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Observing the invisible through imaging mass spectrometry, a window into the metabolic exchange patterns of microbes.

ABSTRACT: Many microbes can be cultured as single-species communities. Often, these colonies are controlled and maintained via the secretion of metabolites. Such metabolites have been an invaluable resource for the discovery of therapeutics (e.g. penicillin, taxol, rapamycin, epothilone). In this article, written for a special issue on imaging mass spectrometry, we show that MALDI-imaging mass spectrometry can be adapted to observe, in a spatial manner, the metabolic exchange patterns of a diverse array of microbes, including thermophilic and mesophilic fungi, cyanobacteria, marine and terrestrial actinobacteria, and pathogenic bacteria. Dependent on media conditions, on average and based on manual analysis, we observed 11.3 molecules associated with each microbial IMS experiment, which was split nearly 50:50 between secreted and colony-associated molecules. The spatial distributions of these metabolic exchange factors are related to the biological and ecological functions of the organisms. This work establishes that MALDI-based IMS can be used as a general tool to study a diverse array of microbes. Furthermore the article forwards the notion of the IMS platform as a window to discover previously unreported molecules by monitoring the metabolic exchange patterns of organisms when grown on agar substrates.

SUBMITTER: Gonzalez DJ

PROVIDER: S-EPMC3543690 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

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Observing the invisible through imaging mass spectrometry, a window into the metabolic exchange patterns of microbes.

Gonzalez David J DJ Xu Yuquan Y Yang Yu-Liang YL Esquenazi Eduardo E Liu Wei-Ting WT Edlund Anna A Duong Tram T Du Liangcheng L Molnár István I Gerwick William H WH Jensen Paul R PR Fischbach Michael M Liaw Chih-Chuang CC Straight Paul P Nizet Victor V Dorrestein Pieter C PC

Journal of proteomics 20120526 16

Many microbes can be cultured as single-species communities. Often, these colonies are controlled and maintained via the secretion of metabolites. Such metabolites have been an invaluable resource for the discovery of therapeutics (e.g. penicillin, taxol, rapamycin, epothilone). In this article, written for a special issue on imaging mass spectrometry, we show that MALDI-imaging mass spectrometry can be adapted to observe, in a spatial manner, the metabolic exchange patterns of a diverse array o ...[more]

PMID: 22641157

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Project description:BackgroundImaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging.MethodsWe developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features.ResultsTo demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells.ConclusionsWe developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.

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Observing the invisible through imaging mass spectrometry, a window into the metabolic exchange patterns of microbes.

Publications

Observing the invisible through imaging mass spectrometry, a window into the metabolic exchange patterns of microbes.

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