GraphCellNet: A deep learning method for integrated single-cell and spatial transcriptomic analysis with applications in development and disease.

Journal: Journal of molecular medicine (Berlin, Germany)
Published Date: Jul 21, 2025

Abstract

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.

Authors

  • Ruoyan Dai
    Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
  • Zhenghui Wang
    Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China.
  • Zhiwei Zhang
    Department of Statistics, University of California, Riverside, California.
  • Lixin Lei
    Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
  • Mengqiu Wang
    Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
  • Kaitai Han
    Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China.
  • Zijun Wang
    School of Chemistry and Chemical Engineering, Shihezi University Shihezi Xinjiang 832003 PR China eavanh@163.com lqridge@163.com 1175828694@qq.com 318798309@qq.com wzj_tea@shzu.edu.cn.
  • Zhenxing Li
    Department of Neurosurgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China. Electronic address: lizhenxing001@126.com.
  • Jirui Zhang
    Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, 102617, China.
  • Qianjin Guo
    Department of Orthopedics, the Second Affiliated Hospital of Luohe Medical College, Luohe Henan, 462300, P.R.China.

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