Single-Cell Expression Variability Implies Cell Function

Abstract

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.

scTenifoldNet: A Machine Learning Workflow for Constructing and Comparing Transcriptome-wide Gene Regulatory Networks from Single-Cell Data

“…This happens even in a group of highly homogeneous cells of the same type. 12 The presence of so-called outlying cells, i.e., cells showing profiles of expression that deviate from those of most other cells, may influence the construction of high-precision scGRNs. Therefore, subsampling offers promise as a technique for handling the noise in the input datasets.…”

What Will B Will B: Identifying Molecular Determinants of Diverse B-Cell Fate Decisions Through Systems Biology

“…Key molecular determinants of cell-to-cell variability in B-cell fate decisions are NF-κB target genes. Indeed, recent single-cell RNA-sequencing analysis found that the most highly variable genes in lymphoid cells were functionally significant and centered around NF-κB and its target genes, including NFKBIA, MYC, IRF4, and AID ( Osorio et al, 2020 ).…”

Increased gene expression variability in BRCA1-associated and basal-like breast tumours

“…across samples) is often ignored and is generally only considered in terms of the impact it has on statistical power. However, gene expression variability has been shown to be under genetic control and important to cellular function [7][8][9][10][11]. Gene expression variability has been used to evaluate transcriptomic data in human disease and development [12][13][14][15].…”