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

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.

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Publication Details

Journal
Research
Published
2025-01-01
DOI
https://doi.org/10.34133/research.0961
Primary Topic
Single-cell and spatial transcriptomics
Type
article
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article

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

Yanbin Yang, Tang Risheng, Juan Wen, Haiyu Zhou et al.
Research
Single-cell and spatial transcriptomics
article

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

Yanbin Yang, Tang Risheng, Juan Wen, Haiyu Zhou, Lijia Yang, Yucheng Huang, Han Huang, Tongtong Deng, Jiying Liu, B. Wang, Chang Liu, Zihao Li, Lifang Zou, Yuhu Zhang, Zhifeng Hao, Hui Wang, Xi Guo, Changkun Liu, Shudong Liu, Shengming Lin, Ling Fei
article en

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.

ResearchVol. 8
Chicago Department of Public Health (US), Jilin University (CN), University of Michigan (US), Shantou University (CN), University of Illinois Chicago (US), University of Chicago (US), Ministry of Education (IR), BGI Group (Japan) (JP), Botswana Geoscience Institute (BW), Guangdong Academy of Medical Sciences (CN), Guangdong Provincial People's Hospital (CN), South China University of Technology (CN)
Openalex Percentile: Top 75%
Single-cell and spatial transcriptomics
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