What makes for good morphology representations for spatial omics?
This review addresses the problem of combining spatial omics and imaging AI for researchers in computational biology and medicine, but it is incremental as it synthesizes existing methods rather than introducing new ones.
The paper reviews how morphological features from imaging AI can be translated or integrated with spatial omics data to predict gene expression and define spatial domains, aiming for a holistic understanding of tissue architecture.
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.