AUTOMATED HIGH-THROUGHPUT N-GLYCAN PREPARATION WORKFLOW ON A COMMERCIALLY AVAILABLE BENCHTOP PIPETTING ROBOT

Importance of the Topic

N-glycan profiling is essential in biopharmaceutical development and quality control, as glycosylation affects drug efficacy, safety, and stability. Automating high-throughput sample preparation enhances reproducibility, minimizes human error, and accelerates analytical workflows.

Objectives and Study Overview

This work presents an end-to-end automated RapiFluor-MS N-glycan preparation workflow using a commercially available Andrew+ benchtop pipetting robot. The primary aims were to halve sample preparation time, improve labeling efficiency, and confirm analytical performance comparable to manual methods.

Methodology

• Automated RapiFluor-MS labeling of released N-glycans in a 96-well plate format.
• Use of single-channel and 8-channel Sartorius pipettors for reagent dispensing.
• Workflow orchestration via OneLab browser-based software, enabling protocol design, execution, and sharing.
• UPLC-FLR analysis on an ACQUITY Glycan BEH Amide column (1.7 µm, 130 Å, 2.1 × 150 mm) with a gradient of 50 mM ammonium formate (pH 4.4) and acetonitrile.
• Fluorescence detection at excitation λ = 265 nm and emission λ = 425 nm.

Used Instrumentation

• Andrew+ automated pipetting platform (Andrew Alliance) with Sartorius single- and 8-channel pipettors.
• OneLab software for automated protocol control.
• Waters ACQUITY UPLC system equipped with Glycan BEH Amide column.
• ACQUITY FLR fluorescence detector.

Main Results and Discussion

• Sample preparation time was reduced approximately two-fold when employing the 8-channel pipettor configuration.
• Automated preparation yielded higher total labeled N-glycan recovery compared to manual workflows (N = 12 replicates per glycan).
• Relative area distributions of four key glycoforms were highly consistent between automated and manual preparations.
• Relative standard deviations for retention time, total area, and relative area were comparable across both methods, demonstrating robust reproducibility.

Benefits and Practical Applications

• Enhanced throughput supports larger sample cohorts in biopharmaceutical R&D and QA/QC settings.
• Consistent labeling efficiency and reduced variability bolster confidence in glycan quantitation.
• Minimized manual handling decreases operator fatigue and risk of pipetting errors.

Future Trends and Potential Applications

• Integration with mass spectrometry detection workflows for comprehensive glycan characterization.
• Extension of automation protocols to alternative glycan labeling chemistries and analyte classes.
• Miniaturization and further multiplexing to meet increasing throughput demands.
• Cloud-based protocol management and remote collaboration for distributed laboratories.

Conclusion

The automated RapiFluor-MS N-glycan preparation on the Andrew+ platform offers a robust, reproducible, and time-efficient workflow. It delivers equivalent or improved analytical performance compared to manual methods, making it a valuable solution for high-throughput glycan analysis in both research and industrial environments.