A HIGH THROUGHPUT LC-MS LIPIDOMICS PLATFORM FOR SCREENING & STRATIFYING CANCER SAMPLES FROM BIOBANKS AND CLINICAL TRIALS

Significance of the Topic

The development of high-throughput lipidomic methods is critical for large-scale screening and stratification of clinical and biobank samples. Rapid and reliable profiling of lipid alterations can provide insights into disease mechanisms, support biomarker discovery, and accelerate translational research in oncology and metabolic disorders.

Objectives and Study Overview

This work aimed to establish a rapid LC-MS lipidomics platform integrating ion mobility spectrometry (IMS) to reduce analysis time and solvent use while maintaining robust lipid class separation. The method was applied to plasma samples from breast cancer patients (n=20) and healthy controls (n=6) to identify lipid species differentially expressed in cancer.

Methodology

• Sample preparation involved protein precipitation of 100 µL plasma with isopropanol, incubation, centrifugation, and supernatant collection.
• A pooled QC was generated from all samples to monitor method performance.
• Chromatography used a Waters BEH C8 1.0×50 mm column with a scaled gradient, reducing runtime to 3.7 min and solvent consumption by 75%.

Used Instrumentation

• Waters Synapt G2-Si mass spectrometer with IMS enabled
• Electrospray ionization in positive mode (50–1200 m/z)
• IMS wave velocity 600 m/s, wave height 40 V
• Data processing via Progenesis QI and statistical analysis with EZinfo

Main Results and Discussion

• Chromatographic scaling maintained lipid class separation comparable to conventional methods.
• IMS provided additional resolution of co-eluting species and improved precursor–fragment assignments, enhancing identification confidence.
• Collision cross-sectional (CCS) values clustered lipid classes distinctly, aiding feature annotation.
• Statistical analysis (OPLS-DA) revealed several phosphatidylcholines (PCs) and triacylglycerols (TAGs) down-regulated in breast cancer, while phosphatidylserines (PSs) were up-regulated, consistent with literature reports implicating phospholipase A2 activity and PS as cancer biomarkers.

Benefits and Practical Applications

• High throughput enables analysis of large cohorts within days instead of weeks.
• Reduced solvent use lowers operational costs and environmental impact.
• IMS and CCS integration increases specificity and database matching accuracy.
• Applicable for biomarker discovery, clinical trial sample screening, and biobank studies.

Future Trends and Opportunities

• Further miniaturization and multiplexing of chromatographic systems.
• Integration of machine learning for automated feature annotation and quantification.
• Expansion to negative ion mode and additional lipid classes.
• Application to multi-omics workflows combining lipidomics with proteomics and metabolomics.

Conclusion

A rapid LC-IMS-MS lipidomics assay was developed, achieving a fourfold reduction in runtime and significant solvent savings while preserving analytical performance. The platform successfully differentiated breast cancer and control plasma lipid profiles, demonstrating its potential for large-scale clinical and translational studies.

References

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