Of Gene Expression and Cell Division Time: A Mathematical Framework for Advanced Differential Gene Expression and Data Analysis

Estimating fold changes of average mRNA and protein molecule counts per cell is the most common way to perform differential expression analysis. However, these gene expression data may be affected by cell division, an often-neglected phenomenon. Here, we develop a quantitative framework that links population-based mRNA and protein measurements to rates of gene expression in single cells undergoing cell division. The equations we derive are easy-to-use and widely robust against biological variability. They integrate multiple “omics” data into a coherent, quantitative description of single-cell gene expression and improve analysis when comparing systems or states with different cell division times. We explore these ideas in the context of resting versus activated B cells. Analyzing differences in protein synthesis rates enables to account for differences in cell division times. We demonstrate that this improves the resolution and hit rate of differential gene expression analysis when compared to analyzing population protein abundances alone. We provide an easy-to-use quantitative framework that links rates of single-cell gene expression to population-level data such as abundances measured by RNA sequencing or mass spectrometry. For populations of dividing cells, this framework integrates multiple layers of omics data for differential gene expression analysis and predicts when cell division is critical in this analysis. Using published human B cell data, we show that the sensitivity of differential gene expression analysis improves noticeably when comparing rates of gene expression instead of abundances.