Inferring gene regulation from stochastic transcriptional variation across single cells at steady state
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pnas.2207392119.pdf
Description
Published version
Size
3.15 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Gupta, Anika
Martin-Rufino, Jorge D
Jones, Thouis R
Subramanian, Vidya
Qiu, Xiaojie
Grody, Emanuelle I
Bloemendal, Alex
Weng, Chen
Niu, Sheng-Yong
Min, Kyung Hoi
Date Issued
2022
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Citation
Gupta, Anika, Martin-Rufino, Jorge D, Jones, Thouis R, Subramanian, Vidya, Qiu, Xiaojie et al. 2022. "Inferring gene regulation from stochastic transcriptional variation across single cells at steady state." Proceedings of the National Academy of Sciences of the United States of America, 119 (34).
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Biology
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1073/PNAS.2207392119
