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

Anika Gupta*, Jorge D Martin-Rufino*, Thouis R Jones, Vidya Subramanian, Xiaojie Qiu, Emanuelle I Grody, Alex Bloemendal, Chen Weng, Sheng-Yong Niu, Kyung Hoi Min, Arnav Mehta, Kaite Zhang, Layla Siraj, Aziz Al'Khafaji, Vijay G Sankaran, Soumya Raychaudhuri, Brian Cleary, Sharon Grossman, Eric S Lander+.
PNAS (2022)


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.

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