The study of single-cell metabolomics is gaining traction in diverse fields of biological and life sciences research. This requires the development of reliable tools to study cell- to-cell differences, along with unbiased automated data interpretation and biomarker identification for untargeted metabolomic profiles. In addition, a systemic workflow that integrates data acquisition, processing, and interpretation is needed to identify and return potential metabolic markers. A crucial aspect is identifying and measuring single cells via MALDI MSI and constructing a solid data structure suitable for the application of unsupervised clustering techniques for further data analysis. Current workflows for single cell MSI are mainly limited to lipidomics, while not focusing on small molecular metabolites, such as those found in the Tricarboxylic Acid (TCA)-cycle. For single cell activation assays, it is essential to measure these small metabolites without molecular degradation to ensure increased reproducibility. Since cell culture results alone provide limited insight, it is also important to evaluate whether the findings obtained from cell cultures can be translated to tissue models, thus contextualizing and transferring these results to another model system.
James-Lucas Cairns (Thu,) studied this question.