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SciSt: single-cell reference-informed spatial gene expression prediction from pathological images.

Yixin Li1, Fan Zhong2, Lei Liu2,3,4

  • 1Institutes of Biomedical Sciences, Fudan University, 130 Dong'an Road, Xuhui District, Shanghai 200032, China.

Briefings in Bioinformatics
|November 20, 2025
PubMed
Summary

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This summary is machine-generated.

This study introduces SciSt, a deep learning model that predicts spatial gene expression from H&E images. SciSt leverages pathological features and biological data for cost-effective, large-scale spatial analysis in disease research.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Digital Pathology

Background:

  • Spatial transcriptomics is crucial for understanding disease mechanisms but limited by sample scarcity and high costs.
  • Clinical histopathology images (H&E-stained) offer a vast, cost-effective resource for spatial analysis.
  • Existing methods struggle to accurately predict spatial gene expression from histopathology images due to limitations in capturing transcriptomic structures.

Purpose of the Study:

  • To develop a novel deep learning framework, SciSt, for predicting spatial gene expression from histopathological images.
  • To enhance biological interpretability and accuracy by integrating pathological features with biologically informed initial gene expressions.
  • To enable efficient and cost-effective large-scale spatial analysis using readily available clinical image archives.
Keywords:
deep learningpathological imagessingle-cell referencespatial gene expression

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Main Methods:

  • Developed SciSt, a deep learning framework integrating pathological features with biologically informed initial gene expressions.
  • Generated initial gene expressions using a weighted strategy combining cell segmentation and single-cell reference data.
  • Validated SciSt on three benchmark datasets and TCGA-BRCA and TCGA-LIHC cohorts for performance and generalization.

Main Results:

  • SciSt achieved state-of-the-art performance in predicting spatial gene expression from H&E images.
  • Outperformed existing models by 21.4% and 13.7% on benchmark datasets.
  • Demonstrated robust generalization capabilities on clinical TCGA cohorts, highlighting its real-world applicability.

Conclusions:

  • SciSt effectively predicts spatial gene expression from histopathological images, overcoming limitations of current methods.
  • The framework enables cross-modal translation between morphology and gene expression, unlocking potential in clinical image archives.
  • Integrating prior biological knowledge significantly improves the interpretability and scalability of biomedical AI models.