Developments and perspectives in high-throughput protein glycomics: enabling the analysis of thousands of samples

Abstract

Glycans expand the structural complexity of proteins by several orders of magnitude, resulting in a tremendous analytical challenge when including them in biomedical research. Recent glycobiological research is painting a picture in which glycans represent a crucial structural and functional component of the majority of proteins, with alternative glycosylation of proteins and lipids being an important regulatory mechanism in many biological and pathological processes. Since interindividual differences in glycosylation are extensive, large studies are needed to map the structures and to understand the role of glycosylation in human (patho)physiology. Driven by these challenges, methods have emerged, which can tackle the complexity of glycosylation in thousands of samples, also known as high-throughput (HT) glycomics. For facile dissemination and implementation of HT glycomics technology, the sample preparation, analysis, as well as data mining, need to be stable over a long period of time (months/years), amenable to automation, and available to non-specialized laboratories. Current HT glycomics methods mainly focus on protein N-glycosylation and allow to extensively characterize this subset of the human glycome in large numbers of various biological samples. The ultimate goal in HT glycomics is to gain better knowledge and understanding of the complete human glycome using methods that are easy to adapt and implement in (basic) biomedical research. Aiming to promote wider use and development of HT glycomics, here, we present currently available, emerging, and prospective methods and some of their applications, revealing a largely unexplored molecular layer of the complexity of life.

Keywords: glycomics; glycoproteomics; high-throughput; mass spectrometry; population studies.

Fig. 1

Total serum N-glycosylation profiles as obtained by HILIC–UHPLC–FLD, CGE–LIF, and MALDI–MS. A) Electropherogram by CGE–LIF after APTS labeling (Reiding et al. 2019). B) Chromatogram by HILIC–UHPLC–FLD after 2-AB labeling (Reiding et al. 2019; Zaytseva et al. 2020). C) Mass spectrum by MALDI–FT–ICR–MS after differential sialic acid esterification (Vreeker et al. 2018; Reiding et al. 2019). The m/z values of the assigned signals in C) correspond to [M + Na]⁺. HILIC–UHPLC–FLD and CGE–LIF can distinguish differences in branching (galactose arm, bisection, and fucose position). Structures are assigned based on exoglycosidase treatment and/or tandem MS data as well as literature knowledge on N-glycan biosynthesis. Some signals correspond to multiple glycan compositions for which the major one is assigned in the figure (CGE–LIF and HILIC–UHPLC–FLD). ^*For full assignments of each signal detected, see Supplemental Tables SI–SIII.

Fig. 2

Simultaneous nanoLC-qTOF-MS glycopeptide profiling of IgG and IgA. A) Extracted ion chromatograms for the most abundant glycopeptide per glycosylation site (SES-H4N5F1S1, IgG1-H4N4F1, IgG4-H3N4F1, TPL-H5N5F1S1, IgG2/3-H3N4F1, ENI-H5N4S2, HYT-H4N4S1, LAGc-H5N5F1S2, IIV-H5N5F1S2, LAGy-H5N5F1S2, and LSL-H5N4S1). Protein names and the first three letters of the amino acid sequence of the respective tryptic peptide are given (Momcilovic et al. 2020). Separation was based on the peptide backbones, clustering the analytes with the same peptide sequence but varying glycan portions. The blue and orange boxes indicate the time windows used to generate summed spectra in B) and C), respectively. B) The 10 most abundant glycopeptides from the IgA1 HYT O-glycopeptide cluster, with their accurate mass and suggested monosaccharide compositions. C) The 10 most abundant glycopeptides from the IgA1/2 LSL N-glycopeptide cluster, with their accurate mass and proposed N-glycan structures (based on tandem MS and literature). *Signals not derived from glycopeptides. This figure is adjusted with permission from Momcilovic et al. (2020).

Fig. 3

Derived N-glycosylation traits. A) N-glycans can be divided into four types, representing their maturation throughout the biosynthetic pathway. B) Per N-glycosylation type, different traits can be calculated as shown here for complex type N-glycans (including their common abbreviations). The calculation of derived glycosylation traits allows the representation of basic glycan biosynthesis steps and enhances data precision.