Most commonly, mass spectrometry imaging has been applied to assess the relative levels of metabolites throughout a tissue. There are two main complications to interpreting these data. First, due to matrix effects, metabolite signal intensities can change between tissue regions independent of metabolite concentration.
Second, metabolite pathway activities cannot be accurately inferred from metabolite levels alone.

To overcome these challenges, we used mass spectrometry imaging to evaluate tissues from animals that had been labeled by 13C-enriched tracers. Then, by modeling the number of 13C-labels incorporated into metabolites and the fraction of metabolite labeled, we aimed to map the distribution of metabolic fluxes throughout tissue.

One notable limitation of our approach is being able to successfully resolve all of a metabolite's isotopologues from background signals, which interfere with the ability to measure full labeling patterns. To that end, we took advantage of trapped ion mobility spectroscopy (TIMS) to separate isotopically labeled versions of the same metabolite from other analytes prior to mass spectrometry analysis. When applied to a model of brain cancer, we found that de novo fatty acid synthesis flux is increased 3-fold in tumors relative to surrounding tissue. We also found that fatty acid elongation flux is elevated 8-fold relative to surrounding healthy tissue, highlighting a potential therapeutic target..

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