2024/07/30 by Eduard Chelebian, Chelebian, Eduard, Christophe Avenel +3
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Computer Vision and Pattern Recognition (cs.CV) #Data Management and Algorithms #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2407.20660
openalex publication_date 2024/07/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.