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<p>Spatial transcriptomics has revolutionized RNA quantification with spatial resolution. Hematoxylin and eosin (H&amp;E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. We introduce ResSAT (Residual networks with Spatial encoding—self-Attention Transformer), a framework for predicting spatially resolved transcriptomic profiles from H&amp;E images by integrating image features, spatial locations, and self-attention transformer-based spot interactions. Benchmarking on 10 × Visium datasets, ResSAT outperforms existing methods and preserved biologically meaningful spatial patterns, promising reduced spatial transcriptomics profiling costs and rapid acquisition of numerous profiles.</p>

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ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot transformer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月26日

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  • Anqi Liu,
  • Yue Zhao,
  • Woong-Ki Kim,
  • Hui Shen,
  • Zhengming Ding,
  • Hong-Wen Deng

摘要

Spatial transcriptomics has revolutionized RNA quantification with spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. We introduce ResSAT (Residual networks with Spatial encoding—self-Attention Transformer), a framework for predicting spatially resolved transcriptomic profiles from H&E images by integrating image features, spatial locations, and self-attention transformer-based spot interactions. Benchmarking on 10 × Visium datasets, ResSAT outperforms existing methods and preserved biologically meaningful spatial patterns, promising reduced spatial transcriptomics profiling costs and rapid acquisition of numerous profiles.