Ludger Ltd, Culham Science Centre, Abingdon, Oxfordshire, United Kingdom.
Leiden University Medical Centre, Centre for Proteomics and Metabolomics, Leiden, Netherlands.
PLoS One. 2019 Jan 17;14(1):e0210759. doi: 10.1371/journal.pone.0210759. eCollection 2019.
Protein O-glycosylation has shown to be critical for a wide range of biological processes, resulting in an increased interest in studying the alterations in O-glycosylation patterns of biological samples as disease biomarkers as well as for patient stratification and personalized medicine. Given the complexity of O-glycans, often a large number of samples have to be analysed in order to obtain conclusive results. However, most of the O-glycan analysis work done so far has been performed using glycoanalytical technologies that would not be suitable for the analysis of large sample sets, mainly due to limitations in sample throughput and affordability of the methods. Here we report a largely automated system for O-glycan analysis. We adapted reductive β-elimination release of O-glycans to a 96-well plate system and transferred the protocol onto a liquid handling robot. The workflow includes O-glycan release, purification and derivatization through permethylation followed by MALDI-TOF-MS. The method has been validated according to the ICH Q2 (R1) guidelines for the validation of analytical procedures. The semi-automated reductive β-elimination system enabled for the characterization and relative quantitation of O-glycans from commercially available standards. Results of the semi-automated method were in good agreement with the conventional manual in-solution method while even outperforming it in terms of repeatability. Release of O-glycans for 96 samples was achieved within 2.5 hours, and the automated data acquisition on MALDI-TOF-MS took less than 1 minute per sample. This largely automated workflow for O-glycosylation analysis showed to produce rapid, accurate and reliable data, and has the potential to be applied for O-glycan characterization of biological samples, biopharmaceuticals as well as for biomarker discovery.
蛋白质 O-糖基化已被证明对广泛的生物过程至关重要,因此人们对研究生物样品中 O-糖基化模式的改变产生了浓厚的兴趣,这些改变可作为疾病生物标志物以及用于患者分层和个性化医疗。鉴于 O-聚糖的复杂性,通常需要分析大量样本才能得出结论。然而,迄今为止,大多数 O-聚糖分析工作都是使用糖分析技术完成的,这些技术不适合分析大量样本集,主要是由于方法的样本通量和可负担性有限。在这里,我们报告了一种用于 O-聚糖分析的自动化系统。我们将 O-聚糖的还原β-消除释放方法改编为 96 孔板系统,并将该方案转移到液体处理机器人上。该工作流程包括 O-聚糖的释放、通过全甲基化进行纯化和衍生化,然后进行 MALDI-TOF-MS 分析。该方法已根据 ICH Q2(R1)分析程序验证指南进行了验证。半自动还原β-消除系统可用于表征和相对定量商业上可获得的标准品中的 O-聚糖。半自动方法的结果与传统的手动溶液法吻合良好,而在重复性方面甚至表现更好。在 2.5 小时内完成了 96 个样本的 O-聚糖释放,而 MALDI-TOF-MS 的自动数据采集每个样本不到 1 分钟。这种用于 O-糖基化分析的自动化工作流程显示出快速、准确和可靠的数据生成能力,有可能应用于生物样品、生物制药以及生物标志物发现中的 O-聚糖表征。