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

## TL;DR

PRTS predicts high-resolution gene activity maps from standard tissue images, making detailed spatial analysis more accessible and affordable.

## Contribution

PRTS introduces a scalable method to generate single-cell spatial transcriptomic data from hematoxylin-and-eosin-stained images.

## Key findings

- PRTS generated transcriptomic profiles for ~60,000 cell tiles per tissue section, a 27-fold increase over conventional ST spots.
- The method achieves accurate predictions using only hematoxylin-and-eosin-stained images, reducing reliance on costly ST technologies.

## Abstract

High-resolution spatial transcriptomics (ST) data provide valuable insights into the molecular dynamics underlying complex biological processes. However, their widespread application remains limited due to high costs and technical challenges. Here, we present PRTS (Pathology-driven Reconstruction of Transcriptomic States), a novel framework that predicts single-cell-resolution ST data directly from histological images. Our results demonstrated that PRTS generated transcriptomic profiles for about 60,000 analyzable cell tiles per tissue section, representing an approximately 27-fold increase in analytical units compared to conventional ST spots and remarkably enhancing spatial resolution. Notably, PRTS achieves accurate cell-level transcriptomic predictions using only hematoxylin-and-eosin-stained tissue images. This method transforms costly ST technologies into a practical and scalable tool, offering a cost-efficient solution for comprehensive ST profiling in hematoxylin-and-eosin-based disease research.