Agakova Anna, Vučković Frano, Klarić Lucija, Lauc Gordan, Agakov Felix
Pharmatics Limited, Edinburgh, UK.
Genos Glycoscience Research Laboratory, Zagreb, Croatia.
Methods Mol Biol. 2017;1503:217-233. doi: 10.1007/978-1-4939-6493-2_17.
Ultra-performance liquid chromatography (UPLC) is the established technology for accurate analysis of IgG Fc N-glycosylation due to its superior sensitivity, resolution, speed, and its capability to provide branch-specific information of glycan species. Correct and cost-efficient preprocessing of chromatographic data is the major prerequisite for subsequent analyses ranging from inference of structural isomers to biomarker discovery and prediction of humoral immune response from characterized changes in glycosylation. The complexity of glycomic chromatograms poses a number of challenges for developing automated data annotation and quantitation algorithms, which frequently necessitated manual or semi-manual approaches to preprocessing, most notably to peak detection and integration. Such procedures are meticulous and time-consuming, and may be a source of confounding due to their dependence on human labelers. Although liquid chromatography is a mature field and a number of methods have been developed for automatic peak detection outside the area of glycomics analysis, we found that hardly any of them are suitable for automatic integration of UPLC glycomic profiles without substantial modifications. In this chapter, we illustrate practical challenges of automatic peak detection of UPLC glycomics chromatograms. We outline a robust, semi-supervised method ACE (Automatic Chromatogram Extraction) for automated alignment and detection of glycan peaks in chromatograms, developed by Pharmatics Limited (UK) in collaboration with Genos Limited (Croatia). Application of the tool requires minimal human interference, which results in a significant reduction in the time and cost of IgG glycomics signal integration using Waters Acquity UPLC instrument (Milford, MA, USA) in several human cohorts with blind technical replicas.
超高效液相色谱法（UPLC）因其卓越的灵敏度、分辨率、速度以及提供聚糖种类分支特异性信息的能力，成为准确分析IgG Fc N-糖基化的成熟技术。色谱数据的正确且经济高效的预处理是后续分析的主要前提，这些分析范围从结构异构体的推断到生物标志物的发现以及根据糖基化特征变化预测体液免疫反应。糖组色谱图的复杂性给开发自动化数据注释和定量算法带来了诸多挑战，这常常需要手动或半手动方法进行预处理，最显著的是用于峰检测和积分。此类程序细致且耗时，并且由于依赖人工标注可能会产生混淆。尽管液相色谱是一个成熟领域，并且在糖组学分析领域之外已经开发了许多用于自动峰检测的方法，但我们发现几乎没有一种方法在不进行大量修改的情况下适用于UPLC糖组图谱的自动积分。在本章中，我们阐述了UPLC糖组色谱图自动峰检测的实际挑战。我们概述了一种稳健的半监督方法ACE（自动色谱图提取），用于色谱图中聚糖峰的自动对齐和检测，该方法由英国的Pharmatics Limited与克罗地亚的Genos Limited合作开发。使用该工具只需极少的人工干预，这使得在美国马萨诸塞州米尔福德市的沃特世Acquity UPLC仪器上对多个具有盲法技术重复的人类队列进行IgG糖组学信号积分的时间和成本显著降低。
