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PRTS: Predicting Single-Cell Spatial Transcriptomic Maps from Histological Images.

Jingyi Wen1, Lingxuan Zou1,2, Jiying Liu3

  • 1School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.

Research (Washington, D.C.)
|November 10, 2025
PubMed
Summary

Pathology-driven Reconstruction of Transcriptomic States (PRTS) predicts single-cell spatial transcriptomics from histology images. This cost-efficient method significantly enhances spatial resolution and analytical units for disease research.

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Area of Science:

  • Genomics
  • Computational Biology
  • Pathology

Background:

  • High-resolution spatial transcriptomics (ST) offers insights into biological processes but faces limitations due to cost and technical complexity.
  • Existing ST methods have lower spatial resolution and fewer analytical units compared to the potential of single-cell analysis.

Purpose of the Study:

  • To introduce PRTS (Pathology-driven Reconstruction of Transcriptomic States), a novel framework for predicting single-cell-resolution ST data from histological images.
  • To demonstrate PRTS's capability to enhance spatial resolution and analytical unit count in transcriptomic profiling.

Main Methods:

  • PRTS framework predicts single-cell transcriptomic states directly from standard histological images.
  • Utilizes hematoxylin-and-eosin (H&E) stained tissue images for transcriptomic predictions.

Main Results:

  • PRTS generates approximately 60,000 analyzable cell tiles per tissue section, a 27-fold increase in analytical units over conventional ST spots.
  • Achieves accurate cell-level transcriptomic predictions, significantly enhancing spatial resolution.
  • Demonstrates the feasibility of using cost-efficient H&E images for high-resolution ST profiling.

Conclusions:

  • PRTS transforms expensive ST technologies into a practical and scalable tool.
  • Offers a cost-efficient solution for comprehensive ST profiling, particularly valuable for H&E-based disease research.
  • Enables deeper molecular insights at single-cell resolution from routine pathology slides.