Mapping enhancer–gene regulatory interactions from single-cell data
Mapping enhancers and their target genes in specific cell types is crucial for understanding gene regulation and human disease genetics. However, accurately predicting enhancer-gene regulatory interactions from single-cell datasets has been challenging. Here we introduce a family of classification models, scE2G, to predict enhancer-gene regulation. These models use features from single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) or multiomic RNA and ATAC-seq data, and are trained on a CRISPR perturbation dataset including >10,000 evaluated element-gene pairs. We benchmark scE2G models against CRISPR perturbations, fine-mapped expression quantitative trait loci and genome-wide association study variant-gene associations and demonstrate state-of-the-art performance at prediction tasks across several cell types and categories of perturbations. We apply scE2G to build maps of enhancer-gene regulatory interactions in heterogeneous tissues and interpret noncoding variants associated with complex traits, nominating regulatory interactions linking INPP4B and IL15 to lymphocyte count. The scE2G models will enable accurate mapping of enhancer-gene regulatory interactions across thousands of human cell types.
Authors
- Lars Michael Steinmetz (ORCID: https://orcid.org/0000-0002-3962-2865)
- Kayla Brand (ORCID: https://orcid.org/0000-0002-8879-628X)
- Andreas R. Gschwind (ORCID: https://orcid.org/0000-0002-0769-6907)
- Maya U. Sheth (ORCID: https://orcid.org/0000-0003-1466-8899)
- Bram L. Gorissen (ORCID: https://orcid.org/0000-0001-5992-0432)
- Evelyn Jagoda
- Judhajeet Ray (ORCID: https://orcid.org/0000-0003-1524-2603)
- Dulguun Amgalan (ORCID: https://orcid.org/0000-0001-6398-3372)
- Christopher S. McGinnis (ORCID: https://orcid.org/0000-0001-6923-9341)
- Danilo Dubocanin (ORCID: https://orcid.org/0000-0003-0330-5814)
- Ansuman T. Satpathy (ORCID: https://orcid.org/0000-0002-5167-537X)
- Glen Munson
- Hjörleifur Einarsson (ORCID: https://orcid.org/0000-0002-0565-9053)
- Wei‐Lin Qiu (ORCID: https://orcid.org/0000-0002-6649-0378)
- Anshul Kundaje (ORCID: https://orcid.org/0000-0003-3084-2287)
- Robin Andersson (ORCID: https://orcid.org/0000-0003-1516-879X)
- X. Rosa (ORCID: https://orcid.org/0000-0001-8297-4279)
- J Engreitz (ORCID: https://orcid.org/0000-0002-5754-1719)
- Jacob Huang
- Anthony S. Tan
- Berk Ustun
- James Galante (ORCID: https://orcid.org/0000-0002-9289-3090)
- Thouis R. Jones
Institutions
- Broad Institute (US)
- University of Copenhagen (DK)
- Lucile Packard Children's Hospital (US)
- University of San Diego (US)
- Cardiovascular Institute of the South (US)
- Foundation Center (US)
- Massachusetts General Hospital (US)
- Novo Nordisk (United States) (US)
- Parker Institute for Cancer Immunotherapy (US)
- Stanford Medicine (US)
- European Molecular Biology Laboratory (DE)
- IT University of Copenhagen (DK)
- Stanford University (US)
Publication Details
- Journal
- Nature Genetics
- Published
- 2026-08-03
- DOI
- https://doi.org/10.1038/s41588-026-02695-8
- Citations
- 22
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 44.74
Funders
- National Science Foundation
- Silicon Valley Community Foundation
- American Heart Association
- Applebaum Foundation
- Novo Nordisk
- National Institutes of Health
- Stanford Research Computing Center, Stanford University
- National Heart, Lung, and Blood Institute
- National Human Genome Research Institute
- Stanford Maternal and Child Health Research Institute