Automated RapiFluor-MS Labeled Glycan Sample Preparation for Disulfide-Rich Glycoprotein

Significance of the Topic

Effective monitoring of N-glycans is critical for the quality control of biotherapeutics, as glycan structures influence protein folding, stability, and clinical performance

Objectives and Overview of the Study

This study presents an automated sample preparation method for profiling N-glycans from disulfide-rich glycoproteins, exemplified by human chorionic gonadotrophin (hCG). It aims to streamline reduction, deglycosylation, labeling, and purification into a rapid workflow compatible with both QC and research environments

Methodology and Instrumentation

The protocol integrates the following steps on an Andrew+ Pipetting Robot:

• Reduction of disulfide bonds using dithiothreitol (DTT) and denaturation surfactant
• Enzymatic release of N-glycans with Rapid PNGase F at elevated temperature
• RapiFluor-MS labeling in anhydrous DMF, followed by optional quenching with ammonium acetate for RP/AX separation
• Solid-phase extraction cleanup using a GlycoWorks HILIC µElution plate
• Purified glycans diluted and transferred for UPLC analysis

Used Instrumentation

• Andrew+ Pipetting Robot with plate gripper
• ACQUITY UPLC H-Class PLUS Bio System
• FLR Detector (Ex 265 nm, Em 425 nm)
• ACQUITY UPLC Glycan BEH Amide Column, 1.7 µm, 2.1×150 mm
• Empower 3 Chromatography Data System

Main Results and Discussion

Automated and manual workflows yielded comparable glycan recovery and relative abundances for a monoclonal antibody standard, with relative standard deviations below 5%. Application to hCG demonstrated enhanced release of previously inaccessible glycoforms when employing DTT reduction. Automated preparation of up to 32 samples in one hour produced consistent profiles, with peak area variations within acceptable QC limits

Benefits and Practical Applications

• Rapid processing of 32 samples per hour
• Reproducible glycan profiles from complex, disulfide-rich proteins
• QC-friendly workflow adaptable to both HILIC and RP/AX separations
• Reduced manual handling and risk of variability

Future Trends and Opportunities

Continued integration of automated glycan sample preparation with high-resolution mass spectrometry will enable deeper structural characterization. Adaptation of the workflow to other complex glycoproteins and miniaturized formats may further enhance throughput. Data analytics and machine learning could improve glycan assignment and tracking in biomanufacturing processes

Conclusion

The automated RapiFluor-MS reducing protocol delivers a robust, high-throughput solution for N-glycan profiling of disulfide-rich glycoproteins, combining rapid sample handling with reliable chromatographic performance. This method supports both research and quality control applications in biotherapeutic development

References

1. Essentials of Glycobiology Third Edition. Cold Spring Harbor Laboratory Press
2. Challenges of Glycosylation Analysis and Control: Drug Discovery Today 2016, 21(5), 740–765
3. Glycan Analysis as Biomarkers for Testicular Cancer: Diagnostics 2019, 9(4), 156
4. N- and O-Glycosylation of hCG Beta Subunit: Glycoconjugate Journal 2020, 37(5), 599–610
5. Rapid Preparation of Released N-glycans for HILIC Analysis: Anal. Chem. 2015, 87(10), 5401–5409
6. Quality Control and Automation Friendly GlycoWorks RapiFluor-MS: Waters Application Note 720005506EN
7. Automated GlycoWorks RapiFluor-MS Preparations on Andrew+: Waters Application Brief 720007008EN