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<p>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 <b>tri</b>angulation cellular community <b>m</b>otif <b>n</b>eural <b>n</b>etwork (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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

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  • Yang Yu,
  • Shuang Wang,
  • Jinpu Li,
  • Meichen Yu,
  • Kyle McCrocklin,
  • Jing-Qiong Kang,
  • Anjun Ma,
  • Qin Ma,
  • Dong Xu,
  • Juexin Wang

摘要

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.