Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.

Authors

Ninghui Hao, Xinxing Yang, Boshen Yan, Dong Li, Junzhou Huang, Xintao Wu, Emily S Ruiz, Arlene Ruiz de Luzuriaga, Chen Zhao, Guihong Wan

Published in

Briefings in bioinformatics. Volume 27. Issue 4. Jul 03, 2026.

Abstract

Spatial omics (SO) enables spatially resolved molecular profiling, while hematoxylin and eosin (H&E) imaging remains the gold standard for morphological assessment in clinical pathology. Recent computational advances increasingly center H&E images in SO analysis and push resolution toward the single-cell level. We systematically review the computational evolution of SO from a histopathology-centered perspective, organizing methods into three paradigms: integration (jointly modeling of paired multimodal data), mapping (inferring molecular profiles from H&E images), and foundation models (learning generalizable representations from large-scale datasets). We summarize actionable modeling directions and persistent gaps, providing a roadmap for developing, and applying computational frameworks in SO.

PMID:
42467990
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.

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