在 Tecan Freedom EVO 平台上自动开发高通量 IgG N-糖基化样品制备方法。

Borna Rapčan, Maja Hanić, Branimir Plavša, Jelena Šimunović, Jerko Štambuk, Frano Vučković, Irena Trbojević-Akmačić, Mislav Novokmet, Gordan Lauc, Genadij Razdorov
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引用次数: 0

摘要

简介聚糖学主要研究聚糖在生物过程中的作用,特别是对抗体等聚糖结合体的折叠、稳定性和受体相互作用的影响,这对我们了解生物学至关重要。免疫球蛋白 G(IgG)N-糖基化的变化与各种生理和病理生理状况有关。然而,耗时的人工样本制备是实施糖化学诊断的局限之一。本研究旨在开发一种在 Tecan Freedom Evo 200 平台上进行样本制备的自动化方法,并将其效率和精确度与人工方法进行比较:最初的方法开发包括 32 个集合血浆技术重复样本。在方法比较中还使用了另外 24 份集合样本,以及从 10,001 只达尔马提亚犬生物库中收集的 78 份随机重复血浆样本,以比较手动和自动方法:结果:开发出了一种新的自动方法。在自动方法中,占样本糖类总量 91% 的糖类峰变化小于 5%,而在手动方法中,占样本总量 92% 的糖类峰变化小于 5%。Passing-Bablok 回归结果表明,在 12 个聚糖峰 (GP) 上,自动方法和手动方法之间没有差异。然而,8 个 GPs 存在系统差异,4 个 GPs 同时存在系统差异和比例差异:结论:所开发的 IgG 聚糖分析自动化样品制备方法减少了接触有害化学物质的机会,简化了工作流程。尽管两种方法之间存在细微差别,但新的自动方法显示出很高的精确度,并被证明与人工方法具有很高的可比性。
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Automated high throughput IgG N-glycosylation sample preparation method development on the Tecan Freedom EVO platform.

Introduction: Glycomics, focusing on the role of glycans in biological processes, particularly their influence on the folding, stability and receptor interactions of glycoconjugates like antibodies, is vital for our understanding of biology. Changes in immunoglobulin G (IgG) N-glycosylation have been associated with various physiological and pathophysiological conditions. Nevertheless, time-consuming manual sample preparation is one of the limitations in the glycomics diagnostic implementation. The study aimed to develop an automated method for sample preparation on the Tecan Freedom Evo 200 platform and compare its efficiency and precision with the manual counterpart.

Materials and methods: The initial method development included 32 pooled blood plasma technical replicates. An additional 24 pooled samples were used in the method comparison along with 78 random duplicates of plasma samples collected from 10,001 Dalmatians biobank to compare the manual and automated methods.

Results: The development resulted in a new automated method. For the automated method, glycan peaks comprising 91% of the total sample glycan showed a variation of less than 5% while 92% of the total sample showed a variation of less than 5% for the manual method. The results of the Passing-Bablok regression indicated no differences between the automated and manual methods for 12 glycan peaks (GPs). However, for 8 GPs systematic difference was present, while both systematic and proportional differences were present for four GPs.

Conclusions: The developed automated sample preparation method for IgG glycan analysis reduced exposure to hazardous chemicals and offered a simplified workflow. Despite slight differences between the methods, the new automated method showed high precision and proved to be highly comparable to its manual counterpart.

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