Spatial Multi-Omics in Drug Discovery

Abstract

Spatial multi-omics (SMO) is revolutionizing drug discovery by integrating molecular profiling with spatial tissue context to provide deep insights into cellular heterogeneity, molecular interactions, and disease microenvironments. Combining spatial transcriptomics, proteomics, metabolomics, and epigenomics, SMO maintains tissue architecture while mapping genes, proteins, metabolites, and regulatory elements at single-cell or subcellular resolution. This approach enables accurate identification of therapeutic targets, elucidation of drug mechanisms, and discovery of spatial biomarkers predictive of drug response or resistance. Widely applied across oncology, neuroscience, autoimmune, and infectious diseases, SMO reveals tumor heterogeneity, immune landscapes, localized cytokine activity, and host-pathogen dynamics. Enhanced by AI, computational modeling, and network analysis, SMO supports integrated data visualization and tissue-level analysis. Its growing use in companion diagnostics, patient stratification, and translational models bridges preclinical and clinical research.