HIGH THROUGHPUT LIPIDOMICS USING ION MOBILITY ENABLED RAPID LC-MS PROFILING SHOWS PROMISE FOR THE ANALYSIS OF HUMAN PLASMA SAMPLES OBTAINED FROM BREAST CANCER PATIENTS

Significance of the Topic

Lipidomics enables detailed mapping of lipid species that play key roles in cell signaling, energy storage and membrane dynamics. Alterations in lipid composition have been linked to metabolic disorders and cancer. Developing high-throughput workflows to profile thousands of clinical samples accelerates biomarker discovery and supports large-scale studies with reduced time and cost.

Objectives and Study Overview

This study aimed to develop and validate a rapid LC-MS lipid profiling assay enhanced by ion mobility spectrometry (IMS) for human plasma samples from breast cancer patients and controls. The goal was to preserve chromatographic resolution while cutting analysis time and solvent use, and to apply the method to identify lipid biomarkers differentiating patient groups.

Methodology and Instrumentation

A rapid LC gradient was scaled down from 13.2 to 3.7 minutes and solvent consumption reduced by 75%. Column inner diameter was reduced fourfold, enabling higher linear velocity at only 2.4-fold lower flow rate. Lipids were extracted from 100 µL plasma with isopropanol, incubated, centrifuged and analyzed directly.

• LC system: Waters BEH C8 column (1.0 × 50 mm, 1.7 µm) with a binary gradient of water/isopropanol/acetonitrile and isopropanol/acetonitrile containing ammonium acetate and formic acid
• Mass spectrometer: Waters Synapt G2-Si with IMS, positive ESI mode, m/z 50–1200, capillary voltage 0.5 kV
• IMS settings: wave velocity 600 m/s, wave height 40 V, enabling collision cross section (CCS) measurement
• Data processing: Progenesis QI for alignment, peak picking and normalization; EZinfo for PCA and OPLS-DA

Main Results and Discussion

Rapid chromatography maintained class separation. Incorporating IMS resolved co-eluting ions and delivered CCS values that grouped lipid classes distinctly. After filtering features with coefficient of variation > 30 %, OPLS-DA and S-plot analysis identified 5 up-regulated and 10 down-regulated lipids in breast cancer samples. Notably, phosphatidylcholines were decreased, suggesting elevated phospholipase A2 activity, while certain phosphatidylserines were elevated, aligning with literature as potential cancer biomarkers.

Benefits and Practical Applications

The workflow reduces per-sample analysis time from over 15 to under 4 minutes, facilitating the processing of large cohorts in days instead of weeks. IMS-enabled CCS measurements improve identification confidence and spectral quality, supporting robust lipid biomarker discovery in clinical and QA/QC settings.

Future Trends and Potential Applications

Further integration of automated sample preparation, expansion of CCS libraries and coupling with machine learning for feature selection will enhance throughput and depth. This approach can be extended to other biofluids and disease contexts, driving translational lipidomics in precision medicine.

Conclusion

A high-throughput LC-IMS-MS lipid profiling assay was developed and applied to breast cancer plasma samples. The method achieved a fourfold reduction in run time, preserved chromatographic performance and leveraged IMS for improved specificity. Identified lipid alterations concur with known cancer biomarkers, demonstrating the assay’s utility for large-scale clinical lipidomics.

References

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