Single-Cell Expression Variability Implies Cell Function

Cells. 2019 Dec 19;9(1):14. doi: 10.3390/cells9010014.


As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or single-cell expression variability (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type's function, from which the scRNA-seq data used to identify HVGs were generated-e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the "variation is function" hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.

Keywords: airway epithelial cell; cell-to-cell variation; dermal fibroblast; induced pluripotent stem cell; lymphoblastoid cell line; scRNA-seq; single-cell RNA sequencing; single-cell expression variability.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Cell Line
  • Gene Expression Profiling / methods*
  • Gene Expression Regulation
  • Gene Regulatory Networks*
  • Humans
  • Organ Specificity
  • Sequence Analysis, RNA / methods
  • Single-Cell Analysis / methods*