A New Lipidomics Software Workflow Demonstrates Disrupted Lipogenesis Induced with Drug Treatment in Leukemia Cells

Significance of the Topic

Lipid metabolism is central to cellular homeostasis and its dysregulation is implicated in various diseases including cancer. High-resolution lipid profiling can reveal perturbations induced by drug treatments, advancing biomarker discovery and mechanistic understanding in leukemia research.

Objectives and Study Overview

This work presents a novel chromatography-based lipidomics workflow integrating high-resolution LC–MS/MS with an in-silico spectral library tool (Lipid Annotator) for automated AMRT database generation and downstream profiling. The approach was applied to acute myeloid leukemia (K562) cells treated with bezafibrate, medroxyprogesterone acetate, and their combination to characterize drug-induced lipidome alterations.

Methodology

Lipids were extracted from treated K562 cells using a modified Folch protocol. Untargeted iterative AutoMS/MS data were acquired in both positive and negative ion modes on an Agilent 6546 LC/Q-TOF coupled to an Agilent 1290 Infinity II UHPLC system with a Poroshell 120 EC-C18 column and a water/methanol–acetonitrile/isopropanol gradient. Data analysis included AMRT database construction via Lipid Annotator, targeted feature extraction in Profinder, and multivariate statistics in Mass Profiler Professional.

Instrumentation

• Agilent 1290 Infinity II UHPLC
• InfinityLab Poroshell 120 EC-C18 column (3.0×100 mm, 2.7 μm)
• Agilent 6546 LC/Q-TOF MS with Jet Stream source
• Iterative AutoMS/MS acquisition (m/z 40–1700, 3 spectra/s)

Main Results and Discussion

The workflow annotated 440 features in positive-ion and 688 in negative-ion mode, generating a custom PCDL with accurate masses and retention times. PCA and correlation analyses distinctly clustered treatment groups, confirming bezafibrate’s dominant effect. Targeted profiling revealed increased triacylglycerols and decreased diacylglycerols under combination treatment, along with novel alterations in bis(monoacylglycero)phosphate, cholesterol esters, cardiolipins, ceramides, and sphingomyelins. Subclass analysis showed a shift from saturated to polyunsaturated phosphatidylcholines. Chromatographic separation and MS/MS spectra enabled resolution of ceramide isomers, enriching structural characterization.

Benefits and Practical Applications

This workflow enhances lipid annotation coverage and structural specificity, facilitating robust differential lipidomics in drug screening, mechanistic studies, and biomarker identification. Automated AMRT database generation accelerates analysis and supports reproducible high-throughput lipid profiling.

Future Trends and Potential Applications

Advances may include integration of machine learning for spectral interpretation, expansion of in-silico libraries, single-cell lipidomics, and real-time LC–MS platform automation. Broader application across clinical research and nutritional studies can further validate lipid biomarkers and therapeutic targets.

Conclusion

The novel lipidomics workflow demonstrated improved coverage and detailed profiling of drug-induced lipidome changes in leukemia cells, corroborating known lipid perturbations and uncovering new class- and isomer-specific alterations. This approach holds promise for comprehensive lipidomic investigations in biomedical research.

References

1. Sartain M, Van de Bittner G, Li X, Koelmel J, Murali A, Stow S. 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.
2. Southam AD, Dolan RF, Brown JR, 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.