Yu Yang, Wang Shuang, Li Jinpu, Yu Meichen, McCrocklin Kyle, Kang Jing-Qiong, Ma Anjun, Ma Qin, Xu Dong, Wang Juexin
Department of Electrical Engineering and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, USA.
Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA.
Nat Commun. 2025 Aug 19;16(1):7737. doi: 10.1038/s41467-025-63141-7.
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.
细胞的空间组织在塑造各种生物系统和疾病微环境中的组织功能和表型方面起着关键作用。然而，关于空间模式中细胞类型之间相互作用的拓扑原理仍知之甚少。在此，我们提出了三角剖分细胞群落基序神经网络（TrimNN），这是一种基于图的深度学习框架，旨在从空间转录组学和蛋白质组学数据中识别保守的空间细胞组织模式，即细胞群落（CC）基序。TrimNN采用半分治方法，在三角剖分空间中高效检测不同大小的过度代表性拓扑基序。通过揭示CC基序，TrimNN揭示了空间分布的细胞类型模式与不同表型之间的关键关联。这些见解为理解生物学和疾病机制提供了基础，并为诊断和治疗干预提供了潜在的生物标志物。
