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This study systematically maps how combinations of approximately 100 transcription factors interact to control gene expression programs using single-cell overexpression screens. The researchers found that different transcription factor combinations can activate shared sets of genes with cell-type specific functions, revealing a modular architecture underlying transcriptional regulation. They also characterized pairwise transcription factor interactions, demonstrated that cooperative interactions enhance transcriptional reprogramming, and developed computational tools to predict the outcomes of combinatorial transcription factor activity.
Why it matters
Understanding how transcription factor combinations drive gene regulatory networks could improve the design of cell reprogramming strategies relevant to regenerative medicine, disease modeling, and cell-based therapies. These findings and predictive tools may help researchers more rationally engineer specific cell states for biomedical applications.
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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.
Source: Combinatorial transcription factor interactions drive modular gene regulatory networks