Divergent combinations of enhancers encode spatial gene expression

Spatial transcriptomics and epigenomics have enabled mapping gene regulation in the tissue context. However, it remains poorly understood how spatial gene expression patterns are orchestrated by enhancers. Here we build eSpatial, a computational framework that deciphers spatially resolved enhancer regulation of gene expression by integrating spatial profiles of gene expression and chromatin accessibility. Applying eSpatial to diverse spatial datasets, including mouse embryo and brain, as well as human melanoma and breast cancer, we reveal a "spatial enhancer code", in which divergent combinations of enhancers regulate the same gene in spatially segregated domains. We validate the spatial enhancer code using public spatial datasets such as VISTA, Allen in situ hybridization (ISH), and H3K27ac MERFISH. Moreover, we conduct transgenic reporter assays and in vivo CRISPR/Cas9-mediated perturbation experiments to confirm the Atoh1 spatial enhancer code in determining Atoh1 spatial expression in mouse embryonic spinal cord and brain. Our study establishes the spatial enhancer code concept, revealing how combinations of enhancers dynamically shape gene expression across diverse biological contexts, providing insights into tissue-specific regulatory mechanisms and tumor heterogeneity.

Authors

Institutions

Publication Details

Journal
Nature Communications
Published
2025-06-02
DOI
https://doi.org/10.1038/s41467-025-60482-1
Citations
2
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
1.61

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Divergent combinations of enhancers encode spatial gene expression

Danni Hong, Yannan Zhao, Zhiyong Liu, Jianwu Dai et al.
2 citations
Nature Communications
Single-cell and spatial transcriptomics
1.61
article

Divergent combinations of enhancers encode spatial gene expression

Danni Hong, Yannan Zhao, Zhiyong Liu, Jianwu Dai, Di Hu, Jialiang Huang, Falong Lu, Muya Shu, Yi Du, Hao Cheng, Jiawei Liu, Bo Xu, Lifang Liu, Ming Zhu
article en
2 citations

Abstract

Spatial transcriptomics and epigenomics have enabled mapping gene regulation in the tissue context. However, it remains poorly understood how spatial gene expression patterns are orchestrated by enhancers. Here we build eSpatial, a computational framework that deciphers spatially resolved enhancer regulation of gene expression by integrating spatial profiles of gene expression and chromatin accessibility. Applying eSpatial to diverse spatial datasets, including mouse embryo and brain, as well as human melanoma and breast cancer, we reveal a "spatial enhancer code", in which divergent combinations of enhancers regulate the same gene in spatially segregated domains. We validate the spatial enhancer code using public spatial datasets such as VISTA, Allen in situ hybridization (ISH), and H3K27ac MERFISH. Moreover, we conduct transgenic reporter assays and in vivo CRISPR/Cas9-mediated perturbation experiments to confirm the Atoh1 spatial enhancer code in determining Atoh1 spatial expression in mouse embryonic spinal cord and brain. Our study establishes the spatial enhancer code concept, revealing how combinations of enhancers dynamically shape gene expression across diverse biological contexts, providing insights into tissue-specific regulatory mechanisms and tumor heterogeneity.

Nature CommunicationsVol. 16(1)
Xiamen University (CN), Chinese Academy of Sciences (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Gezhouba Central Hospital of Sinopharm (CN), Center for Excellence in Brain Science and Intelligence Technology (CN), Institute of Genetics and Developmental Biology (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Youth Innovation Promotion Association of the Chinese Academy of Sciences, Xiamen University, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, Youth Innovation Promotion Association
Openalex Percentile: Top 17%
Single-cell and spatial transcriptomics
1.61
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.