Rapid Microbore Metabolic Profiling (RAMMP) with Ion Mobility for the Lipidomic Investigation of Plasma from Breast Cancer Patients

Significance of the Topic

The rapid and reliable profiling of lipids in biological fluids is critical for discovering disease biomarkers and understanding metabolic alterations in large patient cohorts. Techniques that combine high throughput with robust chromatographic performance enable the analysis of thousands of samples with minimal instrument downtime and consistent data quality.

Objectives and Study Overview

This study aimed to develop and validate a Rapid Microbore Metabolic Profiling (RAMMP) workflow combined with data‐independent acquisition and ion mobility separation for lipidomic analysis of plasma from breast cancer patients versus healthy controls. The goal was to reduce analysis time without sacrificing peak capacity, reproducibility, or confidence in compound identification.

Methodology and Instrumentation

Sample Preparation:

• Pooling of breast cancer and control plasmas.
• Protein precipitation with isopropanol (1:4 ratio), vortex mixing, and centrifugation.
• Collection of supernatant for LC‐MS analysis.

Chromatographic Conditions:

• System: ACQUITY UPLC I‐Class with a 1.0 × 50 mm BEH C8 column at 55 °C.
• Flow Rate: 250 µL/min, injection volume 0.2 µL.
• Gradient: Rapid ramp from aqueous to organic over 3.7 min.

Mass Spectrometry and Ion Mobility:

• Instrument: Synapt G2‐Si HDMS in ESI+ mode.
• Acquisition: Low/high collision energy (5 eV/25 eV) with ion mobility separation and data‐independent acquisition.
• Software: MassLynx for control and Progenesis QI for data processing.

Main Results and Discussion

The RAMMP method reduced LC run time from 15 min to 3.7 min while preserving chromatographic resolution through geometric scaling of column dimensions. Quality control experiments showed consistent retention times and peak shapes across the analytical batch. Ion mobility separation increased feature discrimination per unit time and enabled measurement of collision cross section (CCS) values for enhanced spectral clarity.

Orthogonal partial least squares discriminant analysis (OPLS‐DA) revealed clear separation between breast cancer and healthy control groups. Differential expression analysis identified several lipid classes—phosphatidylcholines, triglycerides, diglycerides, and phosphatidylserines—with significant changes in abundance. Notably, PC 36:2 was underexpressed in cancer samples and confirmed by CCS‐aided database searches.

Benefits and Practical Applications

• High throughput: over fourfold reduction in analysis time compared to conventional UPLC methods.
• Robust reproducibility: stable chromatographic performance across large batches.
• Enhanced confidence: ion mobility and CCS values improve compound identification.
• Efficient instrument utilization: increased sample throughput with minimal maintenance.
• Applicability: suitable for large‐scale lipidomic phenotyping, biomarker discovery, and clinical research.

Future Trends and Applications

• Integration of machine learning for automated feature annotation and pattern recognition.
• Expansion of CCS databases to improve identification of novel lipid species.
• Combination with high‐resolution MS and alternative ionization modes for broader metabolite coverage.
• Application to other biofluids and tissues for comprehensive metabolic phenotyping.
• Development of standardized high‐throughput pipelines for clinical diagnostics and personalized medicine.

Conclusion

The RAMMP workflow with ion mobility and data‐independent acquisition provides a rapid, sensitive, and reproducible platform for large‐scale lipidomic profiling. This approach maintains chromatographic quality while significantly increasing throughput and identification confidence, making it well suited for biomarker discovery in breast cancer and other clinical studies.

References

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