Elemental formula annotation of polar and lipophilic metabolites using 13C, 15N and 34S isotope labelling, in combination with high-resolution mass spectrometry
Patrick Giavalisco,Yan Li,Annemarie Matthes,Aenne Eckhardt,Hans-Michael Hubberten,Holger Hesse,Shruthi Segu,Jan Hummel,Karin Köhl,Lothar Willmitzer +9 more
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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.read more
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The diversity of quinoa morphological traits and seed metabolic composition
TL;DR: In this article , the authors analyzed the seed metabolomes, including amino acids, fatty acids, and saponins, from 471 quinoa cultivars, including two related species, by liquid chromatography -mass spectrometry.
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Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops
Alina Chernova,Pavel V. Mazin,Pavel V. Mazin,Pavel V. Mazin,Svetlana V. Goryunova,Svetlana V. Goryunova,D. V. Goryunov,D. V. Goryunov,Yakov Demurin,Lyudmila Gorlova,Anna Vanyushkina,Waltraud Mair,Nikolai Anikanov,Ekaterina A. Yushina,Ekaterina A. Yushina,Anna Pavlova,Elena Martynova,Elena Martynova,Sergei Garkusha,Zhanna Mukhina,E.G. Savenko,Philipp Khaitovich +21 more
TL;DR: Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has great potential to be used for the assessment of FA profiles of oil crops, and could reveal the presence of longer FAs with the tails of up to 28 carbon atoms.
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SIRT6 is a key regulator of mitochondrial function in the brain
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TL;DR: In this article , the authors combined transcriptomics and metabolomics approaches to characterize the functions of SIRT6 in mouse brains and found that SIRT 6 deficiency in the brain leads to a global downregulation of mitochondria-related genes and pronounced changes in metabolite content.
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Metabolome Identification by Systematic Stable Isotope Labeling Experiments and False Discovery Analysis with a Target-Decoy Strategy
Drew R. Jones,Xusheng Wang,Timothy I. Shaw,Ji-Hoon Cho,Ping-Chung Chen,Kaushik Kumar Dey,Suiping Zhou,Yuxin Li,Nam Chul Kim,Taylor Jp,Kolli U,Li J,Junmin Peng +12 more
TL;DR: A formula-based strategy and algorithm for global metabolite identification and false discovery analysis in untargeted mass spectrometry-based metabolomics that provides a powerful a practical solution for global identification of metabolites with a critical measure of confidence.
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JUMPm: A Tool for Large-Scale Identification of Metabolites in Untargeted Metabolomics.
Xusheng Wang,Ji-Hoon Cho,Suresh Poudel,Yuxin Li,Drew R. Jones,Timothy I. Shaw,Haiyan Tan,Boer Xie,Junmin Peng +8 more
TL;DR: The results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.
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