Single-cell RNA sequencing (scRNA-seq) is a rich resource of cellular heterogeneity, opening new avenues in the study of complex tissues. Tel Aviv University researchers 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. This results demonstrate the power of CPM in reconstructing the continuous spectrum of cell states within heterogeneous tissues.
Overview of the CPM algorithm
a, A flowchart of the CPM pipeline. b–d, Illustration of the steps outlined in a; cell-state space construction (b), deconvolution (c), and extrapolation (d). Dim, dimension.
Availability – https://cran.r-project.org/web/packages/scBio/index.html