SpatialRegNet: spatially resolved mapping of active regulatory network of human and mouse

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ID: 334084
2026
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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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openalex_W7223678107 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Feng Li, Wenli Fan, 康安辉, Wensong Liu, Quan Qi, Zhengliang Zha, Liying Pei, Fang Tian, Yunpeng Zhang, Yanjun Xu
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Journal Nucleic Acids Research
Year 2026
DOI
10.1093/nar/gkag1037
URL
https://doi.org/10.1093/nar/gkag1037
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