Inferring gene regulation from stochastic transcriptional variation across single cells at steady state

Abstract

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

memento: Generalized differential expression analysis of single-cell RNA-seq with method of moments estimation and efficient resampling

“…These observations are consistent with a model of active regulation of multiple related genes each controlled by cis regulatory elements for the same transcription factors with "on" and "off" states [9]. Until recently, studying the distribution of gene expression, in particular the joint distribution of multiple genes, has been technologically challenging and has been mostly pursued in model organisms that can be genetically modified [10,11].…”

Allelic Transcription Factor binding shape transcriptional kinetics observed with scRNA-seq in human cell lines

“…This feature makes GM18278 the only few available cell types for us to investigate large-scale regulatory mechanisms in burst kinetics. Studies with similar ideas have been performed in K562 (Gupta et al, 2022). Secondly, even though our study requires isogenic cell types, it is still applicable for other single-cell RNA-seq data by isolating individual cell types.…”

“…In addition, despite multiple studies that have investigated how gene regulatory factors (transcriptional factors, histone acetylation, DNA methylation, etc.) modulate transcriptional burst, the results are inconsistent among different cell lines and species, probably due to both cell type-specific effects and technical variation (Sánchez and Kondev 2008; Singh et al 2010; Dar et al 2012; Fukaya et al 2016; Faure et al 2017; Nicolas et al 2018; Li et al 2018; Larsson et al 2019; Bartman et al 2019; Dobrinic et al 2021; Gupta et al 2022). Therefore, a transcriptome-wide and unbiased investigation of how regulatory factors modulate transcriptional bursts in human cells is needed to understand how it influences gene expression at the cell population level and further propagates to tissue or organism-level phenotypes.…”

“…Single-molecule techniques like MS2 tagging and Cas-derived systems allow real-time observation of burst kinetics in the context of perturbations to regulatory mechanisms (Singh et al, 2010; Dar et al, 2012; Fukaya et al, 2016; Nicolas et al, 2018; Li et al, 2018; Bartman et al, 2019; Dobrinic et al, 2021). To enable a transcriptome-wide and unperturbed characterization of bursting expression, several other studies explore the potential of using single-cell RNA-seq (scRNA-seq) (Faure et al, 2017; Larsson et al, 2019; Gupta et al, 2022). scRNA-seq has the unique advantage of allowing a snapshot of each cell within the population, capturing the distribution of gene expression over time resulting from the transcriptional bursts.…”

“…To enable a transcriptome-wide and unperturbed characterization of bursting expression, several other studies explore the potential of using single-cell RNA-seq (scRNA-seq) (Faure et al, 2017;Larsson et al, 2019;Gupta et al, 2022). scRNA-seq has the unique advantage of allowing a snapshot of each cell within the population, capturing the distribution of gene expression over time resulting from the transcriptional bursts.…”