Evidence map›Paper›PMID 41210658›Full record

ArticleResearch (Washington, D.C.)2025

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 and 11 more

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Article in Research (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

21 authors.

Jingyi WenSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.ORCID https://orcid.org/0009-0008-0957-5309
Lingxuan ZouSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
Jiying LiuDepartment of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.
Xi GuoSchool of Software Engineering, South China University of Technology, Guangzhou 510006, China.
Changkun LiuSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
Bensu WangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
Tongtong DengDepartment of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
Chang LiuSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
Risheng TangSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 511442, China.
Yanbin YangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
Yucheng HuangDepartment of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China.
Lijia YangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
Hui WangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
Zihao LiSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.
Shengming LinDepartment of Applied Health Science, The University of Illinois Chicago, Chicago, IL 60607, USA.
Shipping LiuBGI-Hangzhou, Hangzhou 310012, China.
Yuhu ZhangDepartment of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China.
Zhifeng HaoDepartment of Mathematics, College of Science, Shantou University, Shantou 515063, China.
Haiyu ZhouDepartment of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China.
Han HuangSchool of Software Engineering, South China University of Technology, Guangzhou 510006, China.
Fei LingSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou 510640, China.ORCID https://orcid.org/0000-0002-3640-4280

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID41210658
PMCPMC12589771

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