Skip to main content

This is a preprint.

It has not yet been peer reviewed by a journal.

The National Library of Medicine is running a pilot to include preprints that result from research funded by NIH in PMC and PubMed.

bioRxiv logoLink to bioRxiv
[Preprint]. 2025 Jun 25:2025.06.14.659727. [Version 2] doi: 10.1101/2025.06.14.659727

Native, Spatiotemporal Profiling of the Global Human Regulome

Lindsay K Pino, Daniele Canzani, Andrea Gutierrez, Julia Robbins, Brian McEllin, Evan Hubbard, Erin Broderick, Anastasiya Prymolenna, Lillian Tatka, J Sebastian Paez, Gaelle Mercenne, Kyle Siebenthall, William E Fondrie, Alexander J Federation
PMCID: PMC12262435  PMID: 40667134

SUMMARY

The regulome, comprising transcription factors, cofactors, chromatin remodelers, and other regulatory proteins, forms the core machinery by which cells interpret signals and execute gene expression programs. Despite its central role in development, disease, and drug response, the regulome remains largely uncharted at scale due to its dynamic, low-abundance, and chromatin-associated nature. Here, we present a method for scalable, regulome profiling for global, compartment-resolved quantification of native regulome proteins. By enriching DNA- and chromatin-associated proteins and profiling them using high-throughput, label-free DIA mass spectrometry, regulome profiling captures chromatin-associated proteins across 36 human cell lines and thousands of perturbations. The resulting Regulome Atlas recovers nearly 60% of known human transcription factors, reveals lineage-specific TF localization, and distinguishes active nuclear engagement from latent, unbound states. We demonstrate that regulome profiles resolve acute immune pathway activation prior to transcriptional changes, identify previously unrecognized drug-induced regulome responses, and enable proteome-scale readouts of compound target engagement and complex remodeling. This work establishes a foundational resource for decoding the regulatory proteome and provides a blueprint for integrating regulome data into next-generation models of cellular behavior.

Full Text Availability

The license terms selected by the author(s) for this preprint version do not permit archiving in PMC. The full text is available from the preprint server.


Articles from bioRxiv are provided here courtesy of Cold Spring Harbor Laboratory Preprints

RESOURCES