Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable quantification

Abstract

Glycoproteomic data are often very complex, reflecting the high structural diversity of peptide and glycan portions. The use of glycopeptide-centered glycoproteomics by mass spectrometry is rapidly evolving in many research areas, leading to a demand in reliable data analysis tools. In recent years, several bioinformatic tools were developed to facilitate and improve both the identification and quantification of glycopeptides. Here, a selection of these tools was combined and evaluated with the aim of establishing a robust glycopeptide detection and quantification workflow targeting enriched glycoproteins. For this purpose, a tryptic digest from affinity-purified immunoglobulins G and A was analyzed on a nano-reversed-phase liquid chromatography–tandem mass spectrometry platform with a high-resolution mass analyzer and higher-energy collisional dissociation fragmentation. Initial glycopeptide identification based on MS/MS data was aided by the Byonic software. Additional MS1-based glycopeptide identification relying on accurate mass and retention time differences using GlycopeptideGraphMS considerably expanded the set of confidently annotated glycopeptides. For glycopeptide quantification, the performance of LaCyTools was compared to Skyline, and GlycopeptideGraphMS. All quantification packages resulted in comparable glycosylation profiles but featured differences in terms of robustness and data quality control. Partial cysteine oxidation was identified as an unexpectedly abundant peptide modification and impaired the automated processing of several IgA glycopeptides. Finally, this study presents a semiautomated workflow for reliable glycoproteomic data analysis by the combination of software packages for MS/MS- and MS1-based glycopeptide identification as well as the integration of analyte quality control and quantification.

Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of Monoclonal Antibody Biologics

“…GraphMS was previously used to analyse LC-MS1 data acquired from Orbitrap Fusion Lumos MS coupled with a nLC system to characterise glycopeptides of human plasma IgG ( Lippold et al, 2020 ). However, the use of the software on BioAccord LC-MS system has not been explored.…”

“…There is an increase rate of new approved monoclonal antibody (mAb) therapeutic products from different IgG subclasses ( Kaplon and Reichert, 2021 ). Each IgG subclass can exhibit different degrees of glycosylation ( Lippold et al, 2020 ). Ensuring the correct glycosylation of the product is therefore a major challenge within bioprocessing operations.…”

“…Doing so offers higher confidence in characterisation and validation of glycopeptides in any complex sample. It has previously been exploited to comprehensively characterise AXL protein, a glycoprotein with higher complex glycosylation heterogeneity compared to antibody molecules, and IgG ( Choo et al, 2019 ; Lippold et al, 2020 ).…”

Glycoinformatics in the Artificial Intelligence Era

“…Another key point regarding data collection is the current limited availability of quantitative data that would allow more accurate profiling. At present, immunoglobulin profiling is by far the most advanced in comparison to other glycoconjugates. , Other prospects can be expected to expand from improved techniques in glycan and tissue imaging. , …”

GlycoBioinformatics

“…Most structural data at this stage is generated by analytical approaches, such as mass spectrometry (MS), high-pressure liquid chromatography (HPLC), and capillary electrophoresis (CE). The articles by Phung et al [ 9 ] and by Lippold et al [ 10 ] suggest ways of combining and customising available MS data analysis tools for glycoproteomic characterization and quantification. The article by Walsh et al [ 11 ], on the other hand, addresses the problems of an irreproducible retention time and peak integration in antibody glycomic analysis using CE, thus allowing small quantitative differences to be detected when comparing similar glycomes by this method.…”