A Rapid, Workflow Driven Approach to Discovery Lipidomics Using Ion Mobility DIA UPLC/MS and Lipostar™

Importance of the Topic

The rapid and accurate profiling of lipids in biological systems enables researchers to uncover metabolic alterations associated with disease states or drug treatments. Employing high‐resolution ion mobility mass spectrometry coupled with advanced informatics accelerates discovery lipidomics by delivering rich, multidimensional data, enhancing confidence in identifications and supporting robust pathway analyses.

Objectives and Study Overview

This study aimed to apply an integrated workflow combining ion mobility‐enabled UPLC/MS and the Lipostar 2 software to monitor time‐dependent changes in the mouse liver lipidome following intravenous administration of gefitinib, a tyrosine kinase inhibitor. The goal was to identify dysregulated lipid species and interpret their roles within relevant metabolic pathways.

Methodology and Instrumentation

Biological Samples and Preparation:

• Liver tissue from C57Bl/6JRj mice dosed at 10 mg/kg gefitinib IV at 0, 0.5, 1, 3, 8, and 24 hours post‐dose.
• Lipid extraction using dichloromethane/methanol with deuterated internal standards; dual extraction to maximize recovery.
• Reconstitution in IPA/ACN with sonication and centrifugation.

Chromatography and Mass Spectrometry:

• ACQUITY UPLC I-Class FTN with a CSH C18 column at 55 °C; flow rate 0.4 mL/min; binary gradient from aqueous ACN to IPA/ACN.
• Waters SYNAPT XS HRMS operating in HDMSE mode, ESI positive/negative, ion mobility separation with nitrogen and helium gases, acquisition range 50–1200 Da, collision energy ramped 25–45 eV.

Data Processing and Informatics:

• Lipostar 2 workflow for raw data import, feature extraction, alignment, smoothing, deisotoping, and deconvolution.
• Multivariate analysis (PCA, PLS-DA) to identify time‐related lipid patterns.
• Lipid identification via database matching, fragmentation rules, CCS, retention time filters, and Mass-MetaSite for drug metabolite detection.
• Quantification using calibration curves and limits of detection/quantification based on internal standards.
• Pathway mapping to connect significant lipids to metabolic and disease pathways.

Main Findings and Discussion

Time-course analysis revealed a clear trajectory in lipid profiles post‐gefitinib administration. Key observations included elevated lysophosphatidylcholines (LPCs) and decreased phosphatidylcholines (PCs) over the sampling period. The identification of the known gefitinib metabolite M6 confirmed efficient in vivo biotransformation. Mapping altered lipids onto a cancer‐related phosphocholine pathway highlighted potential links between drug action and lipid metabolism dysregulation.

Benefits and Practical Applications

This integrated approach offers:

• High‐throughput, information‐rich data acquisition suitable for untargeted lipidomics.
• Customizable workflows accommodating various identification strategies (spectral matching, fragment recognition, CCS exploitation).
• Robust statistical and quantification tools enabling the discovery of putative biomarkers and precise concentration measurements.
• Automated metabolite detection to separate endogenous lipid changes from drug‐related signals.
• Pathway analysis modules for biological interpretation and mechanistic insights.

Future Trends and Opportunities

Ongoing developments in lipidomics will focus on expanding high-quality CCS and spectral libraries, integrating machine learning for automated annotation, and standardizing QA/QC practices through community initiatives. Upcoming instrument advancements may further enhance separation power and sensitivity, enabling deeper coverage of the lipidome and real‐time data processing for translational research applications.

Conclusion

The combination of UPLC-IM-MS and Lipostar 2 provides a streamlined, reproducible workflow for discovery lipidomics. This platform delivers rapid, high‐confidence lipid identification and quantification, facilitating comprehensive pathway analyses in pharmacological and disease studies.

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