Mass spectrometry imaging (MSI) has become a cornerstone of spatial biology research. However, various factors that are intrinsic to the technology limit the quantitative capacity of MSI-based spatial metabolomics and thus reliable interpretation. Here we developed an improved quantitative MSI workflow, based on isotopically 13C-labelled yeast extract as internal standards, to overcome these pitfalls. Using brain and kidney tissue, we demonstrate that this approach allows for quantification of more than 200 metabolic features. Applying our workflow to a stroke model allowed us to not only map metabolic remodelling of the infarct and peri-infarct area over time, but also discover hitherto unnoted remote metabolic remodelling in the histologically unaffected ipsilateral sensorimotor cortex. At day 7 post-stroke, increased levels of neuroprotective lysine and reduced excitatory glutamate levels were found when compared with the contralateral cortex. By day 28 post-stroke, lysine and glutamate levels appeared normal, while decreased precursor pools of uridine diphosphate N-acetylglucosamine and linoleate persisted that were previously linked to vulnerability. Importantly, traditional normalization strategies not using internal standards were unable to visualize these differences. Using 13C-labelled yeast extracts as a normalization strategy establishes a paradigm in quantitative MSI-based spatial metabolomics that greatly enhances reliability and interpretive strength.
Wang et al. (Tue,) studied this question.