Mammalian gene expression variability is explained by underlying cell state

Abstract

Gene expression variability in mammalian systems plays an important role in physiological and pathophysiological conditions. This variability can come from differential regulation related to cell state (extrinsic) and allele-specific transcriptional bursting (intrinsic). Yet, the relative contribution of these two distinct sources is unknown. Here we exploit the qualitative difference in the patterns of covariance between these two sources to quantify their relative contributions to expression variance in mammalian cells. Using multiplexed error robust RNA fluorescent in situ hybridization (MERFISH) we measured the multivariate gene expression distribution of 150 genes related to Ca 2+ signaling coupled with the dynamic Ca 2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca 2+ response to ATP, the remaining variability is effectively at the Poisson limit for most genes. These findings demonstrate that the majority of expression variability results from cell state differences and that the contribution of transcriptional bursting is relatively minimal.

Single-cell gene regulatory network analysis reveals new melanoma cell states and transition trajectories during phenotype switching

“…Interestingly, we show that the heterogeneity is stable over time and across replicates of the same culture, and can be detected regardless of the scRNA-seq technology applied. This strongly suggests it to be a regulated rather than a stochastic process (Foreman and Wollman 2019). Using singlemolecule RNA FISH for a selected panel of marker genes, Shaffer and colleagues observed a similar rare, semi-coordinated transcription in other melanoma cultures (Shaffer et al 2017).…”

“…While it could a priori have been conceivable that 'semi-invasive' melanoma cell lines (based on bulk RNA-seq) would consist of a mixture of cells in either the melanocyte-like or the mesenchymal-like state, our results now establish that these intermediate transcriptomes are largely due to a stable "mixed gene regulatory network" that produces this intermediate transcriptome in all cells, and only partly due to heterogeneity between the cells or random flipping between the states. Recently, Foreman and Wollman analogously demonstrated that expression variability in mammary epithelial cells (MCF10A) results from cell state differences rather than transcriptional bursting (Foreman and Wollman 2019). Notably, these observations were made using MERFISH instead of scRNA-seq.…”

Resource Allocation in Mammalian Systems

“…A lack of sensing precision can propagate downstream to cause stochastic gene expression (Elowitz et al, 2002) (Appendix B). Recent studies have indicated that, when sufficiently accounting for cell phenotype and context, this stochasticity is minimal (Battich et al, 2015;Foreman and Wollman, 2020). This is because individual mammalian cells can mitigate downstream noise of the final machinery products by "smoothing" across time.…”

Caliban: Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

“…It is now well appreciated that individual cells can vary considerably in their behavior, and the ability to capture the temporal evolution of cell-to-cell differences has proven essential to understanding cellular heterogeneity. Increasingly, these dynamic data are being integrated with end-point genomic assays to uncover even more insights into cellular behavior [8][9][10] .…”