Metabolomic Workflow Utilizing Rapid Microbore Metabolic Processing (RAMMP) in Conjunction with SONAR

Importance of the Topic

Rapid and specific metabolomic profiling is essential for clinical research and diagnostic applications. Traditional chromatography methods balance resolution and throughput, but innovations like RAMMP coupled with SONAR DIA offer a pathway to maintain analytical depth while accelerating sample processing.

Objectives and Study Overview

This study aimed to integrate RAMMP with SONAR DIA to achieve high-throughput, quantitative metabolomic analysis of urine samples from pregnant women across three trimesters. Performance with a 300 µm ID column and 3 min gradient (RAMMP) was benchmarked against a 1 mm ID column with a 12 min gradient. Data acquisition employed Waters SONAR DIA, with Progenesis QI for untargeted analysis and Skyline for targeted quantification.

Methodology and Instrumentation

Sample preparation involved centrifugation and dilution of urine samples prior to LC-MS analysis.

• Liquid chromatography: ACQUITY UPLC M-Class system, BEH C18 columns (1.0 mm × 100 mm and 300 µm × 100 mm), gradients from 1 to 95% B in 3–12 min at 100 µL/min (1 mm) or 7 µL/min (300 µm).
• Mass spectrometry: Xevo G2-XS with SONAR DIA, quadrupole scanning across 250–800 Da using a 12 Da window, alternating low (5 eV) and elevated energy (20–55 eV) functions, resolution 30,000 FWHM.

Main Results and Discussion

RAMMP with SONAR maintained equivalent feature detection compared to conventional methods while reducing analysis time fourfold. Decreasing column diameter increased detected feature counts by ~50%. Principal component analysis consistently separated trimester groups for both workflows, with shared discriminating metabolites. Fast SONAR scanning (0.1 s) delivered >10 data points per peak, ensuring quantitative precision and reliable fold-change measurements.

Benefits and Practical Applications

• High-throughput profiling of large sample cohorts with maintained analytical depth.
• Enhanced specificity through multi-dimensional DIA data, reducing interference.
• Robust discrimination of physiological states and potential biomarker identification.
• Quantitative precision supported by rapid scanning capabilities.

Future Trends and Opportunities

Further developments may extend RAMMP-SONAR workflows to other biofluids and complex matrices, integrate automated sample handling, and incorporate advanced bioinformatics for real-time data interpretation. Scaling to population-scale studies and clinical diagnostics could accelerate biomarker validation and personalized medicine applications.

Conclusion

Coupling RAMMP with SONAR DIA enables rapid, specific, and quantitative metabolomic analyses. The workflow demonstrates comparable coverage to conventional methods in a fraction of the time, with reliable discrimination of biological states and strong prospects for large-scale clinical and research applications.

Instrumentation Used

• ACQUITY UPLC M-Class System
• BEH C18 Columns (1.0 mm and 300 µm ID)
• Xevo G2-XS Mass Spectrometer with SONAR DIA
• Progenesis QI Software
• Skyline and EZInfo for data processing and statistical analysis

References

• Gray A.L. et al., Anal. Chem. 2016, 88, 5742–5751.
• Want E.J. et al., Nat. Protoc. 2010, 5, 1005–1018.