With cellular research models you often need a comprehensive view of metabolism at not only the cellular but also the metabolite level to generate accurate biological insights and identify potential therapeutic targets. This presentation highlights the ability to use Seahorse XF technology with LC/MS synergistically to deliver these critical holistic biological insights with enhanced confidence.
In this study we identified two candidate tyrosine kinase inhibitors that had desirable impacts on aerobic metabolism in cancer cells with a Seahorse XF screening strategy. Additionally, by leveraging Seahorse Mitochondrial Stress Test we assessed candidate therapeutics for spare respiratory capacity to assess potential for metabolic switching. To follow up on this mechanistically we conducted both targeted and untargeted metabolomics and lipidomics approaches with Agilent LC/MS systems. From these omics assays we confirmed that one of the drug candidates permitted the cells the ability to switch substrates to beta-oxidation-based metabolism and relieve inhibition.
Methods:
The THP-1 cancer cell line was utilized in this study. Cells were treated for 2 or 18 hours with two tyrosine kinase inhibitors or a vehicle control. Flow cytometry was used for cell count normalization for both Seahorse XF and LC/MS analyses to ensure sampling of the same number of cells from each treatment group. Treated cells were plated into Seahorse XF plates using recommended guidelines and oxygen consumption and extracellular acidification rates were monitored. Separately, treated cells were aliquoted for LC/MS analysis and prepared using a room temperature cell lysis and quenching method. Metabolites and lipids were fractionated with a Captiva EMR-Lipid plate using a Bravo Metabolomics Sample Prep Platform. Metabolite extracts were separated with HILIC-LC, and lipid extracts were separated with RP-LC, and eluents were analyzed with an Agilent Revident LC/Q-TOF and MassHunter Explorer software for chemometrics in a discovery workflow. To identify endogenous metabolites, a database search was performed against a 471-compound subset of the Agilent METLIN Personal Compound Database and Library curated with HILIC-Z retention times from authentic chemical standards, resulting in 101 annotated compounds. To enable lipid annotation, MassHunter Lipid Annotator software was first used to build a lipid database based on in silico MS/MS spectral library matching from MS/MS spectra that were acquired from representative pooled cellular extracts. Specifically, a set of six positive-ion and six negative‑ion mode iterative MS/MS data files were analyzed. This resulted in 562 lipids, representing 16 classes, annotated for positive-ion mode, and 500 lipids, representing 22 classes, annotated for negative-ion mode.