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

Jingyi Wen; Lingxuan Zou; Jiying Liu; Xi Guo; Changkun Liu; Bensu Wang; Tongtong Deng; Chang Liu; Risheng Tang; Yanbin Yang; Yucheng Huang; Lijia Yang; Hui Wang; Zihao Li; Shengming Lin; Shipping Liu; Yuhu Zhang; Zhifeng Hao; Haiyu Zhou; Han Huang; Fei Ling

doi:10.34133/research.0961

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

Research (Wash D C). 2025 Nov 6:8:0961. doi: 10.34133/research.0961. eCollection 2025.

Authors

Jingyi Wen¹, Lingxuan Zou^{1

2}, Jiying Liu³, Xi Guo², Changkun Liu¹, Bensu Wang¹, Tongtong Deng⁴, Chang Liu^{1

5}, Risheng Tang⁵, Yanbin Yang¹, Yucheng Huang⁶, Lijia Yang¹, Hui Wang¹, Zihao Li^{1

7}, Shengming Lin⁸, Shipping Liu⁷, Yuhu Zhang⁴, Zhifeng Hao⁹, Haiyu Zhou⁶, Han Huang^{2

10}, Fei Ling¹

Affiliations

¹ School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
² School of Software Engineering, South China University of Technology, Guangzhou 510006, China.
³ Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
⁴ Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
⁵ School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 511442, China.
⁶ Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China.
⁷ BGI-Hangzhou, Hangzhou 310012, China.
⁸ Department of Applied Health Science, The University of Illinois Chicago, Chicago, IL 60607, USA.
⁹ Department of Mathematics, College of Science, Shantou University, Shantou 515063, China.
¹⁰ Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.

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.