Background
Spatial transcriptomics (ST) technologies reveal the spatial organization of gene expression in tissues, providing critical insights into development, neurobiology, and cancer. However, the high cost and technical complexity of ST limit its broad application, especially at single-cell resolution.
What is PRTS?
A team led by Prof. Fei Ling from South China University of Technology developed PRTS (Pathology-driven Reconstruction of Transcriptomic States), a deep learning framework that predicts single-cell-resolution spatial transcriptomics directly from H&E-stained histology images.
Input: Standard H&E images
Output: Single-cell × gene expression matrix (1,820 highly variable genes)
27x higher resolution compared to conventional ST spots
Trained and validated on mouse brain, human lung cancer, and breast cancer Visium HD datasets
Key Findings
Accurate Spatial Gene Prediction
PRTS reliably predicts the spatial expression patterns of key genes (e.g., Kcnma1, Plp1, Apoe) in mouse brain, matching ground truth ST data.
Single-Cell Annotation
The model identifies 21 cell subtypes (neurons, astrocytes, oligodendrocytes, etc.) with spatial distributions consistent with experimental data.
Robust Performance in Cancer Tissues
PRTS maintains prediction accuracy in human breast and lung cancer tissues, demonstrating generalizability to complex pathological environments.
Future Directions
Clinical Diagnostics: Convert routine H&E slides into transcriptomic maps for cancer subtyping and prognosis.
Drug Discovery: Identify spatially expressed genes in tumor microenvironments for target discovery.
Large-Scale Studies: Enable low-cost spatial transcriptomics for population-level research.
Cross-Platform Integration: Incorporate data from Xenium, Stereo-seq, etc., to build a universal model.
Sources: https://spj.science.org/doi/10.34133/research.0961
Research
News article
Not applicable
PRTS: Predicting Single-Cell Spatial Transcriptomic Maps from Histological Images
6-Nov-2025