A large-scale method for the enrichment and identification of N-glycopeptides in microscale plasma samples

微尺度化学 化学 色谱法 糖肽 比例(比率) 鉴定(生物学) 生物化学 数学 植物 量子力学 生物 物理 数学教育 抗生素
作者
Xinyi Yang,Weijie Qin
出处
期刊:Sepu [Science Press]
卷期号:43 (9): 996-1004
标识
DOI:10.3724/sp.j.1123.2025.04004
摘要

Blood, which forms part of the systemic circulatory system, contains proteins from various tissues and organs. Hence, blood samples are ideal vehicles for studying diseases and physiological states. Plasma is an important component of blood and is essential for clinical proteomics research. Plasma contains rich physiological and pathological information; consequently, it is an ideal medium for discovering disease-related biomarkers. Protein N-glycosylation is a key post-translational modification route. This route is widely involved in biological processes such as intercellular communication, immune regulation, and signal transduction. Changes resulting from aberrant N-glycosylation are closely associated with various pathological conditions, including autoimmune and neurodegenerative diseases and tumors. Hence, N-glycosylation proteomics is highly valuable during biomarker and drug-target development. However, efficiently enriching N-glycopeptides in biological samples before detection by mass spectrometry (MS) is difficult. This is because the highly abundant unmodified peptides result in signal suppression. Consequently, achieving deep N-glycoproteomic coverage is a key challenge, particularly for trace plasma samples, for which in-depth studies are currently lacking. In this study, we developed a strategy for comprehensively profiling trace N-glycopeptides in plasma. This includes an efficient enrichment method in combination with highly sensitive MS. The developed approach integrates glycopeptide enrichment using advanced hydrophilic interaction liquid chromatography (HILIC) with state-of-the-art MS platforms. This significantly enhances detection depth and sensitivity during N-glycosylation analysis using minimal plasma volumes. Selectivity and efficiency during N-glycopeptide enrichment were maximized by systematically optimizing key HILIC-packed stationary-phase parameters. These parameters include chemical composition, pore size, and surface modification. Additionally, the elution gradient was fine-tuned to improve glycopeptide recovery. This optimization process delivered high N-glycopeptide specificity, even in complex plasma matrices. To overcome the limitations of single-platform MS, we implemented a complementary dual-platform strategy. This strategy combines the high-speed, high-resolution capabilities of the Tims TOF Pro 2 instrument with the ultra-high mass accuracy and resolution of the Orbitrap Lumos spectrometer. The former instrument facilitates the rapid and sensitive identification of glycopeptides, particularly for low-abundance species. It exploits the trapped ion mobility spectrometry (TIMS) and parallel accumulated sequential fragmentation (PASEF) technology. The Orbitrap Lumos provides exceptional mass accuracy and high-resolution MS/MS spectra that enable confident glycopeptide structural characterization. This synergistic approach significantly expands the N-glycopeptide identification depth and ensures comprehensive glycosylation-site and glycan-composition coverage. The developed optimized workflow successfully identified 2 962 intact N-glycopeptides using only 20 μg of plasma peptides (equivalent to 0.5 μL of whole plasma). This set a new benchmark for sensitivity in the micro-volume plasma glycoproteome field. This achievement addresses a critical gap, where conventional methods typically require much larger sample volumes. This limits their applicability to clinical and precision medicine settings where sample availability is restricted. The developed platform provides a robust and reliable analytical framework for plasma N-glycoproteomics with significant implications for precision medicine. This method facilitates large-scale clinical studies by enabling highly sensitive glycopeptide profiling from very small plasma volumes. This included the longitudinal monitoring of disease progression and therapeutic responses. Furthermore, it offers a powerful tool for discovering novel N-glycosylation-based biomarkers for use in early disease diagnosis, prognosis, and personalized treatment strategies. In summary, this study advances the technical capabilities of plasma N-glycoproteomics. Additionally, it facilitates the broader use of plasma N-glycoproteomics in biomedical research and clinical diagnostics.
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