Specifying cellular context of transcription factor regulons for exploring context-specific gene regulation programs.

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Cellular identity and disease mechanisms, including cancer, are intricately linked to transcription and transcription factors (TFs).
• Existing data resources for cell line-specific TF-to-target gene annotations are limited, hindering comprehensive analysis.
• Understanding cell-specific regulatory networks is vital for advancing precision medicine and disease research.

Purpose of the Study:

• To develop and validate a robust method for generating cell line-specific transcription factor regulons.
• To create a comprehensive resource of TF-to-target gene annotations across 40 common cell lines.
• To demonstrate the utility of these regulons in analyzing transcriptional dysregulation in cancer.

Main Methods:

• Integrated cellular transcriptome data with transcription factor (TF) binding data to define regulons.
• Developed a straightforward pipeline to capture cell-specific TF binding and transcript expression.
• Benchmarked the method against state-of-the-art approaches using TF knockout experiments.

Main Results:

• Generated cell line-specific regulons for 40 cell lines, encompassing proximal and distal regulatory events.
• Demonstrated that the developed method performs comparably to existing state-of-the-art techniques.
• Showcased the applicability of the pipeline to diverse cell types and its utility in analyzing cancer single-cell datasets.

Conclusions:

• The study provides a valuable computational pipeline and resource for exploring cell line-specific transcriptional regulation.
• The generated regulons facilitate the investigation of transcriptional dysregulation in various cellular contexts, particularly in cancer.
• Network analysis using these cell-type-specific regulons offers powerful insights into disease mechanisms.