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Updated: Sep 15, 2025

Author Spotlight: An Integrated Workflow to Study the Promoter-Centric Spatio-Temporal Genome Architecture in Scarce Cell Populations
Published on: April 21, 2023
Native, Spatiotemporal Profiling of the Global Human Regulome
Researchers developed a scalable method to profile the regulome, identifying key proteins like transcription factors (TFs) in cells. This Regulome Atlas maps TF locations and states, aiding disease and drug response research.
Area of Science:
- Molecular Biology
- Proteomics
- Genomics
Background:
- The cell's regulome, including transcription factors (TFs) and regulatory proteins, controls gene expression but is difficult to study at scale.
- Its dynamic, low-abundance, and chromatin-associated nature presents significant challenges for comprehensive analysis.
Purpose of the Study:
- To develop a scalable method for profiling the regulome, enabling global and compartment-resolved quantification of native regulatory proteins.
- To create a foundational resource for decoding the regulatory proteome and integrating this data into cellular behavior models.
Main Methods:
- Enrichment of DNA- and chromatin-associated proteins.
- High-throughput, label-free DIA mass spectrometry for protein quantification.
- Profiling across 36 human cell lines and thousands of perturbations.
Main Results:
- The developed method, Regulome Profiling, captures chromatin-associated proteins and creates a Regulome Atlas.
- The Atlas includes nearly 60% of known human TFs, reveals lineage-specific TF localization, and distinguishes active from latent TF states.
- Regulome profiles detected immune pathway activation before transcriptional changes and identified novel drug responses.
Conclusions:
- This work establishes a scalable method for regulome profiling, providing a comprehensive resource for understanding gene regulation.
- The Regulome Atlas offers insights into TF dynamics, cellular responses to stimuli, and drug target engagement.
- The findings provide a blueprint for integrating proteomic regulome data into advanced cellular models.
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