Automated Medium and High-Throughput GlycoWorks RapiFluor-MS Preparations on the Andrew+ Pipetting Robot

Significance of the Topic

Glycosylation profiling is a critical quality attribute in biopharmaceutical development, influencing drug safety, efficacy and manufacturing consistency. Rapid and reliable methods for released N-glycan labeling are essential to meet throughput demands in research and quality control laboratories.

Objectives and Study Overview

This study describes the automation of Waters GlycoWorks RapiFluor-MS N-glycan labeling workflows on the Andrew+ pipetting robot. Two protocols were implemented for medium-throughput (24 samples) and high-throughput (48 samples) analyses. Performance was compared to manual preparations, with targets of ≤25 percent deviation in total peak area and ≤5 percent in relative area.

Methodology and Instrumentation

The automated workflows employ the GlycoWorks RapiFluor-MS 24- and 48-Sample Kits for rapid fluorescent and mass-spectrometric detection of released N-glycans. Sample processing steps—denaturation, enzymatic release, labeling, HILIC cleanup and elution—were programmed in OneLab software and executed on the Andrew+ platform. The deck layout included multiple Tip Insertion System modules for different pipette volumes, a storage plate for reagent reservoirs, a vacuum-based microelution plate for cleanup, a Peltier-cooled PCR plate for reactions and microtube modules for enzymes and labeling reagents.

Main Results and Discussion

Automated medium-throughput preparations achieved 87.7 percent recovery relative to manual runs, corresponding to 12.3 percent deviation in total area and 0 percent deviation in relative glycoform distribution. The high-throughput protocol matched medium-throughput manual performance with 23.0 percent deviation in total area and 0 percent in relative area. Intra-run reproducibility remained robust: total area RSD was up to 11.6 percent for 24-sample runs and 15.2 percent for 48-sample runs, while relative area RSD averaged 0.5 percent.

Benefits and Practical Applications

• Increased sample throughput for glycan profiling
• Reduced hands-on time and user variability
• Cost-effective automation with existing laboratory robotics
• Integrated fluorescent and MS detection sensitivity
• Scalable protocols suitable for QA/QC and bioprocess development

Future Trends and Possibilities

Advances in liquid-handling platforms and software connectivity will expand automation of complex glycomics assays. Integration with laboratory information management systems, AI-driven protocol optimization and further scaling beyond 96-well formats are anticipated. Such trends will enhance data consistency, reduce operato­r variability and accelerate biopharmaceutical development.

Conclusion

The Andrew+-automated GlycoWorks RapiFluor-MS protocols for 24- and 48-sample N-glycan analysis demonstrate comparable performance to manual methods, meeting stringent recovery and reproducibility criteria. These workflows offer high efficiency, robust data quality and straightforward method transferability for both medium- and high-throughput laboratories.

Reference

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