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<p>Advances in spatial transcriptomics have enabled high-resolution mapping of tissue architecture at the molecular level, yet integrating its multi-modal data remains challenging. Here, we present stGCL, a framework for accurate and robust integration of gene expression, spatial coordinates, and histological features. stGCL employs a histology-based Vision Transformer to extract morphological features and a multi-modal graph autoencoder with contrastive learning for cross-modal fusion. In addition, we introduce a spatial coordinate correction and registration strategy to support multi-slice integration. We demonstrate that stGCL reliably identifies spatial domains, integrates vertical and horizontal tissue slices, and highlight its generalizability across platforms and resolutions.</p>

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stGCL: a versatile cross-modality fusion method based on multi-modal graph contrastive learning for spatial transcriptomics

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月28日

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  • Na Yu,
  • Daoliang Zhang,
  • Wei Zhang,
  • Zhiping Liu,
  • Xu Qiao,
  • Chuanyuan Wang,
  • Miaoqing Zhao,
  • Weiming Yue,
  • Wei Li,
  • Yang De Marinis,
  • Rui Gao

摘要

Advances in spatial transcriptomics have enabled high-resolution mapping of tissue architecture at the molecular level, yet integrating its multi-modal data remains challenging. Here, we present stGCL, a framework for accurate and robust integration of gene expression, spatial coordinates, and histological features. stGCL employs a histology-based Vision Transformer to extract morphological features and a multi-modal graph autoencoder with contrastive learning for cross-modal fusion. In addition, we introduce a spatial coordinate correction and registration strategy to support multi-slice integration. We demonstrate that stGCL reliably identifies spatial domains, integrates vertical and horizontal tissue slices, and highlight its generalizability across platforms and resolutions.