TY - JOUR
T1 - SpaGRN
T2 - Investigating spatially informed regulatory paths for spatially resolved transcriptomics data
AU - Li, Yao
AU - Liu, Xiaobin
AU - Guo, Lidong
AU - Han, Kai
AU - Fang, Shuangsang
AU - Wan, Xinjiang
AU - Wang, Dantong
AU - Xu, Xun
AU - Jiang, Ling
AU - Fan, Guangyi
AU - Xu, Mengyang
N1 - Publisher Copyright:
© 2025 Elsevier Inc.
PY - 2025/4/16
Y1 - 2025/4/16
N2 - Cells spatially organize into distinct cell types or functional domains through localized gene regulatory networks. However, current spatially resolved transcriptomics analyses fail to integrate spatial constraints and proximal cell influences, limiting the mechanistic understanding of tissue organization. Here, we introduce SpaGRN, a statistical framework that reconstructs cell-type- or functional-domain-specific, dynamic, and spatial regulons by coupling intracellular spatial regulatory causality with extracellular signaling path information. Benchmarking across synthetic and real datasets demonstrates SpaGRN's superior precision over state-of-the-art tools in identifying context-dependent regulons. Applied to diverse spatially resolved transcriptomics platforms (Stereo-seq, STARmap, MERFISH, CosMx, Slide-seq, and 10x Visium), complex cancerous samples, and 3D datasets of developing Drosophila embryos and larvae, SpaGRN not only provides a versatile toolkit for decoding receptor-mediated spatial regulons but also reveals spatiotemporal regulatory mechanisms underlying organogenesis and inflammation.
AB - Cells spatially organize into distinct cell types or functional domains through localized gene regulatory networks. However, current spatially resolved transcriptomics analyses fail to integrate spatial constraints and proximal cell influences, limiting the mechanistic understanding of tissue organization. Here, we introduce SpaGRN, a statistical framework that reconstructs cell-type- or functional-domain-specific, dynamic, and spatial regulons by coupling intracellular spatial regulatory causality with extracellular signaling path information. Benchmarking across synthetic and real datasets demonstrates SpaGRN's superior precision over state-of-the-art tools in identifying context-dependent regulons. Applied to diverse spatially resolved transcriptomics platforms (Stereo-seq, STARmap, MERFISH, CosMx, Slide-seq, and 10x Visium), complex cancerous samples, and 3D datasets of developing Drosophila embryos and larvae, SpaGRN not only provides a versatile toolkit for decoding receptor-mediated spatial regulons but also reveals spatiotemporal regulatory mechanisms underlying organogenesis and inflammation.
KW - 3D regulatory atlas
KW - cellular interaction mapping
KW - gene regulatory network
KW - receptor
KW - receptor-TF-target cascades
KW - spatial autocorrelation analysis
KW - spatially resolved transcriptomics
KW - spatiotemporal dynamics
KW - transcription factor
UR - http://www.scopus.com/inward/record.url?scp=105001714199&partnerID=8YFLogxK
U2 - 10.1016/j.cels.2025.101243
DO - 10.1016/j.cels.2025.101243
M3 - 文章
AN - SCOPUS:105001714199
SN - 2405-4712
VL - 16
JO - Cell Systems
JF - Cell Systems
IS - 4
M1 - 101243
ER -