Science · 2022 · 1.1K citations · 74 references
Life SciencesCell BiologyImmunologyCellular ImmunologyMolecular ImmunologyTranscriptomicsCell Type AnnotationCell-type DeconvolutionImmunogenomicsSingle-cell AnalysisSingle-cell Rna Sequencing AnalysisSingle-cell TranscriptomicsB Cell HeterogeneityCell Type InferenceImmune Cell Subtype ClassificationImmune Cell TranscriptomicsSingle-cell Rna SequencingSingle-cell Rna Sequencing Cell Type AnnotationTissue-specific ExpressionTranscriptomic Cell Type Annotation
Immune cells in human tissues are poorly understood despite their crucial role in health and disease. The study aims to resolve immune cell heterogeneity across tissues by developing CellTypist, a machine‑learning tool for rapid, precise cell‑type annotation, and to establish a foundation for identifying highly resolved immune cell types using a common reference dataset, tissue‑integrated expression analysis, and antigen‑receptor sequencing. The authors surveyed 16 tissues from 12 donors with single‑cell RNA sequencing and VDJ sequencing, generating ~360,000 cells, and applied CellTypist to annotate cell types and analyze tissue‑integrated expression and clonal architecture. They mapped the tissue distribution of finely phenotyped immune cell types, uncovering previously unappreciated tissue‑specific features and the clonal architecture of T and B cells.
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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