Systematic inference of super-resolution cell spatial profiles from histology images

Peng Zhang, Chaofei Gao, Zhuoyu Zhang, Zhiyuan Yuan, Qian Zhang, Ping Zhang, Shiyu Du, Weixun Zhou, Yan Li and Shao Li ()
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Peng Zhang: Tsinghua University
Chaofei Gao: Tsinghua University
Zhuoyu Zhang: Tsinghua University
Zhiyuan Yuan: Fudan University
Qian Zhang: Tsinghua University
Ping Zhang: China Academy of Chinese Medical Sciences
Shiyu Du: China-Japan Friendship Hospital
Weixun Zhou: Chinese Academy of Medical Sciences and Peking Union Medical College
Yan Li: the First Affiliated Hospital of Wannan Medical College
Shao Li: Tsinghua University

Nature Communications, 2025, vol. 16, issue 1, 1-21

Abstract: Abstract Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

Date: 2025
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DOI: 10.1038/s41467-025-57072-6

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