Histopathology-centered Computational Evolution of Spatial Omics: Integration, Mapping, and Foundation Models

TL;DR

This survey reviews the evolution of spatial omics analysis centered on histopathology, highlighting three paradigms—integration, mapping, and foundation models—and discusses future directions and challenges.

Contribution

It systematically categorizes computational methods in spatial omics into three paradigms and analyzes the evolving role of H&E images in this context.

Findings

H&E images serve as spatial context, predictive anchors, and representation backbones.

Three main paradigms: integration, mapping, foundation models.

Identifies persistent gaps in data, biology, and technology.

Abstract

Spatial omics (SO) technologies enable 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 place H&E images at the center of SO analysis, bridging morphology with transcriptomic, proteomic, and other spatial molecular modalities, and pushing resolution toward the single-cell level. In this survey, we systematically review the computational evolution of SO from a histopathology-centered perspective and organize existing methods into three paradigms: integration, which jointly models paired multimodal data; mapping, which infers molecular profiles from H&E images; and foundation models, which learn generalizable representations from large-scale spatial datasets. We analyze how the role of H&E images evolves across these paradigms from spatial context to predictive anchor and ultimately to representation backbone in response to practical constraints such as limited paired data and increasing resolution demands. We further summarize actionable modeling directions enabled by current architectures and delineate persistent gaps driven by data, biology, and technology that are unlikely to be resolved by model design alone. Together, this survey provides a histopathology-centered roadmap for developing and applying computational frameworks in SO.