Automated high throughput IgG N-glycosylation sample preparation method development on the Tecan Freedom EVO platform

Author:

Lauc Gordan1,Novokmet Mislav2,Trbojević-Akmačić Irena2,Vučković Frano2,Štambuk Jerko2,Šimunović Jelena2,Plavša Branimir1,Hanić Maja2,Rapčan Borna1,Razdorov Genadij2

Affiliation:

1. Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia

2. Genos Glycoscience Research Laboratory, Zagreb, Croatia

Abstract

Graphical abstract Highlights • An automated immunoglobulin G (IgG) N-glycan method was developed leveraging positive pressure technology • In general, automated method showed satisfactory precision and agreement with manual method that is in current use • The automated method holds the possibility to significantly streamline sample preparation, enhancing throughput and improving laboratory safety IntroductionGlycomics, 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 methodsThe 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. ResultsThe 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. ConclusionsThe 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.

Publisher

Croatian Society for Medical Biochemistry and Laboratory Medicine

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