Regulation of protein N-glycosylation is essential in human cells. However, large-scale, accurate, and site-specific quantification of glycosylation is still technically challenging. We here introduce SugarQuant, an integrated mass spectrometry-based pipeline comprising protein aggregation capture (PAC)-based sample preparation, multi-notch MS3 acquisition (Glyco-SPS-MS3) and a data-processing tool (GlycoBinder) that enables confident identification and quantification of intact glycopeptides in complex biological samples. PAC significantly reduces sample-handling time without compromising sensitivity. Glyco-SPS-MS3 combines high-resolution MS2 and MS3 scans, resulting in enhanced reporter signals of isobaric mass tags, improved detection of N-glycopeptide fragments, and lowered interference in multiplexed quantification. GlycoBinder enables streamlined processing of Glyco-SPS-MS3 data, followed by a two-step database search, which increases the identification rates of glycopeptides by 22% compared with conventional strategies. We apply SugarQuant to identify and quantify more than 5,000 unique glycoforms in Burkitt’s lymphoma cells, and determine site-specific glycosylation changes that occurred upon inhibition of fucosylation at high confidence.

蛋白质N-糖基化的调节在人类细胞中至关重要。然而，糖基化的大规模，准确和位点特异性定量在技术上仍然具有挑战性。我们在这里介绍SugarQuant，这是一个基于质谱的集成管道，包括基于蛋白质聚集捕获（PAC）的样品制备，多级MS3采集（Glyco-SPS-MS3）和数据处理工具（GlycoBinder），可实现可靠的鉴定和鉴定。复杂生物样品中完整糖肽的定量分析。PAC大大减少了样品处理时间，而不会影响灵敏度。Glyco-SPS-MS3结合了高分辨率MS2和MS3扫描，可增强等压质量标签的报告基因信号，改善N-糖肽片段的检测，并降低多重定量分析的干扰。GlycoBinder可以简化对Glyco-SPS-MS3数据的处理，然后进行两步数据库搜索，与传统策略相比，糖肽的识别率提高了22％。我们应用SugarQuant来识别和量化Burkitt淋巴瘤细胞中的5,000多种独特糖型，并确定以高可信度抑制岩藻糖基化后发生的位点特异性糖基化变化。