TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues

Yang Yu, Shuang Wang, Jinpu Li, Meichen Yu, Kyle McCrocklin, Jing-Qiong Kang, Anjun Ma, Qin Ma (), Dong Xu () and Juexin Wang ()
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Yang Yu: University of Missouri
Shuang Wang: Indiana University Bloomington
Jinpu Li: University of Missouri
Meichen Yu: Indiana University School of Medicine
Kyle McCrocklin: Indiana University Indianapolis
Jing-Qiong Kang: Vanderbilt University
Anjun Ma: The Ohio State University
Qin Ma: The Ohio State University
Dong Xu: University of Missouri
Juexin Wang: Indiana University Indianapolis

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract The spatial organization of cells plays a pivotal role in shaping tissue functions and phenotypes in various biological systems and diseased microenvironments. However, the topological principles governing interactions among cell types within spatial patterns remain poorly understood. Here, we present the triangulation cellular community motif neural network (TrimNN), a graph-based deep learning framework designed to identify conserved spatial cell organization patterns, termed cellular community (CC) motifs, from spatial transcriptomics and proteomics data. TrimNN employs a semi–divide-and-conquer approach to efficiently detect overrepresented topological motifs of varying sizes in a triangulated space. By uncovering CC motifs, TrimNN reveals key associations between spatially distributed cell-type patterns and diverse phenotypes. These insights provide a foundation for understanding biological and disease mechanisms and offer potential biomarkers for diagnosis and therapeutic interventions.

Date: 2025
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DOI: 10.1038/s41467-025-63141-7

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