Strategies for Proteome-Wide Quantification of Glycosylation Macro- and Micro-Heterogeneity

Abstract

Protein glycosylation governs key physiological and pathological processes in human cells. Aberrant glycosylation is thus closely associated with disease progression. Mass spectrometry (MS)-based glycoproteomics has emerged as an indispensable tool for investigating glycosylation changes in biological samples with high sensitivity. Following rapid improvements in methodologies for reliable intact glycopeptide identification, site-specific quantification of glycopeptide macro- and micro-heterogeneity at the proteome scale has become an urgent need for exploring glycosylation regulations. Here, we summarize recent advances in N- and O-linked glycoproteomic quantification strategies and discuss their limitations. We further describe a strategy to propagate MS data for multilayered glycopeptide quantification, enabling a more comprehensive examination of global and site-specific glycosylation changes. Altogether, we show how quantitative glycoproteomics methods explore glycosylation regulation in human diseases and promote the discovery of biomarkers and therapeutic targets.

Keywords: glycoproteomics; label free; mass spectrometry; quantification; stable-isotope labeling.

Figure 1

Protein glycosylation and its macro- and micro-heterogeneity. (A) Depicted N- and O-linked glycan structures that present on proteins. (B) Examples of macro- and micro-heterogeneity. Each glycosylated site on a protein may be only partially occupied by various glycans (i.e., site-specific glycoforms). Macro-heterogeneity indicates the abundance or percentage of all glycosylated forms at each site. Micro-heterogeneity represents the relative abundances of the glycoforms at each site (e.g., G1, G2, G3 at site A and G1, G4, G5 at site B). (C) Available information that different layers of glycosylation analysis can offer. Intact glycopeptide analysis allows quantification at the glycosite (macro-heterogeneity), glycoform (micro-heterogeneity) and glycan levels. The triangle indicates that intact glycopeptide analysis does not characterize glycosidic linkages of the glycan structure (only glycan composition).

Figure 2

Labeling-based strategies for quantitative glycoproteomics. (A) Available positions on an intact glycopeptide for different labeling strategies. For example, chemical labeling reagents react with the amines at peptide N-termini or lysine side chains. (B) Sample preparation workflow for various labeling strategies.

Figure 3

Schematic comparison of DIA- and DDA-based quantitative glycoproteomics.

Figure 4

Scheme of SRM (A) and the strategies for selecting glycopeptide SRM transitions (B).

Figure 5

Multi-layered glycoproteome quantification. (A) Quantification at the intact glycopeptide level. I and J represent the intensities of reporter ions in state A and I’ and J’ represent the intensities of reporter ions in state B. The relative abundances of a glycopeptide between two states (A versus B) were obtained by comparing the intensities of their reporter ions. (B) Quantification at the glycosite level. The summed intensities of all glycoforms on the same glycosite (i.e., glycoforms including G1 to Gn on the purple ball, which represent the glycosite) were compared between two states. (C) Quantification at the glycan level. The summed intensities of all glycoforms on the same glycan (i.e., G1 on purple ball and G1 on blue ball) were compared between two states. (D) The correlation of glycosite quantification between the SugarQuant output and the separate quantitative de-glycoproteome experiments of the same samples from nine cell lines with three biological replicates.