Literature DB >> 28079878

A complete workflow for high-resolution spectral-stitching nanoelectrospray direct-infusion mass-spectrometry-based metabolomics and lipidomics.

Andrew D Southam1, Ralf J M Weber1, Jasper Engel2, Martin R Jones1, Mark R Viant1,2.   

Abstract

Metabolomic and lipidomic studies measure and discover metabolic and lipid profiles in biological samples, enabling a better understanding of the metabolism of specific biological phenotypes. Accurate biological interpretations require high analytical reproducibility and sensitivity, and standardized and transparent data processing. Here we describe a complete workflow for nanoelectrospray ionization (nESI) direct-infusion mass spectrometry (DIMS) metabolomics and lipidomics. After metabolite and lipid extraction from tissues and biofluids, samples are directly infused into a high-resolution mass spectrometer (e.g., Orbitrap) using a chip-based nESI sample delivery system. nESI functions to minimize ionization suppression or enhancement effects as compared with standard electrospray ionization (ESI). Our analytical technique-named spectral stitching-measures data as several overlapping mass-to-charge (m/z) windows that are subsequently 'stitched' together, creating a complete mass spectrum. This considerably increases the dynamic range and detection sensitivity-about a fivefold increase in peak detection-as compared with the collection of DIMS data as a single wide mass-to-charge (m/z ratio) window. Data processing, statistical analysis and metabolite annotation are executed as a workflow within the user-friendly, transparent and freely available Galaxy platform (galaxyproject.org). Generated data have high mass accuracy that enables molecular formulae peak annotations. The workflow is compatible with any sample-extraction method; in this protocol, the examples are extracted using a biphasic method, with methanol, chloroform and water as the solvents. The complete workflow is reproducible, rapid and automated, which enables cost-effective analysis of >10,000 samples per year, making it ideal for high-throughput metabolomics and lipidomics screening-e.g., for clinical phenotyping, drug screening and toxicity testing.

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Year:  2017        PMID: 28079878     DOI: 10.1038/nprot.2016.156

Source DB:  PubMed          Journal:  Nat Protoc        ISSN: 1750-2799            Impact factor:   13.491


  62 in total

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9.  Integrated metabolomics and lipidomics profiling of hippocampus reveal metabolite biomarkers in a rat model of chronic unpredictable mild stress-induced depression.

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