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 Ca2+ signaling coupled with the dynamic Ca2+ response of live cells to ATP. We show that after controlling for cellular phenotypic states such as size, cell cycle stage, and Ca2+ 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.

Predictive features of gene expression variation reveal mechanistic link with differential expression

“…Even for single‐cell experiments, which can control for genetic and macro‐environmental factors, there is ongoing debate as to whether the observed gene‐specific expression variation can be explained by intrinsic (e.g. transcription bursting) or extrinsic (cell‐to‐cell variability) factors (Battich et al , ; Larsson et al , ; Foreman & Wollman, ), or whether these sources are indistinguishable (Eling et al , ). Yet, despite the differences in interpretation of the underlying sources of variation, there is a consensus that genes differ in their expression variation.…”

The circadian oscillator analysed at the single-transcript level

“…Given that previous studies identified cell size as a source of extrinsic variability in transcript counts [26,27,29,35], we quantified cell sizes and found a positive correlation with mRNA number for all genes (Supp figure 2). In fact, we found a linear relationship between the log area and the log mean mRNA number, indicating a power-law scaling (Fig.…”

“…The extrinsic sources of noise in our model, which include area and variable burst size, had a weaker contribution for all three clock genes. Previous studies have shown that the relative proportions of intrinsic versus extrinsic noise is both condition-(cell types, cell states) and gene-specific, and regression models that use cellular features as explanatory variables can account for between 10-80% of the variance, depending on the gene [26,27]. In the cells used here, the strong influence of transcriptional bursting and dominance of intrinsic noise is consistent with live imaging of a Bmal1 transcriptional reporter in the same cell line under similar growth conditions, where intrinsic noise was estimated to be 4-times larger than extrinsic noise [23].…”

“…As we are only able to quantify the role of extrinsic sources that are included in the model, it is nevertheless possible that inclusion of additional sources such as the cell cycle and phenotypic heterogeneity would increase the proportion of extrinsic noise. Nonetheless, as shown previously [14], cells were maintained in conditions that minimised cell-cycle progression and NIH3T3 fibroblasts are phenotypically homogenous, which controls at least for those major sources of extrinsic noise [27,39].…”

High throughput imaging identifies a spatially localized response of primary fetal pulmonary artery endothelial cells to insulin-like growth factor 1 treatment

“…[5][6][7] There is also evidence that after controlling for cell cycle and cell state, the remaining fluctuations are solely due to biochemical reaction noise. 8,9 Recent studies have demonstrated that variability is conferred from mother to siblings cells, 5 inheritable fluctuations in gene expression found within clonal cancer cell lines may lead to drug resistance, 6 and distributions in the number of mitochondria can explain cell death due to TNF-related apoptosis. 7 Regardless of the underlying mechanism, cell to cell variability has broad implications outside of studying mechanisms heterogeneity in clonal cell lines and drug resistance in cancer.…”