Analysis of Rat Urine Using Rapid Microbore Metabolic Profiling (RAMMP) HILIC Chromatography with Ion Mobility-MS

Significance of the Topic

High-throughput and reproducible metabolic profiling is critical for large-scale phenotyping studies. By minimizing instrument time and batch-to-batch variability, researchers can generate consistent, high-quality data across extensive sample cohorts.

Objectives and Study Overview

This work assesses the combined use of Rapid Microbore Metabolic Profiling (RAMMP) with HILIC chromatography and ion mobility-mass spectrometry (IMS-MS) to analyze polar metabolites in rat urine following tienilic acid dosing. The method is benchmarked against conventional UPLC workflows to evaluate throughput, separation performance, and feature detection.

Methodology and Instrumentation

Urine samples from control and tienilic acid-treated rats were diluted 1:10 and analyzed under the following conditions:

• Liquid Chromatography: Waters ACQUITY UPLC I-Class system with BEH Amide column (1.7 µm, 1 mm×50 mm) at 50 °C, 0.2 mL/min flow, 0.1% formic acid in water (A) and acetonitrile (B), 3.3 min gradient.
• Mass Spectrometry: Waters SYNAPT G2-Si with ESI +/-; HDMSE acquisition (50–1200 m/z), low energy 6 eV, elevated energy 20–50 eV, IMS T-wave velocity 700 m/s, pulse height 40 V.
• Data Processing: MassLynx, Progenesis QI for peak picking and label-free quantification, EZInfo for multivariate statistics, TargetLynx for targeted review.

Key Results and Discussion

• Chromatographic run time was shortened from 10 min to 3.3 min with RAMMP, preserving peak shape and retention.
• OPLS-DA and PCA models effectively discriminated control versus tienilic acid-dosed groups.
• Implementing IMS increased overall peak capacity by 51% and revealed 16% additional unique features compared to non-IMS data.
• Experimental collision cross section (CCS) values matched predicted values within 5%, improving compound identification confidence.
• Cumulative identification scores rose by an average of 56% when using the DIA-IMS workflow.

Benefits and Practical Applications

• Substantially increased laboratory throughput with reduced analysis time.
• Enhanced reproducibility across large cohorts by minimizing batch effects.
• Improved specificity and spectral clarity through ion mobility separation.
• Robust label-free quantification suitable for toxicology and biomarker discovery.

Future Trends and Opportunities

Emerging directions include broader adoption of IMS-based data-independent acquisition, expansion of CCS reference libraries, integration with machine learning for automated feature annotation, and real-time QC monitoring to further streamline high-throughput metabolomics.

Conclusion

The combined RAMMP HILIC and IMS-MS approach delivers a rapid, high-capacity platform for polar metabolite profiling in urine. It achieves significant gains in throughput, reproducibility, and identification confidence, supporting large-scale metabolomics applications.

References

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