Literature DB >> 32015496

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

Benedikt Geier1, Emilia M Sogin2, Dolma Michellod2, Moritz Janda2, Mario Kompauer3, Bernhard Spengler3, Nicole Dubilier2,4, Manuel Liebeke5.   

Abstract

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.

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Year:  2020        PMID: 32015496     DOI: 10.1038/s41564-019-0664-6

Source DB:  PubMed          Journal:  Nat Microbiol        ISSN: 2058-5276            Impact factor:   17.745


  29 in total

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

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