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Released N-linked Glycan Analysis Using the BioAccord System

Applications | 2019 | WatersInstrumentation
LC/TOF, LC/HRMS, LC/MS
Industries
Clinical Research
Manufacturer
Waters

Summary

Significance of the Topic


Efficient and reliable analysis of N-linked glycans is essential in biopharmaceutical development to ensure the safety and efficacy of glycoprotein therapeutics. Glycan heterogeneity influences drug stability, bioactivity, and immunogenicity. Traditional fluorescence detection methods offer limited specificity, while mass spectrometry provides detailed structural information but often requires specialized expertise. Integrating both detection modes simplifies workflow, enhances confidence, and supports regulatory compliance in QA/QC and product development.

Objectives and Overview of the Study


This study aims to demonstrate the capabilities of the BioAccord System, a combined LC-optical-MS platform, to streamline N-linked glycan analysis. Key goals include:
  • Automating sample preparation and data acquisition.
  • Collecting complementary fluorescence and high-resolution mass spectrometry data.
  • Accelerating glycan identification and quantification in a regulated environment.

Methodology


The workflow involved:
  • Release of N-linked glycans from the therapeutic fusion protein abatacept using established enzymatic protocols.
  • Labeling with the RapiFluor-MS (RFMS) fluorescent tag via an automated liquid handling system.
  • Hydrophilic interaction liquid chromatography (HILIC) separation on a 150 mm BEH Glycan Amide column.
  • Simultaneous fluorescence (FLR) detection and full-scan MS analysis.
  • Automated data processing with retention time calibration (GU values) and library-based peak assignment in UNIFI software.

Instrumentation


The analysis employed:
  • BioAccord System featuring an ACQUITY UPLC I-Class Plus with optical detector (TUV or FLR).
  • ACQUITY RDa Detector for accurate mass measurements.
  • Andrew Alliance automated sample preparation platform.
  • GlycoWorks RapiFluor-MS N-Glycan Kit for glycan labeling.
  • UNIFI Scientific Information System for compliance-ready control and data processing.

Main Results and Discussion


Analysis of abatacept released glycans highlighted:
  • Identification of 31 N-linked glycan structures within ±10 ppm mass accuracy.
  • Automated GU-based peak annotation delivered high-confidence identifications in the FLR trace.
  • Detection of low-abundance species (e.g., Mannose 5 at 0.2% relative abundance) through extracted ion chromatograms.
  • Seamless integration of fluorescence and MS data improved specificity over single-detection methods.

These results confirm that the BioAccord workflow can reliably profile glycan compositions, including sialylated and complex structures, without manual intervention.

Benefits and Practical Applications


Key advantages of this integrated approach include:
  • Enhanced throughput through automated sample prep and data acquisition.
  • Improved structural confidence by combining orthogonal detection modes.
  • Simplified method deployment in regulated laboratories thanks to compliance-ready software.
  • Scalability for late-stage development and routine QC of biotherapeutics.

Future Trends and Potential Applications


The evolution of glycan analysis is likely to focus on:
  • Further automation and miniaturization to reduce reagent use and increase throughput.
  • Expanded glycan libraries and advanced data analytics powered by machine learning.
  • Integration with multi-attribute methods (MAM) for simultaneous protein and glycan profiling.
  • Real-time monitoring of glycosylation during bioprocessing.

Conclusion


The BioAccord System provides a streamlined, automated platform for N-linked glycan analysis, combining high-resolution MS and fluorescence detection. Its integration with compliance-ready software and automated sample handling simplifies workflow, boosts productivity, and delivers high-confidence glycan characterization suitable for biopharmaceutical R&D and quality control.

Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.

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