Combinatorial transcription factor interactions drive modular gene regulatory networks

Transcription factors (TFs) cooperatively drive gene regulatory networks (GRNs) to establish transcriptional states. Forced induction of TFs in combination can reprogram cell state by supplanting existing GRNs. Thus, TFs and GRNs are the building blocks to engineering transcriptional state. However, one key challenge is that the relationship between TF combinations and GRNs remains largely uncharacterized and difficult to accurately predict. Here, we apply single-cell overexpression screens to map the combinatorial activities of [~]100 TFs to gene expression states. Our analysis identifies diverse TF combinations driving cell-type specific regulatory programs. Notably, different TF combinations induce shared gene sets with cell-type specific functions, suggesting a modular regulatory architecture of the transcriptome. Furthermore, we define pairwise TF interactions and show that cooperative interactions improve transcriptional reprogramming. Finally, we developed tools to predict combinatorial TF phenotypes. These findings improve our understanding of cell state and how to manipulate it for biomedical applications. HIGHLIGHTSO_LICombinatorial over-expression screens for [~]100 transcription factors (TFs). C_LIO_LIDiverse TF combinations drive cell-type specific regulatory programs. C_LIO_LITF regulatory networks reveal a modular regulatory architecture of the transcriptome. C_LIO_LITF-TF interactions and predictive models enhance reprogramming cocktails. C_LI