QUANTIFYING THE LIPIDOME FOR RESPIRATORY DISEASE: A RAPID AND COMPREHENSIVE HILIC-BASED TARGETED APPROACH

Significance of the Topic

Chronic respiratory diseases such as COPD and asthma affect millions worldwide and impose substantial healthcare and socioeconomic burdens. Lipid alterations play critical roles in inflammation, airway remodeling and immune responses, making lipidomic profiling an important tool for understanding disease mechanisms and identifying biomarkers.

Objectives and Study Overview

This work aimed to develop and validate a rapid, high-throughput HILIC-based LC-MS/MS method (LipidQuan) for targeted quantification of over 2,000 lipids across polar and nonpolar classes. The method was applied to plasma from healthy controls, COPD patients and asthma patients to demonstrate its robustness and to uncover disease-related lipid changes.

Methodology and Sample Preparation

Samples underwent protein precipitation using pre-cooled isopropanol (1:5 plasma:IPA) at 4 °C, followed by centrifugation and direct analysis of the supernatant. Method highlights include:

• Hydrophilic interaction chromatography (HILIC) to separate lipids by class within 8 minutes.
• Over 2,000 highly specific MRM transitions based on fatty acyl chains and headgroup fragments.
• Use of stable isotope-labeled standards (SPLASH LIPIDOMIX) spiked prior to extraction for accurate quantification via calibration curves.
• Data processing with TargetLynx or open-source tools such as Skyline, and multivariate analysis in SIMCA-P+ or MetaboAnalyst.

Used Instrumentation

• LC system: Waters ACQUITY UPLC I-Class with FTN or Fixed Loop autosampler.
• Column: ACQUITY UPLC BEH Amide (2.1 × 100 mm, 1.7 µm) operated at 45 °C.
• Mobile phases: A = 95/5 ACN/water, B = 50/50 ACN/water, both with 10 mM ammonium acetate; gradient from 0.1% to 20% B in 2 min, then to 80% B in 3 min, plus 3 min re-equilibration.
• MS system: Waters Xevo TQ-XS or Xevo TQ-S micro, ESI positive/negative, capillary voltage 2.8 kV/1.9 kV, source temp. 120 °C, desolvation 500 °C, MRM acquisition.
• Software: TargetLynx for quantitation, Skyline for open-source processing, SIMCA-P+ and MetaboAnalyst for statistics.

Main Results and Discussion

The LipidQuan method demonstrated high reproducibility (average retention time RSD <2% over 1,500 injections) and robust quantification across more than 2,000 transitions. Class-based separation produced discrete elution bands, allowing use of a minimal set of isotope-labeled standards and cost savings. Calibration curves prepared in plasma showed linear responses and acceptable accuracy. The method resolved isobaric species—e.g. PC(16:0p/22:6) vs. PC(18:2p/20:4)—by exploiting fatty acyl fragment transitions. Multivariate analysis of plasma samples from healthy, COPD and asthma cohorts yielded clear group separation in PLS-DA (R2=0.843, Q2=0.844) without overfitting. Hierarchical clustering of the top 100 discriminating lipids highlighted trends in free fatty acids, phosphatidylcholines, lysophosphatidylcholines, sphingomyelins and ceramides.

Benefits and Practical Applications

• Streamlined workflow from sample prep to biological interpretation.
• High specificity and sensitivity for targeted lipid classes.
• Fast chromatographic run (8 min) and data processing with both commercial and open-source tools.
• Reduced number of isotope standards required, lowering cost.
• Applicability to large cohort studies and routine QA/QC in clinical and industrial laboratories.

Future Trends and Potential Applications

Integration with broader omics platforms will deepen biological insights. Automation of sample handling and data analysis pipelines can further increase throughput. Extension to other biofluids and tissues may reveal novel biomarkers. Advanced bioinformatics approaches, including machine learning, could enhance pattern recognition and predictive diagnostics in respiratory and other diseases.

Conclusion

The LipidQuan HILIC-based targeted LC-MS/MS method provides a rapid, reproducible and comprehensive approach for quantifying diverse lipid classes in plasma. Demonstrated here in a respiratory disease context, it is well suited for large-scale studies and routine applications in research, clinical diagnostics and quality control.

References

• Munjoma N, Isaac G, Plumb RS, Gethings LA. Quantifying the lipidome for a respiratory disease study using LipidQuan: a rapid and comprehensive targeted approach. Application Note 720006542EN; Waters Corporation; 2019.
• Isaac G, Munjoma N, Gethings LA, Plumb RS. LipidQuan for comprehensive and high-throughput HILIC-based LC-MS/MS targeted lipid quantitation. Application Note 720006402EN; Waters Corporation; 2018.