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Peer-reviewedCC BYNature Genetics

Mapping enhancer–gene regulatory interactions from single-cell data

  • Maya U. Sheth1,2,3,4,5
  • Wei‐Lin Qiu6,4,7
  • X. Rosa Ma1,3,5
  • Andreas R. Gschwind1,3,4,5
  • Evelyn Jagoda4,8
  • Anthony S. Tan1,3,5
  • James Galante1,3,7
  • Judhajeet Ray4,8
  • Dulguun Amgalan1,3,4,5
  • Hjörleifur Einarsson6,4,5
  • Bram L. Gorissen9,4,5
  • Danilo Dubocanin3,10
11 more authors
  • Christopher S. McGinnis11,12,8
  • Jacob Huang4,5
  • Glen Munson4,5
  • Kayla Brand1,3,13
  • Ansuman T. Satpathy11,12,5
  • Thouis R. Jones4,7
  • Lars Michael Steinmetz3,14,15
  • Anshul Bharat Kundaje16,3
  • Berk Ustun17
  • J Engreitz18,19,20,21,22
  • Robin Andersson23,24
24 affiliations
  1. 1Basic Sciences and Engineering Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA
  2. 2Department of Bioengineering, Stanford University, Stanford, CA, USA
  3. 3Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
  4. 4The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
  5. 5Stanford University
  6. 6Department of Biology, Section for Computational and RNA Biology, University of Copenhagen, Copenhagen, Denmark
  7. 7University of Copenhagen
  8. 8Broad Institute
  9. 9Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
  10. 10Stanford University School of Medicine
  11. 11Department of Pathology, Stanford University, Stanford, CA, USA
  12. 12Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
  13. 13University of San Diego
  14. 14Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
  15. 15Stanford Genome Technology Center, Stanford University School of Medicine, Palo Alto, CA, USA
  16. 16Department of Computer Science, Stanford University, Stanford, CA, USA
  17. 17Halıcıoğlu Data Science Institute and Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA
  18. 18Basic Sciences and Engineering Initiative, Betty Irene Moore Children's Heart Center, Lucile Packard Children's Hospital, Stanford, CA, USA. [email protected]
  19. 19Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. [email protected]
  20. 20Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. [email protected]
  21. 21Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA. [email protected]
  22. 22The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA. [email protected]
  23. 23Department of Biology, Section for Computational and RNA Biology, University of Copenhagen, Copenhagen, Denmark. [email protected]
  24. 24The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA. [email protected]
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Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{sheth2026mapping,
  title = {{Mapping enhancer–gene regulatory interactions from single-cell data}},
  author = {Maya U. Sheth and Wei‐Lin Qiu and X. Rosa Ma and Andreas R. Gschwind and Evelyn Jagoda and Anthony S. Tan and James Galante and Judhajeet Ray and Dulguun Amgalan and Hjörleifur Einarsson and Bram L. Gorissen and Danilo Dubocanin and Christopher S. McGinnis and Jacob Huang and Glen Munson and Kayla Brand and Ansuman T. Satpathy and Thouis R. Jones and Lars Michael Steinmetz and Anshul Bharat Kundaje and Berk Ustun and J Engreitz and Robin Andersson},
  journal = {Nature Genetics},
  year = {2026},
  month = aug,
  doi = {10.1038/s41588-026-02695-8},
  url = {https://doi.org/10.1038/s41588-026-02695-8},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Mapping enhancer–gene regulatory interactions from single-cell data
AU  - Maya U. Sheth
AU  - Wei‐Lin Qiu
AU  - X. Rosa Ma
AU  - Andreas R. Gschwind
AU  - Evelyn Jagoda
AU  - Anthony S. Tan
AU  - James Galante
AU  - Judhajeet Ray
AU  - Dulguun Amgalan
AU  - Hjörleifur Einarsson
AU  - Bram L. Gorissen
AU  - Danilo Dubocanin
AU  - Christopher S. McGinnis
AU  - Jacob Huang
AU  - Glen Munson
AU  - Kayla Brand
AU  - Ansuman T. Satpathy
AU  - Thouis R. Jones
AU  - Lars Michael Steinmetz
AU  - Anshul Bharat Kundaje
AU  - Berk Ustun
AU  - J Engreitz
AU  - Robin Andersson
PY  - 2026
DA  - 2026/08/01
JO  - Nature Genetics
DO  - 10.1038/s41588-026-02695-8
UR  - https://doi.org/10.1038/s41588-026-02695-8
AB  - 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.
SN  - 1061-4036
SN  - 1546-1718
ER  -