Bai Xiaosheng, Bao Xinyu, Zhang Chuanchao, Shi Qianqian, Chen Luonan
Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
Hubei Engineering Technology Research Center of Agricultural Big Data, Huazhong Agricultural University, Wuhan, 430070, China.
Small Methods. 2025 Feb 17:e2402111. doi: 10.1002/smtd.202402111.
Spatially resolved transcriptomics (SRT) has emerged as a transformative technology for elucidating cellular organization and tissue architecture. However, a significant challenge remains in identifying pathology-relevant spatial functional landscapes within the tissue microenvironment, primarily due to the limited integration of cell-cell communication dynamics. To address this limitation, SpaDCN, a Spatially Dynamic graph Convolutional Network framework is proposed, which aligns cell-cell communications and gene expression within a spatial context to reveal the spatial functional regions with the coherent cellular organization. To effectively transfer the influence of cell-cell communications on expression variation, SpaDCN respectively generates the node layer and edge layer of spatial graph representation from expression data and the ligand-receptor complex contributions and then employs a dynamic graph convolution to switch the propagation of node graph and edge graph. It is demonstrated that SpaDCN outperforms existing methods in identifying spatial domains and denoising expression across various platforms and species. Notably, SpaDCN excels in identifying marker genes with significant prognostic potential in cancer tissues. In conclusion, SpaDCN offers a powerful and precise tool for spatial domain detection in spatial transcriptomics, with broad applicability across various tissue types and research disciplines.
空间分辨转录组学（SRT）已成为一种用于阐明细胞组织和组织结构的变革性技术。然而，在组织微环境中识别与病理相关的空间功能景观仍然是一个重大挑战，主要原因是细胞间通信动态的整合有限。为了解决这一限制，提出了一种空间动态图卷积网络框架SpaDCN，它在空间背景下对齐细胞间通信和基因表达，以揭示具有连贯细胞组织的空间功能区域。为了有效传递细胞间通信对表达变异的影响，SpaDCN分别从表达数据和配体-受体复合物贡献中生成空间图表示的节点层和边层，然后采用动态图卷积来切换节点图和边图的传播。结果表明，SpaDCN在识别不同平台和物种的空间域以及去噪表达方面优于现有方法。值得注意的是，SpaDCN在识别癌症组织中具有显著预后潜力的标记基因方面表现出色。总之，SpaDCN为空间转录组学中的空间域检测提供了一个强大而精确的工具，在各种组织类型和研究领域具有广泛的适用性。
