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

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.