LC-MS FADE: Leveraging Untargeted Lipidomics Data for Fatty Acid Profiling

Fatty acid (FA) profiling has historically been accomplished via gas chromatography-mass spectrometry (GC-MS) and can be used in tandem with liquid chromatography-mass spectrometry (LC-MS) untargeted lipidomics workflows to comprehensively investigate global FA and lipid dynamics. This approach requires both GC-MS and LC-MS platforms, adding expense, acquisition time, and sample consumption, yet lacks the ability to directly correlate fatty acid profiles to intact lipid molecules. To address this issue, we present a workflow to determine FA profiles from lipidomics data sets termed LC-MS fatty acid data extraction (LC-MS FADE). FA data were extrapolated from intact lipid peak areas and expressed as percent fractions. A total of eight disparate lipid extracts, including fetal bovine serum, nutritional yeast, beef liver, canola oil, Viral Producing Cells 1.0, Arabidopsis thaliana, Acheta domesticus, and Escherichia coli K12 were analyzed to assess the accuracy of this strategy. By comparing the LC-MS FADE profile to the GC-MS FAME profile, an average R² value of 0.89 was obtained, demonstrating that LC-MS FADE can successfully and accurately extract FA profiles. LC-MS FADE data sets displayed improved detectivity by identifying more FAs with improved reproducibility compared to GC-MS FAME data. Significantly, lipid class-specific FA insights were obtained using LC-MS FADE that were not accessible with traditional GC-MS methods. LC-MS FADE can also be applied retrospectively to existing lipidomics data sets, enabling extraction of FA profiles from data not originally intended for FA analysis. This method provides additional insight into FA dynamics in complex untargeted lipidomics data sets and can be readily implemented into lipidomic workflows.