Dai Ruoyan, Wang Zhenghui, Zhang Zhiwei, Lei Lixin, Wang Mengqiu, Han Kaitai, Wang Zijun, Li Zhenxing, Zhang Jirui, Guo Qianjin
Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China.
J Mol Med (Berl). 2025 Jul 21. doi: 10.1007/s00109-025-02575-4.
Spatial transcriptomics (ST) integrates gene expression with spatial location, enabling precise mapping of cellular distributions and interactions within tissues, and is a key tool for understanding tissue structure and function. Single-cell RNA sequencing (scRNA-seq) data enhances spatial transcriptomics by providing accurate cell type deconvolution, yet existing methods still face accuracy challenges. We propose GraphCellNet, a model combining cell type deconvolution and spatial domain identification, featuring the Kolmogorov-Arnold Network layer (KAN) to enhance nonlinear feature representation and contextual integration. This design addresses ambiguous cell boundaries and high heterogeneity, improving analytical precision. Evaluated using metrics like Pearson correlation coefficient (PCC), structural similarity index (SSIM), root mean square error (RMSE), Jensen-Shannon divergence (JSD), and Adjusted Rand Index (ARI), GraphCellNet has been applied to various systems, yielding new insights. In myocardial infarction, it identified spatial regions with high Trem2 expression associated with metabolic gene signatures in the infarcted heart. In Drosophila development, it uncovered TWEEDLE dynamics. In human heart development, it identified cell compositions and spatial organization across stages, deepening understanding of cellular spatial dynamics and informing regenerative medicine. KEY MESSAGES: A novel deep learning architecture that effectively captures cellular composition and spatial organization in tissue samples. An innovative KAN layer design that improves the modeling of nonlinear gene expression relationships while maintaining computational efficiency. A graph-based spatial domain identification method that leverages the spatial relationships of cell type information to enhance domain recognition accuracy. Demonstration of the framework's applicability in various biological applications, providing new insights into tissue organization and development.
空间转录组学（ST）将基因表达与空间位置相结合，能够精确绘制组织内细胞分布和相互作用的图谱，是理解组织结构和功能的关键工具。单细胞RNA测序（scRNA-seq）数据通过提供准确的细胞类型反卷积来增强空间转录组学，但现有方法仍面临准确性挑战。我们提出了GraphCellNet，这是一种结合细胞类型反卷积和空间域识别的模型，其特点是具有柯尔莫哥洛夫 - 阿诺德网络层（KAN）以增强非线性特征表示和上下文整合。这种设计解决了细胞边界模糊和高度异质性的问题，提高了分析精度。使用皮尔逊相关系数（PCC）、结构相似性指数（SSIM）、均方根误差（RMSE）、詹森 - 香农散度（JSD）和调整兰德指数（ARI）等指标进行评估，GraphCellNet已应用于各种系统，产生了新的见解。在心肌梗死中，它识别出梗死心脏中与代谢基因特征相关的高Trem2表达的空间区域。在果蝇发育中，它揭示了TWEEDLE动态。在人类心脏发育中，它识别了不同阶段的细胞组成和空间组织，加深了对细胞空间动态的理解，并为再生医学提供了信息。关键信息：一种有效捕获组织样本中细胞组成和空间组织的新型深度学习架构。一种创新的KAN层设计，在保持计算效率的同时改进了非线性基因表达关系的建模。一种基于图的空间域识别方法，利用细胞类型信息的空间关系提高域识别精度。该框架在各种生物学应用中的适用性证明，为组织组织和发育提供了新的见解。
