What makes for good morphology representations for spatial omics?

TL;DR

This review explores how morphological features from imaging AI can be translated or integrated into spatial omics to enhance tissue analysis, including gene expression prediction and spatial domain identification.

Contribution

It introduces a framework for categorizing methods combining spatial omics and morphology, focusing on translation and integration strategies.

Findings

Morphological features can predict gene expression patterns.

Integration of morphology enriches spatial domain analysis.

Framework guides future development of spatial omics-morphology methods.

Abstract

Spatial omics has transformed our understanding of tissue architecture by preserving spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. The intersection of spatial omics and imaging AI presents opportunities for a more holistic understanding. In this review we introduce a framework for categorizing spatial omics-morphology combination methods, focusing on how morphological features can be translated or integrated into spatial omics analyses. By translation we mean finding morphological features that spatially correlate with gene expression patterns with the purpose of predicting gene expression. Such features can be used to generate super-resolution gene expression maps or infer genetic information from clinical H&E-stained samples. By integration we mean finding morphological features that spatially complement gene expression patterns with the purpose of enriching information. Such features can be used to define spatial domains, especially where gene expression has preceded morphological changes and where morphology remains after gene expression. We discuss learning strategies and directions for further development of the field.