SpatialRegNet: spatially resolved mapping of active regulatory network of human and mouse
- 1College of Bioinformatics Science and Technology, Harbin Medical University , 157 Baojian Road, Harbin 150081 ,
- 2Department of Cardiology, the First Affiliated Hospital of Harbin Medical University , Harbin 150001 ,
Abstract
The spatial microenvironment and cellular functional states are governed by spatiotemporally dynamic gene regulatory networks (GRNs), making their dissection essential for understanding disease pathogenesis and normal tissue programs. Despite the rapid development of multiple spatial transcriptomics databases, most lack systematic integration of intracellular GRNs and their spatial coupling patterns. Here, we present SpatialRegNet (https://bio-bigdata.hrbmu.edu.cn/SpatialRegNet/), a comprehensive database curating 1322 tissue slices derived from 152 human and mouse spatial transcriptomic datasets that span 68 tissue types and 93 distinct diseases or disease subtypes, encompassing 3 744 216 spatial spots. The database quantifies spot-level activity for 6733 transcription factor (TF)-mediated regulon networks, which collectively contain over 12.7 million TF–target gene pairs, and archives 802 935 regulon networks that are highly active in distinct spatial niches/cell types. SpatialRegNet embeds a suite of interactive online tools for analyzing regulon networks in spatial contexts, enabling users to interrogate the correlations between regulon network activity and cellular identities, map spatial regulatory heterogeneity and specificity, track dynamic regulon network activity across cell differentiation trajectories, reconstruct intercellular communication signaling cascades, and generate three-dimensional visualizations of regulon network activity. Collectively, SpatialRegNet constitutes a valuable public resource to advance mechanistic investigations of physiological tissue function, disease etiology, and biomarker discovery in spatially resolved GRNs.
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BibTeX
@article{li2026spatialregnet,
title = {{SpatialRegNet: spatially resolved mapping of active regulatory network of human and mouse}},
author = {Feng Li and Wenli Fan and Anhui Kang and Wensong Liu and Quan Qi and Zhengliang Zha and Liying Pei and Fang Tian and Yunpeng Zhang and Yanjun Xu},
journal = {Nucleic Acids Research},
year = {2026},
month = oct,
doi = {10.1093/nar/gkag1037},
url = {https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1037/9001948},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - SpatialRegNet: spatially resolved mapping of active regulatory network of human and mouse
AU - Feng Li
AU - Wenli Fan
AU - Anhui Kang
AU - Wensong Liu
AU - Quan Qi
AU - Zhengliang Zha
AU - Liying Pei
AU - Fang Tian
AU - Yunpeng Zhang
AU - Yanjun Xu
PY - 2026
DA - 2026/10/26
JO - Nucleic Acids Research
DO - 10.1093/nar/gkag1037
UR - https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1037/9001948
AB - The spatial microenvironment and cellular functional states are governed by spatiotemporally dynamic gene regulatory networks (GRNs), making their dissection essential for understanding disease pathogenesis and normal tissue programs. Despite the rapid development of multiple spatial transcriptomics databases, most lack systematic integration of intracellular GRNs and their spatial coupling patterns. Here, we present SpatialRegNet (https://bio-bigdata.hrbmu.edu.cn/SpatialRegNet/), a comprehensive database curating 1322 tissue slices derived from 152 human and mouse spatial transcriptomic datasets that span 68 tissue types and 93 distinct diseases or disease subtypes, encompassing 3 744 216 spatial spots. The database quantifies spot-level activity for 6733 transcription factor (TF)-mediated regulon networks, which collectively contain over 12.7 million TF–target gene pairs, and archives 802 935 regulon networks that are highly active in distinct spatial niches/cell types. SpatialRegNet embeds a suite of interactive online tools for analyzing regulon networks in spatial contexts, enabling users to interrogate the correlations between regulon network activity and cellular identities, map spatial regulatory heterogeneity and specificity, track dynamic regulon network activity across cell differentiation trajectories, reconstruct intercellular communication signaling cascades, and generate three-dimensional visualizations of regulon network activity. Collectively, SpatialRegNet constitutes a valuable public resource to advance mechanistic investigations of physiological tissue function, disease etiology, and biomarker discovery in spatially resolved GRNs.
SN - 0305-1048
SN - 1362-4962
ER -