Cell population mapping from bulk single-cell RNA data

Cell composition analysis of bulk genomics using single-cell data

Amit Frishberg, Naama Peshes-Yaloz, Ofir Cohn, Diana Rosentul, Yael Steuerman, Liran Valadarsky, Gal Yankovitz, Michal Mandelboim, Fuad A. Iraqi, Ido Amit, Lior Mayo, Eran Bacharach, & Irit Gat-Viks

Nature Methods (Research Article)

Abstract—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.