PRINT: A Milestone Technology for Understanding Gene Regulation

In a new study published this week by Nature, researchers at the Broad Institute of MIT and Harvard and Harvard University developed a new computational method – PRINT – that identifies DNA-protein interaction footprints from both bulk and single-cell chromatin accessibility data. By applying PRINT, researchers uncovered the organization and dynamics of cis-regulatory elements (CREs) across different scales, providing deeper insights into how genes are regulated, and paving the way for innovative research in both health and disease. 

‍Advancing Gene Regulation Studies‍

CREs play a critical role in gene expression by binding to regulatory proteins, such as transcription factors (TFs) and histones, influencing fundamental biological processes. However, current methods for studying CREs at high resolution often rely on bulk data, which can mask the variability and specificity of regulatory elements in individual cells. 

In this study, PRINT changes the landscape by analyzing both bulk and single-cell chromatin accessibility data to capture CRE organization at a high resolution. By combining PRINT with a deep learning framework, seq2PRINT, the authors were able to identify the binding dynamics of regulatory proteins across various cell contexts and states, such as cell differentiation and aging. This nuanced view enabled the authors to realize that CREs do not have only two regulation states (open/active or closed/inactive), but that the same CREs can be bound by different sets of TFs across cell types. 

By enabling researchers to track changes in gene regulation in rare cell types or during disease progression, PRINT sheds light into gene regulation dynamics at the single-cell level in physiological and pathological conditions.

“Our study really helped open new opportunities to study how different TFs and nucleosomes combinatorially encode the regulation of gene expression, as well as nominating candidate factors driving diseases,” said co-first author Yan Hu, a graduate student at the Buenrostro Lab at Harvard.

‍A Collaborative Effort‍

The study stems from a collaboration between Hu, Max Horlbeck, MD PhD, a geneticist at Boston Children’s Hospital and postdoctoral fellow at the Buenrostro Lab, and Ruochi Zhang, PhD, a postdoctoral fellow at the Eric and Wendy Schmidt Center at the Broad Institute. 

The Buenrostro Lab aims to create sequencing technologies to better understand gene regulation across health and disease. 

The lab’s research heavily aligns with the Schmidt Center’s work, which converges biology and machine learning to drive biological discoveries. By blending advanced computational methods with experimental biology, PRINT represents the kind of innovation that emerges from interdisciplinary collaboration.

“This is a breakthrough that we couldn’t have accomplished alone without bridging biology and AI through this collaboration,” said Zhang. “Biology and AI form a two-way street—the diverse expertise within our team provides different perspectives on the problem, motivates innovative approaches for investigation, and ultimately drives deeper understanding of the questions we’re addressing.”

Hu, Horlbeck, and Zhang co-led the efforts of the research, with contributions from colleagues at the Buenrostro Lab, Wagers Lab, and the Gene Regulation Observatory (GRO) at the Broad Institute. This work was supported by NIH institutes NHLBI, NHGRI, NIGMS, NICHD, and the common fund, Broad Institute, Schmidt Center, the GRO, Wagers Lab, Harvard Department of Stem Cell and Regenerative Biology, Harvard Stem Cell Institute, and the Impact of Genomic Variation on Function Consortium. Read more about their work in Nature.