Multiscale footprints reveal the organization of cis-regulatory elements

Yan Hu, Max A. Horlbeck, Ruochi Zhang, Sai Ma, Rojesh Shrestha, Vinay K. Kartha, Fabiana M. Duarte, Conrad Hock, Rachel E. Savage, Ajay Labade, Heidi Kletzien, Alia Meliki, Andrew Castillo, Neva C. Durand, Eugenio Mattei, Lauren J. Anderson, Tristan Tay, Andrew S. Earl, Noam Shoresh, Charles B. Epstein, Amy J. Wagers and Jason D. Buenrostro ()
Additional contact information
Yan Hu: Broad Institute of MIT and Harvard
Max A. Horlbeck: Broad Institute of MIT and Harvard
Ruochi Zhang: Broad Institute of MIT and Harvard
Sai Ma: Broad Institute of MIT and Harvard
Rojesh Shrestha: Broad Institute of MIT and Harvard
Vinay K. Kartha: Broad Institute of MIT and Harvard
Fabiana M. Duarte: Broad Institute of MIT and Harvard
Conrad Hock: Broad Institute of MIT and Harvard
Rachel E. Savage: Broad Institute of MIT and Harvard
Ajay Labade: Broad Institute of MIT and Harvard
Heidi Kletzien: Harvard University
Alia Meliki: Broad Institute of MIT and Harvard
Andrew Castillo: Broad Institute of MIT and Harvard
Neva C. Durand: Broad Institute of MIT and Harvard
Eugenio Mattei: Broad Institute of MIT and Harvard
Lauren J. Anderson: Broad Institute of MIT and Harvard
Tristan Tay: Broad Institute of MIT and Harvard
Andrew S. Earl: Broad Institute of MIT and Harvard
Noam Shoresh: Broad Institute of MIT and Harvard
Charles B. Epstein: Broad Institute of MIT and Harvard
Amy J. Wagers: Harvard University
Jason D. Buenrostro: Broad Institute of MIT and Harvard

Nature, 2025, vol. 638, issue 8051, 779-786

Abstract: Abstract Cis-regulatory elements (CREs) control gene expression and are dynamic in their structure and function, reflecting changes in the composition of diverse effector proteins over time1. However, methods for measuring the organization of effector proteins at CREs across the genome are limited, hampering efforts to connect CRE structure to their function in cell fate and disease. Here we developed PRINT, a computational method that identifies footprints of DNA–protein interactions from bulk and single-cell chromatin accessibility data across multiple scales of protein size. Using these multiscale footprints, we created the seq2PRINT framework, which uses deep learning to allow precise inference of transcription factor and nucleosome binding and interprets regulatory logic at CREs. Applying seq2PRINT to single-cell chromatin accessibility data from human bone marrow, we observe sequential establishment and widening of CREs centred on pioneer factors across haematopoiesis. We further discover age-associated alterations in the structure of CREs in murine haematopoietic stem cells, including widespread reduction of nucleosome footprints and gain of de novo identified Ets composite motifs. Collectively, we establish a method for obtaining rich insights into DNA-binding protein dynamics from chromatin accessibility data, and reveal the architecture of regulatory elements across differentiation and ageing.

Date: 2025
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DOI: 10.1038/s41586-024-08443-4

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