Metabolomics: An Improved Lipid Profiling Workflow Demonstrates Disrupted Lipogenesis Induced with Drug Treatment in Leukemia Cells

Significance of the Topic

Chromatographic lipid profiling is critical for understanding lipid metabolism and its alterations under drug treatments. Improved lipid annotation workflows enable more accurate analysis of disease and therapeutic effects.

Aims and Study Overview

This study aimed to develop and apply a novel workflow that uses in silico product ion spectral matching to build a custom accurate mass retention time database for iterative MS/MS lipid annotation. The approach was demonstrated on K562 acute myeloid leukemia cells treated with bezafibrate, medroxyprogesterone acetate and their combination.

Methodology and Instrumentation

Sample Preparation involved culture of K562 cells in RPMI medium, application of four treatments, centrifugation, PBS washes and modified Folch extraction. LC/MS Analysis used a 19-minute reverse phase LC/(MS)/MS method on an Agilent 6546 LC/Q-TOF. Data Analysis employed MassHunter Lipid Annotator to annotate 440 positive and 688 negative ion features, automated generation of a PCDL, Profinder for targeted feature extraction and Mass Profiler Professional for statistical evaluation including PCA.

Main Results and Discussion

PCA revealed distinct clustering by treatment with bezafibrate dominating the combination effect. Lipid class profiling showed increased TAG, decreased DAG and enrichment of polyunsaturated PCs under BaP treatment, consistent with prior reports. Newly observed alterations included changes in BMP, CE, CL, Cer_NS and SM classes. Chromatographic separation coupled with MS/MS enabled resolution of isomeric species such as PC 18:1_22:6 vs PC 16:0_26:0 and Cer_NS d18:1_24:1 vs d18:2_24:0.

Benefits and Practical Applications

The automated workflow accelerates AMRT database creation and enhances lipid annotation depth and accuracy. It supports detailed investigation of lipid alterations in response to therapeutics and is suitable for integration into QA/QC and research environments.

Future Trends and Potential Uses

Expansion of in silico spectral libraries and integration with machine learning may further improve annotation coverage and processing speed. High throughput and single cell lipidomics represent promising advances. Clinical translation for biomarker discovery and lipid pathway analysis is anticipated.

Conclusion

The presented lipidomics workflow delivers comprehensive annotation, robust isomer separation and insightful profiling of drug induced lipidome changes in leukemia cells.

Used Instrumentation

• Agilent 6546 LC/Q-TOF mass spectrometer
• Agilent MassHunter Lipid Annotator software
• Agilent Profinder and Mass Profiler Professional

References

• Sartain M et al Improved Coverage of the Plasma Lipidome Using Iterative MS/MS Data Acquisition Combined with Lipid Annotator Software and 6546 LC/Q-TOF Agilent Application Note 5994-0775EN 2019
• Southam AD et al Drug Redeployment to Kill Leukemia and Lymphoma Cells by Disrupting SCD1 Mediated Synthesis of Monounsaturated Fatty Acids Cancer Research 2015 75(12) 2530-2540