Combinatorial transcription factor interactions drive modular gene regulatory networks

Area of Science:

• Molecular Biology
• Systems Biology
• Genomics

Background:

• Transcription factors (TFs) and gene regulatory networks (GRNs) establish cellular transcriptional states.
• Engineering cell states requires understanding TF combinations and their impact on GRNs, which is currently challenging to predict.

Purpose of the Study:

• To map the combinatorial activities of approximately 100 TFs on gene expression states using single-cell overexpression screens.
• To characterize the relationship between TF combinations and GRNs for improved cell state engineering.

Main Methods:

• Single-cell overexpression screens were employed to assess the effects of TF combinations.
• Analysis of gene expression states resulting from TF induction.
• Identification and characterization of pairwise TF interactions.

Main Results:

• Diverse TF combinations were found to drive cell-type specific regulatory programs.
• Different TF combinations can induce shared gene sets with cell-type specific functions, indicating a modular transcriptome architecture.
• Cooperative TF interactions were shown to enhance transcriptional reprogramming efficacy.

Conclusions:

• The study reveals a modular regulatory architecture of the transcriptome.
• TF-TF interactions and predictive models are crucial for enhancing cell reprogramming cocktails.
• Findings advance the understanding of cell state control for potential biomedical applications.