A RAPID ANALYTICAL PLATFORM FOR BIOFLUID PROFILING IN DISCOVERY METABOLOMICS AND LIPIDOMICS

Significance of the Topic

Rapid metabolomics and lipidomics profiling addresses the challenge of lengthy analysis times, batch effects and data variability in large-scale studies. By reducing run times by 60–75%, these methods enable high-throughput workflows and more consistent data across large cohorts.

Study Objectives and Overview

The main goal was to develop and validate rapid UPLC-MS assays for biofluid profiling in discovery metabolomics and lipidomics. Two complementary methods were optimized: a hydrophilic interaction (HILIC) metabolomics protocol and a reversed-phase lipidomics protocol, both scaled down to minimize cycle time while maintaining chromatographic performance.

Methodology and Instrumentation

• UPLC System: Waters Acquity I-Class with BEH Amide (HILIC) and BEH C8 (lipid) columns, 50×1.0 mm I.D.
• Mass Spectrometer: Waters Synapt G2-Si operated in positive and negative polarity.
• Chromatographic Conditions: 0.2 µL injection, 0.2 mL/min for HILIC at 50 °C; 0.25 mL/min for lipid at 55 °C.
• Chromatographic Gradient: HILIC separation in 2.33 min (99 % B to 50 % B) plus <1 min equilibration; lipid method in 3.70 min total run.
• Ion Mobility: Travelling wave IMS to resolve co-eluting features via collision cross section (CCS).
• Data Processing: MassLynx for acquisition; Progenesis QI for alignment, peak picking and database searches; EzInfo for PCA, OPLS-DA and S-plots.

Main Results and Discussion

• The rapid HILIC method reduced cycle time from 10 min to 3.33 min without loss of retention stability across target compounds.
• The lipid protocol achieved a ~75 % reduction in run time to 3.70 min while preserving class-specific chromatographic regions.
• Analysis of 134 rat urine samples post-treatment completed in 7.5 hours versus >20 hours conventionally.
• Ion mobility doubled the number of detected features by separating overlapping species based on CCS values.
• In human plasma from breast cancer patients, 15 lipids were significant; five upregulated phosphatidylserine species were identified as potential biomarkers.

Benefits and Practical Applications

• Enables acquisition of ~1 000 samples in under three days per assay.
• Reduces mobile phase consumption and minimizes batch-related variability.
• IMS provides an orthogonal separation dimension, improving specificity and identification confidence.
• Applicable to large-scale discovery studies, QA/QC workflows and clinical biomarker screening.

Future Trends and Potential Applications

• Integration of expanded CCS reference libraries for enhanced compound annotation.
• Automation and AI-driven data analysis to accelerate feature selection and interpretation.
• Real-time quality monitoring and adaptive acquisition strategies for ultra-high throughput.
• Application to multi-omic studies combining proteomics and glycomics.

Conclusion

The rapid UPLC-IMS-MS workflows deliver substantial improvements in throughput and data quality for metabolomic and lipidomic profiling. These methods provide robust platforms for large-scale discovery and translational studies.

Reference

• Want EJ, et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc. 2013;8(1):17-32.
• Habchi B, et al. How to really perform high throughput metabolomic analyses efficiently? TrAC Trends Anal Chem. 2016;85:128-139.
• Gray et al. Rapid microbore metabolic profiling UPLC-MS for high-throughput phenotyping. Anal Chem. 2016;88(11):5742-5751.
• Sharma B, Kanwar SS. Phosphatidylserine: A cancer cell targeting biomarker. Semin Cancer Biol. 2017.