Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation

Abstract

Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification.

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

“…Of note, the glycopeptide coverage of Byonic depends highly on the search parameters, fragmentation settings, and the presence and quality of MS/MS spectra. The latter is often compromised due to dynamic range limitations, especially in complex matrices [ 11 , 30 ]. The accuracy of both approaches (MS1 and MS/MS) may be impaired by unexpected peptide modifications, as exemplified for Cys oxidation.…”

“…In the current work, a python script was developed to streamline the connection between GlycopeptideGraphMS identification and LaCyTools quantification ( Supporting Information File 1 ). All tools provided absolute values for glycopeptide quantification, which were subsequently total-area-normalized per glycosylation site, as commonly done in label-free relative quantification in glycoproteomics [ 16 – 17 30 , 32 ].…”

GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

“…Popular and powerful glycoproteomic workflows typically involve standard proteomic sample preparation and protease digestion, coupled with depletion of abundant proteins or enrichment of glycopeptides to enable their measurement. There have also been several advances in glycopeptide quantification strategies including chemical labelling, label-free and data-independent acquisition methods [ 10 ]. Progress in MS technology in particular has enabled deep and sensitive measurement of highly complex glycoproteomes, generating large amounts of high quality data [ 11 ].…”

“…There are several freely available software programs for quantitative label-free glycoproteomics using MS1 or data-dependent acquisition. These include LaCyTools [ 17 ], MassyTools [ 18 ], and GlycoSpectrumScan [ 19 ], which use a predefined list of analytes and masses to interrogate MS1 data, and I-GPA [ 20 ], GlycopeptideGraphMS [ 21 ], GlycoFragwork [ 22 ], and GlycReSoft [ 23 ], which integrate identification and abundance/intensity information for glycopeptides (a recent review is provided in [ 10 ]). Importantly, the complexity of glycan heterogeneity requires that downstream analysis often involves manual processing in addition to standard informatics workflows.…”

Calculating Glycoprotein Similarities From Mass Spectrometric Data

“…As mentioned earlier, the glycan post-translational modification itself undergoes dissociation during tandem MS. Such glycan dissociation, combined with glycosylation heterogeneity are not presently allowed for quantitative proteomics tools.Glycopeptide quantification using MS reaction monitoring has been reviewed in detail elsewhere(6). The following discussion is oriented towards the use of MS reaction monitoring for protein similarity measurement.…”