Mass spectrometry imaging in plants, microbes, and food: a review.

Abstract

Plant health, which affects the nutritional quality and safety of derivative food products, is influenced by symbiotic interactions with microorganisms. These interactions influence the local molecular profile at the tissue level. Therefore, studying the distribution of molecules within plants, microbes, and plant-based food is crucial to assess plant health, ensure the safety and quality of the agricultural products that become part of our food supply, and plan agricultural management practices. Within this framework, the molecular distribution within plant-based samples can be visualized with mass spectrometry imaging (MSI). This review describes key MSI methodologies, highlighting the role they play in unraveling the localization of metabolites, lipids, proteins, pigments, and elemental components across plants, microbes, and food products. Furthermore, investigations that involve multimodal molecular imaging approaches combining MSI with other imaging techniques are described. The advantages and limitations of the different MSI techniques that influence their applicability in diverse agro-food studies are described to enable informed choices for tailored analyses. For example, some MSI technologies involve meticulous sample preparation while others compromise spatial resolution to gain throughput. Key parameters such as sensitivity, ionization bias and fragmentation, reference database and compound class specificity are described and discussed in this review. With the ongoing refinements in instrumentation, data analysis, and integration of complementary techniques, MSI deepens our insight into the molecular biology of the agricultural ecosystem. This in turn empowers the quest for sustainable and productive agricultural practices.

Code and data availability

This is a critical review of mass spectrometry imaging in plants, microbes, and food. The authors explicitly state that no new data, software, or code were generated or included, and no public phenotype datasets, images, analysis code, or models specific to this paper are described. All cited studies are prior work, so

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