratinocytes at the dermal-epidermal junction. Our work thus provides evidence for a functional specialization of human dermal fibroblasts and identifies the partial loss of cellular identity as an important age-related change in the human dermis. These findings have important implications for understanding human skin aging and its associated phenotypes. + +FULL TEXT NOT AVAILABLE \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s42255-022-00531-x.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s42255-022-00531-x.txt new file mode 100644 index 0000000..f8859a2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1038_s42255-022-00531-x.txt @@ -0,0 +1,518 @@ +Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in Type 1 Diabetes +Type 1 Diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The etiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive, and non-diabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time-of-flight, and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreas function. +Introduction +Type 1 diabetes (T1D) is an autoimmune disease which occurs as a consequence of the destruction of insulin-producing beta cells in the islets of Langerhans within the pancreas. This complex disease is characterized by atypical beta-immune interactions including production of beta cell autoantibodies and the immunological attack on beta cells by cytotoxic CD8+ T cells. +T1D autoimmunity has been linked to poorly understood genetic and environmental factors. Genome-wide association studies have implicated multiple loci in T1D, with the major histocompatibility complex (MHC) Class II genes as the dominant susceptibility determinant of this disease. However, the precise cellular context through which T1D susceptibility genes cause the destruction of beta cells remains to be discovered. Addressing this question is particularly challenging since the pancreas is a heterogeneous organ, composed of multiple distinct cell types. +Two nontrivial constraints hamper insights into comprehensive identification of the pathogenic cell types in T1D: (1) the inability to safely biopsy the human pancreas of living donors and (2) the significant disease progression and beta cell destruction by the time patients are clinically diagnosed with T1D. Therefore, the majority of T1D studies have been performed on leukocytes from the peripheral blood, which is not the site of pathogenesis. Of late, the Network for Pancreatic Organ Donors (nPOD) and the Human Pancreas Analysis Program (HPAP) have started collecting pancreatic tissues from hundreds of deceased organ donors diagnosed with T1D. Additionally, given that many T1D patients harbor beta cell autoantibodies (AAbs) in their bloodstream prior to clinical diagnosis, nPOD and HPAP also collect samples from donors with AAbs towards islet proteins but without a medical history of T1D, in hope of elucidating early pathogenic events. +Using these initiatives, we developed a pancreatic islet atlas containing an unprecedented ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time-of-flight (CyTOF), and ~1,000,000 cells using in situ imaging mass cytometry (IMC) in pancreatic tissues of human organ donors collected by HPAP, enabling a resource for extensive exploration and discovery within the pancreatic environment. We also provide an interactive data explorer for simple, direct access to the single-cell transcriptomics data (https://cellxgene.cziscience.com/collections/51544e44-293b-4c2b-8c26-560678423380). Our comprehensive integrative analyses on this unique data set provides cellular and molecular insights into T1D pathogenesis and suggests pancreatic ductal cells may play a role in suppressing CD4+ T cells in pancreatic tissues. +Results +scRNA-seq unravels novel cell states in the human pancreas +To unmask the molecular perturbations occurring in pancreatic tissues during T1D, we constructed 81,313 single-cell RNA-seq (scRNA-seq) libraries from pancreatic islets of 24 human organ donors representing three categories: individuals with T1D (n = 5), those with AAbs toward pancreatic islet proteins but no clinical diagnosis of T1D ('AAB+'; n = 8), and those with neither AAbs nor a history of T1D ('Control'; n = 11) (Fig. 1a and Extended Data Fig. 1 a-b, and Tables S1 to S2). The statistics related to reads per cell across donors demonstrated the high-quality of these data sets (Table S3). We filtered outlier cells, removed doublets, and employed the cell type classifier 'Garnett' (Extended Data Figs. 1c-e, 2a-g, and 3a-e) to cluster 69,645 high-quality cells using 'TooManyCells' (Fig. 1b-c). The resultant classification was confirmed by both canonical gene marker expression for each cell type and by transferring cluster labels from a previous single-nucleus RNA-seq data set consisting of pancreatic islets to our datasets (Fig. 1c and Extended Data Fig. 4 a-c). +Notably, clustering was clearly driven by cell type, and not by confounding factors such as autoantibody status, age, BMI, phenotypic group, or other factors (Extended Data Figs. 2 d-g, Figs. S1 a-k, and S2 a-l). Additional evidence for the lack of technical noise stems from the observation that cell type clustering was preserved when donors from T1D, AAB+, and Control groups were independently clustered (Fig. S3 a-f). +Considering the reported abnormalities of the exocrine pancreas in T1D and recent efforts indicating the enrichment of sequence polymorphisms associated with T1D within the regulatory elements of exocrine cells, we next examined the relationship between pancreatic endocrine and ductal cells. First, we subsetted and reanalyzed the endocrine and ductal cells to achieve a more granular clustering (Fig. 1d). Upon reclustering, the major cell types - alpha cells, beta cells, delta cells, epsilon cells, PP cells, ductal cells, and acinar cells - were easily discernible (Fig. 1d and Extended Data Fig. 4d). In instances where there were two transcriptionally distinct canonical cell types (i.e. Beta-1 and Beta-2), differential gene expression analysis between populations provided further insights into the underlying molecular differences (Tables S4-S7). For example, cells in the Beta-2 cluster expressed higher levels of stress response genes such as NPTX2 and GDF15 when compared to those in the Beta-1 cluster. The activation of stress response genes in beta cells in various hyperglycemic states has been reported previously. Notably, the comparison of cells in the two ductal clusters revealed that while cells in the Ductal-1 cluster were enriched for transcription factors (TFs) associated with the endocrine cell fate (i.e., PDX1 and NKX6-1), those in the Ductal-2 cluster expressed acinar TFs (i.e., PTF1A and GATA4). +A substantial number of cells (4,001) were not included in these canonical cell type clusters, but rather formed their own transcriptionally distinct group on the dendrogram. This cluster comprised 5.7% of all profiled cells, with a mixture of cellular classifications and expression of canonical gene markers. We labeled these cells as 'Hybrid' cells (Figs. 1d and Extended Data Fig. 4d). Notably, the gene expressed most highly and consistently in Hybrid cells was INS, and a comprehensive examination of the cells comprising this cluster ruled out the possibility of them being doublets (Extended Data Figs. 1d and 2 a-e). To further validate the most closely related cell types to these non-canonical cells, we employed a label transfer strategy using a reference pancreatic islet scRNA-seq dataset. We corroborated the assignment of multiple cell types including beta cells and alpha cells to these Hybrid cells (Extended Data Fig. 4 e-f). Cells equivalent to Hybrid cells had been detected earlier and were most recently documented in the adult pancreas of mice and humans. Nonetheless, we excluded hybrid cells for further analysis to eliminate any cells captured potentially as doublets. +Since immune cell-mediated destruction of viable pancreatic cells is the major pathogenic feature of T1D, we examined the intrapancreatic immune cells profiled by scRNA-seq in detail. First, we subsetted and reclustered the cells constituting the 'Immune' cluster from the comprehensive tree (Fig. 1c) and found that this population also contained stellate (RGS5 high) and Schwann (PLP1 high) cells along with immune cells (PTPRC high) (Fig. S4 a-b). Using the Immunological Genome Project (ImmGen) cell type signatures, we further found that the gene signatures of antigen-presenting cells (APCs) such as macrophages, for example CD68, SPI1, CD14, and CD16, were most frequently expressed in the immune cell subset (Fig. S4 b-c), suggesting that these cell types comprise the majority of the identified immune cells that are collected and cultured along with pancreatic islets. +Studies demonstrating that regulatory elements of immune cells harbor the largest number of risk variants associated with T1D imply that immune cells are more susceptible to gene dysregulation compared with other cell types in T1D. To quantify the link between genetic predisposition associated with T1D and cell type-specific gene expression, we used a genetic prioritization model and examined the enrichment of sequence variation associated with T1D across our annotated cell types (Fig. S4 d). As a control, we also examined sequence variation associated with asthma and T2D. This analysis revealed that immune cells were the top cell type associated with T1D and asthma, which are both immune-mediated disorders. In contrast, beta cells were the top cell type associated with T2D (Bonferroni significance threshold of PS-LDSC < 0.05), in agreement with recent reports demonstrating that risk variants for T2D are enriched in active cis-regulatory elements of beta cells. Together, the genetic prioritization model corroborated that gene expression in immune cells is affected by T1D-associated sequence variation. +In addition to successful identification of the major endocrine and exocrine cell types and pancreatic immune cells, we also observed that the overall proportion of these cell types was in accordance with previous work. Each of the major cell types comprised cells from the three donor groups with varying proportions (Fig. 1 e-g, Extended Data Figs. 4 g-h, Figs. S4 e-g, and S5 a-b). As expected, we found that there was a lower proportion of beta cells in the T1D cohort compared to the AAB+ or Control groups (Fig. S5 a-b). Conversely, both acinar and ductal cells comprised a higher portion in the T1D cohort, reflecting the difficulty of isolating high purity islets from T1D donors. Furthermore, within major cell clusters, there were varying degrees of separation based on donor group, which is to be expected due to likely transcriptomic differences among the three donor states (Figs. 1 e-f and S4a). Notably, Ductal-1 cells clearly separated into distinct T1D-enriched and Control-enriched groups (Fig. 1f). Taken together, our data indicate that transcriptomic differences amongst cell types and not technical biases drive the separation of major cellular clades, and that the donor state further segregates within cell types. +Comparison of endocrine and exocrine cells in AAB+ and T1D donors +We next compared transcriptomic divergence of AAB+ and T1D cells from Controls (Fig. 2a). To perform differential expression analysis between donor groups, we used two complementary analytical strategies: (1) grouping individual cells from different donor groups together (Tables S8-S10) or (2) performing pseudo-bulk analysis for each donor (Tables S11-S13). Plotting the average expression levels of the top 3 differentially expressed genes determined by the first strategy across donor groups confirmed that the predicted differential expression is not driven by one or a few donors (Fig. S6). Since pseudo-bulk methods cause cells from individuals with fewer cells to be more heavily weighted, we performed further analysis using genes detected based on the first strategy. Generally, the degree of overlap between dysregulated genes and pathways in AAB+ and T1D states were cell type-dependent (Figs. 2 b-e, S7 a-b, S8 a-b, S9 a-d, S10 a-d, Extended Data Fig 5 a-c). However, some pathways were found to be commonly dysregulated in multiple cell types across T1D and AAB+, including 'apoptotic signaling', various protein folding ontologies, various viral-related ontologies, 'autophagy', 'inflammatory pathways', and 'stress response'. +We next examined the transcriptional changes in the two populations of annotated beta cells, Beta-1 and Beta-2. A large number of genes was downregulated in T1D (9,512 genes) and AAB+ (3,666 genes) Beta-1 cells compared to Controls, many of which overlapped (2,896 genes, 28%; p < 2.2e-16) between the two donor groups (Figs. 2b and S7a). Notable pathways that were frequently downregulated in Beta-1 cells of AAb+ and T1D donors were immune/stress response and apoptosis-related (Figs. 2b and S7a). Given that beta cells are destroyed by immune cells in T1D, it is possible that these remaining Beta-1 cells were not targeted by the immune system. It is also possible that these beta cells are able to survive and function after immunological attack by decreasing immune signaling and apoptotic signaling via downregulation of the TP53 pathway (Figs. 2b and S7a), which is notable given that upregulation of the TP53 pathway and an associated increase in susceptibility to apoptosis has been observed in T1D. Hence, these results suggest that cells from AAb+ donors in this beta cell population are either spared from destruction or employ similar protective molecular mechanisms to enhance survival and function, which is further supported by the fact that the expression of immune checkpoint protein PDL-1 (CD274) is upregulated in AAB+ Beta-1 cells compared to those from Controls. +The Beta-2 cell population displayed a small proportion of genes (4%; 283 genes; p < 2.2e-16) with elevated expression in both T1D and AAB+ cells when compared to Controls (Figs. 2c, and S7b). Additionally, an even smaller number of genes were downregulated in T1D and AAB+ Beta-2 cells when compared to Controls. Nonetheless, several pathways were found to be commonly dysregulated across both donor groups. Two interrelated pathways dysregulated in both T1D and AAB+ Beta-2 cells, namely 'chaperone-mediated protein folding' and 'response to topologically incorrect protein', suggest a dysregulation of protein folding, an essential function for cellular homeostasis. Additionally, the 'TNF-alpha/NF-kappa B signaling' pathway, which has been implicated as an important regulator of autoimmune processes, was significantly downregulated across the two donor groups in the Beta-2 cell population (Figs. 2c and S7b). Together, our differential expression analyses extend earlier studies on the pathways triggering beta cell dysfunction and death. +Given the clear segregation of ductal cell populations by donor group, we next examined the transcriptional changes in the two populations of ductal cells, Ductal-1 and Ductal-2. A large number of genes was upregulated in T1D (7,175 genes) and AAB+ (4,371 genes) Ductal-1 cells when compared to Controls, a significant number of which were common between the two donor groups (Figs. 2d and S8a; 2,283 genes; 25%; p-value < 1e-12). Notable induced pathways upregulated in T1D and AAB+ cells are associated with apoptosis, stress, and immune response (Figs. 2d and S8a). In the Ductal-2 cell population, although many upregulated genes were observed in T1D (6,841 genes), there were not nearly as many upregulated genes in AAB+ cells (1,106 genes) when compared to Controls (Figs. 2e and S8b). Furthermore, in the T1D and AAB+ Ductal-2 cell population, there was a modest but significant overlap between upregulated genes (Figs. 2e and S8b; 11%; p-value < 1e-12). Nevertheless, various gene pathways were found to be significantly upregulated across both ductal populations (Figs. 2 d-e and S8 a-b). Taken together, these findings suggest that although AAb+ donors maintain normoglycemia, significant transcriptional dysregulation is occurring in AAB+ endocrine and exocrine cells that is highly similar to that in T1D. +Next, we directly compared T1D to AAB+ cells (Fig. 2f and Tables S8-S13). For both groups of beta cells, genes associated with autophagy, stress response, and immune-related pathways were activated in AAB+ cells compared to T1D cells (Figs. 2 g-h and S7 a-b). Although similar pathways were upregulated in AAB+ Beta-1 and Beta-2 cells, apoptotic and adaptive immune system signaling were only upregulated in Beta-2 AAB+ cells. These data suggest that this population is undergoing cell death, indicated by the upregulation of adaptive immune cell genes and BCL10. In Ductal cell populations, there was a larger number of upregulated genes in T1D (Figs. 2 i-j, and S8 a-b). Notably, apoptotic, metabolic, protein folding, and immune responses were activated in T1D ductal cells in comparison to AAB+ ductal cells (Figs. 2 i-j and S8 a-b). Remarkably, interferon alpha and beta pathways, known to be critical in T1D disease pathogenesis, were significantly elevated in T1D ductal cells compared to either Control or AAB+ ductal cells (Extended Data Fig. 5d). Our molecular evidence supports more recent findings of exocrine abnormalities in T1D, positioning these exocrine cells in disease pathogenesis. Taken together, AAB+ cells exhibit significant transcriptional changes like those observed in T1D. +Beta cell gene signature is correlated with the anti-GAD titer +Pancreatic tissues from AAb+ donors collected by HPAP can potentially offer a unique insight into the initial molecular events of T1D pathogenesis. A landmark study following patients from birth determined that ~69% of children with multiple islet autoantibodies progressed to T1D after islet autoantibody seroconversion. Among HPAP donors, only one donor with no history of T1D expressed two islet autoantibodies while the other normoglycemic AAb+ donors were anti-glutamic acid decarboxylase (GAD) autoantibody positive. Considering that the longitudinal children study also revealed that the risk of diabetes in children who had no islet autoantibody was 0.4% in contrast to 14% for children expressing a single islet autoantibody, we next focused on the transcriptional landscapes of islets in GAD+ donors and queried for cell types whose transcriptional signature strongly correlated with the GAD titer among the GAD+ subjects. We devised a strategy to determine the number of genes whose expression levels significantly correlated with the GAD titer across GAD+ donors, either positively or negatively. However, we detected only positive correlation of statistical significance between gene expression levels and GAD titers. Strikingly, the top cell type with the largest number of genes (1,473) with significant correlation with the GAD titer in AAb+ donors was Beta-1 cells (Fig. 3a, Table S14). Plotting the average expression levels of cells from each GAD+ donor for these 1,473 genes in Beta-1 cells confirmed this finding (Fig. 3b). To define the identity of genes with an increase in their expression levels correlating with GAD levels, we performed gene-ontology analysis. Our approach highlighted the relevance of endocytosis, lysosome, protein processing in ER and MAPK signaling in Beta-1 cells (Fig. 3c). Additional comparison of the cellular clustering of the one AAb+ donor expressing two autoantibodies (IA-2 and ZnT8; AAB+ #5; HPAP043) with GAD+ donors across the AAB+-specific clustering (Fig. S3b) or all donor-type clustering (Fig. 1c) revealed the distinct transcriptional signature of the double autoantibody-expressing AAb+ donor in comparison to single AAb+ GAD+ donors (Figs. 3d and S2l). This analysis also revealed an overall similarity of GAD+ donors, which modestly displayed GAD level-dependent cell co-segregation (Figs. 3d and S2l). Together, our unbiased strategy puts forward Beta-1 cells as the top cell type whose transcriptional outputs correlate with anti-GAD levels, suggesting the dynamic landscape of transcriptome in normoglycemic autoantibody-positive individuals. +MHC Class II expression is enriched in T1D ductal cells +The major genetic susceptibility determinants of T1D have been mapped to the MHC Class II genes. We therefore sought to determine which cell types or donor states disproportionately express genes in this pathway. Using our scRNA-seq data, we found that genes associated with MHC Class II activity were enriched in Immune, Endothelial, and Ductal clusters (Extended Data Fig. 6 a-d). The lack of enrichment of the immune cell marker PTPRC or other genes associated with immune cells across the endocrine and ductal dendrogram supports the notion that the enrichment of MHC Class II associated genes in ductal cells is not due to immune cell contamination (Extended Data Fig. 4 a,d and Extended Data Fig. 6g). Next, we evaluated the expression of HLA-DPB1, an MHC class II gene associated with T1D risk, and KRT19, a ductal cell marker, across ductal and endocrine cell types. We identified five clusters with high HLA-DPB1 and high KRT19 expression, which accounted for 10.9% of all cells (7,588 cells) (Fig. 4 a-b and Extended Data Fig. 6 e-f). Strikingly, cells from T1D donors disproportionately contributed to this population of MHC Class II-expressing ductal cells (Fig. 4c; p-value < 2.2e-16). This observation is not due to sampling issues pertaining to the difficulty of isolating high purity islets from T1D donors. This conclusion is supported by the fact that even though the Ductal-1 cell population consists of very similar numbers of Control and T1D donor ductal cells (4,217 and 4,154 cells, respectively), there is a marked difference in the percentage of Control versus T1D MHC class II-expressing Ductal-1 cells, at 35% and 91%, respectively (Fig. 4c; p-value < 2.2e-16). +T1D ductal cells assume the transcriptional identity of dendritic cells +Dendritic cells (DCs) are among the major professional antigen-presenting cells expressing MHC Class II proteins with the salient function to ingest antigens and present processed epitopes to T cells, thereby regulating adaptive immune responses by activating or suppressing T cells. Considering that MHC Class II proteins are required for antigen-presentation in dendritic cells, we next evaluated whether there are any other similarities between transcriptional profiles of T1D ductal cells and conventional dendritic cells. Hence, we performed gene-set-enrichment analysis using gene signatures of dendritic cell subtypes, which were recently defined using scRNA-seq profiling in human blood. Remarkably, we found a highly significant enrichment of the DC1 gene signature in Ductal-2 cells of T1D donors, while no other annotated islet cell type revealed such significant and strong enrichment of gene signatures associated with dendritic cell subtypes (Fig. 4d and Extended Data Fig. 7a). DC1 corresponds to the cross-presenting CD141/BDCA-3+ cDC1, which is best marked by CLEC9A. Of note, the enrichment of other dendritic cell subtype gene signatures in T1D ductal cells was not statistically significant (Extended Data Fig. 7a). +To activate T cells, dendritic cells are required to express both MHC Class II proteins and costimulatory proteins CD80 and CD86. In the absence of CD80 and CD86, antigen-presentation by dendritic cells can lead to tolerance and T cell suppression. We found that CD80 and CD86 were not expressed in T1D ductal cells, suggesting a lack of costimulatory signal in these dendritic cell-like ductal cells in T1D donors (Fig. 4e). Additionally, the inhibitory receptor VSIR, which negatively regulates T cell responses, showed higher expression in T1D compared with control ductal cells (Extended Data Fig. 7b). Moreover, the ductal cells in T1D expressed high levels of interferon genes such as ICAM1, ISG20, and IRF7 (Figs. 4f and Extended Data Fig. 5d). Hence, our single-cell transcriptional profiling detected an enrichment of ductal cells with transcriptional similarities to tolerogenic DCs. These results imply an unappreciated role for T1D ductal cells potentially acting as decoy receptors in an apparent attempt to deactivate CD4+ T cells by inducing tolerance during immune invasion of the pancreas. +Multimodal confirmation of MHC Class II+ ductal cells +We next sought to corroborate our transcriptomic-based finding of MHC Class II expression on ductal cells in T1D by employing additional experimental modalities: two high-throughput technologies, CyTOF and IMC, in addition to immunofluorescence experiments. Our integrative approach with CyTOF combined ~ 7,000,000 live, cultured single cells from 12 donors, which had also been profiled by scRNA-seq (4 Control, 4 AAB+, and 4 T1D donors). This additional modality scaled our analytical strategy to millions of cells, measuring the expression levels of 35 proteins (Table S15). Since the strategy we used to annotate cells using scRNA-seq is not applicable to CyTOF measurements, we developed a new machine-learning method to annotate cells based on canonical markers (Extended Data Fig. 8 a-e). Using CyTOF, we identified a population of ductal cells expressing HLA-DR, an MHC Class II protein (Fig. 2a). Notably, we found that cells from T1D donors constituted the largest percentage of this cluster, in agreement with the findings from scRNA-seq (Fig. 5b; p-value < 1e-6). Furthermore, HLA-DR-expressing ductal cells made up a larger percentage of total cells across individual T1D donors compared with Control or AAB+ donors (Fig. 5c, p-value=0.00507). A two-parameter (cytokeratin and HLA-DR) analysis on all single cells analyzed by CyTOF further confirmed the presence of this double-positive population across multiple donors (Fig. 5d and Extended Data Fig. 8 f-g). Notably, these ductal cells did not express CD45, the hallmark of leukocytes (Fig. 5e). The identification of ductal cells with MHC Class II molecules using both scRNA-seq and CyTOF strongly corroborates the increased frequency of this population in T1D. +Having identified a population of ductal cells with MHC Class II molecules enriched in T1D donors by two experimental modalities in our integrative analysis, we next sought to study these ductal cells in pancreatic tissues independent of islet culture by means of anatomical-spatial features in pancreatic tissues by IMC. While measurements with CyTOF and scRNA-seq assays rely on the profiling of dissociated cells, IMC retains spatial information by analyzing tissues fixed directly from the native human pancreas. We again amended our analytical pipeline with an optimized cell annotation approach for the IMC technology. We harnessed the expression levels of 33 proteins quantified by IMC in more than 1 million cells across 143 tissue slides from 19 donors, including 11 individuals not previously assessed by scRNA-seq or CyTOF for an independent validation of our findings (Table S16). This analysis confirmed that MHC Class II-expressing ductal cells were predominately present in T1D donors (Fig. 5 f-h and Extended Data Fig. 9 a-e). MHC Class II-expressing ductal cells were located in all regions of the pancreas (Extended Data Fig. 10a). Remarkably, the frequency of CD11B+ myeloid cells annotated by our analytical strategy in both CyTOF and IMC measurements was highly correlated with the frequency of MHC Class II expressing ductal cells (Extended Data Fig. 10 b-c). Immunofluorescence staining (IF) in native pancreatic tissues, followed by confocal microscopy, verified the existence of MHC Class II-expressing cells in a Control and a T1D donor (Fig. 5i). We identified MHC Class II-expressing ductal cells in both donors; however, there was a pronounced enrichment of MHC Class II-expressing ductal cells in the T1D pancreas (Fig. 5i). Representative examples of IMC measurements in tissues also confirm this finding (Fig. 6). Finally, cellular neighborhood analysis in pancreatic tissues established that HLADR-expressing ductal cells were surrounded by CD4+ T cells and myeloid cells including CD11B+ dendritic cells (Extended Data Fig. 10 d-f; p-value < 1e-2). Together, our multimodal single-cell measurements from transcriptomics to spatial proteomics in ductal cells suggest that ductal cells are transcriptionally similar to tolerogenic DCs, implying an unappreciated role of these exocrine cells in modulating T cell activity in long-term T1D. +Discussion +Employing three high-throughput single-cell technologies, we provided a comprehensive atlas of millions of cells using integrative multi-modal analyses as a molecular microscope to investigate cellular diversity in the pancreas of T1D, AAb+, and non-diabetic human organ donors. These data, including paired samples across technologies, enable an exploration of the pancreatic environment in both healthy and disease states. +We found that AAb+ donors exhibit similar transcriptional changes as T1D donors in various endocrine and exocrine cells, despite these donors retaining normoglycemia. Remarkably, the unique collection of GAD+ donors in the HPAP database allowed us to delineate Beta-1 cells as the primary cell type whose transcriptional outputs correlate with anti-GAD titers, suggesting the existence of dynamic transcriptional landscape in autoantibody-positive individuals. Although it is impossible to discern at present whether these transcriptional changes are contributing to or are byproducts of disease pathogenesis, the mere discovery of molecular phenotypic changes in pancreatic cells of AAb+ individuals should advance our understanding of early pancreatic perturbations occurring in T1D. +The most striking finding arising from our study is that cells of the exocrine compartment show transcriptional and gene ontological changes in the T1D disease setting. Ductal cells from T1D donors, in contrast with those from non-diabetic or AAb+ donors, express high levels of MHC Class II and interferon pathways, are surrounded by CD4+ T cells and dendritic cells and are transcriptionally similar to tolerogenic dendritic cells. Although our study represents the first report of ductal cells expressing MHC Class II proteins in the T1D context, this finding is in accordance with previous literature documenting an elevation of immune cells in the exocrine pancreas of T1D donors and regulation of MHC Class II genes by the interferon signaling pathway. Moreover, the expression of MHC Class II proteins in pancreatic ductal adenocarcinomas has been reported. Recent studies also support a role for epithelial cells as facultative, non-professional antigen-presenting cells in the gut and lung, and expression of MHC Class II proteins in non-lymphoid cells in the pancreas has been shown. We posit that these cells exhibit a tolerogenic response to chronic T cell infiltration in pancreatic tissues and appear to be an ultimately unsuccessful attempt of the pancreas to limit the adaptive T cell response responsible for destroying beta cells. While this interpretation is strongly supported by our multimodal data analysis in human pancreatic tissues, the limitation of our study relates to lack of functional validation of this hypothesis. Our future efforts utilizing mouse genetics will enable us to further validate the functional relevance of these findings. Together, our study provides a unique resource of millions of cells of the pancreatic environment and unmasks exocrine ductal cells as potential responders to immune infiltration in T1D. +One technical question under intense debate in the scRNA-seq community is how to perform differential expression analysis. Squair et al. compared differential expression analysis techniques in scRNA-seq datasets, utilizing bulk RNA-seq data as the ground-truth for measuring false-positives. They concluded that predictions using the pseudo-bulk approach are the most similar to predictions from bulk RNA-seq data. Contradicting Squair et al., Zimmerman et al. published a study comparing techniques for performing differential expression analysis in scRNA-seq datasets and argued that pseudo-replication is acknowledged as one of the most common statistical mistakes in the scientific literature. Instead, they proposed the use of computationally expensive generalized linear mixed models for the analysis of scRNA-seq data. In summary, the contradictory results of these two studies reveal lack of consensus on alternative differential expression methods. Aware of these challenges in the analysis of scRNA-seq data, we took advantage of multimodal measurements such as IMC, CyTOF, and IHC to assess the reproducibility of our novel findings related to ductal cells in T1D donors across independent experimental assays. +Materials and Methods +Experimental model and subject details +Pancreatic islets were procured by the HPAP consortium (RRID:SCR_016202; https://hpap.pmacs.upenn.edu), part of the Human Islet Research Network (https://hirnetwork.org/), with approval from the University of Florida Institutional Review Board (IRB # 201600029) and the United Network for Organ Sharing (UNOS). A legal representative for each donor provided informed consent prior to organ retrieval. For T1D diagnosis, medical charts were reviewed and C-peptide levels were measured in accordance with the American Diabetes Association guidelines (American Diabetes Association 2009). All donors were screened for autoantibodies prior to organ harvest, and AAb positivity was confirmed post tissue processing and islet isolation. +Organs were processed as previously described. Table 1 and 2 summarizes donor information. Pancreatic islets were cultured and dissociated into single cells as previously described (22). Total dissociated cells were used for single cell capture for each of the donors, except AAB+ donor #1 (HPAP019), which was enriched for beta cells. +The C-peptide analysis was performed using a two site immuno-enzymatic assay from Tosoh Bioscience on a Tosoh 2000 auto-analyzer (Tosoh, Biosciences, Inc., South San Francisco, CA). Briefly, the test sample is bound with a monoclonal antibody immobilized on a magnetic solid phase and an enzyme-labeled monoclonal antibody, and then the sample is incubated with a fluorogenic substrate, 4-methylumbelliferyl phosphate (4MUP). The amount of enzyme-labeled monoclonal antibody that binds to the beads is directly proportional to the C-peptide concentration in the test sample. A standard curve is constructed using calibrator of known concentration, and unknown sample concentrations are calculated using the curve. The C-peptide assay is calibrated against WHO IS 84/510 standard. The assay has a sensitivity level of 0.02 ng/mL. To monitor the assay performance, a set of low, medium, and high C-peptide level quality control samples are analyzed several times per day. The inter-assay coefficients of variability for the low, medium, and high C-peptide controls are 3.2%,1.6%, and 1.8%, respectively. The results of the analyses of the long-term monitoring pools have demonstrated a consistently low variation around the target values, thus ensuring result consistency. +Serum from organ donors is tested for GAD, IA-2, mIAA, and ZnT8A autoantibodies by radioligand-binding assay (RIA) as previously described. Micro IAA (mIAA) and ZnT8A were performed with in-house RBA and the assay thresholds (index of 0.010 mIAA and 0.020 for ZnT8A) was set up as 99th percentile of over 100 controls. GAD and IA-2 was performed with NIDDK harmonized standard methods (3) and the upper limits of normal (20 DK units/ml for GAD and 5 DK units/ml for IA-2) was established around the 99th percentile from receiver operating characteristic curves in 500 healthy control subjects and 50 patients with new onset diabetes. In the most recent IASP Workshop, the sensitivity and specificity were 78% and 99% for GAD, 72% and 100% for IA-2, 62% and 99% for mIAA, 74% and 100% for ZnT8A, respectively. +scRNA-seq islet capture, sequencing, and processing +The Single Cell 3' Reagent Kit v2 or v3 was used for generating scRNA-seq data. 3,000 cells were targeted for recovery per donor. All libraries were validated for quality and size distribution using a BioAnalyzer 2100 (Agilent) and quantified using Kapa (Illumina). For samples prepared using 'The Single Cell 3' Reagent Kit v2', the following chemistry was performed on an Illumina HiSeq4000: Read 1: 26 cycles, i7 Index: 8 cycles, i5 index: 0 cycles, and Read 2: 98 cycles. For samples prepared using 'The Single Cell 3' Reagent Kit v3', the following chemistry was performed on an Illumina HiSeq 4000: Read 1: 28 cycles, i7 Index: 8 cycles, i5 index: 0 cycles, and Read 2: 91 cycles. Cell Ranger (10x Genomics; v3.0.1) was used for bcl2fastq conversion, aligning (using the hg38 reference genome), filtering, counting, cell calling, and aggregating (--normalize=none). +scRNA-seq clustering, doublet removal, & cell type classification +Seurat v3.1.5 was used for filtering, UMAP generation, and initial clustering. Genes expressed in at least 3 cells were included, as were cells with at least 200 genes. nFeature, nCount, percent.mt, nFeature vs nCount, and percent.mt vs nCount plots were generated to ascertain the lenient filtering criteria of 200 < nFeature < 8,750, percent.mt < 25, and nCount < 125,000. Data was then log normalized, and the top 2,000 variable genes were detected using the "vst" selection method. The data was then linearly transformed ("scaled"), meaning that for each gene, the mean expression across cells is 0 and the variance across cells is 1. Principle component analysis (PCA) was then carried out on the scaled data, using the 2,000 variable genes as input. We employed two approaches to determine the dimensionality of the data, i.e. how many principal components to choose when clustering: (1) a Jackstraw-inspired resampling test that compares the distribution of p-values of each principle component (PC) against a null distribution and (2) an elbow plot that displays the standard deviation explained by each principal component. Based on these two approaches, 17 PCs with a resolution of 1.2 were used to cluster the cells, and non-linear dimensionality reduction (UMAP) was used with 17 PCs to visualize the dataset. +DoubletFinder v2.0 was used to demarcate potential doublets in the data as previously described, with the following details: 17 PCs were used for pK identification (no ground-truth) and the following parameters were used when running doubletFinder_v3: PCs = 17, pN = 0.25, pK = 0.0725, nExp = nExp_poi, reuse.pANN = FALSE, and sct = FALSE (Fig. S1d). Scrublet v0.2.1 (18) was also used to demarcate potential doublets. We removed all cells that were flagged as doublet by both or either approach. +The raw data for the remaining cells were filtered using the following criteria, which resulted in 69,645 cells remaining: 200 < nFeature < 8,750, percent.mt < 25, and nCount < 100,000. The data were log normalized, the top 2,000 variable genes were detected, the data underwent linear transformation, and PCA was carried out, as described above. Both the Jackstraw-inspired resampling test and an elbow plot of standard deviation explained by each principal component were used to determine the optimal dimensionality of the data, as described above. Based on these two approaches, 26 PCs with a resolution of 1.2 was used to cluster the cells, and UMAP was used with 26 PCs to visualize the 49 clusters detected. +Garnett was used for initial cell classification as previously described. In brief, a cell type marker file (Table S17) with 17 different cell types was compiled using various resources, and this marker file was checked for specificity using the "check_markers" function in Garnett by checking the ambiguity score and the relative number of cells for each cell type. A classifier was then trained using the marker file, with "num_unknown" set to 500, and this classifier was then used to classify cells and cell type assignments were extended to nearby cells, "clustering-extended type" (Louvain clustering) (Fig. S3d). Upon inspection of cluster purity using canonical gene markers of the major pancreatic cell types across the Seurat-generated clusters, we found that the abundant and transcriptionally distinct cell types form generally distinct and unique clusters: beta cells (INS high), alpha cells (GCG high), acinar cells (CPA1 high), ductal cells (KRT19 high), endothelial cells (VWF high) stellate cells (RSG10 high), and immune cells (PTPRC, also known as CD45 or leukocyte common antigen, high) (Fig. S3e). In contrast across the Seurat-generated clusters, the rarer and/or less transcriptionally distinct cell types did not clearly segregate, namely delta cells (SST high), PP cells (PPY high), and epsilon cells (GHRL high). +Integration and label transfer was used to further validate Garnett cell-type assignments as previously described. To label canonical cell types, a previous snRNA-seq data set of adult pancreatic cells (EGAS00001004653) was used as a reference for the "query" datat set presented in this study. First, SCTransform was used to preprocess the data. Briefly, SCTransform uses a generalized linear model (GLM) for each gene with UMI counts as the response variable and sequencing depth as the predictor. To integrate data for UMAP visualization, Seurat integration was used to identify common anchor points between data sets. Seurat uses diagonalized canonical correlation analysis (CCA) followed by L2-normalization and searching for mutual nearest neighbors (MNN). Then, anchors between data sets are compared based on their local neighborhood structure of other anchors to receive "correction vectors". These correction vectors are then subtracted from the query gene expression matrix, resulting in an integrated data set. Similarly for label transfer, these anchors between data sets are instead labeled as discrete cell types and similar anchors assign cell labels from the reference cells to the query cells. To assign canonical cell-type labels to Hybrid cells, the same integration and label transfer process was used but with a previous scRNA-seq pancreatic data set as a reference (GSE145126). +We employed the analytical workflow termed 'TooManyCells', which implements an efficient divisive hierarchical spectral clustering approach along with tree visualizations. We invoked the cellular classifier Garnett, which annotates cell types by training a regression-based classifier from user-provided cell type signatures. Briefly, for the clustering of all cells, the raw data from the 69,645 cells were normalized by total count and gene normalization by median count (TotalMedNorm) followed by term frequency-inverse document frequency (tf-idf) for clustering. For visualization of the comprehensive clustering, the dendrogram was first pruned using the TooManyCells flags '--min-distance-search "15"' and "--smart-cutoff "15"', followed by pruning using the flag '--max-step 6'. +For the clustering of ductal/endocrine cells, data from the ductal/endocrine cell clusters from the comprehensive tree were subsetted and normalized by TotalMedNorm followed by term tf-idf. For visualization of the ductal/endocrine tree, the dendrogram was first pruned using the TooManyCells flags '--min-distance-search "7"' and "--smart-cutoff "7"' followed by pruning using the flag '--max-step 7'. Data from the immune cell cluster from the comprehensive tree were subsetted and normalized by TotalMedNorm followed by tf-idf. For visualization of the immune tree, the dendrogram was first pruned using the TooManyCells flag '--max-step 4'. When individual genes were painted across any of the dendrograms, 'TotalMedNorm' was employed to normalize gene expression. +Differential Gene Expression, GSEA analysis, and Metascape analysis +Differential genes were found using edgeR through TooManyCells with the normalization "NoneNorm" to invoke edgeR single cell preprocessing, including normalization and filtering. For Metascape analysis, less than or equal to 3,000 differential genes (FDR < 0.05 and fold change (FC) > 0.1) were subjected to analysis. The top 20 clusters are displayed and a stringent cut-off of 1e-6 was applied to determine significant gene ontology pathways. For gene-set-enrichment-analysis (GSEA) analysis, GSEA Preranked (4.0.1) was run on a pre-ranked gene list using either user-provided pancreatic gene expression sets or standard hallmark gene signatures provided by the Molecular Signatures Database (MSigDB). Pseudobulk analysis was performed by taking the average of cells within individuals. The differential genes were found using edgeR through multi-sample, multi-group scRNA-seq analysis tool (muscat). The differential genes were filtered based on the combined threshold of p-value < 0.05 and fold change (FC) > 1. +Hybrid cell co-expression, DE analysis and heatmaps +For the differentially expressed genes (FDR < 0.05 and fold change (FC) > 0.1) between every two sample groups, we calculated the shared and unique genes in each cell type, and visualized it with Venn diagrams. The expression levels of the genes in each cell of the three groups were extracted from the median normalized count matrix. Then we aggregate the expression levels in each group by taking the average value of the normalized counts. The mean expression values of the three groups were further normalized by the total expression level of that gene. We visualized the normalized expression level of differential genes with heatmaps. +To examine the co-expression of signature genes of some cell types, we normalized the median normalized matrix with log2(N +1). Then we selected the matrix of selected cell types by marker genes. The distribution of the cells from selected cell types by expression level of two marker genes were shown with geom_density_2d_filled() in ggplot2 package of R. +CyTOF data collection, input files, and preprocessing +Flow CyTOF was performed as described previously. Briefly, after isolating the dissociated cells, barcoding was conducted for donors following the manufacturer's protocol (Fluidigm, 101-0804 B1). Following barcoding, metal-conjugated antibody labeling was carried out in 'FoxP3 permeabilization buffer' (eBioscience, 00-8333) with 1% FBS (Hyclone, Cat# 7207) for 12 hours at 4 C at a concentration of up to 3 million cells per 300 mul of antibody cocktail, followed by twice washing with FoxP3 permeabilization buffer. Cells were then incubated with the DNA intercalator Iridium (Fluidigm, 201192A) at a dilution of 1:4,000 in 2% paraformaldehyde (Electron Microscopy Sciences, 15714) in DPBS (Corning, 21-031-CV) at RT for 1 hr. Mass cytometry data were acquired by CyTOF (Fluidigm). Flow CyTOF data analyses of endocrine cell composition was performed using the Cytobank implement (https://www.cytobank.org/). +Normalized FCS files were pre-processed prior to TooManyCells analysis and visualization using FlowJo Version 10.6.1 by gating all events on singlets according to event length and DNA content and then on live cells based on cisplatin exclusion. The Singlet/Live gated population was exported to a CSV file for TooManyCells analysis. Two dimensional plots were visualized for combinations of individual channels. +TooManyCells clustering for CyTOF +TooManyCells was used to generate cell clades of CyTOF data. Cells with less than a total of 1e-16 signal were removed, leaving 6,945,575 cells. Upon inspection of protein levels across a tree with all cells, endocrine and exocrine compartments were further subsetted leading to a refined analysis of 4,521,988 cells. Quantile normalization of the raw counts was used in the clustering step. The resulting tree was pruned by collapsing nodes with less than (7 MAD X median # cells in nodes) cells within them into their parent nodes. +Imaging mass cytometry (IMC) analysis and Cell Segmentation +IMC was performed as described previously. Cell segmentation of all images was performed with the Vis software package (Visiopharm). All image channels were pre-processed with a 3x3 pixel median filter, then cells were segmented by applying a polynomial local linear parameter-based blob filter to the Iridium-193 DNA channel of each image to select objects representing individual nuclei. Identified nuclear objects were restricted to those greater than 10mum2, then dilated up to 7 pixels to approximate cell boundaries. Per-cell object mean pixel intensities were then exported for further analysis. +TooManyCells clustering for IMC +TooManyCells was used to generate cell clades of IMC data. Cells with less than a total of 1e-16 signal were removed. Upon inspection of protein levels across a tree with all 1,170,001 cells, endocrine and exocrine compartments were further subsetted, leading to the refined analysis of 130,428 cells. The full tree with 1,170,001 cells was used for the assessment of HLA-DR-expressing ductal cells. Quantile normalization of the raw counts was used in the clustering step. The resulting tree was pruned by collapsing nodes containing less than (5 MAD X median # cells in nodes) cells within them into their parent nodes. Subsetting of the tree was done with "--root-cut 3" to focus on node 3 in relevant analyses, with additional pruning of (3 MAD X median # cells in nodes). +Cell-neighborhood analysis for IMC +Three labels were given to cells in the IMC neighborhood analysis: base, neighbor, and distant. Base cells originated from the chosen node, here node 16 in the node 3-focused IMC tree, or node 10 in the complete pruned tree which includes the former node 16. Given the x- and y-coordinates from IMC per cell, each cell's Euclidean distance to a base cell was calculated. If that distance was less than or equal to the chosen value, 20 for the complete pruned tree, the cell was assigned the neighbor label. Otherwise the cell was designated as distant. +Machine-learning method for cell annotation in IMC and CyTOF +To automatically label single cells from proteomic profiles, raw proteomic data along with a signature/marker file (listing unique marker proteins for each cell type) was taken as input. The raw data was normalized with an arcsinh transformation and a cofactor of 200 in case of CyTOF while log transformation followed by unit normalization in case of IMC data. The data was then randomly split into two halves (half donors in one set); cells from 50% of donors are in the training set while the remaining are in the test set. The splitting was done in a stratified fashion based on the disease condition (T1D, AAB+, Control). Semi-supervised learning was employed on the training set (clustering based on proteomics similar cells together) to generate cell labels for the first half of cells based on seeds (cluster centroids) calculated using a handful of labelled cells (0.1-10 percentile cells for each cell type) annotated using markers in the signature file. The annotated training set was used to train an Extreme learning machine (a fast classifier built on a feed forward neural network which does not need training for learning). +IF and confocal microscopy +Tissues were fixed in 10% buffered formalin overnight, washed several times in PBS, then dehydrated through an ethanol and xylenes, then embedded in paraffin and sectioned to 4-8um. Following deparaffinization through xylene and sequential rehydration, slides were subjected to heat antigen retrieval in a pressure cooker with Bulls Eye Decloaking buffer (Biocare). Slides were incubated in primary antibody overnight and secondary antibody conjugated to peroxidase and then developed using Tyramide Signal Amplification (TSA, Akoya Biosciences). Slides were counter stained with DAPI, and then mounted and imaged on Zeiss LSM800. Primary antibodies used for staining were Mouse anti-CK19 (Santa Cruz sc-6278) and Rabbit anti-HLA-DR (Abcam ab92511). +Statistical analysis of box plots with Control, AAB+, and T1D donor states +The D'Agostino & Pearson omnibus normality test was used to assess whether the data from each group was normally distributed. If any group failed the D'Agostino & Pearson omnibus normality test, the Kruskal-Wallis test was applied. If none of the groups failed the D'Agostino & Pearson omnibus normality test, the one-way ANOVA test was applied. +Statistical analysis of cellular neighborhoods +Differential marker expression significance for neighbors in the IMC analysis was determined using permutation tests. For each marker, the distribution of that marker value for each of the designated n neighbors was compared against 100 distributions derived from n random cells across the entire IMC tree. The resulting p-value was calculated by the ratio of the number of permutations that had a lower median marker value than the observed marker value to the total number of permutations. If this value was < 0.5, the value was subtracted from 1 to switch directionality (number of permutations with a higher median value). To account for the two-tailed test, this value was multiplied by 2 for the final p-value calculation. +Statistical analysis of gene signatures in GAD+ donors +Pseudobulk counts of GAD+ donors across all cell types were identified using muscat tool. The GAD levels for each GAD+ donors were retrieved from Table 1. To identify the correlation between gene signatures and GAD levels, the Spearman correlation test was conducted in each cell type. The threshold of correlation > 0.9 and p-value < 0.05 were used to determine the significantly correlated genes with GAD levels. +Assessment of common genetic variants associated with T1D +The CELLEX tool takes the scRNA-seq gene expression matrix as input and evaluates multiple metrics such as differential expression T-statistics, gene enrichment score, expression proportion, and normalized specificity index. The average of these metrics is measured as expression specificity. The GWAS trait data and CELLEX estimates are given as input to CELLECT. CELLECT uses the genetic prioritization model (with a threshold of S-LDSC < 0.05) to quantify the association between the common phylogenetic GWAS signal and cell type expression specificity. +Extended Data +Cell numbers and clustering before complete filtering +a) Pie chart displaying the cell numbers and proportions of each individual donor per donor type. +b) Box plot displaying the average gene number per cell per donor type. +c) UMAP visualization of cell clusters for all cells. +d) Doublets and singlets, as identified using DoubletFinder, across cell clusters visualized by UMAP. +e) UMAP visualization of the normalized gene expression counts of each canonical gene marker of each major cell type. +Doublet removal and UMI counts +a) Doublets and singlets, as identified using Scrublet, across cell clusters visualized by UMAP per individual. +b) Venn diagram indicating the number of cells deemed doublets by DoubletFinder and Scrublet, as well as cells that were commonly identified by both approaches. +c) Table indicating the number of cells removed and the resulting total cell number for each step of filtering. +d) Unique molecular identifier (UMI) counts per cell projected across the dendrogram visualization and clustering of all cells from Figure 1c. Pie charts at the end of the branches display the breakdown of UMI counts per cell within that terminal cluster. Cells begin at the start pin symbol, and from there are partitioned based on similarities and differences in gene expression. +e) UMI counts per cell projected across the dendrogram visualization and clustering of ductal and endocrine cells from Figure 1d. Pie charts at the end of the branches display the breakdown of UMI counts per cell within that terminal cluster. Cells begin at the start pin symbol, and from there are partitioned based on similarities and differences in gene expression. +f) Expression of genes associated with mitochondrial function projected across the dendrogram visualization and clustering of all cells from Figure 1c. +g) Expression of genes associated with mitochondrial function projected across the dendrogram visualization and clustering of ductal and endocrine cells from Figure 1d. +Cell numbers and clustering after complete filtering +a) Pie chart displaying the cell numbers/proportions of each individual donor per donor type. +b) UMAP visualization of cell clusters for all cells. +c) UMAP visualization donor groups across clusters for all cells. +d) UMAP visualization of Garnett cellular classifications across clusters for all cells. +e) UMAP visualization of the normalized gene expression counts of each canonical gene marker of each major cell type. +Marker gene expression confirms canonical cell types +a) Dendrograms highlighting the expression of each canonical gene marker of each major cell type across the dendrogram of all cells in Figure 1c. +b) The classification of our scRNA-seq data was confirmed by a label transfer strategy using a previous single-nucleus RNA-seq data set in pancreatic islets. +c) Bar plot demonstrates percentages of agreement between previous annotation and our strategy using a label-transfer strategy. +d) Dendrograms highlighting the expression of each canonical gene marker of each major cell type across the dendrogram of ductal and endocrine cells in Figure 1d. +e) To further validate the most closely related cell types to Hybrid cells, we used a label transfer strategy to a previous pancreatic islet scRNA-seq data set. In concordance with Garnett and canonical gene markers, we corroborated the assignment of beta, alpha, and PP cells to these Hybrid cells. +f) Bar plot demonstrates annotation results of label transfer for cells grouped as Hybrid cells. +g) Pie chart displaying the cell numbers/proportions of each cell type defined in Figure 1, c and d. +h) Schematic of the human pancreatic islet anatomy and major cell types. +Gene and gene ontology pathways that are shared and different across disease states in Epsilon-1, Epsilon-2, and Immune cells +(a-c) (Left) For each cell type, Venn diagrams indicate the numbers of upregulated and downregulated genes, as well as overlapping genes, across the two disease states. Circles indicate the numbers of genes that are 'T1D enriched' or 'AAB enriched'. p-values presented are the results of hypergeometric CDF tests (one-tailed test for overrepresentation). (Middle) For each cell type, displayed are gene ontology pathways that are shared across T1D and AAB+ cells when compared to Control cells (top) or pathways that are differently enriched in T1D cells vs AAB+ cells (bottom). The top 20 clusters are displayed and a stringent cut-off of 1e-6 was applied to determine significant gene ontology pathways. (Right) Heatmaps displaying the degree of gene expression changes of genes (rows) that are shared (top) or differential (bottom) across AAB+ and T1D disease states. +(d) GSEA analysis plots of FDR q-value vs Normalized Enrichment Score. For both ductal populations, Ductal-1 and Ductal-2, T1D cells were compared to AAB+ or Control cells to determine differentially enriched gene sets. Demarcated in red and labeled are signatures of interest. +Corroboration of HLA-DR+ Ductal cells +(a-b) Dendrograms highlighting the expression of the MHC class II complex (a) or MHC class II activity (b) across the dendrogram of all cells in Figure 1C. Scale bars represent normalized transcript numbers (mean across all MHC class II complex genes (a) or MHC class II activity genes (b)). +(c-d) Dendrograms highlighting the expression of the MHC class II complex (c) or MHC class II activity (d) across the dendrogram of ductal and endocrine cells in Figure 1D. Scale bars represent normalized transcript numbers (mean across all MHC class II complex genes (c) or MHC class II activity genes (d)). +(e-f) Dendrograms highlighting the expression of the HLA-DPB1 (E) or KRT19 (f) across the dendrogram of ductal and endocrine cells in Figure 1D. Scale bars represent normalized transcript numbers. +g) Dendrograms highlighting the expression of the immune-related genes across the dendrogram of ductal and endocrine cells in Figure 1D. Scale bars represent normalized transcript numbers. +(h) Dendrograms highlighting the expression of the BMPR1A across the dendrogram of ductal and endocrine cells in Figure 1D. Scale bars represent normalized transcript numbers. +GSEA analysis across annotated cells types for dendritic cells gene sets. +a) DC1 gene signature is significantly enriched within Ductal-2 cells of T1D donors. Integrated GSEA analysis for dendritic cells gene sets from Villani et al across ranked lists of differentially expressed genes between T1D and control donors. +b) Expression analysis of the inhibitory marker VSIR in dendritic cells demonstrates the high level of this gene in T1D ductal cells compared with control ductal cells. +CyTOF validation of canonical cell types +a) Bar graph displaying the proportion of cells for all major pancreatic cell types from each donor group where cell annotations were obtained by our new machine-learning based strategy using CyTOF measurements across 12 donors. +b) Dendrogram visualization of the immune cell cluster, CD45 positive (+) cells, as determined by the analysis of the flow cytometry by time-of-flight (CyTOF) data. +c) Dendrogram visualization of the beta cell cluster, C-peptide positive (+) cells, as determined by the analysis of the CyTOF data. +d) Dendrogram visualization of the alpha cell cluster, Glucagon positive (+) cells, as determined by the analysis of the CyTOF data. +e) Major cell types projected on TooManyCells tree based on our machine-learning based annotation using CyTOF data (n=6,945,575 cells). +f) Two-parameter CyTOF analysis of HLA-DR and cytokeratin protein expression in single cells from T1D donor #3 (HPAP023). +g) Two parameter CyTOF analysis of HLA-DR and cytokeratin protein expression in single cells from Control donor #3 (HPAP034), a donor with a very low percentage of HLA-DR+ ductal cells as determined by unbiased analysis of CyTOF data with TooManyCells. +IMC validation of HLA-DR+ ductal cells +a) Bar graph displaying the proportion of cells for all major pancreatic cell types from each donor group where cell annotations were obtained by our machine-learning-based strategy using IMC measurements. Further manual inspection of CD19 and FOXP3 staining used for annotating B and Tregs indicated low quality of these markers across tissue slides. +b) Dendrogram visualization of the immune cell cluster, CD45 positive (+) cells, as determined by the analysis of the imaging mass cytometry (IMC) data analysis. +c) Dendrogram visualization of the beta cell cluster, C-peptide positive (+) cells, as determined by the analysis of the IMC data analysis. +d) Dendrogram visualization of the alpha cell cluster, Glucagon positive (+) cells, as determined by the analysis of the IMC data analysis. +e) Major cell types projected on TooManyCells tree as they were annotated by our machine-learning based strategy using IMC data (n=1,170,001 cells). +Cellular neighborhood analysis in IMC data demonstrates the enrichment of CD4+ T cells surrounding HLA-DR+ ductal cells +a) Bar plot displaying the proportion of HLA-DR+ cytokeratin+ cells from each pancreatic region determined by IMC. +b-c) HLA-DR+ cytokeratin+ cells versus percentage of myeloid cells. For each donor group, the median of percentage of each annotated immune subtype and the median HLA-DR+ ductal cell percentage of total cells across all individual donors per donor group was computed. Only myeloid cells demonstrated significant correlation with respect to the number of HLA-DR+ cytokeratin+ cells across donor groups. +d) Dendrogram visualization of the clusters of HLA-DR+ cytokeratin+ cells (red), cells neighboring HLA-DR+ cytokeratin+ (blue), and cells distant from HLA-DR+ cytokeratin+ cells (grey) as determined by leveraging the spatial architecture provided by IMC data. +e) Boxplots showing the normalized protein expression of different canonical markers in cells neighboring HLA-DR+ cytokeratin+ cells (blue) versus cells neighboring random cells (grey). The number of random cells evaluated was equal to the number of HLA-DR+ cytokeratin+ cells. Differential marker expression significance for neighbors in the IMC analysis was determined using permutation tests. For each marker, the distribution of that marker value for each of the designated n neighbors was compared against 100 distributions derived from n random cells across the entire IMC tree. * indicates p-value < 0.01. Total number of cells in both blue and gray groups is 195,633. Box-and-whisker plots (centre, median; box limits, upper (75th) and lower (25th) percentiles; whiskers, 1.5 x interquartile range; points, outliers). +f) CD4+ T cells are the number one immune subtypes enriched at the neighborhood of HLA-DR+ cytokeratin+ cells. Annotation of neighbors of HLA-DR+ cytokeratin+ cells was performed our machine-learning based strategy. +Supplementary Material +Code Availability +Where applicable, scripts used for data processing and analysis have been described in the Supplemental Materials and Methods section and provided on Github https://github.com/GregorySchwartz/multiomics-single-cell-t1d. TooManyCells is a publicly available suite of tools, algorithms, and visualizations (https://github.com/GregorySchwartz/too-many-cells) that was extensively used in this study, and where applicable, the flags used in TooManyCells to generate specific figures are included in the Materials and Methods section. +Competing Interests +M.R.B. has a consulting arrangement with Interius Biotherapeutics. Other authors declare no competing interests. +Data Availability +The GEO accession number associated with this paper is GSE148073. Additional data are publicly available at https://hpap.pmacs.upenn.edu/. Furthermore, a user-friendly web portal for exploration of the scRNA-seq data is available at https://cellxgene.cziscience.com/e/37b21763-7f0f-41ae-9001-60bad6e2841d.cxg/ +HPAP Consortium Authors: +Maria Fasolino1,2,3,4,6,7*, Gregory W. Schwartz2,3,5,6,7*, Abhijeet R. Patil1,2,3,4,6,7, Aanchal Mongia2,3,5,6,7, Maria L. Golson1,4,8, Yue J. Wang1,4, Ashleigh Morgan1,4, Chengyang Liu4,9, Jonathan Schug1, Jinping Liu1,4, Minghui Wu1,4, Daniel Traum1,4, Ayano Kondo1,4, Catherine L. May1,4, Naomi Goldman1,2,3,4,6,7, Wenliang Wang1,2,3,4,6,7, Michael Feldman5,7, Jason H. Moore1,7, Alberto S. Japp2,10, Michael R. Betts2,10, Robert B. Faryabi2,3,5,6,7#, Ali Naji2,4,9#, Klaus H. Kaestner1,3,4#, Golnaz Vahedi1,2,3,4,6,7# +Affiliations: +1Department of Genetics, 2Institute for Immunology, 3Epigenetics Institute, 4Institute for Diabetes, Obesity and Metabolism, 5Department of Pathology and Laboratory Medicine, 6Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; 7Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; 8Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA, 9Department of Surgery, 10Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA. +References +Type 1 diabetes mellitus: much progress, many opportunities +The pathogenesis, natural history, and treatment of type 1 diabetes: time (thankfully) does not stand still +Immune and Pancreatic beta Cell Interactions in Type 1 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susceptibility to the MHC class I genes HLA-B and HLA-A +Dendritic cells in cancer immunology and immunotherapy +Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors +T cell anergy and costimulation +VISTA, a novel mouse Ig superfamily ligand that negatively regulates T cell responses +Single-Cell Mass Cytometry Analysis of the Human Endocrine Pancreas +Increased immune cell infiltration of the exocrine pancreas: a possible contribution to the pathogenesis of type 1 diabetes +Regulation of MHC class II expression by interferon-gamma mediated by the transactivator gene CIITA +Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas +Epithelial MHC Class II Expression and Its Role in Antigen Presentation in the Gastrointestinal and Respiratory Tracts +Highly Multiplexed Image Analysis of Intestinal Tissue Sections in Patients With Inflammatory Bowel Disease +HLA Class II Antigen Processing and Presentation Pathway Components Demonstrated by Transcriptome and Protein Analyses of Islet beta-Cells From Donors With Type 1 Diabetes +Confronting false discoveries in single-cell differential expression +Harmonization of glutamic acid decarboxylase and islet antigen-2 autoantibody assays for national institute of diabetes and digestive and kidney diseases consortia +Early expression of antiinsulin autoantibodies of humans and the NOD mouse: evidence for early determination of subsequent diabetes +The cation efflux transporter ZnT8 (Slc30A8) is a major autoantigen in human type 1 diabetes +Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression +DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors +Comprehensive Integration of Single-Cell Data +Metascape provides a biologist-oriented resource for the analysis of systems-level datasets +muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data +Evidence of gene-gene interaction and age-at-diagnosis effects in type 1 diabetes +Type 1 Diabetes Genetics, C. Confirmation of HLA class II independent type 1 diabetes associations in the major histocompatibility complex including HLA-B and HLA-A +Discernment of human pancreatic cell types using single-cell RNA-seq +a) The transcriptome of single cells from pancreatic islets of 3 donor types (healthy Control donors, autoantibody positive (AAb+) donors, and donors with Type 1 diabetes (T1D)) was ascertained using the 10x Genomics platform. +b) Pie chart displaying the proportion of cells comprised by each donor group. +c) TooManyCells dendrogram visualization and clustering of all cells. Cells begin at the start pin symbol, and are then partitioned based on transcriptional similarities and differences. The color within the branches indicates the proportion of the cells that are classified by the Garnett cellular classification tool (Table S17). Each bifurcation denotes significant transcriptional differences between the two cell groups. Pie charts at the end of the branches display the breakdown of Garnett cellular classification of cells within that terminal cluster. Highlighting or dotted lines surrounding particular clusters of cells with labels define cell types based on Garnett cellular classifications and canonical gene expression. Branch thickness and pie-chart size is proportional to cell number. Branch length is not indicative of any factor, but is merely a means by which to display cells within a defined space. Beta cells (INS high), alpha cells (GCG high), delta cells (SST high), PP cells (PPY high), epsilon cells (GHRL high), acinar cells (CPA1 high), ductal cells (KRT19 high), endothelial cells (VWF high), stellate cells (RSG10 high), and immune cells (PTPRC, also known as CD45 or leukocyte common antigen, high). Percentages provided represent the percentage of total cells. +d) Dendrogram visualization and clustering of ductal and endocrine cells. Highlighting or dotted lines surrounding particular clusters of cells with labels define cell types based on Garnett cellular classifications and canonical gene expression. +(e-f) Group donor type projected across the dendrogram visualization and clustering of all cells from Figure 1C (e) or of endocrine and ductal cells from Figure 1D (f). Pie charts at the end of the branches display the breakdown of donor type within that terminal cluster. +(g) Bar graph displaying the proportion of cells from each donor group for all major pancreatic cell types. The p-values are calculated by the Chi-squared test +AAb+ and T1D donors have both common and distinct transcriptomic changes in endocrine and exocrine cell types +a) For each cell type, two pairwise differential comparisons were carried out: (1) T1D versus Control (referred to as 'T1D upregulated' (T1D/Control) or 'T1D downregulated' (Control/T1D)) and (2) AAB+ versus Control (referred to as 'AAB+ upregulated' (AAB+/Control) or 'AAB+ downregulated' (Control/AAB+)). T1D upregulated genes were then compared to AAB+ upregulated genes to find commonly upregulated genes, and subsequently commonly upregulated gene ontologies and pathways, across these two donor groups; this exact same approach was carried out for downregulated genes as well. +(b-e) (Left) For each cell type, Venn diagrams indicate the numbers of upregulated and downregulated genes, as well as overlapping genes, across the two donor states. (Right) Bar graph displaying notable gene ontologies that are shared across disease states for upregulated and downregulated genes. The p-values presented are the results of hypergeometric CDF tests (one-tailed test for overrepresentation). +(f) Transcriptional differences between cells from T1D and AAb+ donors were determined by directly comparing T1D to AAB+ cells to generate lists of differentially expressed genes that are enriched in T1D cells or AAB+ cells, and enriched gene ontology pathways were discovered from these differential gene lists. +(g-j) (Left) For each cell type, circles indicate the numbers of genes that are 'T31D enriched' or 'AAB enriched'. (Right) Bar graph displaying notable gene ontologies that are enriched for each donor state. +The gene signature of Beta-1 cells in GAD+ donors is correlated with donors' anti-GAD autoantibody titers. +a) Transcriptional outputs of Beta-1 cells positively correlate with the anti-GAD AAb titer in AAb+ donors. In every annotated cell type, we searched for genes whose expression level correlated with anti-GAD AAb levels in normoglycemic GAD+ donors (R2>0.99 and p-value<0.05). +b) Plotting the average expression levels of cells from each GAD+ donor for the top 1,473 genes in Beta-1 cells with statistically significant correlation with the GAD titers corroborated our query. The total number of cells is 6,904. The plot shows a box-and-whisker plot of the given values. Lower 25th percentile (Q1), Interquartile range (IQR), Median (Q2), Upper 75th percentile (Q3). Minimum (Minimum value in the data, Q1-1.5 * IQR), Maximum (Maximum value in the data, Q3+1.5*IQR). The dots represents potential outliers. +c) A gene-ontology analysis in 1,473 genes related to Beta-1 cells using metaScape highlighted the relevance of endocytosis, protein processing in ER and MapK signaling pathway in Beta-1 cells. +d) Comparison of the cell clustering of the one normoglycemic AAb+ donor expressing two autoantibodies (IA-2 and ZnT8) with GAD+ donors using clumpiness revealed the distinct transcriptional signature of the double autoantibody-expressing autoantibody donor and the single autoantibody-expressing GAD+ donors. Clumpiness is a measure for finding the level of aggregation between labels distributed among the leaves of a hierarchical tree and extensively measures the relationships between metadata. Here, each leaf of the dendrogram contains a collection of labels (different AAB donor group). The more the labels group together within the dendrogram, the higher the clumpiness value. This analysis also demonstrates the overall similarity of GAD+ donors, which modestly displayed GAD level-dependent cell co-segregation. +Single-cell RNA-seq profiling enables the identification of MHC Class II-expressing ductal cells with transcriptional similarities to dendritic cells in T1D +a) (Left Top) Dendrogram visualization of co-expression of HLA-DPB1 and KRT19 gene transcripts in individual cells by scRNA-seq across the ductal and endocrine dendrogram from Figure 1D. (Left Bottom) Pie chart demonstrating HLA-DPB1+KRT19+ cells as percentage of total cells. (Right) Magnified view of the clusters of cells with high percentage (25% or greater) of HLA-DPB1+KRT19+ cells with HLA-DPB1 and KRT19 status displayed across these clusters (outlined in red dashed lines) and neighboring clusters of cells. Cells begin at the start pin symbol and from there are partitioned based on similarities and differences in gene expression. +b) (Top) Dendrogram visualization of cellular classification status across the magnified clusters of cells with high percentage (25% or greater) of HLA-DPB1+KRT19+ cells (outlined in red dash lines) and neighboring clusters of cells. (Bottom) Pie chart displaying the relative proportion of cellular classification status of HLA-DPB1+KRT19+ cells. The p-value presented is the result of the Chi-squared test. +c) (Top Left) Dendrogram visualization of donor group across the magnified clusters of cells with a high percentage (25% or greater) of HLA-DPB1+KRT19+ cells (outlined in red) as well as neighboring clusters of cells. (Top Right) Pie chart displaying the relative proportion of HLA-DPB1+KRT19+ cells in Control (top) or T1D (bottom) Ductal-1 cells. The p-value presented is the result of the Fisher exact test. (Bottom Left) Pie chart displaying the relative proportion of donor group of HLA-DPB1+KRT19+ cells. The p-value presented is the result of the Chi-squared test. (Bottom Right) Box plots displaying the HLA-DPB1+KRT19+ cell percentage of total cells per individual across donor groups. 24 total donors: 11 controls, 8 AAB+,and 5 T1D. A box-and-whisker plot is depicted with the box extending from the 25th to 75th percentiles, the line in the middle representing the median, whiskers extending from the minimum to the maximum, and all data points shown. +d) T1D ductal cells are transcriptionally similar to tolerogenic dendritic cells. Gene-set enrichment analysis was performed using gene signatures of DC subtypes, which were recently defined using scRNA-seq in human blood. The DC1 dendritic cell gene signature was enriched in Ductal-2 cells but not Beta-1 cells of T1D donors. +e) The co-stimulatory proteins CD80 or CD86 are not expressed in T1D ductal cells. +f) Ductal cells of T1D donors express interferon-associated genes including ISG20, ICAM1, and IRF7 compared with those of control donors. +Three single-cell resolution protein-based approaches corroborate the existence of MHC Class II-expressing ductal cells in T1D +a) Dendrogram visualization of co-expression of HLA-DR and cytokeratin protein coexpression in single cells analyzed with flow cytometry by time-of-flight (CyTOF). +b) Pie chart displaying HLA-DR+ cytokeratin+ cells and the relative proportions of each donor group from the CyTOF data. The p-value was calculated by the Chi-squared test. +c)Box plots displaying HLA-DR+ cytokeratin+ cell percentage of total cells per individual across donor groups derived from the CyTOF data (p-value=0.00507). Number of donors: AAB+ : 4; Control : 4; T1D : 4. Figure depicts box-and-whisker plot showing the quartiles, minimum non-outlier calculated by (Q1 - 1.5*IQR), 25th percentile/lower quartile Q1, 50th percentile/median Q2, 75th percentile/upper quartile Q3, maximum non-outlier calculated by (Q3 + 1.5*IQR) of the variable (hybrid percentage of total cells per individual) while the whiskers extend to show the rest of the distribution, except for points that are determined to be "outliers" (dots outside whiskers) using a method that is a function of the inter-quartile range. +d) Two-parameter CyTOF analysis of HLA-DR and cytokeratin protein expression in single cells from T1D donor #4 (HPAP028) and T1D donor #5 (HPAP032). +e) CD45 (PTPRC) expression levels in HLA-DR+ cytokeratin+ and HLA-DR+ cytokeratin- single cells. +f) Dendrogram visualization of co-expression of HLA-DR and cytokeratin proteins in single cells analyzed by imaging mass cytometry (IMC). +g) Pie chart displaying HLA-DR+ cytokeratin+ cells and the relative proportions of each donor group from the IMC data. The p-value was calculated by the Chi-squared test. The p-value shows 0.000 by both Chi-square function from scipy.stats (python) for the observed frequency array [34983,3711,4635] : [T1D,AAB+,Control], and cannot provide exact p-value. +h) Box plots displaying HLA-DR+ cytokeratin+ cells as a percentage of total cells per individual across donor groups from the IMC data (p-value=1e-16). Number of donors: AAB+ : 7; Control : 5; T1D : 4. p-value is obtained from a one-way ANOVA test. +i) Representative confocal microscopy image from the pancreas of a T1D donor (top) and Control donor (bottom) displaying HLA-DR+ cytokeratin+ labeled by immunofluorescence (IF). Control (n=3) and T1D (n=2). +Representative examples of IMC measurement corroborates that MHC Class II positive ductal cells are present in pancreatic tissues. +(Left) Imaging mass cytometry (IMC) in a region of interest (ROI) in pancreatic tissue from three representative individual donors for each donor group type (T1D, AAB+, and Control). HLA-DR is a general marker of MHC Class II (HLA-DR) expression, CD99 is a general islet marker, KRT (pan-keratin) is a ductal cell marker, and CD45 (PTPRC) is a general immune cell marker. Notably, HLA-DR+ ductal cells were primarily located in large ductal structures (outlined in yellow). The images presented here are publicly available at https://www.pancreatlas.org/datasets/508. +(Right) HLA typing performed by next-generation sequencing. Comprehensive clinical information about each donor is provided in PANC-DB: https://hpap.pmacs.upenn.edu/. Highlighted in yellow are the particular HLA alleles contributing to the susceptible or protective genotypes, which are abbreviated for each donor on the left side of the figure as follows. The four susceptible genotypes assessed were (1) HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01 (abbreviated as 'DR3', referring to the haplotype bearing the DRB1*03 allele); (2) HLA-DRB1*04:01/02/04/05/08-HLA-DQA1*03:01-HLA-DQB1*03:02/04 (or HLA-DQB1*02) (abbreviated as 'DR4', referring to the haplotype bearing the DRB1*04 allele); (3) HLA-A*24:02; and (4) HLA-B*39:06. The two protective genotypes assessed were (1) HLA-DRB1*15:01-HLA-DQB1*06:02 and (2) HLA-DRB1*07:01-HLA-DQB1*03:03. Notably, HLA-DR+ ductal cells were found across all HLA genotypes, including both susceptible and protective genotypes. +Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in Type 1 Diabetes +Type 1 Diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The etiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive, and non-diabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time-of-flight, and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreas function. +Introduction +Type 1 diabetes (T1D) is an autoimmune disease which occurs as a consequence of the destruction of insulin-producing beta cells in the islets of Langerhans within the pancreas. This complex disease is characterized by atypical beta-immune interactions including production of beta cell autoantibodies and the immunological attack on beta cells by cytotoxic CD8+ T cells. +T1D autoimmunity has been linked to poorly understood genetic and environmental factors. Genome-wide association studies have implicated multiple loci in T1D, with the major histocompatibility complex (MHC) Class II genes as the dominant susceptibility determinant of this disease. However, the precise cellular context through which T1D susceptibility genes cause the destruction of beta cells remains to be discovered. Addressing this question is particularly challenging since the pancreas is a heterogeneous organ, composed of multiple distinct cell types. +Two nontrivial constraints hamper insights into comprehensive identification of the pathogenic cell types in T1D: (1) the inability to safely biopsy the human pancreas of living donors and (2) the significant disease progression and beta cell destruction by the time patients are clinically diagnosed with T1D. Therefore, the majority of T1D studies have been performed on leukocytes from the peripheral blood, which is not the site of pathogenesis. Of late, the Network for Pancreatic Organ Donors (nPOD) and the Human Pancreas Analysis Program (HPAP) have started collecting pancreatic tissues from hundreds of deceased organ donors diagnosed with T1D. Additionally, given that many T1D patients harbor beta cell autoantibodies (AAbs) in their bloodstream prior to clinical diagnosis, nPOD and HPAP also collect samples from donors with AAbs towards islet proteins but without a medical history of T1D, in hope of elucidating early pathogenic events. +Using these initiatives, we developed a pancreatic islet atlas containing an unprecedented ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time-of-flight (CyTOF), and ~1,000,000 cells using in situ imaging mass cytometry (IMC) in pancreatic tissues of human organ donors collected by HPAP, enabling a resource for extensive exploration and discovery within the pancreatic environment. We also provide an interactive data explorer for simple, direct access to the single-cell transcriptomics data (https://cellxgene.cziscience.com/collections/51544e44-293b-4c2b-8c26-560678423380). Our comprehensive integrative analyses on this unique data set provides cellular and molecular insights into T1D pathogenesis and suggests pancreatic ductal cells may play a role in suppressing CD4+ T cells in pancreatic tissues. +Results +scRNA-seq unravels novel cell states in the human pancreas +To unmask the molecular perturbations occurring in pancreatic tissues during T1D, we constructed 81,313 single-cell RNA-seq (scRNA-seq) libraries from pancreatic islets of 24 human organ donors representing three categories: individuals with T1D (n = 5), those with AAbs toward pancreatic islet proteins but no clinical diagnosis of T1D ('AAB+'; n = 8), and those with neither AAbs nor a history of T1D ('Control'; n = 11) (Fig. 1a and Extended Data Fig. 1 a-b, and Tables S1 to S2). The statistics related to reads per cell across donors demonstrated the high-quality of these data sets (Table S3). We filtered outlier cells, removed doublets, and employed the cell type classifier 'Garnett' (Extended Data Figs. 1c-e, 2a-g, and 3a-e) to cluster 69,645 high-quality cells using 'TooManyCells' (Fig. 1b-c). The resultant classification was confirmed by both canonical gene marker expression for each cell type and by transferring cluster labels from a previous single-nucleus RNA-seq data set consisting of pancreatic islets to our datasets (Fig. 1c and Extended Data Fig. 4 a-c). +Notably, clustering was clearly driven by cell type, and not by confounding factors such as autoantibody status, age, BMI, phenotypic group, or other factors (Extended Data Figs. 2 d-g, Figs. S1 a-k, and S2 a-l). Additional evidence for the lack of technical noise stems from the observation that cell type clustering was preserved when donors from T1D, AAB+, and Control groups were independently clustered (Fig. S3 a-f). +Considering the reported abnormalities of the exocrine pancreas in T1D and recent efforts indicating the enrichment of sequence polymorphisms associated with T1D within the regulatory elements of exocrine cells, we next examined the relationship between pancreatic endocrine and ductal cells. First, we subsetted and reanalyzed the endocrine and ductal cells to achieve a more granular clustering (Fig. 1d). Upon reclustering, the major cell types - alpha cells, beta cells, delta cells, epsilon cells, PP cells, ductal cells, and acinar cells - were easily discernible (Fig. 1d and Extended Data Fig. 4d). In instances where there were two transcriptionally distinct canonical cell types (i.e. Beta-1 and Beta-2), differential gene expression analysis between populations provided further insights into the underlying molecular differences (Tables S4-S7). For example, cells in the Beta-2 cluster expressed higher levels of stress response genes such as NPTX2 and GDF15 when compared to those in the Beta-1 cluster. The activation of stress response genes in beta cells in various hyperglycemic states has been reported previously. Notably, the comparison of cells in the two ductal clusters revealed that while cells in the Ductal-1 cluster were enriched for transcription factors (TFs) associated with the endocrine cell fate (i.e., PDX1 and NKX6-1), those in the Ductal-2 cluster expressed acinar TFs (i.e., PTF1A and GATA4). +A substantial number of cells (4,001) were not included in these canonical cell type clusters, but rather formed their own transcriptionally distinct group on the dendrogram. This cluster comprised 5.7% of all profiled cells, with a mixture of cellular classifications and expression of canonical gene markers. We labeled these cells as 'Hybrid' cells (Figs. 1d and Extended Data Fig. 4d). Notably, the gene expressed most highly and consistently in Hybrid cells was INS, and a comprehensive examination of the cells comprising this cluster ruled out the possibility of them being doublets (Extended Data Figs. 1d and 2 a-e). To further validate the most closely related cell types to these non-canonical cells, we employed a label transfer strategy using a reference pancreatic islet scRNA-seq dataset. We corroborated the assignment of multiple cell types including beta cells and alpha cells to these Hybrid cells (Extended Data Fig. 4 e-f). Cells equivalent to Hybrid cells had been detected earlier and were most recently documented in the adult pancreas of mice and humans. Nonetheless, we excluded hybrid cells for further analysis to eliminate any cells captured potentially as doublets. +Since immune cell-mediated destruction of viable pancreatic cells is the major pathogenic feature of T1D, we examined the intrapancreatic immune cells profiled by scRNA-seq in detail. First, we subsetted and reclustered the cells constituting the 'Immune' cluster from the comprehensive tree (Fig. 1c) and found that this population also contained stellate (RGS5 high) and Schwann (PLP1 high) cells along with immune cells (PTPRC high) (Fig. S4 a-b). Using the Immunological Genome Project (ImmGen) cell type signatures, we further found that the gene signatures of antigen-presenting cells (APCs) such as macrophages, for example CD68, SPI1, CD14, and CD16, were most frequently expressed in the immune cell subset (Fig. S4 b-c), suggesting that these cell types comprise the majority of the identified immune cells that are collected and cultured along with pancreatic islets. +Studies demonstrating that regulatory elements of immune cells harbor the largest number of risk variants associated with T1D imply that immune cells are more susceptible to gene dysregulation compared with other cell types in T1D. To quantify the link between genetic predisposition associated with T1D and cell type-specific gene expression, we used a genetic prioritization model and examined the enrichment of sequence variation associated with T1D across our annotated cell types (Fig. S4 d). As a control, we also examined sequence variation associated with asthma and T2D. This analysis revealed that immune cells were the top cell type associated with T1D and asthma, which are both immune-mediated disorders. In contrast, beta cells were the top cell type associated with T2D (Bonferroni significance threshold of PS-LDSC < 0.05), in agreement with recent reports demonstrating that risk variants for T2D are enriched in active cis-regulatory elements of beta cells. Together, the genetic prioritization model corroborated that gene expression in immune cells is affected by T1D-associated sequence variation. +In addition to successful identification of the major endocrine and exocrine cell types and pancreatic immune cells, we also observed that the overall proportion of these cell types was in accordance with previous work. Each of the major cell types comprised cells from the three donor groups with varying proportions (Fig. 1 e-g, Extended Data Figs. 4 g-h, Figs. S4 e-g, and S5 a-b). As expected, we found that there was a lower proportion of beta cells in the T1D cohort compared to the AAB+ or Control groups (Fig. S5 a-b). Conversely, both acinar and ductal cells comprised a higher portion in the T1D cohort, reflecting the difficulty of isolating high purity islets from T1D donors. Furthermore, within major cell clusters, there were varying degrees of separation based on donor group, which is to be expected due to likely transcriptomic differences among the three donor states (Figs. 1 e-f and S4a). Notably, Ductal-1 cells clearly separated into distinct T1D-enriched and Control-enriched groups (Fig. 1f). Taken together, our data indicate that transcriptomic differences amongst cell types and not technical biases drive the separation of major cellular clades, and that the donor state further segregates within cell types. +Comparison of endocrine and exocrine cells in AAB+ and T1D donors +We next compared transcriptomic divergence of AAB+ and T1D cells from Controls (Fig. 2a). To perform differential expression analysis between donor groups, we used two complementary analytical strategies: (1) grouping individual cells from different donor groups together (Tables S8-S10) or (2) performing pseudo-bulk analysis for each donor (Tables S11-S13). Plotting the average expression levels of the top 3 differentially expressed genes determined by the first strategy across donor groups confirmed that the predicted differential expression is not driven by one or a few donors (Fig. S6). Since pseudo-bulk methods cause cells from individuals with fewer cells to be more heavily weighted, we performed further analysis using genes detected based on the first strategy. Generally, the degree of overlap between dysregulated genes and pathways in AAB+ and T1D states were cell type-dependent (Figs. 2 b-e, S7 a-b, S8 a-b, S9 a-d, S10 a-d, Extended Data Fig 5 a-c). However, some pathways were found to be commonly dysregulated in multiple cell types across T1D and AAB+, including 'apoptotic signaling', various protein folding ontologies, various viral-related ontologies, 'autophagy', 'inflammatory pathways', and 'stress response'. +We next examined the transcriptional changes in the two populations of annotated beta cells, Beta-1 and Beta-2. A large number of genes was downregulated in T1D (9,512 genes) and AAB+ (3,666 genes) Beta-1 cells compared to Controls, many of which overlapped (2,896 genes, 28%; p < 2.2e-16) between the two donor groups (Figs. 2b and S7a). Notable pathways that were frequently downregulated in Beta-1 cells of AAb+ and T1D donors were immune/stress response and apoptosis-related (Figs. 2b and S7a). Given that beta cells are destroyed by immune cells in T1D, it is possible that these remaining Beta-1 cells were not targeted by the immune system. It is also possible that these beta cells are able to survive and function after immunological attack by decreasing immune signaling and apoptotic signaling via downregulation of the TP53 pathway (Figs. 2b and S7a), which is notable given that upregulation of the TP53 pathway and an associated increase in susceptibility to apoptosis has been observed in T1D. Hence, these results suggest that cells from AAb+ donors in this beta cell population are either spared from destruction or employ similar protective molecular mechanisms to enhance survival and function, which is further supported by the fact that the expression of immune checkpoint protein PDL-1 (CD274) is upregulated in AAB+ Beta-1 cells compared to those from Controls. +The Beta-2 cell population displayed a small proportion of genes (4%; 283 genes; p < 2.2e-16) with elevated expression in both T1D and AAB+ cells when compared to Controls (Figs. 2c, and S7b). Additionally, an even smaller number of genes were downregulated in T1D and AAB+ Beta-2 cells when compared to Controls. Nonetheless, several pathways were found to be commonly dysregulated across both donor groups. Two interrelated pathways dysregulated in both T1D and AAB+ Beta-2 cells, namely 'chaperone-mediated protein folding' and 'response to topologically incorrect protein', suggest a dysregulation of protein folding, an essential function for cellular homeostasis. Additionally, the 'TNF-alpha/NF-kappa B signaling' pathway, which has been implicated as an important regulator of autoimmune processes, was significantly downregulated across the two donor groups in the Beta-2 cell population (Figs. 2c and S7b). Together, our differential expression analyses extend earlier studies on the pathways triggering beta cell dysfunction and death. +Given the clear segregation of ductal cell populations by donor group, we next examined the transcriptional changes in the two populations of ductal cells, Ductal-1 and Ductal-2. A large number of genes was upregulated in T1D (7,175 genes) and AAB+ (4,371 genes) Ductal-1 cells when compared to Controls, a significant number of which were common between the two donor groups (Figs. 2d and S8a; 2,283 genes; 25%; p-value < 1e-12). Notable induced pathways upregulated in T1D and AAB+ cells are associated with apoptosis, stress, and immune response (Figs. 2d and S8a). In the Ductal-2 cell population, although many upregulated genes were observed in T1D (6,841 genes), there were not nearly as many upregulated genes in AAB+ cells (1,106 genes) when compared to Controls (Figs. 2e and S8b). Furthermore, in the T1D and AAB+ Ductal-2 cell population, there was a modest but significant overlap between upregulated genes (Figs. 2e and S8b; 11%; p-value < 1e-12). Nevertheless, various gene pathways were found to be significantly upregulated across both ductal populations (Figs. 2 d-e and S8 a-b). Taken together, these findings suggest that although AAb+ donors maintain normoglycemia, significant transcriptional dysregulation is occurring in AAB+ endocrine and exocrine cells that is highly similar to that in T1D. +Next, we directly compared T1D to AAB+ cells (Fig. 2f and Tables S8-S13). For both groups of beta cells, genes associated with autophagy, stress response, and immune-related pathways were activated in AAB+ cells compared to T1D cells (Figs. 2 g-h and S7 a-b). Although similar pathways were upregulated in AAB+ Beta-1 and Beta-2 cells, apoptotic and adaptive immune system signaling were only upregulated in Beta-2 AAB+ cells. These data suggest that this population is undergoing cell death, indicated by the upregulation of adaptive immune cell genes and BCL10. In Ductal cell populations, there was a larger number of upregulated genes in T1D (Figs. 2 i-j, and S8 a-b). Notably, apoptotic, metabolic, protein folding, and immune responses were activated in T1D ductal cells in comparison to AAB+ ductal cells (Figs. 2 i-j and S8 a-b). Remarkably, interferon alpha and beta pathways, known to be critical in T1D disease pathogenesis, were significantly elevated in T1D ductal cells compared to either Control or AAB+ ductal cells (Extended Data Fig. 5d). Our molecular evidence supports more recent findings of exocrine abnormalities in T1D, positioning these exocrine cells in disease pathogenesis. Taken together, AAB+ cells exhibit significant transcriptional changes like those observed in T1D. +Beta cell gene signature is correlated with the anti-GAD titer +Pancreatic tissues from AAb+ donors collected by HPAP can potentially offer a unique insight into the initial molecular events of T1D pathogenesis. A landmark study following patients from birth determined that ~69% of children with multiple islet autoantibodies progressed to T1D after islet autoantibody seroconversion. Among HPAP donors, only one donor with no history of T1D expressed two islet autoantibodies while the other normoglycemic AAb+ donors were anti-glutamic acid decarboxylase (GAD) autoantibody positive. Considering that the longitudinal children study also revealed that the risk of diabetes in children who had no islet autoantibody was 0.4% in contrast to 14% for children expressing a single islet autoantibody, we next focused on the transcriptional landscapes of islets in GAD+ donors and queried for cell types whose transcriptional signature strongly correlated with the GAD titer among the GAD+ subjects. We devised a strategy to determine the number of genes whose expression levels significantly correlated with the GAD titer across GAD+ donors, either positively or negatively. However, we detected only positive correlation of statistical significance between gene expression levels and GAD titers. Strikingly, the top cell type with the largest number of genes (1,473) with significant correlation with the GAD titer in AAb+ donors was Beta-1 cells (Fig. 3a, Table S14). Plotting the average expression levels of cells from each GAD+ donor for these 1,473 genes in Beta-1 cells confirmed this finding (Fig. 3b). To define the identity of genes with an increase in their expression levels correlating with GAD levels, we performed gene-ontology analysis. Our approach highlighted the relevance of endocytosis, lysosome, protein processing in ER and MAPK signaling in Beta-1 cells (Fig. 3c). Additional comparison of the cellular clustering of the one AAb+ donor expressing two autoantibodies (IA-2 and ZnT8; AAB+ #5; HPAP043) with GAD+ donors across the AAB+-specific clustering (Fig. S3b) or all donor-type clustering (Fig. 1c) revealed the distinct transcriptional signature of the double autoantibody-expressing AAb+ donor in comparison to single AAb+ GAD+ donors (Figs. 3d and S2l). This analysis also revealed an overall similarity of GAD+ donors, which modestly displayed GAD level-dependent cell co-segregation (Figs. 3d and S2l). Together, our unbiased strategy puts forward Beta-1 cells as the top cell type whose transcriptional outputs correlate with anti-GAD levels, suggesting the dynamic landscape of transcriptome in normoglycemic autoantibody-positive individuals. +MHC Class II expression is enriched in T1D ductal cells +The major genetic susceptibility determinants of T1D have been mapped to the MHC Class II genes. We therefore sought to determine which cell types or donor states disproportionately express genes in this pathway. Using our scRNA-seq data, we found that genes associated with MHC Class II activity were enriched in Immune, Endothelial, and Ductal clusters (Extended Data Fig. 6 a-d). The lack of enrichment of the immune cell marker PTPRC or other genes associated with immune cells across the endocrine and ductal dendrogram supports the notion that the enrichment of MHC Class II associated genes in ductal cells is not due to immune cell contamination (Extended Data Fig. 4 a,d and Extended Data Fig. 6g). Next, we evaluated the expression of HLA-DPB1, an MHC class II gene associated with T1D risk, and KRT19, a ductal cell marker, across ductal and endocrine cell types. We identified five clusters with high HLA-DPB1 and high KRT19 expression, which accounted for 10.9% of all cells (7,588 cells) (Fig. 4 a-b and Extended Data Fig. 6 e-f). Strikingly, cells from T1D donors disproportionately contributed to this population of MHC Class II-expressing ductal cells (Fig. 4c; p-value < 2.2e-16). This observation is not due to sampling issues pertaining to the difficulty of isolating high purity islets from T1D donors. This conclusion is supported by the fact that even though the Ductal-1 cell population consists of very similar numbers of Control and T1D donor ductal cells (4,217 and 4,154 cells, respectively), there is a marked difference in the percentage of Control versus T1D MHC class II-expressing Ductal-1 cells, at 35% and 91%, respectively (Fig. 4c; p-value < 2.2e-16). +T1D ductal cells assume the transcriptional identity of dendritic cells +Dendritic cells (DCs) are among the major professional antigen-presenting cells expressing MHC Class II proteins with the salient function to ingest antigens and present processed epitopes to T cells, thereby regulating adaptive immune responses by activating or suppressing T cells. Considering that MHC Class II proteins are required for antigen-presentation in dendritic cells, we next evaluated whether there are any other similarities between transcriptional profiles of T1D ductal cells and conventional dendritic cells. Hence, we performed gene-set-enrichment analysis using gene signatures of dendritic cell subtypes, which were recently defined using scRNA-seq profiling in human blood. Remarkably, we found a highly significant enrichment of the DC1 gene signature in Ductal-2 cells of T1D donors, while no other annotated islet cell type revealed such significant and strong enrichment of gene signatures associated with dendritic cell subtypes (Fig. 4d and Extended Data Fig. 7a). DC1 corresponds to the cross-presenting CD141/BDCA-3+ cDC1, which is best marked by CLEC9A. Of note, the enrichment of other dendritic cell subtype gene signatures in T1D ductal cells was not statistically significant (Extended Data Fig. 7a). +To activate T cells, dendritic cells are required to express both MHC Class II proteins and costimulatory proteins CD80 and CD86. In the absence of CD80 and CD86, antigen-presentation by dendritic cells can lead to tolerance and T cell suppression. We found that CD80 and CD86 were not expressed in T1D ductal cells, suggesting a lack of costimulatory signal in these dendritic cell-like ductal cells in T1D donors (Fig. 4e). Additionally, the inhibitory receptor VSIR, which negatively regulates T cell responses, showed higher expression in T1D compared with control ductal cells (Extended Data Fig. 7b). Moreover, the ductal cells in T1D expressed high levels of interferon genes such as ICAM1, ISG20, and IRF7 (Figs. 4f and Extended Data Fig. 5d). Hence, our single-cell transcriptional profiling detected an enrichment of ductal cells with transcriptional similarities to tolerogenic DCs. These results imply an unappreciated role for T1D ductal cells potentially acting as decoy receptors in an apparent attempt to deactivate CD4+ T cells by inducing tolerance during immune invasion of the pancreas. +Multimodal confirmation of MHC Class II+ ductal cells +We next sought to corroborate our transcriptomic-based finding of MHC Class II expression on ductal cells in T1D by employing additional experimental modalities: two high-throughput technologies, CyTOF and IMC, in addition to immunofluorescence experiments. Our integrative approach with CyTOF combined ~ 7,000,000 live, cultured single cells from 12 donors, which had also been profiled by scRNA-seq (4 Control, 4 AAB+, and 4 T1D donors). This additional modality scaled our analytical strategy to millions of cells, measuring the expression levels of 35 proteins (Table S15). Since the strategy we used to annotate cells using scRNA-seq is not applicable to CyTOF measurements, we developed a new machine-learning method to annotate cells based on canonical markers (Extended Data Fig. 8 a-e). Using CyTOF, we identified a population of ductal cells expressing HLA-DR, an MHC Class II protein (Fig. 2a). Notably, we found that cells from T1D donors constituted the largest percentage of this cluster, in agreement with the findings from scRNA-seq (Fig. 5b; p-value < 1e-6). Furthermore, HLA-DR-expressing ductal cells made up a larger percentage of total cells across individual T1D donors compared with Control or AAB+ donors (Fig. 5c, p-value=0.00507). A two-parameter (cytokeratin and HLA-DR) analysis on all single cells analyzed by CyTOF further confirmed the presence of this double-positive population across multiple donors (Fig. 5d and Extended Data Fig. 8 f-g). Notably, these ductal cells did not express CD45, the hallmark of leukocytes (Fig. 5e). The identification of ductal cells with MHC Class II molecules using both scRNA-seq and CyTOF strongly corroborates the increased frequency of this population in T1D. +Having identified a population of ductal cells with MHC Class II molecules enriched in T1D donors by two experimental modalities in our integrative analysis, we next sought to study these ductal cells in pancreatic tissues independent of islet culture by means of anatomical-spatial features in pancreatic tissues by IMC. While measurements with CyTOF and scRNA-seq assays rely on the profiling of dissociated cells, IMC retains spatial information by analyzing tissues fixed directly from the native human pancreas. We again amended our analytical pipeline with an optimized cell annotation approach for the IMC technology. We harnessed the expression levels of 33 proteins quantified by IMC in more than 1 million cells across 143 tissue slides from 19 donors, including 11 individuals not previously assessed by scRNA-seq or CyTOF for an independent validation of our findings (Table S16). This analysis confirmed that MHC Class II-expressing ductal cells were predominately present in T1D donors (Fig. 5 f-h and Extended Data Fig. 9 a-e). MHC Class II-expressing ductal cells were located in all regions of the pancreas (Extended Data Fig. 10a). Remarkably, the frequency of CD11B+ myeloid cells annotated by our analytical strategy in both CyTOF and IMC measurements was highly correlated with the frequency of MHC Class II expressing ductal cells (Extended Data Fig. 10 b-c). Immunofluorescence staining (IF) in native pancreatic tissues, followed by confocal microscopy, verified the existence of MHC Class II-expressing cells in a Control and a T1D donor (Fig. 5i). We identified MHC Class II-expressing ductal cells in both donors; however, there was a pronounced enrichment of MHC Class II-expressing ductal cells in the T1D pancreas (Fig. 5i). Representative examples of IMC measurements in tissues also confirm this finding (Fig. 6). Finally, cellular neighborhood analysis in pancreatic tissues established that HLADR-expressing ductal cells were surrounded by CD4+ T cells and myeloid cells including CD11B+ dendritic cells (Extended Data Fig. 10 d-f; p-value < 1e-2). Together, our multimodal single-cell measurements from transcriptomics to spatial proteomics in ductal cells suggest that ductal cells are transcriptionally similar to tolerogenic DCs, implying an unappreciated role of these exocrine cells in modulating T cell activity in long-term T1D. +Discussion +Employing three high-throughput single-cell technologies, we provided a comprehensive atlas of millions of cells using integrative multi-modal analyses as a molecular microscope to investigate cellular diversity in the pancreas of T1D, AAb+, and non-diabetic human organ donors. These data, including paired samples across technologies, enable an exploration of the pancreatic environment in both healthy and disease states. +We found that AAb+ donors exhibit similar transcriptional changes as T1D donors in various endocrine and exocrine cells, despite these donors retaining normoglycemia. Remarkably, the unique collection of GAD+ donors in the HPAP database allowed us to delineate Beta-1 cells as the primary cell type whose transcriptional outputs correlate with anti-GAD titers, suggesting the existence of dynamic transcriptional landscape in autoantibody-positive individuals. Although it is impossible to discern at present whether these transcriptional changes are contributing to or are byproducts of disease pathogenesis, the mere discovery of molecular phenotypic changes in pancreatic cells of AAb+ individuals should advance our understanding of early pancreatic perturbations occurring in T1D. +The most striking finding arising from our study is that cells of the exocrine compartment show transcriptional and gene ontological changes in the T1D disease setting. Ductal cells from T1D donors, in contrast with those from non-diabetic or AAb+ donors, express high levels of MHC Class II and interferon pathways, are surrounded by CD4+ T cells and dendritic cells and are transcriptionally similar to tolerogenic dendritic cells. Although our study represents the first report of ductal cells expressing MHC Class II proteins in the T1D context, this finding is in accordance with previous literature documenting an elevation of immune cells in the exocrine pancreas of T1D donors and regulation of MHC Class II genes by the interferon signaling pathway. Moreover, the expression of MHC Class II proteins in pancreatic ductal adenocarcinomas has been reported. Recent studies also support a role for epithelial cells as facultative, non-professional antigen-presenting cells in the gut and lung, and expression of MHC Class II proteins in non-lymphoid cells in the pancreas has been shown. We posit that these cells exhibit a tolerogenic response to chronic T cell infiltration in pancreatic tissues and appear to be an ultimately unsuccessful attempt of the pancreas to limit the adaptive T cell response responsible for destroying beta cells. While this interpretation is strongly supported by our multimodal data analysis in human pancreatic tissues, the limitation of our study relates to lack of functional validation of this hypothesis. Our future efforts utilizing mouse genetics will enable us to further validate the functional relevance of these findings. Together, our study provides a unique resource of millions of cells of the pancreatic environment and unmasks exocrine ductal cells as potential responders to immune infiltration in T1D. +One technical question under intense debate in the scRNA-seq community is how to perform differential expression analysis. Squair et al. compared differential expression analysis techniques in scRNA-seq datasets, utilizing bulk RNA-seq data as the ground-truth for measuring false-positives. They concluded that predictions using the pseudo-bulk approach are the most similar to predictions from bulk RNA-seq data. Contradicting Squair et al., Zimmerman et al. published a study comparing techniques for performing differential expression analysis in scRNA-seq datasets and argued that pseudo-replication is acknowledged as one of the most common statistical mistakes in the scientific literature. Instead, they proposed the use of computationally expensive generalized linear mixed models for the analysis of scRNA-seq data. In summary, the contradictory results of these two studies reveal lack of consensus on alternative differential expression methods. Aware of these challenges in the analysis of scRNA-seq data, we took advantage of multimodal measurements such as IMC, CyTOF, and IHC to assess the reproducibility of our novel findings related to ductal cells in T1D donors across independent experimental assays. +Materials and Methods +Experimental model and subject details +Pancreatic islets were procured by the HPAP consortium (RRID:SCR_016202; https://hpap.pmacs.upenn.edu), part of the Human Islet Research Network (https://hirnetwork.org/), with approval from the University of Florida Institutional Review Board (IRB # 201600029) and the United Network for Organ Sharing (UNOS). A legal representative for each donor provided informed consent prior to organ retrieval. For T1D diagnosis, medical charts were reviewed and C-peptide levels were measured in accordance with the American Diabetes Association guidelines (American Diabetes Association 2009). All donors were screened for autoantibodies prior to organ harvest, and AAb positivity was confirmed post tissue processing and islet isolation. +Organs were processed as previously described. Table 1 and 2 summarizes donor information. Pancreatic islets were cultured and dissociated into single cells as previously described (22). Total dissociated cells were used for single cell capture for each of the donors, except AAB+ donor #1 (HPAP019), which was enriched for beta cells. +The C-peptide analysis was performed using a two site immuno-enzymatic assay from Tosoh Bioscience on a Tosoh 2000 auto-analyzer (Tosoh, Biosciences, Inc., South San Francisco, CA). Briefly, the test sample is bound with a monoclonal antibody immobilized on a magnetic solid phase and an enzyme-labeled monoclonal antibody, and then the sample is incubated with a fluorogenic substrate, 4-methylumbelliferyl phosphate (4MUP). The amount of enzyme-labeled monoclonal antibody that binds to the beads is directly proportional to the C-peptide concentration in the test sample. A standard curve is constructed using calibrator of known concentration, and unknown sample concentrations are calculated using the curve. The C-peptide assay is calibrated against WHO IS 84/510 standard. The assay has a sensitivity level of 0.02 ng/mL. To monitor the assay performance, a set of low, medium, and high C-peptide level quality control samples are analyzed several times per day. The inter-assay coefficients of variability for the low, medium, and high C-peptide controls are 3.2%,1.6%, and 1.8%, respectively. The results of the analyses of the long-term monitoring pools have demonstrated a consistently low variation around the target values, thus ensuring result consistency. +Serum from organ donors is tested for GAD, IA-2, mIAA, and ZnT8A autoantibodies by radioligand-binding assay (RIA) as previously described. Micro IAA (mIAA) and ZnT8A were performed with in-house RBA and the assay thresholds (index of 0.010 mIAA and 0.020 for ZnT8A) was set up as 99th percentile of over 100 controls. GAD and IA-2 was performed with NIDDK harmonized standard methods (3) and the upper limits of normal (20 DK units/ml for GAD and 5 DK units/ml for IA-2) was established around the 99th percentile from receiver operating characteristic curves in 500 healthy control subjects and 50 patients with new onset diabetes. In the most recent IASP Workshop, the sensitivity and specificity were 78% and 99% for GAD, 72% and 100% for IA-2, 62% and 99% for mIAA, 74% and 100% for ZnT8A, respectively. +scRNA-seq islet capture, sequencing, and processing +The Single Cell 3' Reagent Kit v2 or v3 was used for generating scRNA-seq data. 3,000 cells were targeted for recovery per donor. All libraries were validated for quality and size distribution using a BioAnalyzer 2100 (Agilent) and quantified using Kapa (Illumina). For samples prepared using 'The Single Cell 3' Reagent Kit v2', the following chemistry was performed on an Illumina HiSeq4000: Read 1: 26 cycles, i7 Index: 8 cycles, i5 index: 0 cycles, and Read 2: 98 cycles. For samples prepared using 'The Single Cell 3' Reagent Kit v3', the following chemistry was performed on an Illumina HiSeq 4000: Read 1: 28 cycles, i7 Index: 8 cycles, i5 index: 0 cycles, and Read 2: 91 cycles. Cell Ranger (10x Genomics; v3.0.1) was used for bcl2fastq conversion, aligning (using the hg38 reference genome), filtering, counting, cell calling, and aggregating (--normalize=none). +scRNA-seq clustering, doublet removal, & cell type classification +Seurat v3.1.5 was used for filtering, UMAP generation, and initial clustering. Genes expressed in at least 3 cells were included, as were cells with at least 200 genes. nFeature, nCount, percent.mt, nFeature vs nCount, and percent.mt vs nCount plots were generated to ascertain the lenient filtering criteria of 200 < nFeature < 8,750, percent.mt < 25, and nCount < 125,000. Data was then log normalized, and the top 2,000 variable genes were detected using the "vst" selection method. The data was then linearly transformed ("scaled"), meaning that for each gene, the mean expression across cells is 0 and the variance across cells is 1. Principle component analysis (PCA) was then carried out on the scaled data, using the 2,000 variable genes as input. We employed two approaches to determine the dimensionality of the data, i.e. how many principal components to choose when clustering: (1) a Jackstraw-inspired resampling test that compares the distribution of p-values of each principle component (PC) against a null distribution and (2) an elbow plot that displays the standard deviation explained by each principal component. Based on these two approaches, 17 PCs with a resolution of 1.2 were used to cluster the cells, and non-linear dimensionality reduction (UMAP) was used with 17 PCs to visualize the dataset. +DoubletFinder v2.0 was used to demarcate potential doublets in the data as previously described, with the following details: 17 PCs were used for pK identification (no ground-truth) and the following parameters were used when running doubletFinder_v3: PCs = 17, pN = 0.25, pK = 0.0725, nExp = nExp_poi, reuse.pANN = FALSE, and sct = FALSE (Fig. S1d). Scrublet v0.2.1 (18) was also used to demarcate potential doublets. We removed all cells that were flagged as doublet by both or either approach. +The raw data for the remaining cells were filtered using the following criteria, which resulted in 69,645 cells remaining: 200 < nFeature < 8,750, percent.mt < 25, and nCount < 100,000. The data were log normalized, the top 2,000 variable genes were detected, the data underwent linear transformation, and PCA was carried out, as described above. Both the Jackstraw-inspired resampling test and an elbow plot of standard deviation explained by each principal component were used to determine the optimal dimensionality of the data, as described above. Based on these two approaches, 26 PCs with a resolution of 1.2 was used to cluster the cells, and UMAP was used with 26 PCs to visualize the 49 clusters detected. +Garnett was used for initial cell classification as previously described. In brief, a cell type marker file (Table S17) with 17 different cell types was compiled using various resources, and this marker file was checked for specificity using the "check_markers" function in Garnett by checking the ambiguity score and the relative number of cells for each cell type. A classifier was then trained using the marker file, with "num_unknown" set to 500, and this classifier was then used to classify cells and cell type assignments were extended to nearby cells, "clustering-extended type" (Louvain clustering) (Fig. S3d). Upon inspection of cluster purity using canonical gene markers of the major pancreatic cell types across the Seurat-generated clusters, we found that the abundant and transcriptionally distinct cell types form generally distinct and unique clusters: beta cells (INS high), alpha cells (GCG high), acinar cells (CPA1 high), ductal cells (KRT19 high), endothelial cells (VWF high) stellate cells (RSG10 high), and immune cells (PTPRC, also known as CD45 or leukocyte common antigen, high) (Fig. S3e). In contrast across the Seurat-generated clusters, the rarer and/or less transcriptionally distinct cell types did not clearly segregate, namely delta cells (SST high), PP cells (PPY high), and epsilon cells (GHRL high). +Integration and label transfer was used to further validate Garnett cell-type assignments as previously described. To label canonical cell types, a previous snRNA-seq data set of adult pancreatic cells (EGAS00001004653) was used as a reference for the "query" datat set presented in this study. First, SCTransform was used to preprocess the data. Briefly, SCTransform uses a generalized linear model (GLM) for each gene with UMI counts as the response variable and sequencing depth as the predictor. To integrate data for UMAP visualization, Seurat integration was used to identify common anchor points between data sets. Seurat uses diagonalized canonical correlation analysis (CCA) followed by L2-normalization and searching for mutual nearest neighbors (MNN). Then, anchors between data sets are compared based on their local neighborhood structure of other anchors to receive "correction vectors". These correction vectors are then subtracted from the query gene expression matrix, resulting in an integrated data set. Similarly for label transfer, these anchors between data sets are instead labeled as discrete cell types and similar anchors assign cell labels from the reference cells to the query cells. To assign canonical cell-type labels to Hybrid cells, the same integration and label transfer process was used but with a previous scRNA-seq pancreatic data set as a reference (GSE145126). +We employed the analytical workflow termed 'TooManyCells', which implements an efficient divisive hierarchical spectral clustering approach along with tree visualizations. We invoked the cellular classifier Garnett, which annotates cell types by training a regression-based classifier from user-provided cell type signatures. Briefly, for the clustering of all cells, the raw data from the 69,645 cells were normalized by total count and gene normalization by median count (TotalMedNorm) followed by term frequency-inverse document frequency (tf-idf) for clustering. For visualization of the comprehensive clustering, the dendrogram was first pruned using the TooManyCells flags '--min-distance-search "15"' and "--smart-cutoff "15"', followed by pruning using the flag '--max-step 6'. +For the clustering of ductal/endocrine cells, data from the ductal/endocrine cell clusters from the comprehensive tree were subsetted and normalized by TotalMedNorm followed by term tf-idf. For visualization of the ductal/endocrine tree, the dendrogram was first pruned using the TooManyCells flags '--min-distance-search "7"' and "--smart-cutoff "7"' followed by pruning using the flag '--max-step 7'. Data from the immune cell cluster from the comprehensive tree were subsetted and normalized by TotalMedNorm followed by tf-idf. For visualization of the immune tree, the dendrogram was first pruned using the TooManyCells flag '--max-step 4'. When individual genes were painted across any of the dendrograms, 'TotalMedNorm' was employed to normalize gene expression. +Differential Gene Expression, GSEA analysis, and Metascape analysis +Differential genes were found using edgeR through TooManyCells with the normalization "NoneNorm" to invoke edgeR single cell preprocessing, including normalization and filtering. For Metascape analysis, less than or equal to 3,000 differential genes (FDR < 0.05 and fold change (FC) > 0.1) were subjected to analysis. The top 20 clusters are displayed and a stringent cut-off of 1e-6 was applied to determine significant gene ontology pathways. For gene-set-enrichment-analysis (GSEA) analysis, GSEA Preranked (4.0.1) was run on a pre-ranked gene list using either user-provided pancreatic gene expression sets or standard hallmark gene signatures provided by the Molecular Signatures Database (MSigDB). Pseudobulk analysis was performed by taking the average of cells within individuals. The differential genes were found using edgeR through multi-sample, multi-group scRNA-seq analysis tool (muscat). The differential genes were filtered based on the combined threshold of p-value < 0.05 and fold change (FC) > 1. +Hybrid cell co-expression, DE analysis and heatmaps +For the differentially expressed genes (FDR < 0.05 and fold change (FC) > 0.1) between every two sample groups, we calculated the shared and unique genes in each cell type, and visualized it with Venn diagrams. The expression levels of the genes in each cell of the three groups were extracted from the median normalized count matrix. Then we aggregate the expression levels in each group by taking the average value of the normalized counts. The mean expression values of the three groups were further normalized by the total expression level of that gene. We visualized the normalized expression level of differential genes with heatmaps. +To examine the co-expression of signature genes of some cell types, we normalized the median normalized matrix with log2(N +1). Then we selected the matrix of selected cell types by marker genes. The distribution of the cells from selected cell types by expression level of two marker genes were shown with geom_density_2d_filled() in ggplot2 package of R. +CyTOF data collection, input files, and preprocessing +Flow CyTOF was performed as described previously. Briefly, after isolating the dissociated cells, barcoding was conducted for donors following the manufacturer's protocol (Fluidigm, 101-0804 B1). Following barcoding, metal-conjugated antibody labeling was carried out in 'FoxP3 permeabilization buffer' (eBioscience, 00-8333) with 1% FBS (Hyclone, Cat# 7207) for 12 hours at 4 C at a concentration of up to 3 million cells per 300 mul of antibody cocktail, followed by twice washing with FoxP3 permeabilization buffer. Cells were then incubated with the DNA intercalator Iridium (Fluidigm, 201192A) at a dilution of 1:4,000 in 2% paraformaldehyde (Electron Microscopy Sciences, 15714) in DPBS (Corning, 21-031-CV) at RT for 1 hr. Mass cytometry data were acquired by CyTOF (Fluidigm). Flow CyTOF data analyses of endocrine cell composition was performed using the Cytobank implement (https://www.cytobank.org/). +Normalized FCS files were pre-processed prior to TooManyCells analysis and visualization using FlowJo Version 10.6.1 by gating all events on singlets according to event length and DNA content and then on live cells based on cisplatin exclusion. The Singlet/Live gated population was exported to a CSV file for TooManyCells analysis. Two dimensional plots were visualized for combinations of individual channels. +TooManyCells clustering for CyTOF +TooManyCells was used to generate cell clades of CyTOF data. Cells with less than a total of 1e-16 signal were removed, leaving 6,945,575 cells. Upon inspection of protein levels across a tree with all cells, endocrine and exocrine compartments were further subsetted leading to a refined analysis of 4,521,988 cells. Quantile normalization of the raw counts was used in the clustering step. The resulting tree was pruned by collapsing nodes with less than (7 MAD X median # cells in nodes) cells within them into their parent nodes. +Imaging mass cytometry (IMC) analysis and Cell Segmentation +IMC was performed as described previously. Cell segmentation of all images was performed with the Vis software package (Visiopharm). All image channels were pre-processed with a 3x3 pixel median filter, then cells were segmented by applying a polynomial local linear parameter-based blob filter to the Iridium-193 DNA channel of each image to select objects representing individual nuclei. Identified nuclear objects were restricted to those greater than 10mum2, then dilated up to 7 pixels to approximate cell boundaries. Per-cell object mean pixel intensities were then exported for further analysis. +TooManyCells clustering for IMC +TooManyCells was used to generate cell clades of IMC data. Cells with less than a total of 1e-16 signal were removed. Upon inspection of protein levels across a tree with all 1,170,001 cells, endocrine and exocrine compartments were further subsetted, leading to the refined analysis of 130,428 cells. The full tree with 1,170,001 cells was used for the assessment of HLA-DR-expressing ductal cells. Quantile normalization of the raw counts was used in the clustering step. The resulting tree was pruned by collapsing nodes containing less than (5 MAD X median # cells in nodes) cells within them into their parent nodes. Subsetting of the tree was done with "--root-cut 3" to focus on node 3 in relevant analyses, with additional pruning of (3 MAD X median # cells in nodes). +Cell-neighborhood analysis for IMC +Three labels were given to cells in the IMC neighborhood analysis: base, neighbor, and distant. Base cells originated from the chosen node, here node 16 in the node 3-focused IMC tree, or node 10 in the complete pruned tree which includes the former node 16. Given the x- and y-coordinates from IMC per cell, each cell's Euclidean distance to a base cell was calculated. If that distance was less than or equal to the chosen value, 20 for the complete pruned tree, the cell was assigned the neighbor label. Otherwise the cell was designated as distant. +Machine-learning method for cell annotation in IMC and CyTOF +To automatically label single cells from proteomic profiles, raw proteomic data along with a signature/marker file (listing unique marker proteins for each cell type) was taken as input. The raw data was normalized with an arcsinh transformation and a cofactor of 200 in case of CyTOF while log transformation followed by unit normalization in case of IMC data. The data was then randomly split into two halves (half donors in one set); cells from 50% of donors are in the training set while the remaining are in the test set. The splitting was done in a stratified fashion based on the disease condition (T1D, AAB+, Control). Semi-supervised learning was employed on the training set (clustering based on proteomics similar cells together) to generate cell labels for the first half of cells based on seeds (cluster centroids) calculated using a handful of labelled cells (0.1-10 percentile cells for each cell type) annotated using markers in the signature file. The annotated training set was used to train an Extreme learning machine (a fast classifier built on a feed forward neural network which does not need training for learning). +IF and confocal microscopy +Tissues were fixed in 10% buffered formalin overnight, washed several times in PBS, then dehydrated through an ethanol and xylenes, then embedded in paraffin and sectioned to 4-8um. Following deparaffinization through xylene and sequential rehydration, slides were subjected to heat antigen retrieval in a pressure cooker with Bulls Eye Decloaking buffer (Biocare). Slides were incubated in primary antibody overnight and secondary antibody conjugated to peroxidase and then developed using Tyramide Signal Amplification (TSA, Akoya Biosciences). Slides were counter stained with DAPI, and then mounted and imaged on Zeiss LSM800. Primary antibodies used for staining were Mouse anti-CK19 (Santa Cruz sc-6278) and Rabbit anti-HLA-DR (Abcam ab92511). +Statistical analysis of box plots with Control, AAB+, and T1D donor states +The D'Agostino & Pearson omnibus normality test was used to assess whether the data from each group was normally distributed. If any group failed the D'Agostino & Pearson omnibus normality test, the Kruskal-Wallis test was applied. If none of the groups failed the D'Agostino & Pearson omnibus normality test, the one-way ANOVA test was applied. +Statistical analysis of cellular neighborhoods +Differential marker expression significance for neighbors in the IMC analysis was determined using permutation tests. For each marker, the distribution of that marker value for each of the designated n neighbors was compared against 100 distributions derived from n random cells across the entire IMC tree. The resulting p-value was calculated by the ratio of the number of permutations that had a lower median marker value than the observed marker value to the total number of permutations. If this value was < 0.5, the value was subtracted from 1 to switch directionality (number of permutations with a higher median value). To account for the two-tailed test, this value was multiplied by 2 for the final p-value calculation. +Statistical analysis of gene signatures in GAD+ donors +Pseudobulk counts of GAD+ donors across all cell types were identified using muscat tool. The GAD levels for each GAD+ donors were retrieved from Table 1. To identify the correlation between gene signatures and GAD levels, the Spearman correlation test was conducted in each cell type. The threshold of correlation > 0.9 and p-value < 0.05 were used to determine the significantly correlated genes with GAD levels. +Assessment of common genetic variants associated with T1D +The CELLEX tool takes the scRNA-seq gene expression matrix as input and evaluates multiple metrics such as differential expression T-statistics, gene enrichment score, expression proportion, and normalized specificity index. The average of these metrics is measured as expression specificity. The GWAS trait data and CELLEX estimates are given as input to CELLECT. CELLECT uses the genetic prioritization model (with a threshold of S-LDSC < 0.05) to quantify the association between the common phylogenetic GWAS signal and cell type expression specificity. +Extended Data +Cell numbers and clustering before complete filtering +a) Pie chart displaying the cell numbers and proportions of each individual donor per donor type. +b) Box plot displaying the average gene number per cell per donor type. +c) UMAP visualization of cell clusters for all cells. +d) Doublets and singlets, as identified using DoubletFinder, across cell clusters visualized by UMAP. +e) UMAP visualization of the normalized gene expression counts of each canonical gene marker of each major cell type. +Doublet removal and UMI counts +a) Doublets and singlets, as identified using Scrublet, across cell clusters visualized by UMAP per individual. +b) Venn diagram indicating the number of cells deemed doublets by DoubletFinder and Scrublet, as well as cells that were commonly identified by both approaches. +c) Table indicating the number of cells removed and the resulting total cell number for each step of filtering. +d) Unique molecular identifier (UMI) counts per cell projected across the dendrogram visualization and clustering of all cells from Figure 1c. Pie charts at the end of the branches display the breakdown of UMI counts per cell within that terminal cluster. Cells begin at the start pin symbol, and from there are partitioned based on similarities and differences in gene expression. +e) UMI counts per cell projected across the dendrogram visualization and clustering of ductal and endocrine cells from Figure 1d. Pie charts at the end of the branches display the breakdown of UMI counts per cell within that terminal cluster. Cells begin at the start pin symbol, and from there are partitioned based on similarities and differences in gene expression. +f) Expression of genes associated with mitochondrial function projected across the dendrogram visualization and clustering of all cells from Figure 1c. +g) Expression of genes associated with mitochondrial function projected across the dendrogram visualization and clustering of ductal and endocrine cells from Figure 1d. +Cell numbers and clustering after complete filtering +a) Pie chart displaying the cell numbers/proportions of each individual donor per donor type. +b) UMAP visualization of cell clusters for all cells. +c) UMAP visualization donor groups across clusters for all cells. +d) UMAP visualization of Garnett cellular classifications across clusters for all cells. +e) UMAP visualization of the normalized gene expression counts of each canonical gene marker of each major cell type. +Marker gene expression confirms canonical cell types +a) Dendrograms highlighting the expression of each canonical gene marker of each major cell type across the dendrogram of all cells in Figure 1c. +b) The classification of our scRNA-seq data was confirmed by a label transfer strategy using a previous single-nucleus RNA-seq data set in pancreatic islets. +c) Bar plot demonstrates percentages of agreement between previous annotation and our strategy using a label-transfer strategy. +d) Dendrograms highlighting the expression of each canonical gene marker of each major cell type across the dendrogram of ductal and endocrine cells in Figure 1d. +e) To further validate the most closely related cell types to Hybrid cells, we used a label transfer strategy to a previous pancreatic islet scRNA-seq data set. In concordance with Garnett and canonical gene markers, we corroborated the assignment of beta, alpha, and PP cells to these Hybrid cells. +f) Bar plot demonstrates annotation results of label transfer for cells grouped as Hybrid cells. +g) Pie chart displaying the cell numbers/proportions of each cell type defined in Figure 1, c and d. +h) Schematic of the human pancreatic islet anatomy and major cell types. +Gene and gene ontology pathways that are shared and different across disease states in Epsilon-1, Epsilon-2, and Immune cells +(a-c) (Left) For each cell type, Venn diagrams indicate the numbers of upregulated and downregulated genes, as well as overlapping genes, across the two disease states. Circles indicate the numbers of genes that are 'T1D enriched' or 'AAB enriched'. p-values presented are the results of hypergeometric CDF tests (one-tailed test for overrepresentation). (Middle) For each cell type, displayed are gene ontology pathways that are shared across T1D and AAB+ cells when compared to Control cells (top) or pathways that are differently enriched in T1D cells vs AAB+ cells (bottom). The top 20 clusters are displayed and a stringent cut-off of 1e-6 was applied to determine significant gene ontology pathways. (Right) Heatmaps displaying the degree of gene expression changes of genes (rows) that are shared (top) or differential (bottom) across AAB+ and T1D disease states. +(d) GSEA analysis plots of FDR q-value vs Normalized Enrichment Score. For both ductal populations, Ductal-1 and Ductal-2, T1D cells were compared to AAB+ or Control cells to determine differentially enriched gene sets. Demarcated in red and labeled are signatures of interest. +Corroboration of HLA-DR+ Ductal cells +(a-b) Dendrograms highlighting the expression of the MHC class II complex (a) or MHC class II activity (b) across the dendrogram of all cells in Figure 1C. Scale bars represent normalized transcript numbers (mean across all MHC class II complex genes (a) or MHC class II activity genes (b)). +(c-d) Dendrograms highlighting the expression of the MHC class II complex (c) or MHC class II activity (d) across the dendrogram of ductal and endocrine cells in Figure 1D. Scale bars represent normalized transcript numbers (mean across all MHC class II complex genes (c) or MHC class II activity genes (d)). +(e-f) Dendrograms highlighting the expression of the HLA-DPB1 (E) or KRT19 (f) across the dendrogram of ductal and endocrine cells in Figure 1D. Scale bars represent normalized transcript numbers. +g) Dendrograms highlighting the expression of the immune-related genes across the dendrogram of ductal and endocrine cells in Figure 1D. Scale bars represent normalized transcript numbers. +(h) Dendrograms highlighting the expression of the BMPR1A across the dendrogram of ductal and endocrine cells in Figure 1D. Scale bars represent normalized transcript numbers. +GSEA analysis across annotated cells types for dendritic cells gene sets. +a) DC1 gene signature is significantly enriched within Ductal-2 cells of T1D donors. Integrated GSEA analysis for dendritic cells gene sets from Villani et al across ranked lists of differentially expressed genes between T1D and control donors. +b) Expression analysis of the inhibitory marker VSIR in dendritic cells demonstrates the high level of this gene in T1D ductal cells compared with control ductal cells. +CyTOF validation of canonical cell types +a) Bar graph displaying the proportion of cells for all major pancreatic cell types from each donor group where cell annotations were obtained by our new machine-learning based strategy using CyTOF measurements across 12 donors. +b) Dendrogram visualization of the immune cell cluster, CD45 positive (+) cells, as determined by the analysis of the flow cytometry by time-of-flight (CyTOF) data. +c) Dendrogram visualization of the beta cell cluster, C-peptide positive (+) cells, as determined by the analysis of the CyTOF data. +d) Dendrogram visualization of the alpha cell cluster, Glucagon positive (+) cells, as determined by the analysis of the CyTOF data. +e) Major cell types projected on TooManyCells tree based on our machine-learning based annotation using CyTOF data (n=6,945,575 cells). +f) Two-parameter CyTOF analysis of HLA-DR and cytokeratin protein expression in single cells from T1D donor #3 (HPAP023). +g) Two parameter CyTOF analysis of HLA-DR and cytokeratin protein expression in single cells from Control donor #3 (HPAP034), a donor with a very low percentage of HLA-DR+ ductal cells as determined by unbiased analysis of CyTOF data with TooManyCells. +IMC validation of HLA-DR+ ductal cells +a) Bar graph displaying the proportion of cells for all major pancreatic cell types from each donor group where cell annotations were obtained by our machine-learning-based strategy using IMC measurements. Further manual inspection of CD19 and FOXP3 staining used for annotating B and Tregs indicated low quality of these markers across tissue slides. +b) Dendrogram visualization of the immune cell cluster, CD45 positive (+) cells, as determined by the analysis of the imaging mass cytometry (IMC) data analysis. +c) Dendrogram visualization of the beta cell cluster, C-peptide positive (+) cells, as determined by the analysis of the IMC data analysis. +d) Dendrogram visualization of the alpha cell cluster, Glucagon positive (+) cells, as determined by the analysis of the IMC data analysis. +e) Major cell types projected on TooManyCells tree as they were annotated by our machine-learning based strategy using IMC data (n=1,170,001 cells). +Cellular neighborhood analysis in IMC data demonstrates the enrichment of CD4+ T cells surrounding HLA-DR+ ductal cells +a) Bar plot displaying the proportion of HLA-DR+ cytokeratin+ cells from each pancreatic region determined by IMC. +b-c) HLA-DR+ cytokeratin+ cells versus percentage of myeloid cells. For each donor group, the median of percentage of each annotated immune subtype and the median HLA-DR+ ductal cell percentage of total cells across all individual donors per donor group was computed. Only myeloid cells demonstrated significant correlation with respect to the number of HLA-DR+ cytokeratin+ cells across donor groups. +d) Dendrogram visualization of the clusters of HLA-DR+ cytokeratin+ cells (red), cells neighboring HLA-DR+ cytokeratin+ (blue), and cells distant from HLA-DR+ cytokeratin+ cells (grey) as determined by leveraging the spatial architecture provided by IMC data. +e) Boxplots showing the normalized protein expression of different canonical markers in cells neighboring HLA-DR+ cytokeratin+ cells (blue) versus cells neighboring random cells (grey). The number of random cells evaluated was equal to the number of HLA-DR+ cytokeratin+ cells. Differential marker expression significance for neighbors in the IMC analysis was determined using permutation tests. For each marker, the distribution of that marker value for each of the designated n neighbors was compared against 100 distributions derived from n random cells across the entire IMC tree. * indicates p-value < 0.01. Total number of cells in both blue and gray groups is 195,633. Box-and-whisker plots (centre, median; box limits, upper (75th) and lower (25th) percentiles; whiskers, 1.5 x interquartile range; points, outliers). +f) CD4+ T cells are the number one immune subtypes enriched at the neighborhood of HLA-DR+ cytokeratin+ cells. Annotation of neighbors of HLA-DR+ cytokeratin+ cells was performed our machine-learning based strategy. +Supplementary Material +Code Availability +Where applicable, scripts used for data processing and analysis have been described in the Supplemental Materials and Methods section and provided on Github https://github.com/GregorySchwartz/multiomics-single-cell-t1d. TooManyCells is a publicly available suite of tools, algorithms, and visualizations (https://github.com/GregorySchwartz/too-many-cells) that was extensively used in this study, and where applicable, the flags used in TooManyCells to generate specific figures are included in the Materials and Methods section. +Competing Interests +M.R.B. has a consulting arrangement with Interius Biotherapeutics. Other authors declare no competing interests. +Data Availability +The GEO accession number associated with this paper is GSE148073. Additional data are publicly available at https://hpap.pmacs.upenn.edu/. Furthermore, a user-friendly web portal for exploration of the scRNA-seq data is available at https://cellxgene.cziscience.com/e/37b21763-7f0f-41ae-9001-60bad6e2841d.cxg/ +HPAP Consortium Authors: +Maria Fasolino1,2,3,4,6,7*, Gregory W. Schwartz2,3,5,6,7*, Abhijeet R. Patil1,2,3,4,6,7, Aanchal Mongia2,3,5,6,7, Maria L. Golson1,4,8, Yue J. Wang1,4, Ashleigh Morgan1,4, Chengyang Liu4,9, Jonathan Schug1, Jinping Liu1,4, Minghui Wu1,4, Daniel Traum1,4, Ayano Kondo1,4, Catherine L. May1,4, Naomi Goldman1,2,3,4,6,7, Wenliang Wang1,2,3,4,6,7, Michael Feldman5,7, Jason H. Moore1,7, Alberto S. Japp2,10, Michael R. Betts2,10, Robert B. Faryabi2,3,5,6,7#, Ali Naji2,4,9#, Klaus H. Kaestner1,3,4#, Golnaz Vahedi1,2,3,4,6,7# +Affiliations: +1Department of Genetics, 2Institute for Immunology, 3Epigenetics Institute, 4Institute for Diabetes, Obesity and Metabolism, 5Department of Pathology and Laboratory Medicine, 6Abramson Family Cancer Research Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; 7Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; 8Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA, 9Department of Surgery, 10Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA. +References +Type 1 diabetes mellitus: much progress, many opportunities +The pathogenesis, natural history, and treatment of type 1 diabetes: time (thankfully) does not stand still +Immune and Pancreatic beta Cell Interactions in Type 1 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Endocrine Pancreas and Immune System in Type 1 Diabetes +A practical solution to pseudoreplication bias in single-cell studies +Apoptosis of CD4+ CD25(high) T cells in type 1 diabetes may be partially mediated by IL-2 deprivation +Associations of TP53 codon 72 polymorphism with complications and comorbidities in patients with type 1 diabetes +A dual role for TNF-alpha in type 1 diabetes: islet-specific expression abrogates the ongoing autoimmune process when induced late but not early during pathogenesis +Interferon alpha: The key trigger of type 1 diabetes +Progression of type 1 diabetes from the prediabetic stage is controlled by interferon-alpha signaling +Interferon-gamma drives programmed death-ligand 1 expression on islet beta cells to limit T cell function during autoimmune diabetes +Seroconversion to multiple islet autoantibodies and risk of progression to diabetes in children +Genetics of the HLA region in the prediction of type 1 diabetes +Localization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A +Dendritic cells in cancer immunology and immunotherapy +Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors +T cell anergy and costimulation +VISTA, a novel mouse Ig superfamily ligand that negatively regulates T cell responses +Single-Cell Mass Cytometry Analysis of the Human Endocrine Pancreas +Increased immune cell infiltration of the exocrine pancreas: a possible contribution to the pathogenesis of type 1 diabetes +Regulation of MHC class II expression by interferon-gamma mediated by the transactivator gene CIITA +Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas +Epithelial MHC Class II Expression and Its Role in Antigen Presentation in the Gastrointestinal and Respiratory Tracts +Highly Multiplexed Image Analysis of Intestinal Tissue Sections in Patients With Inflammatory Bowel Disease +HLA Class II Antigen Processing and Presentation Pathway Components Demonstrated by Transcriptome and Protein Analyses of Islet beta-Cells From Donors With Type 1 Diabetes +Confronting false discoveries in single-cell differential expression +Harmonization of glutamic acid decarboxylase and islet antigen-2 autoantibody assays for national institute of diabetes and digestive and kidney diseases consortia +Early expression of antiinsulin autoantibodies of humans and the NOD mouse: evidence for early determination of subsequent diabetes +The cation efflux transporter ZnT8 (Slc30A8) is a major autoantigen in human type 1 diabetes +Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression +DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors +Comprehensive Integration of Single-Cell Data +Metascape provides a biologist-oriented resource for the analysis of systems-level datasets +muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data +Evidence of gene-gene interaction and age-at-diagnosis effects in type 1 diabetes +Type 1 Diabetes Genetics, C. Confirmation of HLA class II independent type 1 diabetes associations in the major histocompatibility complex including HLA-B and HLA-A +Discernment of human pancreatic cell types using single-cell RNA-seq +a) The transcriptome of single cells from pancreatic islets of 3 donor types (healthy Control donors, autoantibody positive (AAb+) donors, and donors with Type 1 diabetes (T1D)) was ascertained using the 10x Genomics platform. +b) Pie chart displaying the proportion of cells comprised by each donor group. +c) TooManyCells dendrogram visualization and clustering of all cells. Cells begin at the start pin symbol, and are then partitioned based on transcriptional similarities and differences. The color within the branches indicates the proportion of the cells that are classified by the Garnett cellular classification tool (Table S17). Each bifurcation denotes significant transcriptional differences between the two cell groups. Pie charts at the end of the branches display the breakdown of Garnett cellular classification of cells within that terminal cluster. Highlighting or dotted lines surrounding particular clusters of cells with labels define cell types based on Garnett cellular classifications and canonical gene expression. Branch thickness and pie-chart size is proportional to cell number. Branch length is not indicative of any factor, but is merely a means by which to display cells within a defined space. Beta cells (INS high), alpha cells (GCG high), delta cells (SST high), PP cells (PPY high), epsilon cells (GHRL high), acinar cells (CPA1 high), ductal cells (KRT19 high), endothelial cells (VWF high), stellate cells (RSG10 high), and immune cells (PTPRC, also known as CD45 or leukocyte common antigen, high). Percentages provided represent the percentage of total cells. +d) Dendrogram visualization and clustering of ductal and endocrine cells. Highlighting or dotted lines surrounding particular clusters of cells with labels define cell types based on Garnett cellular classifications and canonical gene expression. +(e-f) Group donor type projected across the dendrogram visualization and clustering of all cells from Figure 1C (e) or of endocrine and ductal cells from Figure 1D (f). Pie charts at the end of the branches display the breakdown of donor type within that terminal cluster. +(g) Bar graph displaying the proportion of cells from each donor group for all major pancreatic cell types. The p-values are calculated by the Chi-squared test +AAb+ and T1D donors have both common and distinct transcriptomic changes in endocrine and exocrine cell types +a) For each cell type, two pairwise differential comparisons were carried out: (1) T1D versus Control (referred to as 'T1D upregulated' (T1D/Control) or 'T1D downregulated' (Control/T1D)) and (2) AAB+ versus Control (referred to as 'AAB+ upregulated' (AAB+/Control) or 'AAB+ downregulated' (Control/AAB+)). T1D upregulated genes were then compared to AAB+ upregulated genes to find commonly upregulated genes, and subsequently commonly upregulated gene ontologies and pathways, across these two donor groups; this exact same approach was carried out for downregulated genes as well. +(b-e) (Left) For each cell type, Venn diagrams indicate the numbers of upregulated and downregulated genes, as well as overlapping genes, across the two donor states. (Right) Bar graph displaying notable gene ontologies that are shared across disease states for upregulated and downregulated genes. The p-values presented are the results of hypergeometric CDF tests (one-tailed test for overrepresentation). +(f) Transcriptional differences between cells from T1D and AAb+ donors were determined by directly comparing T1D to AAB+ cells to generate lists of differentially expressed genes that are enriched in T1D cells or AAB+ cells, and enriched gene ontology pathways were discovered from these differential gene lists. +(g-j) (Left) For each cell type, circles indicate the numbers of genes that are 'T31D enriched' or 'AAB enriched'. (Right) Bar graph displaying notable gene ontologies that are enriched for each donor state. +The gene signature of Beta-1 cells in GAD+ donors is correlated with donors' anti-GAD autoantibody titers. +a) Transcriptional outputs of Beta-1 cells positively correlate with the anti-GAD AAb titer in AAb+ donors. In every annotated cell type, we searched for genes whose expression level correlated with anti-GAD AAb levels in normoglycemic GAD+ donors (R2>0.99 and p-value<0.05). +b) Plotting the average expression levels of cells from each GAD+ donor for the top 1,473 genes in Beta-1 cells with statistically significant correlation with the GAD titers corroborated our query. The total number of cells is 6,904. The plot shows a box-and-whisker plot of the given values. Lower 25th percentile (Q1), Interquartile range (IQR), Median (Q2), Upper 75th percentile (Q3). Minimum (Minimum value in the data, Q1-1.5 * IQR), Maximum (Maximum value in the data, Q3+1.5*IQR). The dots represents potential outliers. +c) A gene-ontology analysis in 1,473 genes related to Beta-1 cells using metaScape highlighted the relevance of endocytosis, protein processing in ER and MapK signaling pathway in Beta-1 cells. +d) Comparison of the cell clustering of the one normoglycemic AAb+ donor expressing two autoantibodies (IA-2 and ZnT8) with GAD+ donors using clumpiness revealed the distinct transcriptional signature of the double autoantibody-expressing autoantibody donor and the single autoantibody-expressing GAD+ donors. Clumpiness is a measure for finding the level of aggregation between labels distributed among the leaves of a hierarchical tree and extensively measures the relationships between metadata. Here, each leaf of the dendrogram contains a collection of labels (different AAB donor group). The more the labels group together within the dendrogram, the higher the clumpiness value. This analysis also demonstrates the overall similarity of GAD+ donors, which modestly displayed GAD level-dependent cell co-segregation. +Single-cell RNA-seq profiling enables the identification of MHC Class II-expressing ductal cells with transcriptional similarities to dendritic cells in T1D +a) (Left Top) Dendrogram visualization of co-expression of HLA-DPB1 and KRT19 gene transcripts in individual cells by scRNA-seq across the ductal and endocrine dendrogram from Figure 1D. (Left Bottom) Pie chart demonstrating HLA-DPB1+KRT19+ cells as percentage of total cells. (Right) Magnified view of the clusters of cells with high percentage (25% or greater) of HLA-DPB1+KRT19+ cells with HLA-DPB1 and KRT19 status displayed across these clusters (outlined in red dashed lines) and neighboring clusters of cells. Cells begin at the start pin symbol and from there are partitioned based on similarities and differences in gene expression. +b) (Top) Dendrogram visualization of cellular classification status across the magnified clusters of cells with high percentage (25% or greater) of HLA-DPB1+KRT19+ cells (outlined in red dash lines) and neighboring clusters of cells. (Bottom) Pie chart displaying the relative proportion of cellular classification status of HLA-DPB1+KRT19+ cells. The p-value presented is the result of the Chi-squared test. +c) (Top Left) Dendrogram visualization of donor group across the magnified clusters of cells with a high percentage (25% or greater) of HLA-DPB1+KRT19+ cells (outlined in red) as well as neighboring clusters of cells. (Top Right) Pie chart displaying the relative proportion of HLA-DPB1+KRT19+ cells in Control (top) or T1D (bottom) Ductal-1 cells. The p-value presented is the result of the Fisher exact test. (Bottom Left) Pie chart displaying the relative proportion of donor group of HLA-DPB1+KRT19+ cells. The p-value presented is the result of the Chi-squared test. (Bottom Right) Box plots displaying the HLA-DPB1+KRT19+ cell percentage of total cells per individual across donor groups. 24 total donors: 11 controls, 8 AAB+,and 5 T1D. A box-and-whisker plot is depicted with the box extending from the 25th to 75th percentiles, the line in the middle representing the median, whiskers extending from the minimum to the maximum, and all data points shown. +d) T1D ductal cells are transcriptionally similar to tolerogenic dendritic cells. Gene-set enrichment analysis was performed using gene signatures of DC subtypes, which were recently defined using scRNA-seq in human blood. The DC1 dendritic cell gene signature was enriched in Ductal-2 cells but not Beta-1 cells of T1D donors. +e) The co-stimulatory proteins CD80 or CD86 are not expressed in T1D ductal cells. +f) Ductal cells of T1D donors express interferon-associated genes including ISG20, ICAM1, and IRF7 compared with those of control donors. +Three single-cell resolution protein-based approaches corroborate the existence of MHC Class II-expressing ductal cells in T1D +a) Dendrogram visualization of co-expression of HLA-DR and cytokeratin protein coexpression in single cells analyzed with flow cytometry by time-of-flight (CyTOF). +b) Pie chart displaying HLA-DR+ cytokeratin+ cells and the relative proportions of each donor group from the CyTOF data. The p-value was calculated by the Chi-squared test. +c)Box plots displaying HLA-DR+ cytokeratin+ cell percentage of total cells per individual across donor groups derived from the CyTOF data (p-value=0.00507). Number of donors: AAB+ : 4; Control : 4; T1D : 4. Figure depicts box-and-whisker plot showing the quartiles, minimum non-outlier calculated by (Q1 - 1.5*IQR), 25th percentile/lower quartile Q1, 50th percentile/median Q2, 75th percentile/upper quartile Q3, maximum non-outlier calculated by (Q3 + 1.5*IQR) of the variable (hybrid percentage of total cells per individual) while the whiskers extend to show the rest of the distribution, except for points that are determined to be "outliers" (dots outside whiskers) using a method that is a function of the inter-quartile range. +d) Two-parameter CyTOF analysis of HLA-DR and cytokeratin protein expression in single cells from T1D donor #4 (HPAP028) and T1D donor #5 (HPAP032). +e) CD45 (PTPRC) expression levels in HLA-DR+ cytokeratin+ and HLA-DR+ cytokeratin- single cells. +f) Dendrogram visualization of co-expression of HLA-DR and cytokeratin proteins in single cells analyzed by imaging mass cytometry (IMC). +g) Pie chart displaying HLA-DR+ cytokeratin+ cells and the relative proportions of each donor group from the IMC data. The p-value was calculated by the Chi-squared test. The p-value shows 0.000 by both Chi-square function from scipy.stats (python) for the observed frequency array [34983,3711,4635] : [T1D,AAB+,Control], and cannot provide exact p-value. +h) Box plots displaying HLA-DR+ cytokeratin+ cells as a percentage of total cells per individual across donor groups from the IMC data (p-value=1e-16). Number of donors: AAB+ : 7; Control : 5; T1D : 4. p-value is obtained from a one-way ANOVA test. +i) Representative confocal microscopy image from the pancreas of a T1D donor (top) and Control donor (bottom) displaying HLA-DR+ cytokeratin+ labeled by immunofluorescence (IF). Control (n=3) and T1D (n=2). +Representative examples of IMC measurement corroborates that MHC Class II positive ductal cells are present in pancreatic tissues. +(Left) Imaging mass cytometry (IMC) in a region of interest (ROI) in pancreatic tissue from three representative individual donors for each donor group type (T1D, AAB+, and Control). HLA-DR is a general marker of MHC Class II (HLA-DR) expression, CD99 is a general islet marker, KRT (pan-keratin) is a ductal cell marker, and CD45 (PTPRC) is a general immune cell marker. Notably, HLA-DR+ ductal cells were primarily located in large ductal structures (outlined in yellow). The images presented here are publicly available at https://www.pancreatlas.org/datasets/508. +(Right) HLA typing performed by next-generation sequencing. Comprehensive clinical information about each donor is provided in PANC-DB: https://hpap.pmacs.upenn.edu/. Highlighted in yellow are the particular HLA alleles contributing to the susceptible or protective genotypes, which are abbreviated for each donor on the left side of the figure as follows. The four susceptible genotypes assessed were (1) HLA-DRB1*03:01-HLA-DQA1*05:01-HLA-DQB1*02:01 (abbreviated as 'DR3', referring to the haplotype bearing the DRB1*03 allele); (2) HLA-DRB1*04:01/02/04/05/08-HLA-DQA1*03:01-HLA-DQB1*03:02/04 (or HLA-DQB1*02) (abbreviated as 'DR4', referring to the haplotype bearing the DRB1*04 allele); (3) HLA-A*24:02; and (4) HLA-B*39:06. The two protective genotypes assessed were (1) HLA-DRB1*15:01-HLA-DQB1*06:02 and (2) HLA-DRB1*07:01-HLA-DQB1*03:03. Notably, HLA-DR+ ductal cells were found across all HLA genotypes, including both susceptible and protective genotypes. \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1073_pnas_2103240118.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1073_pnas_2103240118.txt new file mode 100644 index 0000000..8749e90 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1073_pnas_2103240118.txt @@ -0,0 +1,123 @@ +Single-cell analyses of renal cell cancers reveal insights into tumor microenvironment, cell of origin, and therapy response +Significance +Renal cell carcinomas (RCCs) are heterogeneous malignancies thought to arise from kidney tubular epithelial cells, and clear cell RCC is the most common entity. This study demonstrates that cell atlases generated from benign kidney and two common RCCs using single-cell RNA sequencing can predict putative cells of origin for more than 10 RCC subtypes. A focused analysis of distinct cell-type compartments reveals the potential role of tumor epithelia in promoting immune infiltration and other molecular attributes of the tumor microenvironment. Finally, an observed association between the lack of immunotherapy response and endothelial cell fraction has important clinical implications. The current study, therefore, significantly contributes toward understanding disease ontogenies and the molecular dynamics of tumor epithelia and the microenvironment. +Diverse subtypes of renal cell carcinomas (RCCs) display a wide spectrum of histomorphologies, proteogenomic alterations, immune cell infiltration patterns, and clinical behavior. Delineating the cells of origin for different RCC subtypes will provide mechanistic insights into their diverse pathobiology. Here, we employed single-cell RNA sequencing (scRNA-seq) to develop benign and malignant renal cell atlases. Using a random forest model trained on this cell atlas, we predicted the putative cell of origin for more than 10 RCC subtypes. scRNA-seq also revealed several attributes of the tumor microenvironment in the most common subtype of kidney cancer, clear cell RCC (ccRCC). We elucidated an active role for tumor epithelia in promoting immune cell infiltration, potentially explaining why ccRCC responds to immune checkpoint inhibitors, despite having a low neoantigen burden. In addition, we characterized an association between high endothelial cell types and lack of response to immunotherapy in ccRCC. Taken together, these single-cell analyses of benign kidney and RCC provide insight into the putative cell of origin for RCC subtypes and highlight the important role of the tumor microenvironment in influencing ccRCC biology and response to therapy. +Renal cell carcinoma (RCC) encompasses several histologically and molecularly diverse tumor groups. The past two decades of research have uncovered a variety of genomic drivers in diverse renal tumor subtypes including the most common subtype, clear cell RCC (ccRCC), and its rare renal tumor counterparts. For instance, the frequent biallelic loss of tumor suppressor genes on chromosome 3p, such as VHL (~90%), PBRM1, SETD2, and BAP1, is a unique characteristic of ccRCCs, while recurrent allelic loss of heterozygosity of chromosomes 1, 2, 6, 10, 13, and 17 is a signature event of classic chromophobe RCC (chRCC), along with frequent TP53 mutations. +It has long been hypothesized that diverse RCC subtypes originate from distinct types of nephron tubular epithelial cells. Thus, identification of cellular orthologs in the benign tissues that share transcriptional signatures with the tumor epithelia of specific RCC subtypes may indicate a putative cell of origin (P-CO). Identifying P-CO transcriptomes provides an appropriate reference to investigate gene expression patterns that are either retained or altered in the tumor epithelia. This knowledge will help refine in vivo disease models and facilitate the exploration of phenotype-genotype associations of disease subtypes. For example, the highly vascularized ccRCC subtype displays high levels of immune cell infiltration, and thus metastatic ccRCC often responds favorably to antiangiogenesis therapies and immunotherapy. By contrast, chRCC and nearly 50% of papillary RCCs (pRCCs) are relatively immune cell-poor/cold. However, the molecular underpinnings dictating whether certain RCC subtypes clinically present as immune cell-rich (hot tumors) or -poor (cold tumors) remain to be fully defined. +Transcriptional landscapes of benign kidney and RCC subtypes curated with bulk RNA sequencing (RNA-seq) provide the average gene expression of all cell types within each tissue. Single-cell sequencing methodologies have been increasingly adopted as a higher-resolution alternative to study gene expression, genomic aberrations, and epigenetic modifications in the constituent cells of various malignancies and their benign counterparts. These methods enable investigations into the significant variations of cell types observed in the tumor and microenvironment across different renal tumor entities, as well as among patients with a given renal tumor. A few studies have examined murine and human benign kidney cell types using single-cell mRNA sequencing (scRNA-seq). Park et al. generated a murine kidney cell atlas and discovered a novel nephron tubular epithelial transitional cell type. The data also helped them map expression of genes associated with chronic kidney diseases to specific cellular compartments. Other recent reports identified a P-CO for pediatric renal Wilms tumor (n = 3 specimens), ccRCC (n = 3), and pRCC (n = 1) and also an intriguing association between the zonation pattern of immune cells and the anatomical location of benign kidney tissues. +To gain a mechanistic understanding of common adult kidney tumors at single-cell resolution, we generated gene expression atlases of benign kidney and RCC tumor samples using a microfluidic droplet-based scRNA-seq platform. These atlases allowed us to address several outstanding questions in RCC pathobiology, including defining the P-CO for diverse RCC molecular subtypes, determining the pathways regulated by tumor epithelial cells, and examining the role of cells constituting the tumor microenvironment in disease pathogenesis and treatment response. +Results +Cell Atlas of Human Benign Adjacent Kidney Tissue. +Recent scRNA-seq of murine and human renal tissue has identified gene signatures of constituent cell types and furthered our understanding of genes whose expressions are linked with genetic traits of chronic kidney diseases and renal cancers. To expand scRNA-seq efforts in human renal cancers, we performed comprehensive genomic profiling of dissociated tissues by scRNA-seq on a cohort of RCCs and benign adjacent kidney tissues (14 samples from nine patients) for which we also had paired whole-exome sequencing (WES) and RNA-seq of corresponding bulk specimens. The benign samples were derived from either cortex or medullary regions, and the tumor specimens represented ccRCC and chRCC (Datasets S1 and S2). As an important analytical caveat, the tubular cells of distal nephron origin were found to display a higher mitochondrial content, reflecting their cellular biology. This observation necessitated separate mitochondrial read thresholds for benign and tumor samples (SI Appendix, Fig. S1 A-C). This difference was corroborated by bulk tissue WES data, which showed higher mitochondrial read coverage in benign compared with tumor tissues, and by previous kidney single-cell and bulk RNA-seq data from the Genotype-Tissue Expression (GTEx) Consortium. A high concordance in the data from bulk RNA-seq and averaged scRNA-seq from the corresponding samples showed that the tissue dissociation step did not significantly alter gene expression patterns. Thus, we proceeded to generate benign and tumor gene expression cell atlases with cell clusters based on data from multiple samples (SI Appendix, Fig. S1 B and C). +The benign renal scRNA-seq atlas demonstrated 26 cell clusters formed by 6,046 cells derived from six samples (representing five patients) (Fig. 1A). We found clusters based on previously characterized lineage-specific markers (Dataset S2), and they spanned both common and rare cell types, including tubular epithelial (clusters 1 to 14), endothelial (clusters 15 to 19), and stromal cells (clusters 20 and 21), as well as immune cells of myeloid and lymphoid branches (clusters 22 to 26). Proximal tubule (PT) cells comprising clusters 1, 2, and 3 (henceforth labeled PT-A, PT-B, and PT-C) expressed known PT markers, including PDZK1IP1 (Fig. 1B). Previous studies have noted stochastic variation in the abundance of PT cells within benign renal cortical and medullary regions. This phenotypic variation was recapitulated in our benign scRNA-seq data, where PT cells accounted for more than 50% of the cells from the cortex region but were less than 10% in medullary (SI Appendix, Fig. S1D and Dataset S1). The PT has been classically divided into three segments (S1 to S3) based on anatomical location and variation in cellular ultrastructure. PT-A cells from these three regions were captured by our data, as evidenced by the distinct expression pattern of markers previously known to be associated with S1, S2, and S3 segments (SI Appendix, Fig. S1E). +Single-cell analysis of benign human kidney reveals novel nephron tubular epithelial cell types. (A) Single-cell atlas of human kidney. t-SNE plot of scRNA-seq data from 6,046 cells obtained from six benign kidney samples. Cell clusters found therein representing 26 cell types are shown. DL, descending limb; DCT, distal convoluted tubule; CNT, connecting duct; Mesa, mesangial cells; Podo, podocytes; Peri, pericytes; vSMC, vascular smooth muscle cells; Mono, monocytes; Macro, macrophages; NK, natural killer cells. (B) Violin plots depicting gene expression patterns of select cell type markers: PDZK1IP1 (all PT cells), ITGB8 (PT-B and -C), PIGR (PT-B and -C), CFH (PT-C), KLK6 (PT-C), CALB1 (IC-PC, CNT), AQP2 (PC), FOXI1 (IC-PC, IC-A, IC-B), and SLC4A1 (IC-PC, IC-A). (C) Trajectory analysis of the three PT cell clusters identified: the common PT-A and rare/novel PT-B and PT-C. (D) As in B except showing stem/progenitor cell markers (VCAM1, VIM, ICAM1) across different cell types. (E) Trajectory analysis of distal tubule IC, PC, and IC-PC populations. (F) Validation of PT-B, PT-C, and IC-PC cells in benign adjacent kidney by RNA-ISH dual staining. PT-B marker ITGB8 (Left) and PT-C marker CFH (Middle) in blue channel and pan-PT cell marker PDZK1IP1 in red channel. IC-PC marker CALB1 in blue channel and IC marker FOXI1 in red channel (Right). (Scale bars for all images, 50 mum.) +From the benign cell atlas we identified three uncharacterized cell type clusters. Among the PT cells, while PT-A (32%) corresponded to the previously studied most common PT cell type, two related but distinct clusters termed PT-B (2.4%) and PT-C (0.7%) represented rarer populations (Fig. 1 A and C). Slingshot trajectory analysis (a trajectory inference method, also called pseudotime analysis from single-cell gene expression data, which orders cells along a trajectory based on similarities in their expression patterns and determines lineage structure by identifying branching events) revealed that the PT-B cluster was more closely related to PT-A cells (Fig. 1C). Notably, both PT-B and PT-C cells were characterized by distinct marker expression, such as ITGB8 and PIGR in PT-B and CFH and KLK6 in PT-C cells (Fig. 1B). Interestingly, PT-B and PT-C cells showed high expression of various renal stem cell marker genes, such as ICAM1, VIM, and VCAM1 (Fig. 1D). In addition to the PT-B and PT-C clusters, the non-PT epithelial cell cluster 10 (2.1%) expressed markers of both intercalated cells (IC) and principal cells (PC), and we have thus termed this third cell type IC-PC (Fig. 1A). This cluster may represent the human equivalent of the transitional cell type between PC and IC cells, which was recently described in an scRNA-seq study of murine kidneys. Supporting this notion, slingshot trajectory analysis showed IC-PC cells as related to both PC and IC cells (Fig. 1E). Finally, RNA in situ hybridization (RNA-ISH) on independent benign kidney tissue samples with select markers confirmed the presence of the PT-B, PT-C, and IC-PC cells (Fig. 1F). +RCC Cell Atlases and P-CO. +Previous studies have hypothesized the P-CO for major RCCs based on their anatomical location, protein expression by immunohistochemistry, and bulk RNA-seq. Determination of the P-CO for various RCC subtypes will help distinguish transcriptomic events in the tumor epithelia that are shared with benign cell types from those which are tumor-specific, uncovering novel tumor biomarkers and disease-specific molecular mechanisms. Toward this goal, RCC tumor atlases were constructed with scRNA-seq data from ~20,500 cells derived from seven ccRCC samples (Dataset S1 and SI Appendix, Fig. S2 A and B) and ~2,500 cells from one chRCC sample (SI Appendix, Fig. S2C). Tumor cell clusters (Dataset S3) were annotated based on known cell type-specific markers. The neoplastic cells representing ccRCC in our cohort overexpressed classic biomarkers of this disease like CA9, ANGPTL4, NDUFA4L2, and NNMT, while chRCC tumor epithelia overexpressed KIT, RHCG, and FOXI1. We examined the gene expression patterns of both erythropoietin (EPO) and its receptor (EPOR) in our scRNA-seq data, where only endothelial cells (afferent/efferent arterioles/descending vasa recta [AEA-DVR]) in the benign kidney showed EPO expression, while EPO was detected mostly in the tumor cells in ccRCC. EPOR expression was detected in most cell types at varying levels in both benign and tumor tissues (SI Appendix, Fig. S2D). Consistent with the phenotype previously deduced by histology and bulk RNA-seq, we observed high immune cell infiltration in ccRCC samples (~30% of total cells sequenced) compared with chRCC (~5% of total cells; SI Appendix, Fig. S2 A-C); a smaller endothelial cell fraction (1.4%) was also observed in chRCC compared with ccRCC. +The tumor cell atlases (SI Appendix, Fig. S2 B and C) were generated from batch-corrected data in which all tumor epithelia clustered together. However, WES of the tumor and matched normal samples revealed somatic copy-number variations (CNV) specific to individual patients (SI Appendix, Fig. S3). To study the effect of CNV on tumor epithelia gene expression, we performed clustering without batch correction. While cells of the tumor microenvironment from different patients clustered according to type, tumor epithelia formed distinct patient-specific clusters (Fig. 2A). Gene expression of the tumor epithelia was specifically influenced by chromosomal aneuploidy, where genes in regions with copy gains and losses showed a corresponding increase or decrease in mRNA expression (Fig. 2B and SI Appendix, Fig. S3). This effect was restricted to the tumor epithelia and not seen among cells comprising the microenvironment (SI Appendix, Fig. S3). +Cell of origin predictions for RCCs. (A) Impact of patient-specific CNV on tumor epithelial cell gene expression. UMAP plot of cell types captured from seven different ccRCC samples, where tumor epithelial cells clustered according to patient, while nontumor cells from different patients clustered according to cell types. (B) Individual examples reemphasize the association between genome-wide CNV gains and losses and single-cell gene expression patterns in the tumor epithelia. (C) Delineation of the P-CO for various RCCs. The "radar" plots indicate the probabilities based on a random forest classifier of a given query gene expression dataset (single-cell data from tumor epithelia of RCCs or bulk data from benign renal tissues, different anatomic locations, and tumors) to resemble a given benign epithelial cell type (periphery), as depicted by the spokes/radii. The predicted closest normal cell types for the various tumor tissues analyzed include the following. (Row 1) benign: bulk renal cortex, bulk cortico-medullary, bulk medullary; ccRCC tumors: bulk ccRCC, single-cell ccRCC; (row 2) oncocytic renal tumors: bulk chRCC, single-cell chRCC, HOT; papillary type-1 tumors: bulk pRCC type-1, bulk MTSCC; (row 3) papillary type-2 tumors: bulk pRCC type-2, TRCC, HLRCC, CIMP (-1, -2); (row 4) rare molecular subtypes: KRAS-mutant types 1 and 2 and MTOR-mutant types 1 to 3. (D) Lineage-specific marker validation by RNA-ISH dual staining. ITGB8 expression (blue) validates PT-B as P-CO for ccRCC (Top). CA9 (red) is a general biomarker of ccRCC. As in Top, except using ALPK2 as a second PT-B marker (Bottom). (E) Mutual exclusivity observed in FOXI1 and L1CAM dual stains reveals the distinct identity of two tumor epithelial cell types in a HOT. (Scale bar, 50 mum.) +To nominate P-COs for different RCC subtypes, we trained a random forest model with expression profiles of the 12 benign tubular epithelial cell types. This model was then validated using bulk RNA-seq data obtained from TCGA (The Cancer Genome Atlas) benign renal tissues that Lindgren et al. reclassified to distinct anatomical locations, including 49 cortex, 19 corticomedullary, and 36 medullary samples. As expected, we observed abundant PT and thick ascending limb (TAL) cells from cortex and medullary regions, respectively, in the TCGA benign renal samples (Fig. 2C); in the radar plots, spoke length depicts the probability of shared gene signatures between the samples of interest and the 12 benign cell types from our atlas. +We used this model to identify similarities between the tumor epithelia scRNA-seq data from ccRCC and chRCC samples to predict their corresponding P-COs. The ccRCC data from both the tumor epithelia scRNA-seq and bulk RNA-seq (TCGA) showed the highest probability of matching to the RNA expression profile of the rare PT-B cells (Fig. 2C). To corroborate this observation, we performed dual RNA-ISH with the classic ccRCC marker CA9 and two markers of PT-B cells (ITGB8 and ALPK2) on ccRCC tumor tissues and observed coexpression of CA9/ITGB8 and CA9/ALPK2 (Fig. 2D). +In contrast, among the oncocytic renal tumors analyzed, data from both chRCC tumor epithelia scRNA-seq and bulk chRCC RNA-seq (TCGA) were similar to the RNA expression of IC (Fig. 2C). Hybrid oncocytic tumors (HOTs) are intriguing renal tumors found predominantly in patients with Birt-Hogg-Dube syndrome that demonstrate morphologic and immunohistochemical features overlapping with renal oncocytoma and chRCC. Our in-house RNA-seq data from a HOT was most similar to that of IC-PC cells (Fig. 2C). Intrigued by these results, we performed RNA-ISH for FOXI1, a key transcription factor expressed in IC cells and oncocytic/chromophobe tumors (Fig. 2E). We found FOXI1 staining in ~50% of the tumor epithelia and reasoned that subtracting the IC-A gene signature from the tumor RNA-seq data may identify markers corresponding to the second (FOXI1-negative) tumor epithelial population. Using this approach, we identified L1CAM overexpression in HOTs, wherein it is relevant to note that L1CAM is expressed in PC cells in the benign kidney. Dual RNA-ISH of FOXI1 and L1CAM showed mutually exclusive expression within HOT epithelia, thereby validating markers specific for these distinct epithelial populations within the same HOT (Fig. 2E). +In P-CO analysis of additional RCC subtypes, most subtypes from the pRCC types 1 and 2 disease spectrum showed the highest probability of matching to the gene signature of the PT-B cluster, while some subtypes (hereditary leiomyomatosis and renal cell carcinoma (HLRCC), CpG island methylator phenotype (CIMP), and pRCC type 1) showed additional similarity to thin ascending limb (tAL) cells to varying degrees (Fig. 2C). The presence of activating hotspot mutations in KRAS (~0.6% recurrence) and MTOR (~2.0%) from the TCGA pan-kidney cancer data are infrequent events mainly associated with the understudied rare RCC subtypes that lack the classic molecular and histologic features that define the more common renal tumor subtypes (e.g., chromosome 3p loss and VHL and MET mutations). Applying our model to these rare molecular subtypes revealed five different patterns with high probabilities of matching to either tAL/PC or PT-B/tAL signatures among KRAS-mutated cases, as well as to PT-B/tAL and connecting tubule (CNT)/IC-PC among MTOR-mutated cases, suggesting distinct cells of origin for these diverse subtypes (Fig. 2C). +Tumor Epithelial Cells Drive Immune Cell Infiltration in ccRCC. +Immune cell infiltration-associated phenomena in the tumor microenvironment are robust predictors of response to immunotherapy and have been strongly linked with high somatic mutation/neoantigen burden. RCCs, however, are typically characterized by an immune-hot phenotype despite a low mutation burden. To gain insights into this paradox and better understand the milieu of RCC cells and their microenvironment, we compared differentially expressed genes (>0.5 log2FC and 60 cell types. Many of these cell types were discrete, whereas others, especially in the lens and cornea, formed continua corresponding to known developmental transitions that persist in adulthood. Having profiled each tissue separately, we performed an integrated analysis of the entire anterior segment, revealing that some cell types are unique to a single structure, whereas others are shared across tissues. The integrated cell atlas was then used to investigate cell type-specific expression patterns of more than 900 human ocular disease genes identified through either Mendelian inheritance patterns or genome-wide association studies. +The anterior segment of the eye is a complex set of interconnected structures, comprising the cornea, conjunctiva, iris, ciliary body (CB), crystalline lens, and aqueous humor outflow pathways; the outflow pathways, in turn, include the trabecular meshwork (TM), Schlemm canal, and ciliary muscle (Fig. 1 A and B). Together, these structures fulfill two prerequisites of vision: 1) ensuring that light reaches the retina and 2) ensuring that it is optimally focused. The transparent cornea and lens provide the refractive power of the eye. The iris determines how much light reaches the retina. The CB produces the aqueous humor that nourishes the cornea and lens and removes waste products. Finally, the outflow pathways drain aqueous humor from the anterior chamber. +Human anterior segment and cells of the central cornea. (A) The human eye depicted in sagittal section. (B) The anterior segment, which includes the cornea, iris, CB, and lens. The limbus, representing the transition between peripheral cornea and sclera, houses the aqueous outflow structures including the TM and Schlemm canal. (C) Central cornea comprises three primary cellular layers: epithelium, stroma, and endothelium. The stratified epithelium is composed of basal, wing, and superficial cells delineated in the boxed area. (D) Clustering of 37,485 single-nucleus expression profiles from human central cornea visualized by uniform manifold approximation and projection (UMAP). Here and in subsequent UMAPs, arbitrary colors are used to distinguish clusters deemed to be distinct by unsupervised analysis. (E) Feature plots demonstrating DE genes corresponding to the epithelial subtypes. (F) Dot plot showing genes selectively expressed in cells of the central cornea, with gradient expression patterns noted in the epithelial subtypes. In this and subsequent figures, the size of each circle is proportional to the percentage of nuclei within a cluster expressing the gene and the color intensity depicts the average normalized transcript count in expressing cells. (G) Corneal superficial epithelium immunostained for KRT78 (green). (H) Fluorescent RNA ISH for BCAS1 (red) highlights superficial epithelium, and NECTIN4 (green) highlights both wing cells and superficial cells. (I) ISH for BCAS1 (red) highlights superficial epithelium and LAMA3 (green) highlights basal cells. (J) Transit amplifying cells are identified via ISH for TOP2A (green). (K) Corneal stromal fibroblasts are highlighted by ISH for ANGPTL7 (green); the superficial epithelium is highlighted by ISH for BCAS1 (red). (L) Corneal endothelium immunostained for CA3 (red). AF, autofluorescence; K_Epi, corneal epithelium; TA, transit amplifying; K_Fibro, corneal fibroblasts; K_Endo, corneal endothelium. Yellow bars: 20 microm. +Because each of the structures within the anterior segment is essential for proper functioning of the eye, dysfunction of any one of them leads to vision loss. Indeed, the three leading causes of global blindness among adults ages >50 y involve anterior segment structures to varying extents: cataract, glaucoma, and uncorrected refractive error. Other conditions with manifestations primarily in the anterior segment include dry eye disease, corneal dystrophies, anterior uveitis and trachoma. Although most of these entities can be treated, few if any can be cured with current approaches. +To build a better understanding of the complex tissues comprising the human anterior segment, we generated a cell atlas using high-throughput single-nucleus RNA sequencing (snRNAseq). We profiled 195,248 single nuclei from six nondiseased anterior segment tissues, applied computational methods to cluster them based on transcriptomic similarity, and used histological techniques to assign cell type identities to the clusters. After investigating each tissue independently, we pooled them and performed an integrated analysis. In this way, we were able to show that some cell types are confined to specific tissues, whereas others are shared across tissues. Finally, we used this cell atlas to investigate cell-type-specific expression patterns of over 900 genes that have been implicated in susceptibility to human ocular diseases. +Results +Six tissues:central cornea, corneoscleral wedge (CSW), TM, iris, CB, and lens:were dissected from eyes of six individual donors with no histories of ocular disease (SI Appendix, Table S1). Tissues were obtained postmortem within <6 h from death in all but one case, dissected within an hour of enucleation, and frozen for further processing. Nuclei were prepared and profiled using a droplet-based method. Altogether, we obtained high-quality transcriptomes from 195,248 single nuclei from which we generated a cell atlas. An additional eight eyes were used for histological analysis (SI Appendix, Table S2). +Cornea. +The transparent, avascular cornea forms a tough layer that, alongwith the sclera, encases the delicate intraocular tissues. Beyond its protective function, the cornea provides the principal refracting surface of the eye. The cornea is composed of 3 primary cellular layers: epithelium, stroma, and endothelium (Fig. 1C). The corneal epithelium is a nonkeratinizing stratified squamous epithelium. Its cells can be divided into three histologically distinct sublayers: an innermost single layer of columnar basal cells, a two- to three-cell-thick intermediate layer of "wing" or polygonal suprabasal cells, and a two-cell-thick superficial layer of plate-like squamous cells. The basal cell layer contains two cell types: some divide continuously (transit amplifying cells) whereas others arrest and subsequently migrate superficially to become wing and then surface cells. The avascular and acellular Bowman layer separates the epithelium from the lamellar stroma; and the Descemet membrane, a basement membrane, separates the lamellar stroma from the endothelium. +We isolated the central 6 mm of the cornea and processed it separately from the peripheral cornea and limbus, discussed below. We recovered transcriptomes from 37,485 single nuclei and divided them into 7 clusters using computational analysis (Materials and Methods). Using established markers, we annotated them as corneal epithelial cells (four clusters, 81.3%), corneal endothelial cells (14.5%), stromal keratocytes (also known as corneal fibroblasts, 3.6%), and immune cells (0.5%) (Fig. 1 D-F and SI Appendix, Fig. S1A, and Dataset S1). +Among corneal epithelial clusters, defined by a high expression of PAX6, KRT12, and TACSTD2 (Fig. 1E), we localized differentially expressed (DE) genes by immunohistochemistry and in situ hybridization (ISH) in order to assign each cluster to cells populating distinct sublayers. The basal cluster was characterized by an enriched expression of extracellular matrix (ECM)- and adhesion-related genes including LAMA3 (Fig. 1 E and I). The transit amplifying cells were distinguished from committed basal cells by selective expression of the cell proliferation marker MKI67 and other genes involved in DNA replication and cell division including BIRC5, UBE2C, and TOP2A (Fig. 1 E and J). The superficial-most squamous epithelial cells were characterized by the expression of multiple keratins (KRT4, KRT24, KRT78) and mucins (MUC4, MUC16, MUC21, MUC22), as well as BCAS1 (Fig. 1 E, H, I, and K and SI Appendix, Fig. S2B). The middle layer wing cells were characterized by higher levels of KRT3 expression and graded expression of multiple genes, representing the transitional state between basal and superficial cells (Fig. 1 E, F, and H and SI Appendix, Fig. S2A). +Two other clusters corresponded to the corneal endothelium (CA3; Fig. 1 F and L) and corneal stromal keratocytes (ANGPTL7; Fig. 1 F and K). Finally, the small cluster of immune cells contained both macrophages (likely corneal dendritic cells) and lymphocytes; DE genes within the cluster included PTPRC/CD45, CYTIP, FYB1, ELMO, and lymphocyte- associated marker IKZF1 (Fig. 1F). The somata of neurons that innervate the cornea are located proximal to the tissue itself, so we did not identify any in our dataset. +Limbus. +The limbus is the annular region between the avascular clear cornea and the vascularized opaque sclera (Fig. 2A). The limbus is not anatomically discrete but contains components distinct from both cornea and sclera. Externally, it contains the transition zone of the ocular surface where corneal and conjunctival epithelia meet. Herein, limbal epithelial stem cells (LSCs), accounting for less than 1 to 2% of proliferating basal epithelial cells, help maintain corneal epithelial integrity, which keeps the cornea avascular and transparent. Internally, the limbus includes the TM, Schlemm canal, and specialized outflow vessels through which aqueous humor drains from the anterior chamber. +Cell types of the internal and external limbus derived from dissection of CSW. (A) Diagram of the limbus, representing the transitional tissue between peripheral corneal and sclera. (B) Clustering of 52,309 single-nucleus expression profiles derived from CSW tissue visualized by UMAP. (C) Feature plots showing a selection of genes enriched in ocular surface epithelium subtypes. (D) ISH for BCAS1 (red) highlights the superficial epithelium in both the cornea and conjunctiva, whereas KRT12 (green) highlights basal and wing cells of the cornea. A transitional area is noted in the limbus where KRT12 expression tapers off and is absent in the conjunctival epithelium. Here, and in F, G, and I, the images represent a cropped version of a larger montage obtained from a continuous meridional section spanning cornea, limbus, and sclera, providing anatomical landmarks to support labeling. For an example of the full image, see SI Appendix, Fig. S3F. (E) ISH for KRT12 (green) highlights basal and wing cells of the cornea, and RARRES1 (green) highlights superficial and some wing cells of the conjunctiva. A transition area within the limbus is noted. (F) ISH for NTRK2 (red) highlights the basal epithelium of the cornea, and PDGFC (green) highlights mostly the basal epithelium of the conjunctiva. (G) Immunostaining against PECAM1 (red) highlights endothelial cells lining vessels in the subepithelial stromal tissues of the external limbus. Immunostaining against PDPN (green) highlights lymphatic endothelium lining a subset of these vessels. (H) Transit amplifying basal cells within the limbus are highlighted with ISH for TOP2A (green); basal and wing cells in the limbal area demonstrate KRT12 (red) expression as visualized by ISH. (I) A goblet cell in the conjunctiva visualized with immunostaining against MUC5AC (green). (J) Conjunctival melanocytes visualized by ISH for PAX3 (green) and KIT (red). (K) Uveal melanocytes visualized by ISH for PAX3 (green) and MET (red). Mo, macrophage; Conj, conjunctival; CC_VenEndo, Collector Channel/Venous Endothelium; Vasc, vascular; Endo, endothelium, K, cornea; Fibro, fibroblast. (Scale bars: 100 microm.) +To ensure adequate representation of cell types in both the internal and external limbus, we separated the TM from the CSW, processed each separately, and merged the data into a single dataset of 52,309 transcriptomes (Fig. 2 B and C and SI Appendix, Figs. S1B and S4 A and D, and Dataset S1). Of these, 11,108 were identified as ocular surface epithelial cells based on expression of canonical epithelial markers CDH1, CLDN1, and keratin genes; we reclustered this group to identify rare types and ensure optimal capture of gradient differences (SI Appendix, Figs. S1C and S3A). This clustering result was then incorporated into the limbus dataset (Dataset S1). +Ocular surface epithelium. +We identified nine groups of ocular surface epithelial cells, which could be divided into corneal, limbal, and conjunctival populations based on established markers and histological validation (Fig. 2B and SI Appendix, Fig. S3A). Those with robust KRT12, KRT3, and/or KRT24 expression were corneal, whereas those with KRT15, KRT14, KRT13, and/or KRT7 expression were conjunctival or limbal (Fig. 2C and SI Appendix, Fig. S4D). The three corneal clusters corresponded to the superficial, wing, and basal types described above (Fig. 1D). The three conjunctival clusters were all positive for IGFBP3, previously shown to be specific to conjunctival epithelium; among them, we identified basal (e.g., KRT14+ PDGFC+), superficial (e.g., KRT7+LCN2+WFDC2+ENTPD2+), and wing clusters (e.g., KRT13+KRT15+BCAS1+RARRES1+) (SI Appendix, Fig. S3 B and C). The three limbal clusters demonstrated expression profiles reflective of their transitional location; interestingly, limbal superficial and basal cells were transcriptionally more similar to their conjunctival counterparts whereas the limbal wing cells were more transcriptionally similar to their corneal counterparts. Superficial limbal epithelial cells were distinguished and confirmed histologically as WFDC2-ENTPD1+BCAS1+RARRES1+ (SI Appendix, Fig. S3H). Limbal basal cells were KRT15+KRT12-GJB6-, whereas limbal wing cells were KRT15+KRT12+GJB6+, consistent with prior reports. We visualized the expression patterns of the limbal and conjunctival cells with ISH for KRT12, PDGFC, WFDC2, ENTPD1, RARRES1, and BCAS1 (Fig. 2 D-F and SI Appendix, Fig S3H). +Together, the nine clusters related to each other through expression similarities and differences along two orthogonal axes: the corneal-limbal-conjunctival axis, as described above, and the superficial-wing-basal axis. For example, superficial cells of all three regions expressed KRT4 and BCAS1, superficial and wing cells of all three regions expressed NECTIN4, and basal cells of all three regions expressed LAMA3 (SI Appendix, Fig. S3B). Regarding differences, MUC16 was the predominant mucin expressed by superficial corneal cells, whereas MUC4 was the predominant mucin expressed by the limbal and conjunctival counterparts. Similarly, corneal basal epithelial cells were strongly NTRK2 positive, whereas those in the conjunctiva were NTRK2 negative but PDGFC and LGR6 positive (Fig. 2 C and F and SI Appendix, Fig. S3F). BCAS1 expression was limited to the superficial epithelial layer in the cornea but present in both the superficial and wing epithelial cells of the conjunctiva (Fig. 2D). These layers of the conjunctiva were also positive for RARRES1 and MECOM, unlike their counterparts in the cornea (Fig. 2E and SI Appendix, Fig. S3F). +Three epithelial cell populations that have been reported to be present in this region did not form discrete clusters, namely, transit amplifying cells, limbal progenitor cells (LPCs), and LSCs. The first of these, the transit amplifying cell, was observed in the cornea, as described above, but did not form a distinct cluster in this smaller dataset (3,696 basal epithelia in CSW sample compared to 8,614 in central cornea; Fig. 2H and SI Appendix, Fig. S3D). Second, although recent reports described separate clusters of LPCs, we found that certain markers identified in those studies (e.g., GPHA2, CDH19, and FRZB) were expressed by cells in a discrete lobe within the limbal basal cluster (SI Appendix, Fig. S3E). Other putative LPC/LSC markers were generally enriched in both our limbal and conjunctival basal populations or were more diffusely expressed or undetectable (SI Appendix, Fig. S3G, and Dataset S1). Finally, one study reported a discrete LSC cluster, but we find striking transcriptomic similarities between that type and the cells we identify as vascular endothelial cells here and in a previous report. We therefore hesitate to nominate LPCs or LSCs here as discrete, recognizable types. +Goblet cells. +A distinct cluster corresponding to the specialized, mucin-producing goblet cells of the ocular surface was identified based on enrichment of MUC5AC (Fig. 2I). This cluster was visualized within the conjunctival epithelium and also expressed ATP2C2, AGR3, and TFF1. +Melanocytes. +Two distinct clusters were composed of melanocytes. They shared canonical melanocyte markers, such as PAX3, TYR, and MITF, but were transcriptionally distinct (Fig. 2C and SI Appendix, Fig. S4 C and D). Histology supported the presence of two distinct cell types, which we annotate as uveal and conjunctival, corresponding to their locations (Fig. 2 J and K). The uveal type, derived from both the TM and CSW, represented melanocytes in the ciliary muscle and selectively expressed ERBB4, NRG3, and MET; the conjunctival type, derived exclusively from the CSW, represented melanocytes nestled among the basal epithelial cells of the conjunctiva and selectively expressed LEF1, PLPPR4, and KIT. +Trabecular meshwork. +Two clusters of closely related cells demonstrated DE genes previously identified via scRNAseq analysis of TM tissue. ISH confirmed the localization of these genes to the aqueous drainage structures within the iridocorneal angle, with some clearly confined to the TM and others extending into contiguous tissues of the iris root and ciliary muscle (SI Appendix, Fig. S4 E-G). The two clusters were tentatively annotated as TM fibroblasts and uveal fibroblasts, with the latter characterized by genes most strongly visualized at the uveal base of the TM, the iris root, and ciliary muscle. Although TM cells are known to have characteristics of vascular endothelia, smooth muscle, and macrophages as well as fibroblasts, we assigned fibroblast as the predominant descriptor due to their transcriptomic relatedness to other fibroblasts in the dataset (e.g., those in the sclera and cornea) (SI Appendix, Fig. S4F). This does not contradict their hybrid nature, and indeed, we note DE genes in these clusters reminiscent of the above types. For example, MYOC, CEMIP, CDH23, SLC4A10, FAM155A, KCNIP1, PDE1C, ADAM12, MICAL2, and LMX1B were among the top 20 DE genes for the TM cluster, whereas BMP5, EYA1, BICC1, DCN, APOD, KCNT2, ABCA8, IGFBP5, DYNC1I1, and MGP were for the uveal cluster (Dataset S1). Gradient expression patterns were noted within and across both clusters (SI Appendix, Fig. S4H), potentially representing further specialization as described in Integrated Analysis. +Vessel endothelium. +Transcriptomic profiles identified four clusters of vessel endothelial cells. Based on our previous work, we identified one as Schlemm canal endothelium expressing FN1 and PLAT and a second as vascular endothelium, expressing ALPL (SI Appendix, Fig. S4D). A third corresponded to endothelium lining the collector channels, aqueous veins, and scleral venous plexuses, expressing ACKR1/DARC, AQP1, SELE, and COL15A1. The fourth endothelial cluster selectively expressed lymphatic markers including LYVE1, PROX1, CCL21, and FLT4 and was localized to subepithelial vessels within the conjunctival stroma using immunostaining against PDPN (Fig. 2G). We assigned these cells to the conjunctival lymphatic endothelium. +Additional cell types. +Seven additional clusters were identified as pericytes (NOTCH3 and PDGFRB), macrophages (LYVE1+CD163+), lymphocytes (CD69+), mast cells (IL1RL1+CPA3+), ciliary muscle cells (DES and CHRM3), Schwann cells (LGI4 and CDH19), corneal stromal fibroblasts (KERA and MME), and scleral fibroblasts (TXNB, FBLN1). An eighth cluster, provisionally labeled FibroX, was transcriptomically similar to scleral fibroblasts but exhibited distinct markers such as EBF2 and SHISA6 (SI Appendix, Fig. S4D). +Iris. +The iris functions as a diaphragm akin to those in manufactured optical systems. The iris can be divided into five principal parts: 1) the anterior border layer, consisting of a dense meshwork of fibroblasts and melanocytes; 2) the stroma, made up of loose connective tissue and a lower density of fibroblasts and melanocytes, along with blood vessels, axons surrounded by Schwann cells, and immune cells such as macrophages ("clump cells"), mast cells, and lymphocytes; (3) the sphincter muscle, made up of spindle-shaped (mononucleated) smooth muscle cells bundled into units of 5 to 8; 4) the dilator muscle, a group of pigmented myoepithelial cells sometimes called anterior pigmented epithelium (APE); and 5) the posterior pigmented epithelium (PPE) facing the posterior chamber (Fig. 3A). +Cells of the iris and CB. (A) Diagram of the human iris, consisting of the anterior border layer, the stroma, the sphincter muscle, APE, and PPE, shown in greater detail within the box. (B) Clustering of transcriptomesderived from iris tissue visualized by UMAP. (C) Dot plot showing genes selectively expressed in cells of the iris. (D) Iris sphincter muscle cells immunostained for desmin (DES; red). (E) Uveal melanocytes within the anterior border layer and stroma of the iris immunostained for Melan-A (MLANA; red). (F) Iris fibroblasts and vessel endothelium within the iris stroma immunostained with PDPN (green) and PECAM1 (red), respectively. (G) Immunostaining against RELN (red) highlights the iris PPE and to a lesser extent iris APE; the latter is also positive for DES (green), highlighting its contractile role as the iris dilator. (H) Diagram of the human CB, consisting of the ciliary muscle, ciliary stroma, and ciliary processes, shown in greater detail within the box. (I) Clustering of 34,132 single-nucleus expression profiles derived from CB tissue visualized by UMAP. (J) Dot plot showing genes selectively expressed in cells of the CB. (K) Ciliary stromal fibroblasts immunostained with PDPN (green) and vessel endothelium with PECAM1 (red). (L) Uveal melanocytes within the ciliary stroma immunostained for MLANA (red). (M) CB-NPCE and CB-PCE immunostained with LRP2 (green) and RELN (red), respectively. (N) Immunostaining against CRB1 (green) highlights a subset of NPCE situated in proximity with neighboring ciliary processes. (O) RNA ISH for MECOM (green) highlights NPCE lining a ciliary process. Vasc_Endo, vascular endothelium; CM, ciliary muscle, CP, ciliary process; White scale bars: 100 microm; yellow bars: 20 microm. +From iris samples, we obtained 57,422 nuclei that formed 9 clusters (Fig. 3 B and C and SI Appendix, Fig. S1D). We were able to assign all 9 to cell types. Iris stromal fibroblasts, expressing DCN, APOD, MYOC, RARRES1, and PDPN, were identified in the stroma and anterior border layer of the iris via immunostaining against PDPN (Fig. 3F). The melanocytes were identified through their selective expression of melanocyte marker PAX3, as well as EDNRB, MITF, MLANA, MLPH, and TYR (Fig. 3 C and E). Smooth muscle cells comprising the iris sphincter muscle, responsible for constricting the pupil upon cholinergic stimulation, were identified by selective expression of the muscarinic receptor CHRM3, as well as DES (Fig. 3 C and D). The iris dilator muscle, responsible for dilating the pupil upon adrenergic stimulation, selectively expressed the alpha-1a adrenergic receptor ADRA1A. Both dilator and sphincter muscle cells expressed contractile genes, including MYH11 and ATP2A2 (Fig. 3C). The large, intensely pigmented cells of the PPE were visualized with immunostaining against RELN (Fig. 3G). Vascular endothelial cells, macrophages, lymphocytes, and Schwann cells were identified by expression of canonical markers (see above and ref.). +Ciliary Body. +The CB consists of four main components: 1) ciliary muscle, responsible for accommodation and assistance in removal of aqueous humor; 2) stroma, the vascularized connective tissue core; 3) pigmented ciliary epithelium (PCE); and 4) nonpigmented ciliary epithelium (NPCE). The PCE and NPCE are single-layered epithelia that lie adjacent to each other, oriented apex-to-apex, and connected by gap junctions (Fig. 3H). Both play important roles in maintaining the blood-aqueous barrier and secreting aqueous humor. +Analysis of 34,132 CB single nucleus transcriptomes yielded 10 clusters (Fig. 3 I and J and SI Appendix, Fig. S1F). The most abundant corresponded to the PCE and NPCE. DE genes in the PCE included ATP6V1C2, encoding the H+ vacuolar ATPase; SLC23A2, encoding a sodium-dependent ascorbate transporter; and SLC4A4, encoding the sodium-bicarbonate cotransporter (NBC) (Fig. 3J). Other DE genes included MAMDC1, CCBE1, DCT, and numerous additional solute transporters (SLC35G1, SLC38A11, SLC24A5, SLC9B2, SLC7A2, and SLC7A6), consistent with the PCE's role alongside the NPCE in secreting aqueous humor. We visualized the PCE via immunostaining against RELN (Fig. 3M). DE genes that distinguished NPCE from PCE included the vitreous humor components, COL9A1, COL9A3, and OPTC, reflecting the NPCE's close association with this gel, as well as ATP1A2, NECTIN3, CACNA1E, ENPP2, and the chloride transporter BEST2. The NPCE was visualized with immunostaining against LRP2, MECOM, and CRB1, with the latter being expressed by a subset of cells populating areas of the ciliary processes that were in contact with other processes (Fig. 3 M-O). +Cell types comprising the other clusters were determined by the expression of canonical markers. They included DES and MYH11 for muscle, DCN and PDPN for fibroblasts (Fig. 3K), LGI4 and SCN7A for Schwann cells, MLANA for melanocytes (Fig. 3L), PECAM1 for vascular endothelium (Fig. 3K), ADCY3 for pericytes, CD163 for macrophages, and CD96 for lymphocytes (Fig. 3J). +Lens. +The lens occupies the space between the iris and the vitreous (Fig. 1A). It is suspended in position by the elastic zonular fibers running between the equatorial lens capsule and the ciliary processes (Fig. 1B). The lens substance, composed of the central nucleus, concentrically layered cortical fibers, and lens epithelium, is enveloped by an elastic basement membrane called the lens capsule (Fig. 4A). Throughout life, lens epithelial cells that retain the capacity to divide migrate toward the postequatorial region, where they terminally differentiate into cortical lens fiber cells. Mature lens fiber cells lack most organelles, including the nucleus, ribosomes, and mitochondria, rendering them highly transparent. +Cells of the crystalline lens. (A) The human crystalline lens consists of lens epithelium and lens fiber cells. (B) Clustering of 13,900 single-nucleus expression profiles derived from lens tissue visualized by UMAP. A continuum is observed ranging from anterior epithelium to lens fiber cells. (C) Dot plot showing genes selectively expressed in cells of the lens. (D) Feature plots demonstrating DE genes corresponding to cells of the lens. (E and F) Lens epithelial cells visualized with ISH. Expression of CACNA1A (green) is evident in both anterior and equatorial epithelial cells, whereas ATP8B4 (red) is confined to the anterior epithelium. (G) ISH demonstrates the expression of GPR160 (green) by early lens fiber cells and CAV1 (red) by more mature fiber cells. (H and I) Transitional lens epithelial cells in the post equatorial region are positive for SLC1A2 (red) as visualized by ISH; early fiber cells are positive for GPR160 (green); and fiber cells are positive for UCHL1 (green). (White scale bars; 100 microm; yellow bars: 20 microm.) +Computational analysis divided 13,900 lens cell transcriptomes into 5 clusters (Fig. 4B and SI Appendix, Fig. S1E, and Dataset S1). Three were classified as lens epithelial cells due to their robust expression of the ocular epithelial marker PAX6. Their regional localizations as visualized by ISH for selectively expressed markers (ATP8B4, CACNA1A1, and SLC1A2) identified them as anterior, equatorial, and transitional types (Fig. 4 C-F, H, and I). Of note, cells in the transitional cluster expressed genes supporting differentiation into fiber cells (e.g., JAG1, NAP1L4, BACH2). The remaining two clusters, visualized with ISH for GPR160, CAV1, and UCHL1, corresponded to nucleated fiber cells at different stages of maturity (Fig. 4 G-I). +The arrangement of these clusters mirrored the migratory and developmental trajectory of lens epithelial cells. Rather than being discrete, the clusters were continuous, describing a gradient leading from epithelial cell on one end of the spectrum to lens fiber cell on the other, as well as gradients of expression within each cluster (Fig. 4 C and D). +Scattered TOP2A+ cells were present in the lens epithelial clusters suggesting the capacity of a subset to proliferate, and a small group of cells was PTPRC+, suggestive of a resident immune population as recently described. Neither of these rare populations was large enough to generate a discrete cluster in our dataset. +Integrated Analysis. +In several cases, cell types identified in one tissue resembled those in one or more other tissues. This observation raised the question of which cell types were present in multiple tissues and which were tissue specific. To address this issue, we pooled and reclustered data from iris, CB, cornea, CSW, and TM. The initial analysis indicated that cell types in the lens were distinct from those in the other structures, so they were omitted from this analysis. Altogether, integration yielded 34 clusters (Fig. 5A and SI Appendix, Figs. S5 A and B and S6A). A list of the DE genes from each type, as well as those from lens, is compiled in Dataset S1. +Integrated analysis of cells populating the human anterior segment. (A) Clustering of expression profiles pooled for the integrated analysis and visualized by UMAP. (B) Stacked bar chart indicating proportions within each cluster contributed by separate tissue sources. Transcriptional relatedness indicated by dendrogram. (C-E) Dot plot showing common and DE genes in fibroblast types (C),vessel endothelial types (D), and uveal epithelium. (F) Violin plots showing common and selectively expressed genes in the two pericyte clusters. Here and in other violin plots, the scale on y axis represents expression level, calculated as a normalized log(UMI+1) value. (G) Feature plots demonstrating expression patterns within the macrophage cluster. (H) Dot plot showing cell-type-specific expression of PAX3, MET, KIT, and LEF1. (I) Iris fibroblasts selectively expressing ETNPPL (red), visualized with RNA ISH. (J) TM fibroblasts selectively expressing NEB (red), visualized with RNA ISH. (K) Schlemm canal (SC) endothelium expressing FN1 (red), visualized with RNA ISH. (L) SC endothelium expressing PKHD1L1 (red), visualized with RNA ISH. (M) Melanocytes within the basal layer of the conjunctiva visualized with ISH for PAX3 (red) and LEF1 (green). Consistent with expression pattern seen in H, vascular endothelium lining a vessel within the conjunctival stroma is also noted to be positive for LEF1 (green). (N) Melanocytes within the iris stroma, visualized with RNA ISH for PAX3 (red), do not express LEF1 (green, absent). The iris sphincter is instead positive for LEF1, consistent with expression noted in H. (O) Uveal melanocytes within the iris anterior border layer and stroma visualized with ISH for MET (red) and PAX3 (green). (P) Melanocytes within the iris stroma, visualized with ISH for PAX3 (red), do not express KIT (green, absent). L, Limbus; Conj Epi, conjuctival epithelium. Remaining abbreviations as in previous figures. (White scale bars: 100 microm.) +We tallied the origins of the cells within each cluster. In many cases, cells of a particular type were derived mostly or entirely from a single tissue:for example PCE cells from the CB, sphincter muscle from the iris, and goblet cells from the CSW (Fig. 5B and SI Appendix, Fig. S5B). In some cases, however, cells derived from multiple tissues coclustered. This was not surprising for immune cells, melanocytes, and Schwann cells but was unexpected for some other cell types. +Epithelium. +Twelve clusters were identified as epithelial cells. Most were tissue specific, deriving primarily from iris, CB, CSW, or cornea/CSW, with the latter group presumably reflecting the presence of corneal tissue in the CSW (Fig. 5B). Based on transcriptomic similarity, they formed two clades. One comprised ocular surface types, and the other comprised uveal (iris and CB) types, consistent with their embryological origins from the surface ectoderm or the neuroectoderm of the optic cup, respectively. Transcriptional similarity further mirrored embryological origin among the four uveal epithelial types: the NPCE of the CB was transcriptionally more similar to the PPE of the iris, together representing the inner layer of the uveal bilayer (derived from the inner layer of the optic cup), and the PCE of the CB was more similar to the APE of the iris, together representing the outer layer of the uveal bilayer (derived from the outer layer of the optic cup) (Fig. 5B and E). Among ocular surface epithelial types, the limbal superficial and basal cell clusters were similar to their conjunctival counterparts and clustered together (i.e., "L_Conj_Epi-Superficial," "L_Conj_Epi-Basal"). +Endothelium. +Four clusters of vessel endothelial cells were identified through the expression of the canonical marker PECAM1 (Fig. 5D). All four clusters were transcriptomic neighbors, and three were largely tissue specific (Fig. 5B). Two clusters corresponded to the vascular endothelium, likely reflecting different portions of the vascular tree. One of these, CC_VenEndo, was predominantly derived from the CSW tissue and expressed markers previously associated with collector channels and aqueous veins (AQP1, POSTN, COL15A1); it may also contain endothelium lining of other vessels of the venous tree, especially within scleral venous plexuses. The other, Vasc_Endo, was present in multiple tissues and may correspond to capillary endothelium and/or endothelium lining vessels of the arterial tree. Two other clusters corresponded to the lymphatic endothelium (PDPN+, PKHD1L1+, SI Appendix, Fig. S6H), predominantly localized to the conjunctival subepithelial stroma, and to the Schlemm canal endothelium (FN1+ PLAT+, Fig. 5 D and K). The predominantly TM source of this latter cluster supports the notion that the Schlemm canal endothelium is a unique endothelium specialized for the conventional outflow path. Immunostaining against PKHD1L1 highlighted closely related conjunctival lymphatics and Schlemm canal endothelia (Fig. 5L) but not blood vessels. +Pericytes. +Two distinct pericyte clusters were identified by the shared expression of PDGFRB, ADCY3, JAG1, and NOTCH3 (Fig. 5F). Pericyte1 (LAMA2+, TRPC4+) was derived mostly from TM and CB, whereas Pericyte2 (ID4+, TRPC6+, RGS6+) was derived almost exclusively from the CSW (Fig. 5B and SI Appendix, Fig. S6 A and I-L). +Fibroblasts. +Six transcriptomically related clusters expressed genes diagnostic of fibroblasts (e.g., PDGFRA, COL6A3, DCN, PRRX1, CDH11; Fig. 5 B and C). Five were assigned based on tissue source and histological validation: TM fibroblasts derived from tissue within the TM and CSW dissections; iris fibroblasts derived predominantly from the iris; ciliary fibroblasts derived primarily from the ciliary muscle tissue within the TM, CSW, and CB (annotated as "uveal fibroblasts" in the TM/CSW atlas, above); corneal fibroblasts, derived predominantly from cornea and corneal component of the CSW dissection; and scleral fibroblasts derived exclusively from the scleral and/or limbal component of the CSW dissection. The sixth cluster, FibroX, from limbus, remains uncharacterized. +Three of the fibroblast clusters:iris, ciliary, and TM fibroblasts:were closely related and shared numerous markers (e.g., MYOC, PDPN, and RSPO2). However, each expressed distinct markers as follows that enabled histological validation to support their predominant tissue source: WIF1 and ETNPPL for iris fibroblasts; BMP5, PI16, and C7 for ciliary fibroblasts; and NEB, NELL2, UNC5D, and TMEM178A for TM fibroblasts (Fig. 5 I and J and SI Appendix, Figs. S4 E and F and S6 C-F). In addition, a subset of cells from each one of these clusters was derived from the TM tissue dissection (Fig. 5B) and shared DE genes with TM cell types previously described and validated as "BeamA," "BeamB," and "JCT" in a previous scRNAseq study. For example, markers of Beam A (e.g., BMP5) were expressed predominantly by the ciliary fibroblast cluster representing cells populating the ciliary muscle and uveal base of the TM; markers of Beam B (TMEFF2) and JCT (CHI3L1) were expressed by largely nonoverlapping populations within the iris and TM fibroblast clusters, respectively, and exhibited correspondingly nonoverlapping staining within the iridocorneal angle on histology (SI Appendix, Figs. S4 E-G and S6B) In a contemporaneous scRNAseq study of the TM with largely consistent results, DE markers of TM1 overlapped with the ciliary fibroblast cluster and those of TM2 overlapped with the current TM fibroblast cluster. We envision two possible explanations for the difference between the current and previous studies. One is that some cells in the TM dissections may have been derived from neighboring tissues:a complication that only became apparent when we profiled those tissues separately. We favor the alternative, that the similarity is genuine, which would be consistent with the TM being populated to varying degrees by three subsets of neural-crest-derived mesenchymal cells derived by migration from the contiguous iris and CB. In this view, the three clusters may be considered "outflow fibroblasts," populating a key area within the iridocorneal angle where aqueous drains through the conventional or uveoscleral pathway. +Melanocytes. +In the CSW atlas described earlier, two types of melanocytes were identified, namely, conjunctival and uveal, with the latter deriving from the ciliary muscle component of the CSW dissection. An integrated analysis supported this distinction; melanocytes derived from the iris and CB coclustered with the uveal melanocytes (MET+) identified in the CSW, and these cells remained distinct from the conjunctival melanocytes (KIT+ and LEF1+) (Fig. 5 M-P and SI Appendix, Fig. S5C). +Macrophages. +Although all macrophages grouped together in a single cluster regardless of tissue source, a closer inspection indicated substantial heterogeneity. Macrophages of corneal origin were too few in number to form a distinct cluster but segregated within a discrete zone, demonstrating the expression of CXCR4 and ITGA4 but not LYVE1 or F13A1. In contrast, the remainder of the macrophage cluster, which derived from the other tissues, expressed LYVE1, STAB1, and F13A1 (Fig. 5G and SI Appendix, Fig. S5D). Although immune cell heterogeneity in particular must be interpreted with caution in a small postmortem donor pool, all donors contributed similarly to the macrophage population, and the distribution of subpopulations was similar across all donors. +Disease Associations. +Many genes have been implicated in susceptibility to ocular diseases that result from defects in the anterior segment. We explored expression patterns of 924 disease-associated genes to the cell types in our atlas. +Mendelian Genes. +Anterior segment dysgenesis (ASD). +Congenital and developmental abnormalities of the anterior segment can affect the iris, iridocorneal angle drainage structures, and cornea. Due to dysregulated aqueous outflow, ~50% of patients with ASD develop glaucoma. Genes implicated in ASD and glaucoma include PAX6, PITX2, FOXC1, CYP1B1, LTBP2, FOXE3, PITX3, B3GLCT, COL4A1, PXDN, and CPAMD8. Many of these genes, which play well-defined roles in ocular development, continue to be expressed in relevant adult cell types within the anterior segment (Fig. 6 A and B and SI Appendix, Fig. S7A). +Expression of ocular disease-associated genes. (A) Feature plots demonstrating enriched expression of glaucoma-associated genes within cell types localized to the iridocorneal angle drainage structures. (B) Dot plot showing cataract- and EL-associated genes. (C) Feature plot showing expression patterns of genes implicated in corneal dystrophies. (D) Dot plot showing cell-type-specific enrichment scores of genes identified through GWAS for common ocular conditions or traits. Major retinal cell types from normal macula are also included. Cell type abbreviations are as in previous figures. H1/H2, horizontal cells; BC, bipolar cells; AC, amacrine cells; RGC, retinal ganglion cells. +Glaucoma. +Glaucoma is a phenotypically heterogeneous disease with a final common outcome of optic nerve degeneration, with elevated IOP representing the only known modifiable risk factor. We explored expression patterns of Mendelian genes associated with glaucoma (including ASD-related glaucoma) and elevated IOP, including MYOC, ANGPT1, ANGPT2, and LMX1B as well as PITX2, FOXC1, LTBP2, and CPAMD8. As expected, most of them were robustly expressed in outflow pathway cells and other glaucoma-relevant cell types (Fig. 6A and SI Appendix, Fig. S7). +Cataract. +Defined as opacity in the crystalline lens, cataract is the leading cause of blindness worldwide and generally follows a bimodal age distribution corresponding to congenital and age-related onset. Hereditary congenital cataracts, presenting at birth or during infancy, have been associated with highly penetrant genetic mutations in lens crystallins, growth factors, transcription factors, connexins, intermediate filament proteins, membrane proteins, the protein degradation apparatus, and a variety of other pathways including lipid metabolism. Crystallin genes were strongly expressed across lens epithelial and fiber cell types with gradient differences for a subset of genes. For example, CRYAB, CRYGS, and CRYBB2 were broadly expressed across all lens clusters (spanning both epithelial and fiber cell types), whereas CRYBB3 and CRYBA1 were most prominently expressed in transitional epithelium and early fiber cells and CRYBA4, CRYGD, and CRYBB1 were enriched in lens fibers (Fig. 6B). The connexin gene GJA3 demonstrated a similar expression pattern beginning in transitional epithelium and extending variably into the fiber cells. Among other associated congenital cataract genes, we noted peak expression either in the early fiber cells (e.g., LIM2, TDRD7) or the more mature fiber cells (e.g., BFSP1, BFSP2, TMEM114, HSF4). Only a minority of cataract-associated genes demonstrated predominant expression in the lens epithelial (as opposed to fiber cell) clusters, and most of them were in the setting of pleomorphic syndromes (PAX6, RGS6, TSPAN12). +Corneal dystrophies. +The corneal dystrophies are inherited conditions that impair the transparency of the cornea, leading to reduced vision and in some cases chronic ocular pain. Although these conditions have variable presentations, many have clinical findings predominantly localized within one of the three major layers of the cornea. We found a tight correspondence between cell-type-specific expression and the predominant clinical finding. Genes implicated in epithelial corneal dystrophies, including KRT3 and KRT12 (Meesman) and COL17A1 (recurrent erosion), were expressed predominantly in the wing and basal cells of the corneal epithelium (Fig. 2C and SI Appendix, Fig. S2A). Genes implicated in endothelial dystrophies, including COL8A2 (Posterior polymorphous) and SLC4A11 (Congenital Hereditary type 2), were expressed predominantly in the corneal endothelium (Fig. 6C). Finally, genes implicated in stromal dystrophies, including CHST6 (macular), DCN (congenital stromal), and TGFBI (lattice and granular), were expressed in keratocytes, with the latter also expressed in the epithelium (Fig. 6C). +Ectopia lentis. +Ectopia lentis (EL) is a condition in which the crystalline lens is abnormally positioned within the eye, most often due to damaged, dysfunctional, or absent zonular fibers. The most common cause of EL is trauma, but mutations have been implicated in both isolated cases (e.g., ADAMTSL4) and in syndromes such as Marfan syndrome (FBN1), Weill Marchesani syndrome (ADAMTS10, ADAMTS17) Knobloch syndrome (COL18A1), aniridia (PAX6), and homocystinuria (CBS, MTR, MTHFR). EL-associated genes were consistently expressed in the nonpigmented ciliary epithelial and/or the equatorial and transitional lens epithelial cells, which are located in areas adjacent to zonules, suggesting that they contribute to ongoing structural integrity of this important apparatus (Fig. 6B). LOXL1, implicated in pseudoexfoliation syndrome:a condition in which zonular fibers become brittle over time:was also expressed most prominently in the equatorial lens epithelium (Fig. 6B). +Susceptibility Genes Nominated by GWAS Analysis. +Susceptibility to common ocular conditions such as myopia, age-related cataract, and primary open angle glaucoma (POAG) is conferred by multiple common genetic variants with small effect sizes, many of which have been identified by genome-wide association study (GWAS). In addition to interrogating each gene individually (SI Appendix, Fig. S8 A-H), we developed a gene expression score (Materials and Methods) to explore whether GWAS-identified genes associated with these complex traits were more likely to be expressed in specific subsets of cell types. Because both the anterior and posterior segments are involved in POAG and myopia, we applied this analysis to key retinal cell types, alongside the anterior segment atlas (Fig. 6D and SI Appendix, Fig. S7B). Patterns were broadly consistent with our current knowledge of disease mechanisms. Genes associated with cataract were enriched in lens cell types. Genes associated with POAG were most enriched in TM cells, cell types of outflow tissues within the iridocorneal angle (ciliary and iris fibroblasts), and corneal endothelium (e.g., POU6F2, associated with thin central corneal thickness, a highly heritable trait and independent risk factor for development of POAG). Moderate enrichment scores in retinal pigment epithelium (RPE), Muller glia, scleral fibroblasts, CB epithelium, and vessel endothelium are consistent with the disease's complex pathophysiology and potential diversity of phenotypes. Genes associated with vertical cup-to-disk ratio (VCDR) demonstrated similar enrichment patterns to POAG, albeit they were less pronounced. Finally, myopia-associated genes were most strongly enriched in cells of the neural retina (specifically retinal ganglion cells, RPEs, and cones), as well as uveal epithelium and corneal endothelium. +Discussion +We used snRNAseq to profile cells comprising the human anterior segment, generating discrete cell atlases of the cornea, limbus, iris, CB, and lens. We then merged these datasets to perform an integrated analysis that enabled holistic appraisal of these cells across contiguous and interdependent ocular tissues. Finally, we used the atlases to interrogate cell-type-specific expression patterns of disease-associated genes, gleaning insights into where they act. +Tissue-Specific and Shared Cell Types. +Our analysis of anterior segment structures defined many clusters, each presumably belonging to a single cell type or a small number of closely related types that were too transcriptomically similar to separate in the current dataset. . Adding these results to those from our recent analysis of the TM and contiguous structures but taking shared types from the integrated analysis into account, we estimate that there are at least 40 cell types in the human anterior segment. Our results are generally concordant with those of a recent whole eye atlas, but the larger number of anterior segment cells in our dataset than theirs (195,248 vs. <30,000) allowed us to document far more cell types. +It was apparent that some of the types isolated from a particular tissue were very similar to ones isolated from other tissues. This is unsurprising for three reasons. First, although the structures we analyzed can be roughly separated by dissection, some are contiguous without sharply defined borders. For example, the iris root, TM, and ciliary muscle share a common insertion area at the iridocorneal angle (Fig. 1B) and limbus is continuous with the cornea and sclera (Fig. 2A) such that discrete anatomical dissection at the cellular level is impossible. Second, some of the major cell types, such as macrophages, are motile and can easily cross tissue boundaries. Third, some types, such as melanocytes, Schwann cells, and outflow fibroblasts populating the iridocorneal angle, are derived from the neural crest, a migratory population that invades multiple structures during development. +By combining transcriptomes from contiguous tissues, reclustering them, and visualizing key markers with histological methods, we were able to determine which types are shared and which are primarily confined to specific tissues. Most epithelial types were tissue specific whereas immune cells (mast cells, macrophages, and lymphocytes) and Schwann cells (crest derived) were shared. For other groups, distribution was more complex. For pericytes, melanocytes, and endothelia, one type populated multiple tissues, whereas the other (or others for endothelia) was tissue specific. Of particular interest were the melanocytes, with a conjunctival type derived from the CSW on the surface of the eye and a uveal type inside the eye present in multiple structures. The substantial differences between their transcriptomes may carry important implications in disease research. Uveal melanoma and conjunctival melanoma:representing serious oncologic diagnoses:behave very differently in the clinical setting and consequently require different treatment approaches; their distinct molecular features may provide insight into pathogenesis and therapeutic strategies. +Additional insights were gleaned for fibroblasts. Despite representing a diverse group of cells that play a key role in maintaining health and driving disease, fibroblasts throughout the body have historically been poorly characterized. Single-cell technologies have contributed significantly to advancing our understanding of their heterogeneity and contributions to disease states. Our current work extends this analysis to fibroblasts within the anterior segment of the eye, particularly those populating the TM. Indeed, only by examining the TM together with surrounding contiguous tissues were we able to appreciate both its relatedness to the cornea, CB, and iris as well as its unique aspects. Among the anterior segment fibroblast cells, we found the TM cells to be most closely related to fibroblasts residing in the CB and iris tissues. In contrast, fibroblasts from the cornea and sclera were distinctly different, with each defined by a unique set of ECM and collagen genes. +A prominent illustration of tissue-specific cell types exhibiting relationships corresponding to their contiguity and embryological origins involved the cells populating the uveal epithelial bilayer lining the iris and CB, whose origins trace to the neuroectodermal bilayer of the optic cup. The iris PPE demonstrated the closest transcriptomic relation to the nonpigmented CB NPCE, together comprising the inner layer of the uveal epithelial bilayer derived from the inner layer of the embryological optic cup. Similarly, the iris APE demonstrated the closest relation to the pigmented CB PCE, together comprising the outer layer of the uveal epithelial bilayer derived from the outer layer of the embryological optic cup. Although we did not integrate posterior segment tissues into our analysis, we speculate that the RPE may show similarity to the other two cell types in this outer uveal epithelial layer. Similarly, the embryological origin of the neural retina links it to the inner uveal layer, which may account for some residual neural-type expression patterns in the CB nonpigmented epithelium and the iris PPE (e.g., CRB1, LRRN2, CACNA1E). +A caveat to the classification system is that we defined clusters or types using clustering algorithms that, while nominally unsupervised, depend on the parameters we choose. This is especially relevant in cases where gene expression differences occur on a continuum among cells. For example, in several instances, we noted that the expression of specific genes displayed gradients within a cluster, suggesting the existence of either additional closely related cell types or cell-state-related patterns of expression. Conversely, given the limited numbers of cells used, there may be tissue-specific differences among nominally shared types that we were unable to detect. +Disease-Associated Genes. +Our cell atlas of the human anterior segment provides a valuable resource for analyzing genes implicated in ocular disease. Several results were noteworthy. For example, it expands our insight into the complexity of glaucoma, where outflow fibroblasts (including those populating the TM, CB, and iris), corneal endothelium, and vessel endothelia (including those populating Schlemm canal, lymphatic, and blood vessels) emerged as key cell types expressing genes associated with POAG and IOP. In cataract, transitional lens epithelium and early fiber cells expressd the majority of disease-associated genes. In myopia, the uveal epithelium, RPE, and various neural cell types in the retina appeared to stand out as important expressors of associated genes. +Materials and Methods +Tissue Acquisition, Dissection, and Processing. +Human ocular tissues were obtained postmortem either from Massachusetts General Hospital (MGH) via the Rapid Autopsy Program or from The Lion's Eye Bank in Murray, Utah. The acquisition and use of human tissue were approved by the Human Study Subject Committees (Dana Farber/Harvard Cancer Center Protocol No. 13-416, University of Utah Protocol IRB_00010201) (SI Appendix, Tables S1 and S2). Human tissue used for immunohistochemistry (IHC) was also provided by the Lions Vision Gift, Portland, OR. No ocular disease was reported in any of the human donors, and no abnormalities were noted during dissection. Single nuclei libraries were generated, sequenced, and analyzed by methods similar to those in van Zyl et al. with modifications detailed in SI Appendix, Methods. +Histology. +CSWs or whole globes were fixed, frozen, and sectioned in a cryostat. Sections were processed for IHC or ISH. Antibodies and probes used are listed in SI Appendix, Table S3. Images were acquired on Zeiss LSM 710 confocal microscopes and analyzed using ImageJ (NIH). +Supplementary Material +Reviewers: S.B., Washington University in St Louis School of Medicine; R.L., Northwestern University; and J.S., New York University. +Competing interest statement: T.v.Z. is employed by Regeneron. J.R.S. is a consultant to Biogen. This work was performed entirely at Harvard University, with no funding from either Regeneron or Biogen. Regeneron and Biogen scientists have had no access to the data. +This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas.2200914119/-/DCSupplemental. +Data Availability +The accession number at Gene Expression Omnibus for the raw data reported in this paper is GSE199013. Data can also be visualized in the Broad Institute's Single Cell Portal. +Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: The Right to Sight: An analysis for the Global Burden of Disease Study +Massively parallel digital transcriptional profiling of single cells +Anatomy and physiology of the cornea +Survivin: Key regulator of mitosis and apoptosis and novel target for cancer therapeutics +Dominant-negative cyclin-selective ubiquitin carrier protein E2-C/UbcH10 blocks cells in metaphase +DNA topoisomerase II and its growing repertoire of biological functions +Single-cell transcriptomics identifies a unique entity and signature markers of transit-amplifying cells in human corneal limbus +Single cell transcriptomics reveals the heterogeneity of the human cornea to identify novel markers of the limbus and stroma +The anatomy of the limbus +Human limbal epithelial stem cell regulation, bioengineering and function +The limbus: Structure and function +Corneal epithelial biology: Lessons stemming from old to new +Keratin 13 is a more specific marker of conjunctival epithelium than keratin 19 +Cytokeratin 15 can be used to identify the limbal phenotype in normal and diseased ocular surfaces +The spectrum of cytokeratins expressed in the adult human cornea, limbus and perilimbal conjunctiva +Expression of insulin-like growth factor binding protein-3 in pterygium tissue +Comparative analysis of human conjunctival and corneal epithelial gene expression with oligonucleotide microarrays +In vivo and in vitro expression of connexins in the human corneal epithelium +A single cell atlas of human cornea that defines its development, limbal progenitor cells and their interactions with the immune cells +Molecular characteristics and spatial distribution of adult human corneal cell subtypes +Single-cell transcriptomics identifies limbal stem cell population and cell types mapping its differentiation trajectory in limbal basal epithelium of human cornea +Molecular identity of human limbal heterogeneity involved in corneal homeostasis and privilege +Cell atlas of aqueous humor outflow pathways in eyes of humans and four model species provides insight into glaucoma pathogenesis +Goblet cells of the conjunctiva: A review of recent findings +Molecular taxonomy of human ocular outflow tissues defined by single-cell transcriptomics +The many faces of the trabecular meshwork cell +Cross-tissue organization of the fibroblast lineage +Adult vitreous structure and postnatal changes +Zinn's zonule +The lens growth process +The lens equator: A platform for molecular machinery that regulates the switch from cell proliferation to differentiation in the vertebrate lens +Jagged 1 is necessary for normal mouse lens formation +Nucleosome assembly proteins NAP1L1 and NAP1L4 modulate p53 acetylation to regulate cell fate +A comprehensive spatial-temporal transcriptomic analysis of differentiating nascent mouse lens epithelial and fiber cells +Resident immune cells of the avascular lens: Mediators of the injury and fibrotic response of the lens +Anterior eye development and ocular mesenchyme: New insights from mouse models and human diseases +Integrated single-cell atlas of endothelial cells of the human lung +Phenotype-genotype correlations and emerging pathways in ocular anterior segment dysgenesis +Axenfeld-Rieger syndrome in the age of molecular genetics +Glaucoma in adults-screening, diagnosis, and management: A review +Mutations and mechanisms in congenital and age-related cataracts +Early and late clinical landmarks of corneal dystrophies +Molecular pathogenesis and management strategies of ectopia lentis +Genomic locus modulating corneal thickness in the mouse identifies POU6F2 as a potential risk of developing glaucoma +Multi-species single-cell transcriptomic analysis of ocular compartment regulons +A transcriptome atlas of the mouse iris at single-cell resolution defines cell types and the genomic response to pupil dilation +Uveal and conjunctival melanoma: Close together:But only distantly related +Eye morphogenesis and patterning of the optic vesicle +van Zyl ., Cell atlas of the human ocular anterior segment: Tissue-specific and shared cell types. Gene Expression Omnibus. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE199013. Deposited 20 March 2022. +van Zyl ., Cell atlas of the human ocular anterior segment: Tissue-specific and shared cell types. Broad Single Cell Portal. https://singlecell.broadinstitute.org/single_cell/study/SCP1841. Deposited 20 March 2022. \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1093_hmg_ddab140.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1093_hmg_ddab140.txt new file mode 100644 index 0000000..f0fa845 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1093_hmg_ddab140.txt @@ -0,0 +1,3 @@ +Abstract The human neural retina is a light sensitive tissue with remarkable spatial and cellular organization. Compared with the periphery, the central retina contains more densely packed cone photoreceptor cells with unique morphologies and synaptic wiring. Some regions of the central retina exhibit selective degeneration or preservation in response to retinal disease and the basis for this variation is unknown. In this study, we used both bulk and single-cell RNA sequencing to compare gene expression within concentric regions of the central retina. We identified unique gene expression patterns of foveal cone photoreceptor cells, including many foveal-enriched transcription factors. In addition, we found that the genes RORB1, PPFIA1 and KCNAB2 are differentially spliced in the foveal, parafoveal and macular regions. These results provide a highly detailed spatial characterization of the retinal transcriptome and highlight unique molecular features of different retinal regions. + +FULL TEXT NOT AVAILABLE \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1126_science_aat5031.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1126_science_aat5031.txt new file mode 100644 index 0000000..63346c7 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_1126_science_aat5031.txt @@ -0,0 +1,124 @@ +Spatio-temporal immune zonation of the human kidney +Tissue-resident immune cells are important for organ homeostasis and defense. The epithelium may contribute to these functions directly, or via crosstalk with immune cells. We used single cell RNA sequencing to resolve the spatio-temporal immune topology of the human kidney. We reveal anatomically-defined expression patterns of immune genes within the epithelial compartment, with anti-microbial peptide transcripts evident in pelvic epithelium in the mature, but not fetal kidney. A network of tissue-resident myeloid and lymphoid immune cells was evident in both fetal and mature kidney, with post-natal acquisition of transcriptional programs that promote infection-defense capabilities. Epithelial-immune crosstalk orchestrated localization of anti-bacterial macrophages and neutrophils to the regions of the kidney most susceptible to infection. Overall, our study provides a global overview of how the immune landscape of the human kidney is zonated to counter the dominant immunological challenge. +The kidneys play a vital role in organism homeostasis but can be affected by a number of prevalent and life-limiting conditions where recognition and response to pathogen and danger signals is critical. These responses are mediated by a network of immune cells but our understanding of human kidney-resident immune cells and their cell signaling circuitry is limited. Anatomically, each kidney is comprised of the cortex containing glomeruli (where filtrate is generated), and medulla where urine is concentrated (Fig. 1A). The functional subunit of the kidney is the nephron, made up of a glomerulus, proximal tubule (PT) (where filtered electrolytes are reabsorbed), loop of Henle (LOH) (that generates the intrarenal sodium gradient required for urine concentration) and collecting ducts (CD) that coalesce in the kidney pelvis. Urine subsequently flows into the ureter and on to the bladder (Fig. 1A). The principal infectious challenge arises from bacteria ascending the ureter into the kidney pelvis, and we have previously shown that the hypersaline environment within the medulla may promote anti-microbial responses. +Here, we used droplet encapsulation high throughput single cell RNA sequencing (scRNAseq) (10x Genomics platform) and flow and mass cytometry to define the global immune landscape of the human kidney. We studied single cell suspensions from 14 mature human kidneys and 6 fetal kidneys (Table S1, S2, and Fig. S1). We captured 114,113 droplets from mature kidneys, yielding 40,268 cells following rigorous quality control, as described previously (Fig. S2A). We loaded 739 highly variable genes into a principal component analysis and identified clusters that were manually curated into four major cellular compartments based on canonical marker expression; endothelial, immune, fibroblast/myofibroblast, and epithelium (Figs. 1B, S3A-F). +Within the endothelial cell clusters, glomerular endothelial cells (GE), vasa recta (VR), and peritubular capillaries (PCap) were identified (Figs. 1B, S3E-F). Nephron epithelial cells were evident, including podocytes (Podo), PT, LOH, connecting nephron tubule (CNT), intercalated cells (IC) and principal cells (PC) of the collecting duct, as well as pelvic epithelium (PE) (Fig. 1B). Immune cell populations included mononuclear phagocytes (MNPs), B cells, T cells and NK cells (Fig. 1B), and their presence confirmed by mass cytometry in n=3 additional adult kidney samples (Fig. S4) that were flushed to minimise intravascular contamination. +To explore the spatial distribution of these cells across the kidney, we sought to assign a depth-estimate of the sample from which the cells originated, termed 'Pseudodepth' (Fig. S5, Table S3, Supp. methods). We observed enrichment of GE, Podo and PT cells in samples predicted to be cortical/cortico-medullary in pseudodepth, whilst PE and transitional epithelium were limited to medulla/pelvic pseudodepth (Fig. 1C), as predicted by their known arrangement. This analysis revealed an asymmetrical distribution of immune cells, with B cells almost exclusively located in cortical samples, whilst MNPs were enriched in deeper samples (Figs. 1C-D, S4G, S5D). +We next analysed single cell transcriptomes from fetal kidneys obtained at 7-16 post-conception weeks (PCW). Based on the analyses of 33,865 droplets, yielding 27,203 annotated cells after rigorous quality control (Fig. S1A and S2B), we identified immune, endothelial, developing nephron epithelium and stromal cell clusters based on canonical marker expression and informed by previous transcriptional analyses of fetal kidney (Figs. 1E, S6, S7, Table S10). Cells from various developmental stages of nephrogenesis (Fig. 1F) were evident in our single cell data (Fig. 1E, G-H), with cap mesenchyme (CM) dominating at 7-8 PCW (Fig. 1G-H). From 9 PCW, podocytes were more evident, and by 12 weeks, cells from across nephrogenesis were present, including PT, LOH, CD and PE (Fig. 1E, G-H). +Much of the knowledge about the developing immune system in the human fetus is inferred from murine studies. Fetal kidneys at the gestational age captured in our study (7-16 PCW), would be expected to contain macrophages with potentially three developmental origins; yolk sac progenitors, aorta-gonad-mesonephros (AGM) hematopoetic stem cells (HSCs) and fetal liver HSCs. The extent to which other myeloid and lymphoid cells populate the human fetal kidney is unclear. In our fetal kidney dataset (representing 7-16 PCW), several immune cell clusters were evident (Fig. 1E). Macrophages and some dendritic cells (DCs) were present at the earliest developmental stage (Fig. 1H). Monocytes, T cells and NK cells appeared from 9 PCW, whilst B cells were present at later developmental stages from 12 PCW (Fig. 1H). These data demonstrate that immune cell subsets exhibit different temporal patterns of localization to the human fetal kidney. +Terminally differentiated fetal nephron epithelial cells (Fig. S7) showed transcriptional similarity to their mature counterparts (Figs. S8-9), particularly in proximal nephron components, whilst CNT and PE showed less similarity (Fig. 2A). Immune gene ontology (GO) terms were enriched across the mature nephron, particularly in the pelvic epithelium, including 'innate immune' and 'anti-microbial response' genes (Figs. 2B, S10B). We verified this pattern of gene expression in bulk transcriptomic data, which similarly demonstrated highest expression of immune genes in the pelvis (Fig. S10C). In contrast, there was little or no expression of immune genes in fetal kidney epithelium (Fig. 2B). +We hypothesized that these spatially distinct immune gene expression patterns may be related to the dominant infection threat in the post-natal renal tract, which occurs via bacteria ascending the ureter from the bladder, most commonly uropathogenic Escherichia coli (UPEC). UPEC-associated molecules such as flagellin are sensed by extracellular toll-like receptors (TLRs). In mature kidneys, we observed higher expression of the flagellin receptor TLR5, and its down-stream signalling molecule MyD88, in the PE compared with the proximal nephron epithelium (Fig. S10D). Epithelial cells may directly contribute to organ defense by secreting antimicrobial peptides (AMPs). AMP expression was highest in the mature pelvic epithelium (Fig. 2B), including serum amyloid A1 (SAA1), which inhibits biofilm formation in UPEC, and Lipocalin 2 (LCN2), an iron chelator with bacteriostatic effects, the deficiency of which results in susceptibility to recurrent urinary tract infections (UTI). +To validate the differential expression and functional significance of these AMPs in kidney epithelium, we measured transcripts in bulk human kidney samples ex vivo, and demonstrated high expression of LCN2 and SAA1 in medulla/pelvis samples that increased following the addition of UPEC (Fig. 2C). Similarly, in vivo in a murine model of pyelonephritis, Lcn2 and Saa1 were more highly expressed in the medulla/pelvis compared with cortex, and expression upregulated 24 hours following urethral challenge with UPEC (Fig. 2C). Thus, the distinct expression patterns of AMPs in human kidney likely facilitate protective epithelial responses in the region most vulnerable to ascending bacterial infection. This zonated epithelial innate immune capability is acquired post-natally and was not evident in fetal kidney. +Analysis of the immune compartment in mature kidney identified, and delineated the defining markers of, resident MNP, neutrophil, mast, pDC, B, CD4 T, CD8 T, NK and NKT cell clusters (Figs. 3A, S11, Supp data 7, 8). Lymphocyte subsets expressed molecules associated with tissue-residency (Fig. S12A) and a recently defined Hobit/Blimp1-containing murine resident-lymphocyte signature (Fig. S12B), consistent with the conclusion that the mature human kidney houses bona-fide tissue-resident lymphocytes. The B cell cluster included IgM, IgG and IgA-expressing cells (Fig. S12C). Cytokine and transcription factor expression did not indicate any specific polarization of CD4 T cells (Fig. S12D). Within the NK cluster were cells with dual expression of gamma and delta TCR, and markers typically associated with mucosal-associated invariant T cell (MAIT) (Fig. S12D). +Within the myeloid compartment, we identified 4 distinct clusters of MNPs (Fig. 3B and S13A-D). MNPs comprise monocytes, macrophages and DCs with several subsets described based on surface markers, function and ontogeny; expression of CD11c, major histocompatibility complex (MHC) class II molecules and CD14 identifies monocyte-derived macrophages with an avid phagocytic capacity, whilst CD11c+/MHCII+/CD14- are classical myeloid DC (cDC) with the ability to migrate and present or cross-present antigen to T cells. cDC can be further subdivided into cDC1 that are CD141(THBD) + and XCR1+, and cDC2 that express CD1c. CD11c+/MHCII+/CD14+ macrophages, cDC1 and cDC2 have previously been described in the human kidney. In our scRNAseq dataset, all four MNP clusters expressed ITGAX (CD11c) and HLA-DRA (an MHCII gene), with the highest expression of MHCII in MNPc (Fig. 3C). Some cells in this cluster expressed CD1C, XCR1, CLEC9A and CD141 (THBD) (Fig. 3C) indicating that it included cDC1 and cDC2 cells (Fig. S13E-F). MNPa and MNPd contained cells expressing CD14, whilst MNPb expressed CD16 (FCGR3A) (Fig. 3C). MNPa was transcriptionally similar to 'classical' monocytes, and MNPb to 'non-classical' monocytes (Fig. S13C-D), and both showed transcriptional overlap with intestinal macrophages (Fig. S13G), which are predominantly monocyte-derived. MNPd showed little transcriptional similarity to circulating monocytes or monocyte-derived macrophages in intestine or skin (Fig. S13C-D, G) suggesting that these cells may have a different origin or that their transcriptome is re-programmed by the tissue environment. Of note, RUNX1, a TF used to fate-map yolk sac-derived cells, was differentially expressed in MNPd (Fig. S13H), raising the possibility that they may populate the kidney pre-natally. +Macrophages and cDCs are functionally distinguished by their capacity to phagocytose and destroy ingested material or to process and present antigen to CD4 T cells respectively. In mature kidneys, 'Antigen processing and presentation' and 'T cell co-stimulation' were the top GO terms associated with MNPc (Fig. S13I), consistent with their identity as cDC. 'Defense response to bacterium' and 'Neutrophil degranulation' genes were expressed by monocyte-derived MNP subsets, MNPa and MNPb (Fig. S13I), including anti-microbial genes such as S100A8 and S100A9, IL1B and lysozyme (LYZ) (Fig. S13J). +CD11c+MHCII+CD14+ MNPs in mature human kidney are enriched in inner regions of the kidney and specialized for defense against UPEC. However, in the current scRNAseq experiment two subsets of MNP were found to express CD14, MNPa and MNPd (Fig. 3C), with the single cell transcriptomes indicating functional diversity, with MNPa alone specialized in anti-bacterial activities (Fig. S13I). Using marker genes identified by our scRNAseq dataset (Table S6), we confirmed the presence of MNPa-d in adult kidney by flow cytometry (Fig. S14A-B) and compared the phagocytic capacity of MNPa and MNPd (Fig. S14B). MNPa demonstrated avid uptake of fluorescently labeled UPEC, in contrast to MNPd (Fig. 3D), in keeping with the GO term analysis. +In the fetal kidney, the lymphoid compartment contained CD4 T cells, with few CD8 T cells detectable (in contrast to the mature kidney), as well as B, NK, and innate lymphoid cells (Fig. 3E). There were several subsets of myeloid cells, including monocytes, two subsets of macrophages (MPhage1 and MPhage2), cDC1 and cDC2, pDCs and mast cells (Fig. 3E). We also observed proliferating counterparts of MPhage1, cDC2, monocytes, B cells and NK cells (Figs. 3E, S15). The MPhage 1 population showed transcriptional similarity to murine kidney F4/80high yolk sac-derived macrophages (Fig. S16A). The MPhage 2 subset had transcriptional overlap with MPhage 1 but also expressed pro-inflammatory genes (Figs. 3C, E, S16B). MPhage 1, dominated the resident-immune cell population in fetal kidneys at 7-10 PCW (Fig. 3F) but at later stages, MPhage 2 increased in number along with other immune cells (Fig. 3F). The emergence of MPhage 2 at later time-points may reflect a different origin, or increasing exposure of the same precursor cell to a pro-inflammatory stimulus. Fetal kidney DCs were predominantly cDC2 with few cDC1 (Fig. 3F), as observed in mature kidneys (Figs. 3B, S13E-F). The emergence of kidney CD4, CD8 T cells and B cells at >10 PCW mirrors the development of fetal thymus and spleen. +To investigate the relationship between immune cell subsets in fetal and mature human kidney, we quantified their transcriptional similarity (Figs. S16C, S17A). MNPd was the only mature kidney MNP cluster with similarity to fetal kidney Mphage1 (S16C), and both clusters expressed MRC1, C1QC, CD163, and MAF (Figs. 3C, S16D). Trajectory analysis demonstrated a temporal progression of the MPhage1 transcriptome towards that of MNPd (Fig. S16F). Mature kidney MNPa and MNPb were highly transcriptionally similar to fetal kidney monocytes (Fig. 3C, S16C). +We next asked how the transcriptome of kidney immune cells changed over developmental time. Previous studies indicate that fetal monocytes may be less 'pro-inflammatory' and fetal DCs less effective at stimulating CD4 T cells than their mature counterparts. We found that fetal kidney monocytes were less enriched for 'pro-inflammatory' M1 gene expression than mature kidney MNPa and MNPb (Fig. 3G). Both fetal macrophage subsets, as well as MNPd in the mature kidney, were skewed towards an anti-inflammatory M2 transcriptome (Fig. 3G). Monocyte-derived macrophages in the mature kidney showed increased expression of 'Phagocytosis' and 'Defense response to bacterium' genes compared with fetal monocytes (Fig. S16G). Similarly, 'antigen processing and presentation' genes and HLA-DRA were more highly expressed in mature kidney DCs compared with those in the fetus (Figs. 3C, S16G, F). In the lymphoid compartment, fetal kidney B cells showed no evidence of class switching (in contrast to their mature counterparts) (Fig. S17B), whilst fetal CD8 T cells expressed little GZMH, a cytotoxic effector molecule (Fig. S17B). Functionally, fetal kidney CD8 T cells and NK cells showed reduced enrichment for 'T cell receptor signalling' and 'NK cell mediated immunity' relative to mature kidney CD8 and NK cells respectively (Fig. S17C). +Epithelial and endothelial cells can communicate with immune cells via chemokines that orchestrate immune cell position, and cytokines that promote immune cell function. To investigate epithelial-immune cross-talk in the kidney, we assessed chemokine ligand-receptor interactions (Figs. 4A and Fig S18A). CX3CL1 was expressed in CNT and PE, and its receptor CX3CR1 on monocyte-derived macrophages (MNPa-b) and DCs. Analysis of bulk RNA sequencing data demonstrated two peaks of CX3CL1 expression across kidney pseudodepth, in keeping with the single cell analysis (Fig. 4A, S18B). Using a CX3CL1 reporter mouse, we confirmed high expression of CX3CL1 protein in the kidney pelvis, with the potential to position CX3CR1-expressing MNPs (Fig. S18C). Analysis of CX3CR1 expression across kidney pseudodepth similarly showed some transcripts in the cortex, but greatest expression in the deeper regions of the kidney (Fig. S18B). Given the marked bacterial phagocytic capacity of MNPa (Fig. 3D), this pattern of chemokine expression would place them in a pelvic position to combat ascending UPEC. Notably, MNPd showed little expression of CX3CR1, and in human kidney sections there were few CD206/163 positive cells in the medulla and pelvis compared to the cortex (Fig. S18D). +The ligand-receptor analysis also highlighted interactions between PE and neutrophils via expression of CXCL1-3, CXCL5-6 and CXCL8 and their receptors on neutrophils (Fig. 4A). We validated this, demonstrating that CK17 (KRT17)+ cells (a marker of PE) in human kidney pelvis express CXCL8 and LCN2 (Fig. 4B, S19A). This finding is clinically important since genetic variants of CXCL8 and CXCR1 are associated with susceptibility to pyelonephritis in humans. To determine if this epithelial-immune cross-talk may promote neutrophil recruitment to the pelvis during UTI, samples of human kidney were incubated with UPEC. At baseline, CXCL8 levels were significantly higher in the medulla/pelvis samples compared with the cortex (Fig. 4C), with a marked increase observed following UPEC challenge (Fig. 4C). Similarly, in vivo, in a mouse model of pyelonephritis, Cxcl1 and Cxcl2 (murine orthologues of human CXCL8) were higher in the medulla/pelvis samples and in PE at baseline and further increased during infection (Fig. 4C, S19B). The functional importance of this response to promote neutrophil chemotaxis during infection was evident by the accumulation of LysMhigh/CD11b+ neutrophils in the PE (Fig. 4D, S19C). Overall, expression of neutrophil-recruiting chemokines was highest in PE, with some expression of CXCL2/CXCL3 in principal cells, in line with previous murine studies. In the fetal kidney, there was little expression of any neutrophil-recruiting chemokine in the distal nephron and PE (Fig. 4E). +Here we investigated immune capability in the human kidney and determined how it changes over developmental time and anatomical space. We found that anti-microbial immunity is spatially zonated but this feature was not evident pre-natally. Fetal kidney epithelium showed little immune gene expression, in line with the view that it occupies a relatively sterile environment, where anatomically polarized anti-microbial defense is redundant. We show that a variety of immune cell populations are established in the human kidney in the first trimester with distinct temporal patterns, but they differ from mature kidney immune cells, with post-natal acquisition of transcriptional programmes that promote pro-inflammatory and infection defense capabilities. The mature kidney MNP compartment was dominated by two monocyte-derived macrophage populations, specialised for anti-bacterial function, but also contained a smaller M2-enriched macrophage population that was transcriptionally similar to fetal kidney macrophages, potentially indicating pre-natal seeding, consistent with mouse studies. +In summary, our study provides a comprehensive description of immune topology in the human kidney, providing a resource, which adds to the recently published murine kidney dataset, to facilitate the future study of pathogenic mechanisms and the identification of therapeutic targets in immune and infectious kidney diseases. +Supplementary Material + Author contributions: +B.J.S. and J.R.F. analyzed the data, with contributions from M.D.Y., S.B., T.J.M. F.V.B., M.D.C.V.H., and M.R.C. Samples were curated and/or experiments performed by: F.V.B., J.R.F., B.J.S., R.A.B., D.M.O., R.V-T., E.S., K.W.L., A.M.R., J.L.F., J.U.L.S., S.J.F., C.G., N.R., L.M., T.A., J.N.A., A.C.P.R., I.M., S.F., C.J., D.R., J.N., A.F., J.B., S.Lis., S.Lin. and G.D.S. Pathological expertise was provided by A.Y.W., N.S.. B.J.S., J.R.F and M.R.C. wrote the manuscript. 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Anatomy of the human kidney. +B. UMAP plot of 40,268 human mature kidney cells. Compartments illustrated in colors (red, immune; blue, vasculature; green, nephron; mauve, stroma). Annotations derived from compartment-specific analysis (Fig. S8, 11, 13). +C. UMAP plots illustrating the contribution made by cells from biopsies at inferred biopsy depths, with density contours colored according to compartments [B]. +D. Barplots showing the proportion of immune cells at each inferred biopsy depth. +E. UMAP plot of 27,203 human fetal kidney cells. Compartments illustrated in colors (red, immune; blue, vasculature; green, developing nephron; mauve, stroma). Annotations derived from compartment-specific analysis (Fig. S7, 15). +F. Diagram illustrating steps in development of the nephron through early fetal life. The ureteric bud (UB) undergoes branching and instructs development of cap mesenchyme (CM) into renal vesicle (RV) and subsequently S-shaped body (SSB). UB forms distal nephron structures, whilst SSB forms proximal structures. +G. Proportional contribution of fetal developing nephron cell types at distinct developmental time points. Cell types are colored as in Fig S7F. +H. UMAP plots illustrating the contribution made by cells from kidneys at discrete developmental time points, with density contours colored according to compartments [E]. PCW, post-conception weeks. Annotations: MNP, mononuclear phagocyte; MPhage, macrophage; NO, neutrophil; Mast, mast cell; pDC, plasmacytoid dendritic cell; B, B cell; NK, natural killer cell; NKT, natural killer T cell; CD4 T, CD8 T, CD4 and CD8 T cell; MK, megakaryocyte; AVRE, ascending vasa recta endothelium; DVRE, descending vasa recta endothelium; PCE, peritubular capillary endothelium; GE, glomerular endothelium; PE, pelvic epithelium; TE, transitional epithelium of ureter; LOH, loop of Henle; CNT, connecting tubule; PC, principal cell; IC (A+B), type A and B intercalated cells; Podo, podocyte; PT, proximal tubule; dPT, distinct proximal tubule; EPC, epithelial progenitor cell; Fib, fibroblast; MFib, myofibroblast; CM, cap mesenchyme; Prl-CM, proliferating cap mesenchyme; RV, renal vesicle; SSB, S shaped body; UB, ureteric bud. +Gene expression patterns in the developing and mature nephron +A. Heatmap of mean similarity scores between fetal and mature nephron cell types. +B. Upper panel: heatmap of mean scaled scores for immune process genesets (Innate immune response, GO:0045087; Defense response, GO:0006952; Immune response, GO:0006955; Antimicrobial humoral response, GO:0019730). Lower panel: heatmap of mean expression values of antimicrobial peptides (AMPs) amongst pelvic epithelium marker genes. Point size shows the fraction of cells with non-zero expression. +C. Log10 transformed relative expression levels of LCN2 and SAA1 (human) and Lcn2 and Saa1/2 (murine) in kidneys following UPEC challenge (measured by qPCR, values relative to unstimulated cortical samples (n = 3 (human), n = 6 (murine), ANOVA - ***. p < 0.0005; **, p <0.005; *, p < 0.05; NS, not significant). Boxplots show median values and interquartile range. C, cortex; M/P, medulla/pelvis. +Myeloid cell populations in the mature and developing kidney +A. UMAP plot illustrating the cell populations identified in 7803 mature kidney immune cells. The myeloid sub-compartment is circled and lymphoid cells de-colored. +B. UMAP plot illustrating four subsets of mononuclear phagocyte (MNPa-d), neutrophils, mast cells, and plasmacytoid dendritic cells, after reanalysis of 1347 cells of the mature kidney myeloid sub-compartment. +C. Violin plots showing expression levels of canonical myeloid population markers. +D. Efficiency of FITC-labelled UPEC phagocytosis by CD14+ HLA-DR+ CD36+ (MNPa) and CD14+ HLA-DR+ CD206+ (MNPd) cells from adult human kidney, by flow cytometry (n = 4; *, p < 0.05 (Wilcoxson rank sum test)). Representative plot showing technical replicates. +E. UMAP plot illustrating the cell populations identified in 6847 fetal immune cells. Lymphoid cells are decolored. +F. Plot showing proportional contribution of cell types identified in [3E] to the fetal immune compartment over developmental time. PCW, post-conception weeks. Lymphoid cells are decolored. +H. Plot showing geneset scores of M1 and M2 macrophage polarisation signatures (derived from GSE5099 - LPS vs Il4 stimulated bone marrow derived macrophages) amongst fetal and mature MNP. Grey points, single cell geneset scores; colored points, mean scores for each cell type. +Spatial topology of myeloid cell populations in the mature kidney +A. Heatmap of chemokine ligand-receptor interactions between mature myeloid and nephron cell types arranged by proximal to distal nephron organization. Point size indicates permutation p value (CellPhoneDB). Color indicates the scaled mean expression level of ligand and receptor (Mol1/2). +B. Confocal microscopy images illustrating co-expression of CK17 (white), CXCL8 (green), and LCN2 (red) in human medullary and pelvic epithelium. M/P, medulla/pelvis. Scale bars 20 mum. +C. Plots illustrating Log10 transformed relative expression of CXCL1 and CXCL8 (human) and Cxcl1 and Cxcl2 (murine) in response to UPEC at distinct kidney depths, by qPCR. Values relative to unstimulated cortex (n = 3 (human), n = 6 (murine), ANOVA - ***, p < 0.0005; **, p <0.005; *, p < 0.05; NS, not significant). Boxplots show median values and interquartile range. C, cortex; M/P, medulla/pelvis. +D. Confocal microscopy images of kidneys from LysM-GFP transgenic reporter mice stained with anti-GFP (green), phalloidin (white), and anti-CD11b (red) after catheterisation with UPEC or PBS control. Left panels: full depth tiles from cortex to pelvis. Right panels: zoom of the region highlighted (yellow). Scale bars 70 mum. +E. Heatmap of mean expression values of neutrophil recruiting chemokines. Point size shows the fraction of cells with non-zero expression. \ No newline at end of file diff --git a/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_15252_embj_2018100811.txt b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_15252_embj_2018100811.txt new file mode 100644 index 0000000..8aaedc2 --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/resources/publications/DOI_10_15252_embj_2018100811.txt @@ -0,0 +1 @@ +FULL TEXT NOT AVAILABLE \ No newline at end of file From d993b0287fdb218a2079fce5dd68acd37427b69e Mon Sep 17 00:00:00 2001 From: Caroline Eastwood Date: Fri, 14 Nov 2025 10:20:17 +0000 Subject: [PATCH 6/9] added a notebook analysis example of 30 CxG datasets --- Notebooks/amica_granularity_showcase.ipynb | 986 ++++++++++++++++++ .../graphs/cxg_annotate/report_generator.py | 51 +- .../cxg_annotate/statistics_generator.py | 112 ++ 3 files changed, 1130 insertions(+), 19 deletions(-) create mode 100644 Notebooks/amica_granularity_showcase.ipynb create mode 100644 cellsem_agent/graphs/cxg_annotate/statistics_generator.py diff --git a/Notebooks/amica_granularity_showcase.ipynb b/Notebooks/amica_granularity_showcase.ipynb new file mode 100644 index 0000000..d31140d --- /dev/null +++ b/Notebooks/amica_granularity_showcase.ipynb @@ -0,0 +1,986 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "fb8d19fc", + "metadata": {}, + "source": [ + "# AMICA: Improving Cell Type Annotation Granularity\n", + "\n", + "This notebook demonstrates the progress of the AMICA (Automated Mapping and Identification of Cell Annotations) agent in improving the granularity of cell type annotations from scientific literature." + ] + }, + { + "cell_type": "markdown", + "id": "c7f92239", + "metadata": {}, + "source": [ + "## 1. The Problem: Inconsistent Annotations\n", + "\n", + "Manually curated cell type annotations often vary in specificity. Authors might use general terms (e.g., \"endothelial cell\") when a more precise term exists (e.g., \"endothelial cell of arteriole\"). This inconsistency makes large-scale data integration and analysis challenging.\n", + "\n", + "**Goal:** Use an AI agent (AMICA) to automatically suggest more granular and contextually appropriate ontology terms based on information extracted from the source publication." + ] + }, + { + "cell_type": "markdown", + "id": "60788ebc", + "metadata": {}, + "source": [ + "## 2. Setup and Data Loading\n", + "\n", + "First, let's install the necessary libraries and load the results from all the `groundings.tsv` files generated by the annotation pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "51c5b5c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (2.3.1)\n", + "Requirement already satisfied: oaklib in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (0.6.23)\n", + "Requirement already satisfied: numpy>=1.26.0 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from pandas) (2.3.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in 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rdflib-shim->funowl>=0.2.0->oaklib) (0.6.1)\n", + "\u001b[33mWARNING: scipy 1.16.0 does not provide the extra 'scipy'\u001b[0m\u001b[33m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pandas oaklib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dd20e120", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully loaded 855 records from 29 datasets.\n" + ] + } + ], + "source": [ + "import os\n", + "import pandas as pd\n", + "from pathlib import Path\n", + "\n", + "# Set the display option to show full content of columns\n", + "pd.set_option('display.max_colwidth', None)\n", + "\n", + "output_dir = Path('../cellsem_agent/graphs/cxg_annotate/resources/output/')\n", + "grounding_files = list(output_dir.glob('**/groundings.tsv'))\n", + "\n", + "all_data = []\n", + "for f in grounding_files:\n", + " try:\n", + " df = pd.read_csv(f, sep='\\t')\n", + " all_data.append(df)\n", + " except Exception as e:\n", + " print(f\"Could not read {f}: {e}\")\n", + "\n", + "if all_data:\n", + " combined_df = pd.concat(all_data, ignore_index=True)\n", + " print(f\"Successfully loaded {len(combined_df)} records from {len(grounding_files)} datasets.\")\n", + "else:\n", + " print(\"No grounding files found or loaded.\")" + ] + }, + { + "cell_type": "markdown", + "id": "93874ba3", + "metadata": {}, + "source": [ + "## 3. Improved Granularity Examples \n", + "\n", + "Improved granularity cases is when AMICA provides a more specific ontology term than the author's original annotation. We can identify these cases by checking if the author's term is an **ancestor** of the agent's term in the Cell Ontology.\n", + "\n", + "For example, `cell` (CL:0000000) is an ancestor of `fibroblast` (CL:0000057). If the author mapped to `cell` and the agent mapped to `fibroblast`, it's an improvement." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "50ac0fbc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully connected to Cell Ontology.\n" + ] + }, + { + "data": { + "text/html": [ + "
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AnnotationFull Name (from paper)Author MappingAgent's Granular MappingDataset
0Prolif-MacNot availablemacrophage (CL:0000235)cycling macrophage (CL:4033076)1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique
1IC-PCintercalated cell–principal cellcolumnar/cuboidal epithelial cell (CL:0000075)renal intercalated cell (CL:0005010)f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique
2Afferent / Efferent Arteriole Endothelial Cellendothelial cell of the afferent/efferent arterioles (EC-AEA)endothelial cell (CL:0000115)endothelial cell of arteriole (CL:1000412)0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique
3Ascending Vasa Recta Endothelial Cellascending vasa recta endothelial cellendothelial cell (CL:0000115)vasa recta ascending limb cell (CL:1001131)0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique
4C-PCcortical principal cellkidney collecting duct principal cell (CL:1001431)kidney cortex collecting duct principal cell (CL:1000714)0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique
..................
123OFFxNot availableOFF-bipolar cell (CL:0000750)OFFx cell (CL:4033036)8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique
124RB1Not availableON-bipolar cell (CL:0000749)rod bipolar cell (CL:0000751)8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique
125B cell cyclingNot availableB cell (CL:0000236)cycling B cell (CL:4033068)2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique
126cycling DCsNot availabledendritic cell (CL:0000451)cycling dendritic cell (CL:4033070)2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique
127cycling gd Tcycling gammadelta T cellgamma-delta T cell (CL:0000798)cycling gamma-delta T cell (CL:4033072)2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique
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" + ], + "text/plain": [ + " Annotation \\\n", + "0 Prolif-Mac \n", + "1 IC-PC \n", + "2 Afferent / Efferent Arteriole Endothelial Cell \n", + "3 Ascending Vasa Recta Endothelial Cell \n", + "4 C-PC \n", + ".. ... \n", + "123 OFFx \n", + "124 RB1 \n", + "125 B cell cycling \n", + "126 cycling DCs \n", + "127 cycling gd T \n", + "\n", + " Full Name (from paper) \\\n", + "0 Not available \n", + "1 intercalated cell–principal cell \n", + "2 endothelial cell of the afferent/efferent arterioles (EC-AEA) \n", + "3 ascending vasa recta endothelial cell \n", + "4 cortical principal cell \n", + ".. ... \n", + "123 Not available \n", + "124 Not available \n", + "125 Not available \n", + "126 Not available \n", + "127 cycling gammadelta T cell \n", + "\n", + " Author Mapping \\\n", + "0 macrophage (CL:0000235) \n", + "1 columnar/cuboidal epithelial cell (CL:0000075) \n", + "2 endothelial cell (CL:0000115) \n", + "3 endothelial cell (CL:0000115) \n", + "4 kidney collecting duct principal cell (CL:1001431) \n", + ".. ... \n", + "123 OFF-bipolar cell (CL:0000750) \n", + "124 ON-bipolar cell (CL:0000749) \n", + "125 B cell (CL:0000236) \n", + "126 dendritic cell (CL:0000451) \n", + "127 gamma-delta T cell (CL:0000798) \n", + "\n", + " Agent's Granular Mapping \\\n", + "0 cycling macrophage (CL:4033076) \n", + "1 renal intercalated cell (CL:0005010) \n", + "2 endothelial cell of arteriole (CL:1000412) \n", + "3 vasa recta ascending limb cell (CL:1001131) \n", + "4 kidney cortex collecting duct principal cell (CL:1000714) \n", + ".. ... \n", + "123 OFFx cell (CL:4033036) \n", + "124 rod bipolar cell (CL:0000751) \n", + "125 cycling B cell (CL:4033068) \n", + "126 cycling dendritic cell (CL:4033070) \n", + "127 cycling gamma-delta T cell (CL:4033072) \n", + "\n", + " Dataset \n", + "0 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique \n", + "1 f801b7a9-80a6-4d09-9161-71474deb58ae_cxg_dataset_unique \n", + "2 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique \n", + "3 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique \n", + "4 0b75c598-0893-4216-afe8-5414cab7739d_cxg_dataset_unique \n", + ".. ... \n", + "123 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique \n", + "124 8623d55f-d91c-41c2-ae68-ed2072fd268d_cxg_dataset_unique \n", + "125 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique \n", + "126 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique \n", + "127 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique \n", + "\n", + "[128 rows x 5 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "from oaklib import get_adapter\n", + "from pathlib import Path\n", + "\n", + "try:\n", + " cl_adapter = get_adapter(\"ols:cl\")\n", + " print(\"Successfully connected to Cell Ontology.\")\n", + "except Exception as e:\n", + " cl_adapter = None\n", + " print(f\"Could not connect to Cell Ontology via OLS: {e}\")\n", + "\n", + "improved_examples = []\n", + "if cl_adapter and 'combined_df' in locals():\n", + " for _, row in combined_df.iterrows():\n", + " author_id = str(row.get('cl_id', ''))\n", + " agent_id = str(row.get('grounding_cl_id', ''))\n", + "\n", + " if author_id and agent_id and author_id != agent_id and 'CL:' in author_id and 'CL:' in agent_id:\n", + " try:\n", + " # Check if the author's term is an ancestor of the agent's more specific term\n", + " ancestors = cl_adapter.ancestors(agent_id, predicates=['rdfs:subClassOf', 'BFO:0000050'])\n", + " if author_id in ancestors:\n", + " # Safely parse the enrichment column to get the full_name\n", + " full_name = \"Not available\"\n", + " try:\n", + " # The enrichment data is stored as a string representation of a dictionary.\n", + " # We need to convert it to a proper dictionary.\n", + " # Using eval is a direct way, but be cautious with untrusted data.\n", + " # A safer method is using json.loads after replacing single quotes.\n", + " enrichment_str = row['enrichment']\n", + " if isinstance(enrichment_str, str):\n", + " enrichment_data = json.loads(enrichment_str.replace(\"'\", \"\\\"\"))\n", + " full_name = enrichment_data.get('full_name', 'Not available')\n", + " except (json.JSONDecodeError, TypeError, SyntaxError):\n", + " # Handles cases where the string is not a valid dict or is NaN\n", + " pass\n", + "\n", + " improved_examples.append({\n", + " \"Annotation\": row['annotation_text'],\n", + " \"Full Name (from paper)\": full_name,\n", + " \"Author Mapping\": f\"{row['cl_label']} ({author_id})\",\n", + " \"Agent's Granular Mapping\": f\"{row['grounding_cl_label']} ({agent_id})\",\n", + " \"Dataset\": Path(row['dataset_name']).name\n", + " })\n", + " except Exception as e:\n", + " # OLS can be flaky, so we continue on error\n", + " pass\n", + "\n", + "if improved_examples:\n", + " improved_df = pd.DataFrame(improved_examples)\n", + " # Reorder columns to be more intuitive\n", + " improved_df = improved_df[[\n", + " \"Annotation\",\n", + " \"Full Name (from paper)\",\n", + " \"Author Mapping\",\n", + " \"Agent's Granular Mapping\",\n", + " \"Dataset\"\n", + " ]]\n", + " display(improved_df)\n", + "else:\n", + " print(\"No clear examples of improved granularity were found.\")" + ] + }, + { + "cell_type": "markdown", + "id": "310565f5", + "metadata": {}, + "source": [ + "## 4. Analysis: Where Can AMICA Improve?\n", + "\n", + " The primary area for improvement is **Regressions**: cases when the agent suggests a *less* specific term than the author. This is tested by checking whether the agent's CL term is an anscestor of the author's CL term.\n", + "\n", + "To improve the agent's performance, test with:\n", + "- Tissue context \n", + "- Species context (very common with immune cells)\n", + "\n", + "(Note: Cases where the agent returns `NO MATCH found` are not treated as failures. Instead, they potential missing CL terms.)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "eaeb7077", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Limitations: Agent Returned a Less Specific Term ---\n" + ] + }, + { + "data": { + "text/html": [ + "
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AnnotationAuthor Granular MappingAgent's General MappingDataset
0retinal bipolar neuron type AON-bipolar cell (CL:0000749)retinal bipolar neuron (CL:0000748)d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique
1retinal bipolar neuron type BOFF-bipolar cell (CL:0000750)retinal bipolar neuron (CL:0000748)d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique
2retinal bipolar neuron type CON-bipolar cell (CL:0000749)retinal bipolar neuron (CL:0000748)d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique
3retinal bipolar neuron type DON-bipolar cell (CL:0000749)retinal bipolar neuron (CL:0000748)d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique
4AntiBplasma cell (CL:0000786)antibody secreting cell (CL:0000946)1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique
...............
172basal2basal cell of epidermis (CL:0002187)basal cell (CL:0000646)f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique
173spinousspinous cell of epidermis (CL:0000649)spinous cell (CL:4052060)f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique
174Macrophagecolon macrophage (CL:0009038)macrophage (CL:0000235)2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique
175acinarpancreatic acinar cell (CL:0002064)acinar cell (CL:0000622)37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique
176acinar_minor_mhcclassIIpancreatic acinar cell (CL:0002064)acinar cell (CL:0000622)37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique
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" + ], + "text/plain": [ + " Annotation Author Granular Mapping \\\n", + "0 retinal bipolar neuron type A ON-bipolar cell (CL:0000749) \n", + "1 retinal bipolar neuron type B OFF-bipolar cell (CL:0000750) \n", + "2 retinal bipolar neuron type C ON-bipolar cell (CL:0000749) \n", + "3 retinal bipolar neuron type D ON-bipolar cell (CL:0000749) \n", + "4 AntiB plasma cell (CL:0000786) \n", + ".. ... ... \n", + "172 basal2 basal cell of epidermis (CL:0002187) \n", + "173 spinous spinous cell of epidermis (CL:0000649) \n", + "174 Macrophage colon macrophage (CL:0009038) \n", + "175 acinar pancreatic acinar cell (CL:0002064) \n", + "176 acinar_minor_mhcclassII pancreatic acinar cell (CL:0002064) \n", + "\n", + " Agent's General Mapping \\\n", + "0 retinal bipolar neuron (CL:0000748) \n", + "1 retinal bipolar neuron (CL:0000748) \n", + "2 retinal bipolar neuron (CL:0000748) \n", + "3 retinal bipolar neuron (CL:0000748) \n", + "4 antibody secreting cell (CL:0000946) \n", + ".. ... \n", + "172 basal cell (CL:0000646) \n", + "173 spinous cell (CL:4052060) \n", + "174 macrophage (CL:0000235) \n", + "175 acinar cell (CL:0000622) \n", + "176 acinar cell (CL:0000622) \n", + "\n", + " Dataset \n", + "0 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique \n", + "1 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique \n", + "2 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique \n", + "3 d5c67a4e-a8d9-456d-a273-fa01adb1b308_cxg_dataset_unique \n", + "4 1873a18a-66fd-4a4d-8277-a872c93f5b59_cxg_dataset_unique \n", + ".. ... \n", + "172 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique \n", + "173 f512b8b6-369d-4a85-a695-116e0806857f_cxg_dataset_unique \n", + "174 2872f4b0-b171-46e2-abc6-befcf6de6306_cxg_dataset_unique \n", + "175 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique \n", + "176 37b21763-7f0f-41ae-9001-60bad6e2841d_cxg_dataset_unique \n", + "\n", + "[177 rows x 4 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "regressions = []\n", + "\n", + "if cl_adapter and 'combined_df' in locals():\n", + " for _, row in combined_df.iterrows():\n", + " author_id = str(row.get('cl_id', ''))\n", + " agent_id = str(row.get('grounding_cl_id', ''))\n", + " \n", + " # Case: Agent provided a less granular term (regression)\n", + " if author_id and agent_id and author_id != agent_id and 'CL:' in author_id and 'CL:' in agent_id:\n", + " try:\n", + " # Get the ancestors of the AUTHOR's term\n", + " ancestors = cl_adapter.ancestors(author_id)\n", + " # Check if the AGENT's term is in the author's ancestor list\n", + " if agent_id in ancestors:\n", + " regressions.append({\n", + " 'Annotation': row['annotation_text'],\n", + " 'Author Granular Mapping': f\"{row['cl_label']} ({author_id})\",\n", + " 'Agent\\'s General Mapping': f\"{row['grounding_cl_label']} ({agent_id})\",\n", + " 'Dataset': Path(row['dataset_name']).name\n", + " })\n", + " except Exception as e:\n", + " # OLS can be flaky, so we continue on error\n", + " pass\n", + "\n", + "print(\"--- Limitations: Agent Returned a Less Specific Term ---\")\n", + "if regressions:\n", + " regressions_df = pd.DataFrame(regressions)\n", + " display(regressions_df)\n", + "else:\n", + " print(\"No clear examples of regressions were found.\")" + ] + }, + { + "cell_type": "markdown", + "id": "f9f7693c", + "metadata": {}, + "source": [ + "## 5. Overall Performance Statistics\n", + "\n", + "To get a quantitative measure of AMICA's performance, we can categorize every annotation in the dataset into one of the following buckets:\n", + "\n", + "- **Improved Granularity**: The agent provided a more specific term (a subclass or `part_of` the author's term).\n", + "- **Exact Match**: The agent's term is identical to the author's term.\n", + "- **Regression**: The agent provided a less specific term (an ancestor of the author's term). The current percentage (22%) does not reflect the full t\n", + "- **Other (unrelated)**: The agent's term and the author's term are valid but unrelated hierarchically.\n", + "- **No Match Found**: The agent could not find a grounding for the annotation. Potential new CL terms.\n", + "\n", + "---\n", + "\n", + "### Note on “Regression”\n", + "\n", + "The current **20.7% regression rate is inflated** by many *false regressions* caused by inconsistencies in the source annotations rather than agent errors.\n", + "\n", + "**Example:** \n", + "In one dataset, the author's 'defined cell set' such as **“Lymphoid_T/NK”** are broad categories but are linked to many highly specific CL terms (e.g., *natural killer cell*, *effector memory T cell*, *activated CD8+ αβ T cell*).\n", + "\n", + "- **Agent’s mapping:** Correctly assigns the logical parent term **CL:0000542 (lymphocyte)**. \n", + "- **Analysis outcome:** Incorrectly flags these as regressions because it compares the agent's CL term to each specific author mapping.\n", + "\n", + "To better measure true agent performance, the author's datasets will be pre-processed in the future to filter cases where the 'author-defined cell set' (the text label) represents a different, broader concept than the 'CL-defined cell set'.\n", + "\n", + "---\n", + "\n", + "This analysis provides a high-level overview of how often the agent adds value, stays consistent, or requires further refinement." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fc8bdc42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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improved_granularity12814.97%
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regression17720.7%
other779.01%
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" + ], + "text/plain": [ + " Count Percentage\n", + "improved_granularity 128 14.97%\n", + "exact_match 401 46.9%\n", + "regression 177 20.7%\n", + "other 77 9.01%\n", + "no_match 56 6.55%\n", + "total 855 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "stats = {\n", + " 'improved_granularity': 0,\n", + " 'exact_match': 0,\n", + " 'regression': 0,\n", + " 'other': 0,\n", + " 'no_match': 0,\n", + " 'total': 0\n", + "}\n", + "\n", + "if cl_adapter and 'combined_df' in locals():\n", + " total_records = len(combined_df)\n", + " stats['total'] = total_records\n", + "\n", + " for _, row in combined_df.iterrows():\n", + " author_id = str(row.get('cl_id', ''))\n", + " agent_id = str(row.get('grounding_cl_id', ''))\n", + "\n", + " # Category: No Match\n", + " if not agent_id or agent_id == 'nan' or 'NO MATCH' in agent_id:\n", + " stats['no_match'] += 1\n", + " continue\n", + " \n", + " # Ensure both are valid CL terms for comparison\n", + " if not author_id or author_id == 'nan' or 'CL:' not in author_id or 'CL:' not in agent_id:\n", + " # If author_id is invalid, we can't compare.\n", + " # We could count this in a separate category, but for now we'll skip comparison.\n", + " continue\n", + "\n", + " # Category: Exact Match\n", + " if author_id == agent_id:\n", + " stats['exact_match'] += 1\n", + " continue\n", + "\n", + " try:\n", + " # Category: Improved Granularity\n", + " # Check if author_id is an ancestor of agent_id (is_a or part_of)\n", + " agent_ancestors = cl_adapter.ancestors(agent_id, predicates=['rdfs:subClassOf', 'BFO:0000050'])\n", + " if author_id in agent_ancestors:\n", + " stats['improved_granularity'] += 1\n", + " continue\n", + "\n", + " # Category: Regression\n", + " # Check if agent_id is an ancestor of author_id\n", + " author_ancestors = cl_adapter.ancestors(author_id, predicates=['rdfs:subClassOf'])\n", + " if agent_id in author_ancestors:\n", + " stats['regression'] += 1\n", + " continue\n", + " \n", + " # If none of the above, it's an 'other' relationship\n", + " stats['other'] += 1\n", + "\n", + " except Exception as e:\n", + " # OLS can be flaky, so we count errors as 'other' for now\n", + " # print(f\"Could not process comparison for {author_id} and {agent_id}: {e}\")\n", + " stats['other'] += 1\n", + "\n", + "\n", + "# Create a DataFrame for displaying the statistics\n", + "stats_df = pd.DataFrame.from_dict(stats, orient='index', columns=['Count'])\n", + "stats_df['Percentage'] = (stats_df['Count'] / stats_df.loc['total', 'Count'] * 100).round(2).astype(str) + '%'\n", + "\n", + "# Remove the total from the percentage calculation display\n", + "stats_df.loc['total', 'Percentage'] = ''\n", + "\n", + "display(stats_df)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cellsem-agent-py3.12", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/cellsem_agent/graphs/cxg_annotate/report_generator.py b/cellsem_agent/graphs/cxg_annotate/report_generator.py index aeb9972..68627cc 100644 --- a/cellsem_agent/graphs/cxg_annotate/report_generator.py +++ b/cellsem_agent/graphs/cxg_annotate/report_generator.py @@ -2,6 +2,7 @@ import pandas as pd from oaklib import get_adapter + def analyze_groundings(output_dir): """ Analyzes all groundings.tsv files in the output directory and generates a report. @@ -25,32 +26,44 @@ def analyze_groundings(output_dir): groundings_file = os.path.join(dataset_path, "groundings.tsv") if os.path.exists(groundings_file): report_lines.append(f"\n## Dataset: {dataset_folder}") - df = pd.read_csv(groundings_file, sep='\t') - + df = pd.read_csv(groundings_file, sep=" ") + improved_count = 0 - + for _, row in df.iterrows(): - author_cl_id = row.get('cl_id') - agent_cl_id = row.get('grounding_cl_id') + author_cl_id = row.get("cl_id") + agent_cl_id = row.get("grounding_cl_id") - if pd.notna(author_cl_id) and pd.notna(agent_cl_id) and author_cl_id != agent_cl_id: + if ( + pd.notna(author_cl_id) + and pd.notna(agent_cl_id) + and author_cl_id != agent_cl_id + ): if cl_adapter: try: # Check if author's term is an ancestor of the agent's term - if author_cl_id in cl_adapter.ancestors(agent_cl_id): + if author_cl_id in cl_adapter.ancestors( + agent_cl_id, + predicates=["rdfs:subClassOf", "BFO:0000050"], + ): improved_count += 1 - good_examples.append({ - "dataset": dataset_folder, - "annotation_text": row['annotation_text'], - "author_mapping": f"{row['cl_label']} ({author_cl_id})", - "agent_mapping": f"{row['grounding_cl_label']} ({agent_cl_id})", - "enrichment": row['enrichment'] - }) + good_examples.append( + { + "dataset": dataset_folder, + "annotation_text": row["annotation_text"], + "author_mapping": f"{row['cl_label']} ({author_cl_id})", + "agent_mapping": f"{row['grounding_cl_label']} ({agent_cl_id})", + "enrichment": row["enrichment"], + } + ) except Exception as e: - print(f"Could not process ontology check for {author_cl_id} and {agent_cl_id}: {e}") - + print( + f"Could not process ontology check for {author_cl_id} and {agent_cl_id}: {e}" + ) - report_lines.append(f"Found {improved_count} instances of improved granularity.") + report_lines.append( + f"Found {improved_count} instances of improved granularity." + ) report_lines.append("\n# Good Examples of Improved Granularity") for ex in good_examples: @@ -60,14 +73,14 @@ def analyze_groundings(output_dir): report_lines.append(f"- **Agent's Mapping:** {ex['agent_mapping']}") report_lines.append(f"- **Enrichment Info:** `{ex['enrichment']}`") - report_content = "\n".join(report_lines) report_file_path = os.path.join(output_dir, "granularity_report.md") with open(report_file_path, "w") as f: f.write(report_content) - + print(f"Report generated at {report_file_path}") + if __name__ == "__main__": current_dir = os.path.dirname(os.path.abspath(__file__)) output_directory = os.path.join(current_dir, "resources", "output") diff --git a/cellsem_agent/graphs/cxg_annotate/statistics_generator.py b/cellsem_agent/graphs/cxg_annotate/statistics_generator.py new file mode 100644 index 0000000..53c2abd --- /dev/null +++ b/cellsem_agent/graphs/cxg_annotate/statistics_generator.py @@ -0,0 +1,112 @@ +import os +import pandas as pd +from pathlib import Path +from oaklib import get_adapter + + +def generate_statistics(): + """ + Analyzes all groundings.tsv files to generate performance statistics. + """ + output_dir = Path(__file__).parent.joinpath("resources", "output") + grounding_files = list(output_dir.glob("**/groundings.tsv")) + + if not grounding_files: + print(f"No grounding files found in {output_dir}") + return + + all_data = [pd.read_csv(f, sep="\\t") for f in grounding_files] + combined_df = pd.concat(all_data, ignore_index=True) + total_annotations = len(combined_df) + + if total_annotations == 0: + print("No annotations found to analyze.") + return + + print( + f"Analyzing {total_annotations} total annotations from {len(grounding_files)} datasets..." + ) + + try: + cl_adapter = get_adapter("ols:cl") + print("Successfully connected to Cell Ontology.") + except Exception as e: + cl_adapter = None + print(f"Could not connect to Cell Ontology via OLS: {e}") + print( + "Cannot determine 'Improved' or 'Suboptimal' status without ontology access." + ) + return + + improved_count = 0 + identical_count = 0 + no_match_count = 0 + less_specific_count = 0 + other_suboptimal_count = 0 + + for _, row in combined_df.iterrows(): + author_id = str(row.get("cl_id", "")) + agent_id = str(row.get("grounding_cl_id", "")) + + # Handle cases where either ID is missing or not a valid CL ID string upfront + if ( + pd.isna(row.get("cl_id")) + or pd.isna(row.get("grounding_cl_id")) + or not author_id + or not agent_id + ): + other_suboptimal_count += 1 + continue + + # Category 1: No Match Found by Agent + if "NO MATCH" in agent_id: + no_match_count += 1 + continue + + # Category 2: Identical Mapping + if author_id == agent_id: + identical_count += 1 + continue + + # For hierarchy checks, both must be valid CL IDs + if "CL:" not in author_id or "CL:" not in agent_id: + other_suboptimal_count += 1 + continue + + try: + # Category 3: Improved Granularity (Author's term is an ancestor of the agent's term) + if author_id in cl_adapter.ancestors(agent_id): + improved_count += 1 + # Category 4: Less Specific Mapping (Agent's term is an ancestor of the author's term) + elif agent_id in cl_adapter.ancestors(author_id): + less_specific_count += 1 + # All other cases (e.g., different branches of the ontology) + else: + other_suboptimal_count += 1 + except Exception: + # If any ontology lookup fails, count it as other/suboptimal + other_suboptimal_count += 1 + + print("\n--- AMICA Performance Statistics ---") + if total_annotations > 0: + improved_percent = (improved_count / total_annotations) * 100 + identical_percent = (identical_count / total_annotations) * 100 + no_match_percent = (no_match_count / total_annotations) * 100 + less_specific_percent = (less_specific_count / total_annotations) * 100 + other_suboptimal_percent = (other_suboptimal_count / total_annotations) * 100 + + print(f"\nTotal Annotations Analyzed: {total_annotations}") + print("-" * 45) + print(f"Improved Granularity: {improved_count} ({improved_percent:.2f}%)") + print(f"Identical Mapping: {identical_count} ({identical_percent:.2f}%)") + print( + f"Less Specific Mapping: {less_specific_count} ({less_specific_percent:.2f}%)" + ) + print(f"No Match Found: {no_match_count} ({no_match_percent:.2f}%)") + print( + f"Other (e.g., different branch): {other_suboptimal_count} ({other_suboptimal_percent:.2f}%)" + ) + + +if __name__ == "__main__": + generate_statistics() From 0a361cd3c779faa60b88c4c5d2f4cfacabb57846 Mon Sep 17 00:00:00 2001 From: Caroline Eastwood Date: Fri, 14 Nov 2025 10:24:42 +0000 Subject: [PATCH 7/9] Update amica_granularity_showcase.ipynb --- Notebooks/amica_granularity_showcase.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/Notebooks/amica_granularity_showcase.ipynb b/Notebooks/amica_granularity_showcase.ipynb index d31140d..2d8c1d3 100644 --- a/Notebooks/amica_granularity_showcase.ipynb +++ b/Notebooks/amica_granularity_showcase.ipynb @@ -570,6 +570,7 @@ "To improve the agent's performance, test with:\n", "- Tissue context \n", "- Species context (very common with immune cells)\n", + "- Pre-process author's datasets to filter cases where the 'author-defined cell set' (the text label) represents a different, broader concept than the 'CL-defined cell set'. \n", "\n", "(Note: Cases where the agent returns `NO MATCH found` are not treated as failures. Instead, they potential missing CL terms.)" ] @@ -788,8 +789,8 @@ "\n", "- **Improved Granularity**: The agent provided a more specific term (a subclass or `part_of` the author's term).\n", "- **Exact Match**: The agent's term is identical to the author's term.\n", - "- **Regression**: The agent provided a less specific term (an ancestor of the author's term). The current percentage (22%) does not reflect the full t\n", - "- **Other (unrelated)**: The agent's term and the author's term are valid but unrelated hierarchically.\n", + "- **Regression**: The agent provided a less specific term (an ancestor of the author's term).\n", + "- **Other (unrelated)**: The agent's term and the author's term are unrelated hierarchically.\n", "- **No Match Found**: The agent could not find a grounding for the annotation. Potential new CL terms.\n", "\n", "---\n", From 3c121f3745811c5b51713703ec7e55b51168b044 Mon Sep 17 00:00:00 2001 From: Caroline Eastwood Date: Thu, 11 Dec 2025 10:15:07 +0000 Subject: [PATCH 8/9] updated the notebook to include "broad term" filtering results --- Notebooks/amica_granularity_showcase.ipynb | 425 +++++++++++++++++++++ 1 file changed, 425 insertions(+) diff --git a/Notebooks/amica_granularity_showcase.ipynb b/Notebooks/amica_granularity_showcase.ipynb index 2d8c1d3..e9aa5e2 100644 --- a/Notebooks/amica_granularity_showcase.ipynb +++ b/Notebooks/amica_granularity_showcase.ipynb @@ -961,6 +961,431 @@ "\n", "display(stats_df)" ] + }, + { + "cell_type": "markdown", + "id": "3ff195e1", + "metadata": {}, + "source": [ + "## 6. In-depth Analysis of Regressions and \"Other\" Mismatches (Filtered)\n", + "\n", + "To better understand the agent's performance, we can apply the same filtering logic used in the `filtered_data_stats.py` script. This involves excluding annotations that were originally labeled as 'broad term' or 'overlaps' in the ground truth data. By removing this noise, we can get a clearer picture of true regressions and other types of mismatches.\n", + "\n", + "### Filtered Regression and \"Other\" Cases\n", + "First, we'll define the function to read the ground truth data and then process the annotations to find the specific examples for each category." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a1a4b602", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ Connected to Cell Ontology (OLS).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages/eutils/__init__.py:4: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n" + ] + } + ], + "source": [ + "# --- CELL 1: Imports & Setup ---\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from pathlib import Path\n", + "from oaklib import get_adapter\n", + "\n", + "# Initialize Ontology Adapter\n", + "# (This might take a moment to connect)\n", + "try:\n", + " cl_adapter = get_adapter(\"ols:cl\")\n", + " print(\"✅ Connected to Cell Ontology (OLS).\")\n", + "except Exception as e:\n", + " print(f\"⚠️ Could not connect to OLS: {e}\")\n", + " cl_adapter = None" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2ef2e241", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input Directory: /Users/ce12/Documents/GitHub/cellsem-agent/cellsem_agent/graphs/cxg_annotate/resources/input/matrix_outputs_30\n", + "Output Directory: /Users/ce12/Documents/GitHub/cellsem-agent/cellsem_agent/graphs/cxg_annotate/resources/output\n" + ] + } + ], + "source": [ + "# --- CELL 2: Configuration ---\n", + "\n", + "# Adjust this path to point to your 'resources' folder relative to this notebook\n", + "# Based on your previous messages, it seems to be here:\n", + "BASE_PATH = Path(\"../cellsem_agent/graphs/cxg_annotate/resources\")\n", + "\n", + "INPUT_DIR = BASE_PATH / \"input/matrix_outputs_30\"\n", + "OUTPUT_DIR = BASE_PATH / \"output\"\n", + "\n", + "print(f\"Input Directory: {INPUT_DIR.resolve()}\")\n", + "print(f\"Output Directory: {OUTPUT_DIR.resolve()}\")\n", + "\n", + "# Verification check\n", + "if not INPUT_DIR.exists() or not OUTPUT_DIR.exists():\n", + " print(\"⚠️ WARNING: One or more directories do not exist. Please check the path.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0ac9d527", + "metadata": {}, + "outputs": [], + "source": [ + "# --- CELL 3: Helper Functions ---\n", + "\n", + "def get_match_type_map(dataset_folder_name):\n", + " \"\"\"\n", + " Reads the ground truth input file to get 'match_type' for each (text, id) pair.\n", + " Handles column mapping (author_cell_type -> annotation_text).\n", + " \"\"\"\n", + " input_path = INPUT_DIR / f\"{dataset_folder_name}.tsv\"\n", + " if not input_path.exists():\n", + " input_path = INPUT_DIR / f\"{dataset_folder_name}.csv\"\n", + " \n", + " if input_path.exists():\n", + " try:\n", + " df = pd.read_csv(input_path, sep='\\t')\n", + " \n", + " # 1. Normalize column names (Input specific -> Standard)\n", + " if 'author_cell_type' in df.columns: df['annotation_text'] = df['author_cell_type']\n", + " if 'CL_ID' in df.columns: df['cl_id'] = df['CL_ID']\n", + " \n", + " # 2. Check if required columns exist\n", + " if 'match_type' not in df.columns: return {}\n", + "\n", + " # 3. Clean and Normalize values\n", + " df['cl_id'] = df['cl_id'].astype(str).str.strip()\n", + " df['annotation_text'] = df['annotation_text'].astype(str).str.strip()\n", + " # Convert \"Broad Term\" -> \"broad_term\"\n", + " df['match_type'] = df['match_type'].astype(str).str.lower().str.replace(' ', '_')\n", + " \n", + " # Return Dictionary: {(text, id) : match_type}\n", + " return df.set_index(['annotation_text', 'cl_id'])['match_type'].to_dict()\n", + " except Exception as e:\n", + " # print(f\"Error reading input for {dataset_folder_name}: {e}\")\n", + " return {}\n", + " return {}\n", + "\n", + "def calculate_stats(df, match_type_map, filter_noisy=False):\n", + " \"\"\"\n", + " Calculates performance stats. \n", + " If filter_noisy=True, it skips 'broad_term' and 'overlaps'.\n", + " \"\"\"\n", + " stats = {\"Improved\": 0, \"Exact\": 0, \"Regression\": 0, \"No Match\": 0, \"Other\": 0, \"Total\": 0}\n", + " \n", + " for _, row in df.iterrows():\n", + " author_cl = str(row.get('cl_id', '')).strip()\n", + " # Get raw agent string to check for 'NO MATCH' text\n", + " agent_raw = str(row.get('grounding_cl_id', '')).strip()\n", + " text = str(row.get('annotation_text', '')).strip()\n", + " \n", + " # Skip invalid author entries\n", + " if not author_cl or author_cl.lower() == 'nan': continue\n", + "\n", + " # --- FILTERING STEP ---\n", + " match_type = match_type_map.get((text, author_cl), \"unknown\")\n", + " \n", + " if filter_noisy:\n", + " if match_type in ['broad_term', 'overlaps']:\n", + " continue # Skip this row completely\n", + " \n", + " stats[\"Total\"] += 1\n", + "\n", + " # --- CATEGORIZATION ---\n", + " \n", + " # 1. No Match Found\n", + " if not agent_raw or agent_raw.lower() in ['nan', 'none'] or 'no match' in agent_raw.lower():\n", + " stats[\"No Match\"] += 1\n", + " continue\n", + " \n", + " # 2. Exact Match\n", + " if author_cl == agent_raw:\n", + " stats[\"Exact\"] += 1\n", + " continue\n", + " \n", + " # 3. Ontology Checks\n", + " try:\n", + " # Improvement (Agent is Child OR Part_of Author)\n", + " if cl_adapter and author_cl in cl_adapter.ancestors(agent_raw, predicates=['rdfs:subClassOf', 'BFO:0000050']):\n", + " stats[\"Improved\"] += 1\n", + " continue\n", + " # Regression (Agent is Parent of Author - strict is_a)\n", + " if cl_adapter and agent_raw in cl_adapter.ancestors(author_cl, predicates=['rdfs:subClassOf']):\n", + " stats[\"Regression\"] += 1\n", + " continue\n", + " except:\n", + " pass\n", + " \n", + " # 4. Other\n", + " stats[\"Other\"] += 1\n", + " \n", + " return stats" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "da8207b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 29 datasets. Processing...\n", + "✅ Analysis Complete.\n", + "✅ Analysis Complete.\n" + ] + } + ], + "source": [ + "# --- CELL 4: Main Analysis Loop ---\n", + "\n", + "raw_results = []\n", + "clean_results = []\n", + "\n", + "grounding_files = list(OUTPUT_DIR.glob('**/groundings.tsv'))\n", + "print(f\"Found {len(grounding_files)} datasets. Processing...\")\n", + "\n", + "for file_path in grounding_files:\n", + " dataset_name = file_path.parent.name\n", + " try:\n", + " df = pd.read_csv(file_path, sep='\\t')\n", + " mapping = get_match_type_map(dataset_name)\n", + " \n", + " # 1. Calculate RAW stats (All data, no filtering)\n", + " raw = calculate_stats(df, mapping, filter_noisy=False)\n", + " for cat, count in raw.items():\n", + " if cat != \"Total\":\n", + " raw_results.append({\n", + " \"Dataset\": dataset_name, \n", + " \"Category\": cat, \n", + " \"Count\": count, \n", + " \"Scope\": \"Raw (All Data)\"\n", + " })\n", + " \n", + " # 2. Calculate CLEAN stats (Filter out broad terms)\n", + " clean = calculate_stats(df, mapping, filter_noisy=True)\n", + " for cat, count in clean.items():\n", + " if cat != \"Total\":\n", + " clean_results.append({\n", + " \"Dataset\": dataset_name, \n", + " \"Category\": cat, \n", + " \"Count\": count, \n", + " \"Scope\": \"Cleaned (High Quality)\"\n", + " })\n", + " \n", + " except Exception as e:\n", + " print(f\"Error processing {dataset_name}: {e}\")\n", + "\n", + "print(\"✅ Analysis Complete.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9b832a3d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Summary Table (Aggregate Percentages):\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ScopeCleaned (High Quality)Raw (All Data)
Category  
Exact51.50%46.90%
Improved16.73%14.97%
Regression13.20%20.70%
No Match7.32%6.55%
Other11.24%10.88%
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# --- CELL 5: Visualization ---\n", + "\n", + "import seaborn as sns\n", + "\n", + "# Combine data\n", + "viz_df = pd.DataFrame(raw_results + clean_results)\n", + "\n", + "# Group by Scope and Category to get aggregate sums\n", + "summary_df = viz_df.groupby(['Scope', 'Category'])['Count'].sum().reset_index()\n", + "\n", + "# Calculate percentages based on the total for that Scope\n", + "total_raw = summary_df[summary_df['Scope'] == 'Raw (All Data)']['Count'].sum()\n", + "total_clean = summary_df[summary_df['Scope'] == 'Cleaned (High Quality)']['Count'].sum()\n", + "\n", + "def get_pct(row):\n", + " total = total_raw if row['Scope'] == 'Raw (All Data)' else total_clean\n", + " return (row['Count'] / total) * 100\n", + "\n", + "summary_df['Percentage'] = summary_df.apply(get_pct, axis=1)\n", + "\n", + "# --- PLOTTING ---\n", + "plt.figure(figsize=(12, 6))\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "# Custom order for logical flow\n", + "order = [\"Exact\", \"Improved\", \"Regression\", \"No Match\", \"Other\"]\n", + "\n", + "# Create Bar Chart\n", + "ax = sns.barplot(\n", + " data=summary_df, \n", + " x='Category', \n", + " y='Percentage', \n", + " hue='Scope', \n", + " order=order,\n", + " palette={\"Raw (All Data)\": \"#a1c9f4\", \"Cleaned (High Quality)\": \"#005a32\"}\n", + ")\n", + "\n", + "plt.title(\"Impact of Filtering 'Broad Terms' on Agent Performance\", fontsize=16)\n", + "plt.ylabel(\"Percentage of Annotations (%)\", fontsize=12)\n", + "plt.xlabel(None)\n", + "plt.legend(title=\"Data Scope\")\n", + "\n", + "# Add percentage text labels on top of bars\n", + "for container in ax.containers:\n", + " ax.bar_label(container, fmt='%.1f%%', padding=3, fontsize=10)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "# --- TABLE DISPLAY ---\n", + "print(\"\\nSummary Table (Aggregate Percentages):\")\n", + "pivot_table = summary_df.pivot(index='Category', columns='Scope', values='Percentage').reindex(order)\n", + "display(pivot_table.style.format(\"{:.2f}%\").background_gradient(cmap=\"Greens\", axis=None))" + ] } ], "metadata": { From 0482256585892a67a3f9663664673393a1d1d45a Mon Sep 17 00:00:00 2001 From: Caroline Eastwood Date: Thu, 11 Dec 2025 12:44:30 +0000 Subject: [PATCH 9/9] added tissue context results --- Notebooks/amica_granularity_showcase.ipynb | 881 +++++++++++++++++++-- 1 file changed, 799 insertions(+), 82 deletions(-) diff --git a/Notebooks/amica_granularity_showcase.ipynb b/Notebooks/amica_granularity_showcase.ipynb index e9aa5e2..7bb30e2 100644 --- a/Notebooks/amica_granularity_showcase.ipynb +++ b/Notebooks/amica_granularity_showcase.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "51c5b5c4", "metadata": {}, "outputs": [ @@ -112,6 +112,74 @@ "Requirement already satisfied: pyjsg>=0.11.6 in 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pygments>=2.7.2 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from pytest>=2.8.1->pytest-logging<2016.0.0,>=2015.11.4->prefixcommons>=0.1.12->linkml-runtime>=1.5.3->oaklib) (2.19.2)\n", "Requirement already satisfied: SQLAlchemy-Utils<0.39.0,>=0.38.2 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from semsql>=0.3.1->oaklib) (0.38.3)\n", "Requirement already satisfied: sphinx>=4.0 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from sphinx-click>=6.0.0->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (8.2.3)\n", "Requirement already satisfied: docutils in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from sphinx-click>=6.0.0->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (0.21.2)\n", @@ -216,15 +294,16 @@ "Requirement already satisfied: arrow>=0.15.0 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from isoduration->jsonschema[format]>=4.0.0->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (1.3.0)\n", "Requirement already satisfied: types-python-dateutil>=2.8.10 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from arrow>=0.15.0->isoduration->jsonschema[format]>=4.0.0->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (2.9.0.20250708)\n", "Requirement already satisfied: et-xmlfile in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from openpyxl->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (2.0.0)\n", - "Requirement already satisfied: sortedcontainers in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from pytrie->curies>=0.6.6->oaklib) (2.4.0)\n", - "Requirement already satisfied: rdflib-jsonld==0.6.1 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from rdflib-shim->funowl>=0.2.0->oaklib) (0.6.1)\n", - "\u001b[33mWARNING: scipy 1.16.0 does not provide the extra 'scipy'\u001b[0m\u001b[33m\n", - "\u001b[0mRequirement already satisfied: arrow>=0.15.0 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from isoduration->jsonschema[format]>=4.0.0->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (1.3.0)\n", + "Requirement already satisfied: wrapt<2,>=1.10 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from deprecated->linkml-runtime>=1.5.3->oaklib) (1.17.2)\n", + "Requirement already satisfied: arrow>=0.15.0 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from isoduration->jsonschema[format]>=4.0.0->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (1.3.0)\n", "Requirement already satisfied: types-python-dateutil>=2.8.10 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from arrow>=0.15.0->isoduration->jsonschema[format]>=4.0.0->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (2.9.0.20250708)\n", "Requirement already satisfied: et-xmlfile in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from openpyxl->linkml>1.7.10->sssom<0.5.0,>=0.4.4->oaklib) (2.0.0)\n", "Requirement already satisfied: sortedcontainers in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from pytrie->curies>=0.6.6->oaklib) (2.4.0)\n", "Requirement already satisfied: rdflib-jsonld==0.6.1 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from rdflib-shim->funowl>=0.2.0->oaklib) (0.6.1)\n", "\u001b[33mWARNING: scipy 1.16.0 does not provide the extra 'scipy'\u001b[0m\u001b[33m\n", + "\u001b[0mRequirement already satisfied: sortedcontainers in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from pytrie->curies>=0.6.6->oaklib) (2.4.0)\n", + "Requirement already satisfied: rdflib-jsonld==0.6.1 in /Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages (from rdflib-shim->funowl>=0.2.0->oaklib) (0.6.1)\n", + "\u001b[33mWARNING: scipy 1.16.0 does not provide the extra 'scipy'\u001b[0m\u001b[33m\n", "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n", "Note: you may need to restart the kernel to use updated packages.\n" ] @@ -236,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "dd20e120", "metadata": {}, "outputs": [ @@ -256,7 +335,7 @@ "# Set the display option to show full content of columns\n", "pd.set_option('display.max_colwidth', None)\n", "\n", - "output_dir = Path('../cellsem_agent/graphs/cxg_annotate/resources/output/')\n", + "output_dir = Path('../cellsem_agent/graphs/cxg_annotate/resources/output/raw_output')\n", "grounding_files = list(output_dir.glob('**/groundings.tsv'))\n", "\n", "all_data = []\n", @@ -288,10 +367,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "id": "50ac0fbc", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "/Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages/eutils/__init__.py:4: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n", + "/Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages/eutils/__init__.py:4: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " import pkg_resources\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -577,7 +668,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "eaeb7077", "metadata": {}, "outputs": [ @@ -795,7 +886,7 @@ "\n", "---\n", "\n", - "### Note on “Regression”\n", + "### Note on “less specific mappings”\n", "\n", "The current **20.7% regression rate is inflated** by many *false regressions* caused by inconsistencies in the source annotations rather than agent errors.\n", "\n", @@ -814,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "fc8bdc42", "metadata": {}, "outputs": [ @@ -977,32 +1068,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "a1a4b602", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "✅ Connected to Cell Ontology (OLS).\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/ce12/Documents/GitHub/cellsem-agent/.venv/lib/python3.12/site-packages/eutils/__init__.py:4: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " import pkg_resources\n" - ] } ], "source": [ @@ -1024,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "2ef2e241", "metadata": {}, "outputs": [ @@ -1032,8 +1107,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Input Directory: /Users/ce12/Documents/GitHub/cellsem-agent/cellsem_agent/graphs/cxg_annotate/resources/input/matrix_outputs_30\n", - "Output Directory: /Users/ce12/Documents/GitHub/cellsem-agent/cellsem_agent/graphs/cxg_annotate/resources/output\n" + "Input Directory: /Users/ce12/Documents/GitHub/cellsem-agent/cellsem_agent/graphs/cxg_annotate/resources/output/pandasaurus_cxg_outputs_30\n", + "Output Directory: /Users/ce12/Documents/GitHub/cellsem-agent/cellsem_agent/graphs/cxg_annotate/resources/output/raw_output\n" ] } ], @@ -1044,8 +1119,8 @@ "# Based on your previous messages, it seems to be here:\n", "BASE_PATH = Path(\"../cellsem_agent/graphs/cxg_annotate/resources\")\n", "\n", - "INPUT_DIR = BASE_PATH / \"input/matrix_outputs_30\"\n", - "OUTPUT_DIR = BASE_PATH / \"output\"\n", + "INPUT_DIR = BASE_PATH / \"output/pandasaurus_cxg_outputs_30\"\n", + "OUTPUT_DIR = BASE_PATH / \"output/raw_output\"\n", "\n", "print(f\"Input Directory: {INPUT_DIR.resolve()}\")\n", "print(f\"Output Directory: {OUTPUT_DIR.resolve()}\")\n", @@ -1214,13 +1289,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "9b832a3d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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ScopeCleaned (High Quality)Raw (All Data)Cleaned (High Quality)Raw (All Data)
Category
Exact51.50%46.90%Exact53.11%46.90%
Improved16.73%14.97%Improved17.30%14.97%
Regression13.20%20.70%Regression12.43%20.70%
No Match7.32%6.55%No Match7.57%6.55%
Other11.24%10.88%Other9.59%10.88%
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -1386,6 +1461,648 @@ "pivot_table = summary_df.pivot(index='Category', columns='Scope', values='Percentage').reindex(order)\n", "display(pivot_table.style.format(\"{:.2f}%\").background_gradient(cmap=\"Greens\", axis=None))" ] + }, + { + "cell_type": "markdown", + "id": "a18be62e", + "metadata": {}, + "source": [ + "## 7. Effect of Tissue Context on Problematic Annotations\n", + "\n", + "To validate the tissue context enhancement, we took **147 problematic annotations** (92 regressions + 55 other errors from the cleaned dataset) and reran AMICA with tissue context enabled.\n", + "\n", + "### Experimental Design\n", + "- **Baseline (Cleaned)**: The same 147 annotations from the original cleaned output\n", + "- **Enhanced (Cleaned + Tissue Context)**: Those same 147 annotations reprocessed with tissue context\n", + "\n", + "**Key Question:** Does adding tissue context reduce regressions and improve annotation quality for these problematic cases?\n", + "\n", + "**Note:** We're comparing the same 147 annotations before and after tissue context, NOT comparing raw vs cleaned (since these annotations were already filtered - they passed the cleaning step but were still problematic)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "a73cbac7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 23 datasets with tissue context. Processing...\n", + "✅ Tissue Context analysis complete. Found 147 problematic annotations to track.\n" + ] + } + ], + "source": [ + "# --- CELL 7.1: Load Tissue Context Results and Identify Comparison Set ---\n", + "\n", + "# Path to the tissue context output\n", + "TISSUE_CONTEXT_OUTPUT_DIR = BASE_PATH / \"output/output_tissue_context\"\n", + "\n", + "# First, collect all the problematic annotations that were rerun with tissue context\n", + "# These are our comparison set - we need to find these same annotations in Raw and Cleaned outputs\n", + "problematic_annotations = [] # Will store (annotation_text, dataset_name, cl_id) tuples\n", + "\n", + "tissue_context_results = []\n", + "grounding_files_tc = list(TISSUE_CONTEXT_OUTPUT_DIR.glob('**/groundings.tsv'))\n", + "\n", + "print(f\"Found {len(grounding_files_tc)} datasets with tissue context. Processing...\")\n", + "\n", + "for file_path in grounding_files_tc:\n", + " dataset_name = file_path.parent.name\n", + " try:\n", + " df = pd.read_csv(file_path, sep='\\t')\n", + " \n", + " # Track which annotations are in the tissue context set\n", + " for _, row in df.iterrows():\n", + " problematic_annotations.append({\n", + " 'annotation_text': str(row.get('annotation_text', '')).strip(),\n", + " 'dataset_name': dataset_name,\n", + " 'cl_id': str(row.get('cl_id', '')).strip()\n", + " })\n", + " \n", + " # Use the pandasaurus input for match_type mapping\n", + " mapping = get_match_type_map(dataset_name)\n", + " \n", + " # Calculate stats with filtering (tissue context was only applied to cleaned data)\n", + " tc_stats = calculate_stats(df, mapping, filter_noisy=True)\n", + " for cat, count in tc_stats.items():\n", + " if cat != \"Total\":\n", + " tissue_context_results.append({\n", + " \"Dataset\": dataset_name, \n", + " \"Category\": cat, \n", + " \"Count\": count, \n", + " \"Scope\": \"Cleaned + Tissue Context\"\n", + " })\n", + " except Exception as e:\n", + " print(f\"Error processing {dataset_name}: {e}\")\n", + "\n", + "print(f\"✅ Tissue Context analysis complete. Found {len(problematic_annotations)} problematic annotations to track.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "42e2cabd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting the same 147 annotations from raw output (baseline - before tissue context)...\n", + "✅ Extracted 147 baseline annotations from AMICA's original output\n" + ] + } + ], + "source": [ + "# --- CELL 7.2: Extract Same Annotations from Raw Output (Baseline - Before Tissue Context) ---\n", + "\n", + "# Create a lookup set for fast checking\n", + "prob_set = {(item['annotation_text'], item['dataset_name'], item['cl_id']) for item in problematic_annotations}\n", + "\n", + "print(f\"Extracting the same {len(prob_set)} annotations from raw output (baseline - before tissue context)...\")\n", + "\n", + "# Get AMICA's original output for these same 147 annotations\n", + "# This is what AMICA produced WITHOUT tissue context\n", + "baseline_results = []\n", + "\n", + "grounding_files = list(OUTPUT_DIR.glob('**/groundings.tsv'))\n", + "\n", + "for file_path in grounding_files:\n", + " dataset_name = file_path.parent.name\n", + " try:\n", + " df = pd.read_csv(file_path, sep='\\t')\n", + " \n", + " # Filter to only the problematic annotations\n", + " df['annotation_text_clean'] = df['annotation_text'].astype(str).str.strip()\n", + " df['cl_id_clean'] = df['cl_id'].astype(str).str.strip()\n", + " \n", + " # Create a mask for rows that are in our problematic set\n", + " mask = df.apply(\n", + " lambda row: (row['annotation_text_clean'], dataset_name, row['cl_id_clean']) in prob_set,\n", + " axis=1\n", + " )\n", + " \n", + " df_subset = df[mask].copy()\n", + " \n", + " if len(df_subset) > 0:\n", + " # Get the match_type mapping from pandasaurus\n", + " mapping = get_match_type_map(dataset_name)\n", + " \n", + " # Calculate stats using the same categorization logic as tissue context\n", + " # This shows what AMICA originally produced for these 147 annotations\n", + " baseline_stats = calculate_stats(df_subset, mapping, filter_noisy=True)\n", + " for cat, count in baseline_stats.items():\n", + " if cat != \"Total\":\n", + " baseline_results.append({\n", + " \"Dataset\": dataset_name,\n", + " \"Category\": cat,\n", + " \"Count\": count,\n", + " \"Scope\": \"Before Tissue Context\"\n", + " })\n", + " except Exception as e:\n", + " print(f\"Error processing {dataset_name}: {e}\")\n", + "\n", + "print(f\"✅ Extracted {sum([r['Count'] for r in baseline_results])} baseline annotations from AMICA's original output\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f59cabfa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "📊 Impact of Tissue Context on 147 Problematic Annotations\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ScopeBefore Tissue ContextAfter Tissue Context
Category  
Exact0.00%9.52%
Improved0.00%2.72%
Regression62.59%48.30%
No Match0.00%2.72%
Other37.41%36.73%
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "✨ Impact of Tissue Context:\n", + " • Regression Rate: 62.59% → 48.30%\n", + " • Absolute Reduction: 14.29 percentage points\n", + " • Relative Reduction: 22.8%\n", + " • Regression Count: 92 → 70 (22 fewer regressions)\n" + ] + } + ], + "source": [ + "# --- CELL 7.3: Before vs After Tissue Context Comparison ---\n", + "\n", + "# Rename tissue context results for clarity\n", + "tissue_context_results_renamed = [\n", + " {**item, 'Scope': 'After Tissue Context'} \n", + " for item in tissue_context_results\n", + "]\n", + "\n", + "# Combine baseline and tissue context results\n", + "comparison_df = pd.DataFrame(baseline_results + tissue_context_results_renamed)\n", + "\n", + "# Aggregate by Scope and Category\n", + "comparison_summary = comparison_df.groupby(['Scope', 'Category'])['Count'].sum().reset_index()\n", + "\n", + "# Calculate percentages for each scope\n", + "def get_comparison_pct(row):\n", + " scope_total = comparison_summary[comparison_summary['Scope'] == row['Scope']]['Count'].sum()\n", + " return (row['Count'] / scope_total) * 100\n", + "\n", + "comparison_summary['Percentage'] = comparison_summary.apply(get_comparison_pct, axis=1)\n", + "\n", + "# Display the comparison table\n", + "print(\"\\n📊 Impact of Tissue Context on 147 Problematic Annotations\\n\")\n", + "\n", + "pivot_comparison = comparison_summary.pivot(index='Category', columns='Scope', values='Percentage')\n", + "# Reorder columns\n", + "pivot_comparison = pivot_comparison[['Before Tissue Context', 'After Tissue Context']].reindex(order)\n", + "display(pivot_comparison.style.format(\"{:.2f}%\").background_gradient(cmap=\"RdYlGn_r\", axis=None))\n", + "\n", + "# Calculate improvement metrics\n", + "before_reg = comparison_summary[(comparison_summary['Scope'] == 'Before Tissue Context') & \n", + " (comparison_summary['Category'] == 'Regression')]['Percentage'].values\n", + "after_reg = comparison_summary[(comparison_summary['Scope'] == 'After Tissue Context') & \n", + " (comparison_summary['Category'] == 'Regression')]['Percentage'].values\n", + "\n", + "# Handle case where category might not exist\n", + "before_reg = before_reg[0] if len(before_reg) > 0 else 0\n", + "after_reg = after_reg[0] if len(after_reg) > 0 else 0\n", + "\n", + "regression_reduction = before_reg - after_reg\n", + "\n", + "print(f\"\\n✨ Impact of Tissue Context:\")\n", + "print(f\" • Regression Rate: {before_reg:.2f}% → {after_reg:.2f}%\")\n", + "if before_reg > 0:\n", + " print(f\" • Absolute Reduction: {regression_reduction:.2f} percentage points\")\n", + " print(f\" • Relative Reduction: {(regression_reduction/before_reg)*100:.1f}%\")\n", + " \n", + " # Calculate actual counts\n", + " before_total = comparison_summary[comparison_summary['Scope'] == 'Before Tissue Context']['Count'].sum()\n", + " before_count = int(before_reg * before_total / 100)\n", + " after_count = int(after_reg * before_total / 100)\n", + " count_reduction = before_count - after_count\n", + " \n", + " print(f\" • Regression Count: {before_count} → {after_count} ({count_reduction} fewer regressions)\")\n", + "else:\n", + " print(f\" • Absolute Reduction: {regression_reduction:.2f} percentage points\")" + ] + }, + { + "cell_type": "markdown", + "id": "ec3ba7bc", + "metadata": {}, + "source": [ + "### Key Findings\n", + "\n", + "The progressive analysis reveals how both data quality and algorithmic enhancements contribute to AMICA's performance:\n", + "\n", + "#### 🔍 Step 1: Data Quality Filtering\n", + "Removing \"broad term\" and \"overlaps\" annotations from ground truth significantly reduces false regressions:\n", + "- These cases represent mismatches between author-defined cell sets and CL-defined cell sets\n", + "- Example: Author label \"Lymphoid_T/NK\" (broad category) mapped to specific terms like \"natural killer cell\"\n", + "- **Impact:** Large reduction in apparent regressions (mostly false positives)\n", + "\n", + "#### 🧬 Step 2: Tissue Context Enhancement \n", + "Adding anatomical context from papers helps AMICA make more specific mappings:\n", + "- **Coverage:** 78/147 problematic annotations (53.1%) had tissue context available\n", + "- **Mechanism:** Disambiguates general terms using anatomical location\n", + " - \"native cell\" → specific cell type based on tissue location\n", + " - \"endothelial cell\" → \"endothelial cell of [specific vessel type]\" based on tissue\n", + "- **Impact:** Further reduction in true regressions (2.84 pp, representing 22.8% relative improvement)\n", + "\n", + "#### 🎯 Future Opportunities\n", + "1. **Expand tissue context coverage** beyond 53.1% through better paper extraction\n", + "2. **Add species context** particularly for immune cells\n", + "3. **Incorporate developmental stage** information for cell type disambiguation\n", + "4. **Continue ground truth curation** to reduce remaining annotation inconsistencies" + ] + }, + { + "cell_type": "markdown", + "id": "ea7061ec", + "metadata": {}, + "source": [ + "## 8. Overall Impact: Full Dataset with Tissue Context\n", + "\n", + "While Section 7 focused on the 147 problematic annotations, let's now look at the overall impact of tissue context across **all annotations** that were reprocessed.\n", + "\n", + "This shows the complete progression:\n", + "1. **Raw (All Data)**: Original AMICA output with all annotations\n", + "2. **Cleaned (High Quality)**: After filtering broad terms and overlaps\n", + "3. **Cleaned + Tissue Context**: Full reprocessing with tissue context enabled\n", + "\n", + "**Note:** The tissue context run processed all available annotations from the input directory, showing the real-world impact when tissue context is enabled for a complete dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e008dfb3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "📊 AMICA REGRESSION REDUCTION: COMPLETE JOURNEY\n", + "================================================================================\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 StageTotal AnnotationsRegressionsRegression Rate
0🔴 Raw (All Data)85517720.70%
1🟠 Cleaned (Filtered)7409212.43%
2🟢 Cleaned + Tissue Context740709.46%
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "🎯 OVERALL IMPACT SUMMARY\n", + "================================================================================\n", + "Starting Point: 20.70% (177 regressions)\n", + "After Filtering: 12.43% (92 regressions)\n", + "After Tissue Context: 9.46% (70 regressions)\n", + "\n", + "Total Reduction: 11.24 percentage points\n", + "Total Improvement: 54.3%\n", + "Regressions Eliminated: 107 out of 177\n", + "================================================================================\n" + ] + }, + { + "data": { + "image/png": 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IiMSrU6eOxVhq+fkjx4md1Pmjll8Ug4IRI0bY76l50qpVKxsQsarfvn17q+vH74jHLBIMHjzYrl2cwRKr4xxQwTHmfGXgMW7cONewYUP7PSdFMVBiQEWWAFs4uBcRkVyKs9t7rC3J5yWSLhX+KW6OooiIiIiIiIiISBnR9j0REREREREREUk5TUqJiIiIiIiIiEjKaVJKRERERERERERSTpNSIiIiIiIiIiKScpqUEskgGzZscGeeeaadEBLixI5DDz3UjnYNccQsJ+Nh9erV9j1fEznppJPs9zwOOOAAd9hhh7nu3bu7t99+O6l75Ojcr776qljPffrpp919992X1PVFRLIxbr/77ruxGBt9cAx3NEbzPTHcX4OjxFMlbDs4Zapz5852DyIimdoXzjTb6nc/99xz1vdOh7CvXtz74PmMN3QGmqSDJqVEMsjdd9/tzjvvPLfTTjvFfrZ+/Xq3cOFCV7NmTVdQUFCq699www3unXfecXPmzHH5+fmuZcuW7pJLLnHz5s0r9jUuuOACO/q2OOhUzJw5033zzTeluGsRkcyP2xUrVrR/E2Ojj9NPP93tvffe9j1fo0aNGmVxOR123nln16NHD3sfIiKZ2BeW5IR99VNPPdU9++yz2/ybxo0bu7p167rnn38+BXcoUpgmpUQyBCstr7/+ug1eQq+88opr2rSprXJMnz69VK+x++67uzp16rg999zTrjlo0CB32mmnuTvvvNOVBwZprMBPnDixXK4vIpJpcZsYG33suuuubscdd7Tv+RqV7pVp7v+NN95wa9asSet9iMj2rai+sJQc7Q8L28Vx7rnnuvHjx6e9TZLtjyalRDIEmUtt27a1VevQSy+95Fq1auVOPPFEN3/+/CLThEuqW7du7vPPP3erVq2yf3/55ZfuwgsvtO19Bx98sDVQPgXYp/+ef/75bsyYMfY921JOOeUUd9BBB7kjjzzSDRs2zP3111+x6+fl5bmXX37Zbdy4sUzvW0QkU+N2Mls9iKWsTPPwMZZ4ee2111o2K9e/9dZb3ebNm2Nb73jezTff7A4//HD30EMP2c+feuop+zmxmy0Yn332Wew12Pp99dVX2+/atWvnPvnkk0L3wP23adPG3o+ISCbF1DfffNMWOFu0aGFZP2Tge8S6cePGWb+V3xPfiipL4WPwiy++6I499lh3xBFHuNtuu839+eefsVh82WWXWZZW69at3Ycffui2bNlimVvHH3+8ldG49NJL3XfffVfouq+99po77rjjLF4PHTrUtkQnQl+b+/X3OXXq1NjveG0Wion1xGliOZm1jz/+uMXmo446yj366KOx5yfTV49u31uyZIk755xz3CGHHGL3QR/d495+//13244ukkqalBLJEDSiNDyhb7/91n366ac2IUUDWaVKlVJnSyVK1/UN3N9//20N7j777ONeeOEFG+QwweS3dfj0Xxq53r17W4NNg85gh0aZCSmewypXeP1q1arZhJqISK7H7WQRS9u3b28PH2OHDBnifvvtN/fkk0+6sWPH2iTS8OHDY39DRhMDHwYbHTp0sCynBx980N100002ucVkFQOSX3/91Z7PBNbXX39tA5wbb7zRPfzww3H3ccwxxyRdY1BEpDxjKrWRrrjiCtexY0frl3bt2tUNGDDA+sYemT1k/bOIS81U4iD92aIQK6l3ylcmuPwiK+i/ElOnTJliEzTEzlmzZrkRI0ZYn5gJLCauwuv7+qncx9y5c92ECRPiXpNFhYsuushi84wZM9zgwYMttod9enZGsKOB98lr9+/f3yamHnvsMZvM4h5++umnpPvq0Xpd/OzAAw+0toISHtzLihUr7PcVKlSwCTC1BZJqmpQSyQA0cqxq+wkijwa2evXqlinF3voTTjjBGqCyRAOITZs2WaNJ8fPrrrvO1a9f3zVv3txWp5iwgk//ZZKpcuXKrlKlSu722293J598sqtXr55lTDVr1sx98cUXhV6jSZMmbtmyZWV63yIimRi3WbkOH2Q8/RdiKdsr/BYLFiNmz55tAwxW9RmcsHrOAIKJKq9Pnz6uQYMGVgNk0qRJNrhgAaNhw4Y2mGHAwuCHv6GIOpNRxHQyBBhURfE+GJiEma4iIumMqWQTkc1DjaT99tvP9erVy/qckydPjj2HLCZqmNJv7du3r2Uy/fDDD0W+DjGZLCkmX6666iqbVPLb1WrXrm1ZREzakCVFn5vsJ57LhBf1/6iTGmYSUa+VySYWj7kek0RRZGfVqlXLYjMxmswlJpbC7KcaNWrY3/M+6HsTu1mg4PMgK4rPh10NyfbVQ2RF8XPag0aNGtnnNnDgwFgmLtRnl3T4f1VOEUkrVrNZ+aBBijYeTET5GiQ0xDRsCxYssAa1LPgT/cjCYpKJxpiVG1ahWFmnYaKRToQtewykHnjgAWsM6UzQYJJ6HWJiTSc7icj2ELej2azE1WSwBYPrsh0kxM/8NmuwEBD+DZNY9957b+xnDKhWrlxpAygmmhhQeWz3iCJO8xq//PKLDZ5ERNIdU4ltTMCEmOyfNm1a7N9M8nj0ZeG35CXCNruwH0v20c8//2z/ZjLfI35yP2xzC+Mkk2PcF1/BwoHHwiwFxn2Wqkd/mkl/7t0jLoc1BonpZCqBvnV4P/7fZMgm21cP0R5wjzvs8G9eChN9IfXZJR00KSWSAXwjFKYD03gx0UNjw0RUiIaorCalfN2R/fff37KlzjrrLOsQsIpDCjOvH65IhUjv7devn+vUqZOtvvM9W/iieF9hAygikotxG2QvlQYDFTJYw0GXxyEVixcvtu932WWXQn/Dav3RRx9d6PkM0BIVL09UA8tnCvj3JSKS7pgaxjmP34fPSXRK338V6g6f76/jXzt8vUSv7eNt+Pph/9a/bvSemCQjPpN1VRR/gmsoUd852b76tl4jSn12SQf9jxPJAKxKsFriV2r83vKqVavalg0mofyDffNsxQhTbUuDgQ+pv/vuu6/ViPr+++8tnZitIezrX7t2bZGNO0XOu3TpYrVO2OdPijFbT6LP530VZwVHRCSb43ZJhRNBrL6zbYOfMcHFg3g/cuTIIgvo8jfr1q2LPZ8H9U0WLVpkWzQYIIXFzRNtzeB9MGCJZn6JiKQrphLb/ES8t3DhwliWUkksX7489j2ZRnvssUfCuEe/mJhIHPW4NzJWw9engHlYRHyvvfaKy5Dl+WQpkQ3lYzTXpV5UspLtq4fIKmMxOnwuWwrZAh6+R/XZJdU0KSWSAViRYGtFeFoSW/c4EpefN23aNPZgXz1b7qg5kiwGOuyzpzHjtagHxeQX+9J9h4BTN7g2p5Qw6cR+/nAgRENLzSiuxfPpHHAtfsZ1uH504ESDzcSXiEgux+2S2m233Syjaf369Ta5T+bpNddcYwOcpUuXuuuvv95iMwsVibD9gsK8LFywMMBWPhYvuBbZUhQJpi4VgztO76PAbxTvgzoqypQSkUyJqfR5CwoKLL6xne6RRx6xwuNsXysp+r5M0s+bN8+NHj3aTttLhHpMLLgSO4mb7GCgHhWTThwM4fnYSp0pyllwz1FnnHGGLS6QKcXWvzlz5th9lGSrdLJ99RDjCrZos8jB58lhGRR3D98Pnz9b/ERSSZNSIhmCQcjHH39s37N6QkNDem4Ue9eZ4CGDKll33HGH1XuiVgmDGFZtaOApzgj2uvsteDSgNFY0oOwtZ7AETgChMeNUj8svv9wa1G7dutn1SHWmoxCuQpFSTKqxfw0RkVyM26XBpBHxmLjLCjYxlhV1BjfEVlbZw3pRURyTzolUDIjYysGJVRyT7mutcBoV8Z1rsXjQo0ePuGt89NFHcXWsRETSGVOp50Q85CRSYhvZ/ffff3/cVuVkEC85GIKTo5l0uvjii4t8LifTkYl05ZVXWv+Wfi795nALND+nwDoZR8Tynj17xl2HxYGJEyfaRBAlLyg0zmQY95GsZPvqIRY2OB2Q2rR8ntzTPffcYwsSoP1hsVltgaRahX+Kk+snIuWO1W1OwaBOE6vmuYIVeU5CYUVIRCSX5ErcZtWdQQiZVmEBdRGRXImpLPbm5eVZZpDiXNFbA1nEINNWdaUklfS/TSRDcKwrx9pGi5pns61bt9pxur179073rYiIlLlcidvcPye9aqAmIumUKzE1W+Xn51udKk1ISarpf5xIBiFNOLovPJuRZt2uXTurayIikouyPW5z39w/70NEJN2yPaZmK2pdUTA9UekQkfKm7XsiIiIiIiIiIpJyypQSEREREREREZGU06SUiIiIiIiIiIiknCalREREREREREQk5TQpJSIiIiIiIiIiKadJKRERERERERERSTlNSomIiIiIiIiISMppUkpERERERERERFJOk1IiIiIiIiIiIuJS7X9sOzK8v0KkegAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# --- CELL 8.1: Overall Regression Reduction Journey ---\n", + "\n", + "# Calculate the progression of regression rates across all stages\n", + "\n", + "# Get totals from each stage\n", + "raw_total = summary_df[summary_df['Scope'] == 'Raw (All Data)']['Count'].sum()\n", + "clean_total = summary_df[summary_df['Scope'] == 'Cleaned (High Quality)']['Count'].sum()\n", + "\n", + "# Get regression counts and rates\n", + "raw_reg_count = summary_df[\n", + " (summary_df['Scope'] == 'Raw (All Data)') & \n", + " (summary_df['Category'] == 'Regression')\n", + "]['Count'].values[0]\n", + "\n", + "clean_reg_count = summary_df[\n", + " (summary_df['Scope'] == 'Cleaned (High Quality)') & \n", + " (summary_df['Category'] == 'Regression')\n", + "]['Count'].values[0]\n", + "\n", + "# Get percentages\n", + "raw_reg_pct = summary_df[\n", + " (summary_df['Scope'] == 'Raw (All Data)') & \n", + " (summary_df['Category'] == 'Regression')\n", + "]['Percentage'].values[0]\n", + "\n", + "clean_reg_pct = summary_df[\n", + " (summary_df['Scope'] == 'Cleaned (High Quality)') & \n", + " (summary_df['Category'] == 'Regression')\n", + "]['Percentage'].values[0]\n", + "\n", + "# From Section 7: 147 problematic annotations improved from 92 → 70 regressions\n", + "problematic_count = len(prob_set)\n", + "tc_before_reg = 92 # Regressions in the 147 problematic cases before tissue context\n", + "tc_after_reg = 70 # Regressions in the 147 problematic cases after tissue context\n", + "\n", + "# Calculate blended result: \n", + "# - 147 annotations use improved tissue context results (70 regressions)\n", + "# - Remaining annotations keep their cleaned results\n", + "remaining_annotations = clean_total - problematic_count\n", + "remaining_regressions = clean_reg_count - tc_before_reg\n", + "\n", + "blended_regression_count = tc_after_reg + remaining_regressions\n", + "blended_regression_rate = (blended_regression_count / clean_total) * 100\n", + "\n", + "# Create summary table\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"📊 AMICA REGRESSION REDUCTION: COMPLETE JOURNEY\")\n", + "print(\"=\"*80 + \"\\n\")\n", + "\n", + "summary_data = {\n", + " 'Stage': [\n", + " '🔴 Raw (All Data)',\n", + " '🟠 Cleaned (Filtered)',\n", + " '🟢 Cleaned + Tissue Context'\n", + " ],\n", + " 'Total Annotations': [\n", + " int(raw_total),\n", + " int(clean_total),\n", + " int(clean_total)\n", + " ],\n", + " 'Regressions': [\n", + " int(raw_reg_count),\n", + " int(clean_reg_count),\n", + " int(blended_regression_count)\n", + " ],\n", + " 'Regression Rate': [\n", + " f'{raw_reg_pct:.2f}%',\n", + " f'{clean_reg_pct:.2f}%',\n", + " f'{blended_regression_rate:.2f}%'\n", + " ]\n", + "}\n", + "\n", + "summary_table = pd.DataFrame(summary_data)\n", + "\n", + "# Display with styling\n", + "display(summary_table.style\n", + " .set_properties(**{'text-align': 'center', 'background-color': '#f8f9fa'})\n", + " .set_table_styles([\n", + " {'selector': 'th', 'props': [('text-align', 'center'), ('font-weight', 'bold'), ('background-color', '#e9ecef'), ('color', '#495057')]},\n", + " {'selector': 'td', 'props': [('padding', '10px'), ('color', '#212529')]}\n", + " ])\n", + " .background_gradient(subset=['Regressions'], cmap='RdYlGn_r')\n", + ")\n", + "\n", + "# Overall impact summary\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"🎯 OVERALL IMPACT SUMMARY\")\n", + "print(\"=\"*80)\n", + "print(f\"Starting Point: {raw_reg_pct:.2f}% ({int(raw_reg_count)} regressions)\")\n", + "print(f\"After Filtering: {clean_reg_pct:.2f}% ({int(clean_reg_count)} regressions)\")\n", + "print(f\"After Tissue Context: {blended_regression_rate:.2f}% ({int(blended_regression_count)} regressions)\")\n", + "print(f\"\\nTotal Reduction: {raw_reg_pct - blended_regression_rate:.2f} percentage points\")\n", + "print(f\"Total Improvement: {((raw_reg_pct - blended_regression_rate)/raw_reg_pct)*100:.1f}%\")\n", + "print(f\"Regressions Eliminated: {int(raw_reg_count - blended_regression_count)} out of {int(raw_reg_count)}\")\n", + "print(\"=\"*80)\n", + "\n", + "# Visualization\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "\n", + "stages = ['Raw\\n(All Data)', 'Cleaned\\n(Filtered)', 'Cleaned + TC\\n(on problematic)']\n", + "regression_rates = [raw_reg_pct, clean_reg_pct, blended_regression_rate]\n", + "regression_counts = [raw_reg_count, clean_reg_count, blended_regression_count]\n", + "totals = [raw_total, clean_total, clean_total]\n", + "\n", + "# Color gradient from red to green\n", + "colors = ['#e74c3c', '#f39c12', '#27ae60']\n", + "\n", + "# Create bars\n", + "bars = ax.bar(stages, regression_rates, color=colors, alpha=0.8, edgecolor='black', linewidth=1.5)\n", + "\n", + "# Add percentage and count labels on bars\n", + "for i, (bar, rate, count, total) in enumerate(zip(bars, regression_rates, regression_counts, totals)):\n", + " height = bar.get_height()\n", + " # Percentage label\n", + " ax.text(bar.get_x() + bar.get_width()/2., height + 0.5,\n", + " f'{rate:.2f}%',\n", + " ha='center', va='bottom', fontsize=14, fontweight='bold')\n", + " # Count label\n", + " ax.text(bar.get_x() + bar.get_width()/2., height/2,\n", + " f'{int(count)}/{int(total)}',\n", + " ha='center', va='center', fontsize=11, color='white', fontweight='bold')\n", + "\n", + "# Add reduction arrows and labels\n", + "for i in range(len(stages)-1):\n", + " x_start = i + 0.15\n", + " x_end = i + 0.85\n", + " y = max(regression_rates[i], regression_rates[i+1]) + 3\n", + " \n", + " reduction = regression_rates[i] - regression_rates[i+1]\n", + " relative_reduction = (reduction / regression_rates[i]) * 100\n", + " \n", + " # Arrow\n", + " ax.annotate('', xy=(x_end, y), xytext=(x_start, y),\n", + " arrowprops=dict(arrowstyle='->', lw=2, color='green'))\n", + " \n", + " # Reduction label\n", + " ax.text((x_start + x_end)/2, y + 1.5,\n", + " f'↓ {reduction:.2f} pp\\n({relative_reduction:.1f}%)',\n", + " ha='center', va='bottom', fontsize=10, \n", + " bbox=dict(boxstyle='round', facecolor='lightgreen', alpha=0.7))\n", + "\n", + "# Styling\n", + "ax.set_ylabel('Regression Rate (%)', fontsize=13, fontweight='bold')\n", + "ax.set_title('AMICA Regression Reduction: Impact of Data Filtering + Tissue Context', \n", + " fontsize=15, fontweight='bold', pad=20)\n", + "ax.set_ylim(0, max(regression_rates) + 8)\n", + "ax.grid(axis='y', alpha=0.3, linestyle='--')\n", + "ax.set_axisbelow(True)\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] } ], "metadata": {