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Elemental formula annotation of polar and lipophilic metabolites using 13C, 15N and 34S isotope labelling, in combination with high-resolution mass spectrometry

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TLDR
A comprehensive multi-isotope labelling-based strategy using fully labelled plant tissues, in combination with a fractionated metabolite extraction protocol, which can be applied in either an automated database-dependent or a database-independent analysis of the plant polar metabolome and lipidome.
Abstract
The unbiased and comprehensive analysis of metabolites in any organism presents a major challenge if proper peak annotation and unambiguous assignment of the biological origin of the peaks are required. Here we provide a comprehensive multi-isotope labelling-based strategy using fully labelled (13) C, (15) N and (34) S plant tissues, in combination with a fractionated metabolite extraction protocol. The extraction procedure allows for the simultaneous extraction of polar, semi-polar and hydrophobic metabolites, as well as for the extraction of proteins and starch. After labelling and extraction, the metabolites and lipids were analysed using a high-resolution mass spectrometer providing accurate MS and all-ion fragmentation data, providing an unambiguous readout for every detectable isotope-labelled peak. The isotope labelling assisted peak annotation process employed can be applied in either an automated database-dependent or a database-independent analysis of the plant polar metabolome and lipidome. As a proof of concept, the developed methods and technologies were applied and validated using Arabidopsis thaliana leaf and root extracts. Along with a large repository of assigned elemental compositions, which is provided, we show, using selected examples, the accuracy and reliability of the developed workflow.

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The diversity of quinoa morphological traits and seed metabolic composition

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Metabolome Identification by Systematic Stable Isotope Labeling Experiments and False Discovery Analysis with a Target-Decoy Strategy

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JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics.

TL;DR: The results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.
References
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Journal ArticleDOI

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Journal ArticleDOI

Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics

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