As technology expands what it is possible to accurately measure, so too the challenges faced by modern mass spectrometry applications expand. A high level of accuracy in lipid quantitation across thousands of chemical species simultaneously is demanded. While relative changes in lipid amounts with varying conditions may provide initial insights or point to novel targets, there are many questions that require determination of lipid analyte absolute quantitation. Glycerophospholipids present a significant challenge in this regard, given the headgroup diversity, large number of possible acyl chain combinations, and vast range of ionization efficiency of species. Lipidomic output is being used more often not just for profiling of the masses of species, but also for highly-targeted flux-based measurements which put additional burdens on the quantitation pipeline. These first two challenges bring into sharp focus the need for a robust lipidomics workflow including deisotoping, differentiation from background noise, use of multiple internal standards per lipid class, and the use of a scriptable environment in order to create maximum user flexibility and maintain metadata on the parameters of the data analysis as it occurs. As lipidomics technology develops and delivers more output on a larger number of analytes, so must the sophistication of statistical post-processing also continue to advance. High-dimensional data analysis methods involving clustering, lipid pathway analysis, and false discovery rate limitation are becoming standard practices in a maturing field.
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