Determination of Abundant Metabolite Matrix Adducts Illuminates the Dark Metabolome of MALDI-Mass Spectrometry Imaging Datasets.

Spatial metabolomics using mass spectrometry imaging (MSI) is a powerful tool to map hundreds to thousands of metabolites in biological systems. One major challenge in MSI is the annotation of m/z values, which is substantially complicated by background ions introduced throughout the chemicals and equipment used during experimental procedures. Among many factors, the formation of adducts with sodium or potassium ions, or in case of matrix-assisted laser desorption ionization (MALDI)-MSI, the presence of abundant matrix clusters strongly increases total m/z peak counts. Currently, there is a limitation to identify the chemistry of the many unknown peaks to interpret their biological function. We took advantage of the co-localization of adducts with their parent ions and the accuracy of high mass resolution to estimate adduct abundance in 20 datasets from different vendors of mass spectrometers. Metabolites ranging from lipids to amines and amino acids form matrix adducts with the commonly used 2,5-dihydroxybenzoic acid (DHB) matrix like [M + (DHB-H2O) + H]+ and [M + DHB + Na]+. Current data analyses neglect those matrix adducts and overestimate total metabolite numbers, thereby expanding the number of unidentified peaks. Our study demonstrates that MALDI-MSI data are strongly influenced by adduct formation across different sample types and vendor platforms and reveals a major influence of so far unrecognized metabolite-matrix adducts on total peak counts (up to one third). We developed a software package, mass2adduct, for the community for an automated putative assignment and quantification of metabolite-matrix adducts enabling users to ultimately focus on the biologically relevant portion of the MSI data.

Spatial metabolomics of in situ host-microbe interactions at the micrometre scale.

Spatial metabolomics describes the location and chemistry of small molecules involved in metabolic phenotypes, defence molecules and chemical interactions in natural communities. Most current techniques are unable to spatially link the genotype and metabolic phenotype of microorganisms in situ at a scale relevant to microbial interactions. Here, we present a spatial metabolomics pipeline (metaFISH) that combines fluorescence in situ hybridization (FISH) microscopy and high-resolution atmospheric-pressure matrix-assisted laser desorption/ionization mass spectrometry to image host-microbe symbioses and their metabolic interactions. The metaFISH pipeline aligns and integrates metabolite and fluorescent images at the micrometre scale to provide a spatial assignment of host and symbiont metabolites on the same tissue section. To illustrate the advantages of metaFISH, we mapped the spatial metabolome of a deep-sea mussel and its intracellular symbiotic bacteria at the scale of individual epithelial host cells. Our analytical pipeline revealed metabolic adaptations of the epithelial cells to the intracellular symbionts and variation in metabolic phenotypes within a single symbiont 16S rRNA phylotype, and enabled the discovery of specialized metabolites from the host-microbe interface. metaFISH provides a culture-independent approach to link metabolic phenotypes to community members in situ and is a powerful tool for microbiologists across fields.