Cross-tissue immune cell analysis reveals tissue-specific features in humans
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 dataset, tissue-integrated expression analysis, and antigen receptor sequencing.
| Errataetall: |
CommentIn: Science. 2022 May 13;376(6594):695-696. - PMID 35549410 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:376 |
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| Enthalten in: |
Science (New York, N.Y.) - 376(2022), 6594 vom: 13. Mai, Seite eabl5197 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Domínguez Conde, C [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 17.05.2022 Date Revised 14.02.2024 published: Print-Electronic CommentIn: Science. 2022 May 13;376(6594):695-696. - PMID 35549410 Citation Status MEDLINE |
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| doi: |
10.1126/science.abl5197 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM340775742 |
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| 500 | |a CommentIn: Science. 2022 May 13;376(6594):695-696. - PMID 35549410 | ||
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| 520 | |a 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 dataset, tissue-integrated expression analysis, and antigen receptor sequencing | ||
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