Protein N-Glycosylation Analysis Strategy and Procedures

The glycan chain is located in the outermost layer of the cell and is the first molecule encountered and recognized by other cells, antibodies, invading viruses and bacteria. N-glycans regulate a wide range of cellular functions, including cell-cell and cell-matrix adhesion, cell proliferation, cell survival and immune system response, by affecting protein folding, recognition, clearance, secretion and construction.

The difficulty in N-glycosylation analysis lies in identifying the glycosylation modification sites of proteins and in analyzing the highly complex N-glycans attached to the glycosylation sites. N-glycosylation modifications are not guided by a template and are performed by multiple enzymes acting together. It is highly influenced by different physiological conditions, and multiple glycosylation sites may exist on a single protein. Moreover, the same glycan chain modification can occur at multiple glycosylation sites, which is called macroheterogeneity of protein glycosylation. In addition, different glycan modifications can occur at the same glycosylation site, which is called microheterogeneity of glycosylation. Complex glycan chains can form stereo-isomers and regio-isomers. A comprehensive study of these targets can make the analysis exceptionally complex.

N-glycosylation Analysis Strategies

1. Intact analysis: MS analysis of intact glycoproteins under denaturing or non-denaturing conditions. This method allows to obtain complete protein information of glycoproteins as well as to directly analyze the combination of post-translational modifications and modifications. This technique is challenging due to the large microhomogeneities exhibited by glycoprotein structures and the limitations of existing techniques to effectively separate glycoproteins from proteins.

2. Bottom-up glycoproteomic analysis: The glycan chains and peptide chains of glycoproteins are analyzed separately. The released N-glycans are isolated and structurally analyzed. In addition, the digestion of glycoproteins into glycopeptides allows the study of specific glycosylated content using a peptide-centered proteomic analysis. The advantage of this method is that the specific site of glycosylation on the protein can be identified and that site-specific information can be used to identify different glycosylation sites for a specific glycan structure. It also helps to understand the molecular structure of the protein.

N-glycan Analysis Procedures

• N-glycan release

The main methods for N-glycan release are enzymatic and chemical.

An earlier and more effective method for the chemical release of N-glycans is hydrazinolysis. However, anhydrous hydrazine is a highly toxic and explosive solvent and the released N-glycans under such conditions are subject to further degradation and isomerization, resulting in inhomogeneity of the sugar chains and causing some interference in the subsequent analysis. Therefore, the use of hydrazinolytic methods has been greatly reduced and replaced by milder enzymatic methods.

Glycosidases are a powerful tool in N-glycan analysis and are divided into exonucleolytic and endonucleolytic glycosidases. The release of N-glycans from glycoproteins using the endo-glycosidase method is the most direct and the simplest. Endoglycosidases can cut intact N-glycans from peptide chains or hydrolyze the glycosidic bonds within N-glycans, cutting long glycans into shorter oligosaccharide fragments for easy analysis.

• N-glycan labeling

Free N-glycans lack chromophore or fluorophore properties, making chromatography-based analysis difficult. N-glycans are also not ideal analytes for MS. Its strong hydrophilicity leads to inefficient desolvation, resulting in suboptimal ionization efficiency during MS analysis. Therefore, chemical labeling (i.e. derivatization) of N-glycans is often used prior to analysis to modulate the physicochemical properties of N-glycans and increase the sensitivity of the assay.

N-glycan derivatization can be divided into four main types: (1) holomethylation modification, (2) reducing end modification, (3) sialic acid modification, and (4) multiple labeling modification of N-glycans.

• N-glycan instrument detection

The separation and structural analysis of N-glycans are generally performed by liquid chromatography (LC), mass spectrometry (MS), capillary electrophoresis (CE), etc.

• Analysis of sample data of N-glycans

For large numbers of samples, a large amount of N-glycan data needs to be statistically analyzed. If the data are normally distributed, independent sample t-test (T-test) or analysis of variance (ANOVA) can be used to determine the differences between samples and to test the data for homogeneity of variance. If the sample data have homogeneity of variance, the ANOVA results are retained and further parametric tests are performed. If the sample does not have homogeneity of variances, further analysis by non-parametric tests is required.

Discriminant analysis (DA) is used to assess the adequacy of the classification system. Unlike ANOVA or multiple ANOVA analysis, which predicts one or more continuous dependent variables by one or more independent variables, DA is a powerful analytical method to determine the validity of the prediction of a set of variables across various samples. The main data processing methods commonly used in DA are principal component analysis (PCA) or partial least squares (PLS).

Even if there is a statistical difference in one or some N-glycan data between two groups of samples with different conditions, this does not necessarily mean that this or these N-glycans can be used as a diagnostic marker to distinguish between the two. Further subject characteristic curve (ROC) analysis is required. In ROC analysis, curves are plotted based on the true positive rate (sensitivity) and false positive rate (specificity). These area under the curve (AUC) plots are then used to distinguish whether or not they can be used as biomarkers. In addition, in clinical diagnostic analysis, samples are sometimes studied in multivariate analysis in combination with multiple markers.

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