Illuminating the Cellular and Molecular Response to Drug Treatment by Combining Bioenergetic Measurements with Untargeted Metabolomics

Significance of the Topic

The integration of real-time cellular bioenergetic profiling with untargeted metabolomics and lipidomics offers a comprehensive view of how cancer cells adapt to kinase inhibitor treatment. This combined approach enables researchers to link changes in whole-cell energy fluxes with molecular metabolite and lipid alterations, advancing our understanding of drug mechanisms and aiding the development of targeted therapies.

Objectives and Study Overview

This study aimed to characterize the metabolic response of the THP-1 acute monocytic leukemia cell line to two tyrosine kinase inhibitors, SU1498 and AG-879. By applying both Seahorse XF functional assays and high-resolution LC/Q-TOF metabolomics, the work sought to map cellular energy shifts and identify key molecular changes induced by these treatments.

Methodology and Sample Preparation

THP-1 cells were cultured in supplemented RPMI medium and exposed to SU1498, AG-879, or DMSO vehicle for short (1–2 h) and extended (18 h) intervals. Cell viability and counts were determined using an Agilent Novocyte Quanteon flow cytometer to normalize data. The Seahorse XF Real-Time ATP Rate Assay and Mito Stress Test quantified glycolytic and mitochondrial ATP production rates and respiratory parameters. For molecular profiling, metabolites and lipids were extracted via an automated dual extraction workflow on an Agilent Bravo Liquid Handling Platform. Polar metabolites were separated by HILIC-LC and analyzed by an Agilent Revident LC/Q-TOF; lipid extracts underwent iterative MS/MS and were annotated using Agilent Lipid Annotator.

Used Instrumentation

• Agilent Seahorse XF Pro analyzer
• Agilent Bravo Liquid Handling Platform
• Agilent Novocyte Quanteon flow cytometer
• Agilent 1290 Infinity II Bio LC
• Agilent Revident LC/Q-TOF
• MassHunter Explorer software
• Agilent Lipid Annotator software

Main Results and Discussion

Seahorse XF screening of 80 kinase inhibitors highlighted AG-879 and SU1498 for their selective reduction of mitochondrial ATP production in THP-1 cells, with minimal effects on healthy PBMCs. Both compounds increased glycolytic ATP generation while decreasing mitochondrial ATP rates over time, indicating mitochondrial uncoupling. Untargeted metabolomics revealed decreases in glycolytic intermediates such as glucose phosphates after treatment, alongside an increase in pantothenic acid, a precursor for TCA cycle coenzyme A. Lipidomics identified elevated triacylglycerol levels and altered ceramide profiles, suggesting shifts in lipid storage and signaling.

Practical Benefits and Applications

Combining functional bioenergetics with molecular omics delivers a powerful platform for rapid screening of anticancer drugs, enabling correlation of whole-cell energy flux alterations with specific metabolite and lipid perturbations. This workflow supports more informed decisions in drug discovery, target validation, and mechanistic studies.

Future Trends and Applications

Future efforts may focus on expanding qualitative flux analyses, applying Seahorse XF substrate oxidation tests to determine fuel flexibility, and mining unidentified metabolic features. Integration with isotope tracing and multi-omics datasets could further elucidate dynamic pathway remodeling under drug pressure.

Conclusion

The combined application of Seahorse XF bioenergetics and untargeted LC/Q-TOF metabolomics and lipidomics provides a deep, multi-scale perspective on cancer cell metabolic responses to kinase inhibitors, enhancing mechanistic insights and accelerating therapeutic development.

References

1. Van de Bittner GC et al. An Automated Dual Metabolite + Lipid Sample Preparation Workflow for Mammalian Cell Samples. Agilent Technical Overview 5994-5065EN, 2022.
2. Kam Y et al. Rapid Bioenergetic Functional Screening of Anticancer Drug Candidates. Agilent Application Note 5994-5651EN, 2023.
3. Mohsin S et al. High Confidence Targeted Data Mining of Untargeted High-Resolution Data for Lipids in Plasma. ASMS Poster MP 463, 2023.
4. Hyunh K et al. A Comprehensive, Curated, High-Throughput Method for the Detailed Analysis of the Plasma Lipidome. Agilent Application Note 5994-3747EN, 2021.