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 65211, USA.
Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN 47405, USA.
Res Sq. 2025 Jan 17:rs.3.rs-5584635. doi: 10.21203/rs.3.rs-5584635/v1.
The spatial arrangement of cells plays a pivotal role in shaping tissue functions in various biological systems and diseased microenvironments. However, it is still under-investigated of the topological coordinating rules among different cell types as tissue spatial patterns. Here, we introduce the angulation cellular community otif eural etwork (), a bottom-up approach to estimate the prevalence of sizeable conservative cell organization patterns as Cellular Community () motifs in spatial transcriptomics and proteomics. Different from clustering cell type composition from classical top-down analysis, TrimNN differentiates cellular niches as countable topological blocks in recurring interconnections of various types, representing multicellular neighborhoods with interpretability and generalizability. This graph-based deep learning framework adopts inductive bias in CCs and uses a semi-divide and conquer approach in the triangulated space. In spatial omics studies, various sizes of CC motifs identified by TrimNN robustly reveal relations between spatially distributed cell-type patterns and diverse phenotypical biological functions.
细胞的空间排列在塑造各种生物系统和疾病微环境中的组织功能方面起着关键作用。然而，作为组织空间模式，不同细胞类型之间的拓扑协调规则仍未得到充分研究。在这里，我们介绍了角度细胞群落神经网络（TrimNN），这是一种自下而上的方法，用于估计在空间转录组学和蛋白质组学中作为细胞群落（CC）基序的相当大的保守细胞组织模式的普遍性。与经典自上而下分析中聚类细胞类型组成不同，TrimNN将细胞生态位区分为各种类型反复互连中可数的拓扑块，代表具有可解释性和普遍性的多细胞邻域。这种基于图的深度学习框架在CC中采用归纳偏差，并在三角剖分空间中使用半分治方法。在空间组学研究中，TrimNN识别出的各种大小的CC基序有力地揭示了空间分布的细胞类型模式与不同表型生物学功能之间的关系。
