Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics

Abstract

Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels. Spatial monoomics technology has evolved from monoomics to spatial multiomics. Moreover, the spatial resolution, high-throughput detection capability, capture efficiency, and compatibility with various sample types of omics technology have considerably advanced. Despite the technological advances in this field, data analysis frameworks have stagnated. Current challenges include incomplete spatial monoomics data analysis pipeline, overly complex data analysis tasks, and few established spatial multiomics data analysis strategies. In this review, we systematically summarize recent developments of various spatial monoomics techniques and improvements in related data analysis pipeline. On the basis of the spatial multiomics technology, we propose a data integration strategy with cross-platform, cross-slice, and cross-modality. We summarize the potential applications of spatial monoomics technology, aiming to provide researchers and clinicians with a better understanding of how such applications have advanced. Spatial multiomics technology is expected to substantially impact biology and precision medicine through measurements of cellular tissue structures and the extraction of biomolecular features.