{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Dominguez Conde C"],"funding":["Cancer Research UK","European Research Council","NIDDK NIH HHS","NIAID NIH HHS","NHLBI NIH HHS","National Institute for Health Research (NIHR)","Wellcome Trust"],"pagination":["eabl5197"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7612735"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["376(6594)"],"pubmed_abstract":["Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference da"],"journal":["Science (New York, N.Y.)"],"pubmed_title":["Cross-tissue immune cell analysis reveals tissue-specific features in humans."],"pmcid":["PMC7612735"],"funding_grant_id":["646794","P01 AI106697","206194","U19 AI128949","211276/D/18/Z","CL-2021-14-004","P30 DK132710","U01 HL145547","K22 AI066976","RP-2017-08-ST2-002","10208","105924/Z/14/Z","108413/A/15/D","213555/Z/18/Z","105924"],"pubmed_authors":["Needham EJ","Rainbow DB","Tuck L","Meyer KB","King HW","Wells SB","Mahbubani KT","Pritchard S","Jones JL","Jarvis LB","Mamanova L","Huang N","Tuong ZK","Elmentaite R","Bolt L","Fasouli ES","Suchanek O","Prete M","Menon DK","Bayraktar OA","Dominguez Conde C","Park J","Saeb-Parsy K","Xu C","Richardson L","James LK","Chen D","Sims PA","Gomes T","Polanski K","Yosef N","Li T","Campos L","Richoz N","Clatworthy MR","Mousa HS","Howlett SK","Szabo PA","Rahmani E","Farber DL","Teichmann SA"],"additional_accession":[]},"is_claimable":false,"name":"Cross-tissue immune cell analysis reveals tissue-specific features in humans.","description":"Despite their crucial role in health and disease, our knowledge of immune cells within human tissues remains limited. We surveyed the immune compartment of 16 tissues from 12 adult donors by single-cell RNA sequencing and VDJ sequencing generating a dataset of ~360,000 cells. To systematically resolve immune cell heterogeneity across tissues, we developed CellTypist, a machine learning tool for rapid and precise cell type annotation. Using this approach, combined with detailed curation, we determined the tissue distribution of finely phenotyped immune cell types, revealing hitherto unappreciated tissue-specific features and clonal architecture of T and B cells. Our multitissue approach lays the foundation for identifying highly resolved immune cell types by leveraging a common reference da","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-04-04T02:24:09.094Z","creation":"2025-02-19T01:34:42.72Z"},"accession":"S-EPMC7612735","cross_references":{"pubmed":["35549406"],"doi":["10.1126/science.abl5197"]}}