Improving Glycan Profiling in Process Development Using Empower Spectral Library

Significance of the Topic

Glycosylation of monoclonal antibodies is a critical quality attribute directly linked to therapeutic efficacy and safety. Accurate profiling of released N-glycans is essential during process development and manufacturing to monitor cell culture conditions, ensure batch consistency, and support regulatory compliance. Integrating mass spectrometry into traditional fluorescence or UV glycan assays enhances detection specificity and accelerates peak identification.

Objectives and Study Overview

This work demonstrates a workflow combining LC-FLR-MS with an Empower™ spectral library for rapid, putative identification of unknown glycans and co-eluted species. A user-created library based on RapiFluor-MS labeled standards is applied to process development samples to improve productivity and confidence in glycan profiling.

Methodology and Instrumentation

Sample Preparation:

• Library: RapiFluor-MS labeled glycan standard diluted in water/DMF/ACN; 2 µL injected.
• Test Sample: N-glycans released from mAb standard and labeled using the GlycoWorks RapiFluor-MS N-Glycan Kit; 10 µL injected.

LC-FLR-MS Conditions:

• System: ACQUITY UPLC H-Class Bio with FLR (λex 265 nm, λem 425 nm) and QDa mass detector.
• Column: ACQUITY UPLC Glycan BEH Amide (130 Å, 1.7 µm, 2.1 × 150 mm) at 60 °C.
• Gradient: 25–46% aqueous (50 mM ammonium formate, pH 4.4) over 35 min; followed by wash and re-equilibration to 55 min.
• MS Settings: ESI+ mode, 500–1250 m/z, centroid acquisition at 2 points/s, cone 15 V, capillary 1.5 kV.
• Data System: Empower 3 CDS with spectral library search (noise threshold, retention time pre-search, multi-library support).

Main Results and Discussion

Library Creation:
Using a 55 min method, 18 RFMS-labeled glycan peaks were separated and incorporated into a spectral library. Empower allows flexible addition of MS spectra and labels to new or existing libraries.

Unknown Glycan Identification:
In a process development sample, two peaks absent in the reference were observed. Spectral matching assigned them as FA1 and Man5. Cross-validation with glucose unit (GU) values enhanced assignment confidence.

Co-elution Detection:
A single FLR peak initially annotated as Man5 contained two co-eluted species. Iterative spectral matching identified Man5 as the dominant and FA1G1 as the minor component, demonstrating the value of MS data in resolving co-elutions.

Benefits and Practical Applications of the Method

• Accelerates glycan peak assignment through automated spectral matching.
• Improves specificity by combining orthogonal FLR and MS detection.
• Supports method flexibility with user-defined libraries and retention time filters.
• Enables detection of co-elutions and low-abundance glycans.
• Facilitates streamlined reporting and audit-ready documentation within Empower CDS.

Future Trends and Applications

Advancements in high-throughput MS detectors and enhanced labeling reagents will further refine glycan profiling speed and sensitivity. Expansion of shared spectral libraries and machine learning-driven matching algorithms can enable real-time process monitoring and predictive quality control in biopharmaceutical manufacturing.

Conclusion

The Empower spectral library-enhanced LC-FLR-MS workflow offers a robust, flexible, and automated approach for glycan profiling in process development. By leveraging MS spectral information, it delivers rapid putative identification of unknowns, uncovers co-elutions, and streamlines reporting, thereby supporting efficient quality attribute monitoring throughout the product lifecycle.

Reference

1. Shields RL et al., J Biol Chem. 277:26733–26740 (2002)
2. Consgave EFJ et al., Waters Application Note 720005352EN (2015)
3. Lauber MA et al., Anal Chem. 87:5401–5409 (2015)