Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.
Department of Genome Sciences, University of Washington, Seattle, WA, USA.
Analyst. 2019 Jun 7;144(11):3601-3612. doi: 10.1039/c9an00486f. Epub 2019 May 8.
Porous graphitized carbon (PGC) based chromatography achieves high-resolution separation of glycan structures released from glycoproteins. This approach is especially valuable when resolving structurally similar isomers and for discovery of novel and/or sample-specific glycan structures. However, the implementation of PGC-based separations in glycomics studies has been limited because system-independent retention values have not been established to normalize technical variation. To address this limitation, this study combined the use of hydrolyzed dextran as an internal standard and Skyline software for post-acquisition normalization to reduce retention time and peak area technical variation in PGC-based glycan analyses. This approach allowed assignment of system-independent retention values that are applicable to typical PGC-based glycan separations and supported the construction of a library containing >300 PGC-separated glycan structures with normalized glucose unit (GU) retention values. To enable the automation of this normalization method, a spectral MS/MS library was developed of the dextran ladder, achieving confident discrimination against isomeric glycans. The utility of this approach is demonstrated in two ways. First, to inform the search space for bioinformatically predicted but unobserved glycan structures, predictive models for two structural modifications, core-fucosylation and bisecting GlcNAc, were developed based on the GU library. Second, the applicability of this method for the analysis of complex biological samples is evidenced by the ability to discriminate between cell culture and tissue sample types by the normalized intensity of N-glycan structures alone. Overall, the methods and data described here are expected to support the future development of more automated approaches to glycan identification and quantitation.
多孔石墨化碳 (PGC) 基色谱可实现从糖蛋白中释放的聚糖结构的高分辨率分离。当解析结构相似的异构体和发现新的和/或样品特异性聚糖结构时,这种方法特别有价值。然而,由于尚未建立系统独立的保留值来归一化技术变化,因此 PGC 基分离在糖组学研究中的实施受到限制。为了解决这一限制,本研究结合使用水解葡聚糖作为内标和 Skyline 软件进行采集后归一化,以减少 PGC 基聚糖分析中的保留时间和峰面积技术变化。这种方法允许分配适用于典型 PGC 基聚糖分离的系统独立保留值,并支持构建包含>300 种 PGC 分离聚糖结构的库,其归一化葡萄糖单位 (GU) 保留值。为了实现这种归一化方法的自动化,开发了葡聚糖梯的光谱 MS/MS 库,实现了对异构体聚糖的可靠区分。该方法的实用性通过两种方式得到证明。首先,为了告知生物信息学预测但未观察到的聚糖结构的搜索空间,基于 GU 库开发了两种结构修饰(核心岩藻糖基化和双分支 GlcNAc)的预测模型。其次,通过归一化 N-聚糖结构的强度能够区分细胞培养和组织样本类型,证明了该方法在分析复杂生物样本中的适用性。总体而言,这里描述的方法和数据有望支持未来开发更自动化的聚糖鉴定和定量方法。