Optimization and Application of LC-MS/MS Techniques for Characterizing Glycans, Proteins, and Metabolites in Complex Biological Samples

Proteomics is a large-scale study of proteins within a cell, tissue, or organism under specific physiological conditions. It involves analyzing protein expression, structure, functions, interactions, and modifications, providing critical insights into biological regulation in both health and disease. As a key component of disease biomarker research, proteomics is particularly important for understanding complex disorders. Among post-translational modifications, glycosylation is one of the most prevalent and functionally significant, influencing protein stability, signaling, immune responses, and cell-cell interactions. Aberrant glycosylation patterns are associated with various diseases, including neurodegenerative disorders and cancers, where specific glycan and glycopeptide isomers serve as potential biomarkers. Metabolomics, the study of small-molecule metabolites involved in cellular processes, complements proteomics by offering a snapshot of an organism’s physiological state. As part of the multi-omics framework, metabolomics follows the genome, transcriptome, and proteome, providing a comprehensive view of disease progression and metabolic regulation. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful analytical platform for characterizing proteins, glycoproteins, glycans, and metabolites, offering high sensitivity and resolution. This dissertation presents advancements in LC-MS/MS methodologies to enhance biomolecular characterization. A FAIMS-assisted approach was developed to improve the separation of permethylated glycan isomers, while mesoporous graphitized carbon (MGC) chromatography combined with ultraviolet photodissociation enabled enhanced structural characterization of glycopeptide and glycan isomers. Additionally, an isotopic labeling strategy was established to facilitate the simultaneous analysis of up to sixteen glycomics samples in a single run. A label-free, bottom-up proteomics workflow was optimized and applied to investigate the cognitive and cardiovascular effects of antidiabetic drugs, including DPP4 and SGLT2 inhibitors, in type 2 diabetes patients with mild cognitive impairment. Furthermore, comprehensive serum metabolomics profiling of narcolepsy type 1 (NT1) patients was conducted to identify potential disease markers. By integrating advanced LC-MS/MS techniques into proteomics, glycomics, and metabolomics research, this work contributes to biomarker discovery, disease characterization, and therapeutic development, reinforcing the critical role of multi-omics approaches in modern biomedical science.