Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels

Abstract

Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans. Genetic variation can change how much a gene is turned on or off in a tissue or a population of cells of the same type. However, this averaging of expression levels across a cell population masks an important aspect of gene expression regulation, namely its variability. Recent work in humans has indicated that nearby (cis) genetic factors minimally influence this variability. We have combined genetic measurements with flow cytometry single-cell protein levels to resolve the genetic control of gene expression variability in human immune cells. Importantly, we have demonstrated that whilst genetic variants near the target genes (cis) rarely influence variability, there is still an extensive genetic contribution from genetic loci faraway, or on a separate chromosome (trans). Furthermore, we have resolved that these trans genetic effects regulate the expression of other nearby genes, which leads to changes in gene expression variability of our target proteins. Our findings can be explained by an evolutionary balance between the cis regulation of gene expression levels, and the downstream consequences on gene expression variability.