期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2022
卷号：119
期号：34
DOI：10.1073/pnas.2207392119
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Significance
Deciphering gene regulatory networks can help elucidate the molecular underpinnings that define cellular identity and disease processes. Current approaches to discover regulation compare different cell types or employ cellular perturbations. We show that, with enough data, it should be possible to identify regulatory relationships within a cell type without need for perturbation, by leveraging the intrinsic stochasticity in transcriptional bursting across individual cells at steady-state. Importantly, time-shifted correlations in RNA expression make it possible to distinguish covariation due to regulatory relationships within a cell state from covariation due to undetected cell states. Here, we present a theoretical framework for this approach and discuss future experimental design.
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.
