Mass Spectrometry Based Multi‐Omics Integration: Analytical Methods and Biological Applications

RATIONALE: Biological systems are regulated through strongly interconnected molecular layers that cannot be accurately resolved using single-omics approaches. Although genomics and transcriptomics provide essential regulatory information, they often face obstacles to reflect functional molecular outcomes. Mass spectrometry (MS)-based multi-omics integration is currently recognized as a central analytical strategy to overcome this limitation by enabling direct, high-resolution measurement of proteins, metabolites, and lipids, thereby supporting systems-level biological interpretation and translational discovery. METHODS: This review critically examines mass spectrometry-based multi-omics approaches through analysis of published literature, with a focus on integrating proteomic, metabolomic, lipidomic, and spatial omics data. Computational frameworks and translational applications relevant to biomarker discovery and precision medicine are highlighted. RESULTS: MS-centered multi-omics integration significantly enhances molecular coverage, quantitative accuracy, and pathway-level interpretation by combining various analytical layers. Applications across cancer biology, metabolic disorders, neurodegenerative diseases, and environmental research have exhibited improved biomarker robustness and mechanistic resolution contrast with single-omics studies. Recent developments in spatial and single-cell MS address cellular heterogeneity, while integrative computational approaches minimise challenges associated with data complexity, normalization, and cross platform variability. CONCLUSIONS: Mass spectrometry-based multi-omics integration represents a rapidly evolving analytical approach for systems biology and translational research. Continued advances in MS instrumentation, acquisition strategies, and computational integration are expected to further improve biological interpretability that fastens the discovery of clinically and biologically relevant molecular signatures.