# The deep learning framework Hist2Prot infers single-cell spatial proteomics from histology to predict biomarkers and clinical outcomes

Source: [nature.com](https://www.nature.com/articles/s43018-026-01270-8) · 2026-11-13 · Medicine
Page: https://themorning.science/story/the-deep-learning-framework-hist2prot-infers-single-cell-spatial-proteomics-from-455b0a18408481d0
Kind: research · Impact: medium

## In brief

- Hist2Prot is a deep learning framework described by Fan et al. in Nature Cancer, published online 13 November 2026.
- It aims to infer single-cell spatial proteomic patterns from routine hematoxylin and eosin (H&E) histology slides.
- The model is proposed to predict molecular biomarkers, prognosis and clinical outcomes.

## Why it matters

If validated, it suggests that routinely collected H&E slides may contain information about spatial protein patterns that can be used for biomarker and outcome prediction.

## Timeline

- 2026-11-13: Published online in Nature Cancer

## Coverage

- [The deep learning framework Hist2Prot infers single-cell spatial proteomics from histology to predict biomarkers and clinical outcomes](https://www.nature.com/articles/s43018-026-01270-8) — nature.com, 2026-11-13

Tags: spatial proteomics, histology, deep learning, biomarkers, cancer, pathology

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Brief generated by AI (claude-haiku-5-5) from the reporting listed under Coverage. Headlines belong to their publishers. The Morning Science – https://themorning.science