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

Author

Listed:
  • 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
    The Ohio State University)

  • Qin Ma

    (The Ohio State University
    The Ohio State University)

  • Dong Xu

    (University of Missouri
    University of Missouri)

  • Juexin Wang

    (Indiana University Indianapolis)

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.

Suggested Citation

  • Yang Yu & Shuang Wang & Jinpu Li & Meichen Yu & Kyle McCrocklin & Jing-Qiong Kang & Anjun Ma & Qin Ma & Dong Xu & Juexin Wang, 2025. "TrimNN: characterizing cellular community motifs for studying multicellular topological organization in complex tissues," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63141-7
    DOI: 10.1038/s41467-025-63141-7
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