期刊
NATURE METHODS
卷 16, 期 4, 页码 327-+出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/s41592-019-0355-5
关键词
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资金
- European Research Council [637885]
- Israeli Centers of Research Excellence (I-CORE) Center [41/11]
- Broad-Israel Science Foundation (ISF) [1168/14, ISF 1824/13]
- Edmond J. Safra Center for Bioinformatics at Tel Aviv University
- Shulamit Aloni Scholarship
- ISF [288/16]
- European Research Council (ERC) [637885] Funding Source: European Research Council (ERC)
Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. We introduce Cell Population Mapping (CPM), a deconvolution algorithm in which reference scRNA-seq profiles are leveraged to infer the composition of cell types and states from bulk transcriptome data ('scBio' CRAN R-package). Analysis of individual variations in lungs of influenza-virus-infected mice reveals that the relationship between cell abundance and clinical symptoms is a cell-state-specific property that varies gradually along the continuum of cell-activation states. The gradual change is confirmed in subsequent experiments and is further explained by a mathematical model in which clinical outcomes relate to cell-state dynamics along the activation process. Our results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.
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