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[一种用于微量血浆样本中N-糖肽富集与鉴定的大规模方法]

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

作者信息

Yang Xin-Yi, Qin Wei-Jie

机构信息

School of Basic Medical Science,Anhui Medical University,Hefei 230032,China.

State Key Laboratory of Medical Proteomics,National Center for Protein Sciences (Beijing),Beijing Proteome Research Center,Beijing Institute of Lifeomics,Beijing 102206,China.

出版信息

Se Pu. 2025 Sep;43(9):996-1004. doi: 10.3724/SP.J.1123.2025.04004.

Abstract

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.

摘要

血液作为体循环系统的一部分,含有来自各种组织和器官的蛋白质。因此,血液样本是研究疾病和生理状态的理想载体。血浆是血液的重要组成部分,对临床蛋白质组学研究至关重要。血浆包含丰富的生理和病理信息;因此,它是发现疾病相关生物标志物的理想介质。蛋白质N-糖基化是一种关键的翻译后修饰途径。该途径广泛参与细胞间通讯、免疫调节和信号转导等生物过程。N-糖基化异常导致的变化与各种病理状况密切相关,包括自身免疫性疾病、神经退行性疾病和肿瘤。因此,N-糖基化蛋白质组学在生物标志物和药物靶点开发中具有很高的价值。然而,在通过质谱(MS)检测之前,有效地富集生物样品中的N-糖肽具有挑战性。这是因为大量未修饰的肽会导致信号抑制。因此,实现深度N-糖蛋白质组覆盖是一个关键挑战,特别是对于微量血浆样本,目前缺乏深入研究。在本研究中,我们开发了一种策略,用于全面分析血浆中的微量N-糖肽。这包括一种有效的富集方法与高灵敏度MS相结合。所开发的方法将先进的亲水相互作用液相色谱(HILIC)用于糖肽富集与最先进的MS平台相结合。这在使用最小血浆体积进行N-糖基化分析时显著提高了检测深度和灵敏度。通过系统优化关键的HILIC填充固定相参数,使N-糖肽富集的选择性和效率最大化。这些参数包括化学成分、孔径和表面修饰。此外,对洗脱梯度进行了微调以提高糖肽回收率。即使在复杂的血浆基质中,这种优化过程也能提供高N-糖肽特异性。为了克服单平台MS的局限性,我们实施了一种互补的双平台策略。该策略将Tims TOF Pro 2仪器的高速、高分辨率能力与Orbitrap Lumos光谱仪的超高质量精度和分辨率相结合。前一种仪器有助于快速、灵敏地鉴定糖肽,特别是对于低丰度物种。它利用了捕集离子淌度质谱(TIMS)和平行累积连续碎裂(PASEF)技术。Orbitrap Lumos提供了出色的质量精度和高分辨率MS/MS谱图,能够可靠地进行糖肽结构表征。这种协同方法显著扩展了N-糖肽鉴定深度,并确保了全面的糖基化位点和聚糖组成覆盖。所开发并优化的工作流程仅使用20μg血浆肽(相当于0.5μL全血)就成功鉴定了2962种完整的N-糖肽。这在微量血浆糖蛋白质组领域为灵敏度设定了新的基准。这一成果填补了一个关键空白,传统方法通常需要大得多的样本量,这限制了它们在样本获取受限的临床和精准医学环境中的适用性。所开发的平台为血浆N-糖蛋白质组学提供了一个强大且可靠的分析框架,对精准医学具有重要意义。该方法通过能够从非常少量的血浆中进行高灵敏度糖肽分析,促进了大规模临床研究,包括疾病进展和治疗反应的纵向监测。此外,它为发现基于N-糖基化的新型生物标志物提供了一个强大工具,用于早期疾病诊断、预后和个性化治疗策略。总之,本研究提升了血浆N-糖蛋白质组学的技术能力。此外,它促进了血浆N-糖蛋白质组学在生物医学研究和临床诊断中的更广泛应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4d39/12412023/032ca62b6fa5/2A93E92A-7AE1-439e-BD9D-90F4E2F64AFD-F001.jpg

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