Advances in MS-Based Glycomics for Biomedical Research

Abstract

Glycosylation is a highly complex and functionally diverse post-translational modification that modulates protein folding, stability, cell signaling, and immune response. Aberrant glycosylation is associated with numerous diseases, including cancer, neurodegenerative disorders, and infections, making glycans attractive targets for biomarker discovery and therapeutic development. Mass spectrometry (MS) has become a widely used analytical tool, for glycomics analysis due to its high sensitivity, specificity, and structural resolution. This review highlights advances in MS-based glycomics, encompassing sample preparation, ionization techniques, and fragmentation strategies. Quantitative strategies, including stable isotope labeling, isobaric tagging, and label-free approaches, are also examined for their roles in precise glycan quantification. The review also explores bioinformatics tools and the growing integration of artificial intelligence and machine learning for glycan structure prediction and data interpretation.