Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study

Martin, Jean-Charles, Maillot, Matthieu, Mazerolles, Gérard, Verdu, Alexandre, Lyan, Bernard, Migné, Carole, Defoort, Catherine, Canlet, Cecile, Junot, Christophe, Guillou, Claude, Manach, Claudine, Jabob, Daniel, Bouveresse, Delphine Jouan-Rimbaud, Paris, Estelle, Pujos-Guillot, Estelle, Jourdan, Fabien, Giacomoni, Franck, Courant, Frédérique, Favé, Gaëlle, Le Gall, Gwenaëlle, Chassaigne, Hubert, Tabet, Jean-Claude, Martin, Jean-Francois, Antignac, Jean-Philippe, Shintu, Laetitia, Defernez, Marianne, Philo, Mark, Alexandre-Gouaubau, Marie-Cécile, Amiot-Carlin, Marie-Josephe, Bossis, Mathilde, Triba, Mohamed N., Stojilkovic, Natali, Banzet, Nathalie, Molinié, Roland, Bott, Romain, Goulitquer, Sophie, Caldarelli, Stefano and Rutledge, Douglas N. (2015) Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study. Metabolomics, 11 (4). pp. 807-821. ISSN 1573-3882

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Abstract

The metabo-ring initiative brought together five nuclear magnetic resonance instruments (NMR) and 11 different mass spectrometers with the objective of assessing the reliability of untargeted metabolomics approaches in obtaining comparable metabolomics profiles. This was estimated by measuring the proportion of common spectral information extracted from the different LCMS and NMR platforms. Biological samples obtained from 2 different conditions were analysed by the partners using their own in-house protocols. Test #1 examined urine samples from adult volunteers either spiked or not spiked with 32 metabolite standards. Test #2 involved a low biological contrast situation comparing the plasma of rats fed a diet either supplemented or not with vitamin D. The spectral information from each instrument was assembled into separate statistical blocks. Correlations between blocks (e.g., instruments) were examined (RV coefficients) along with the structure of the common spectral information (common components and specific weights analysis). In addition, in Test #1, an outlier individual was blindly introduced, and its identification by the various platforms was evaluated. Despite large differences in the number of spectral features produced after post-processing and the heterogeneity of the analytical conditions and the data treatment, the spectral information both within (NMR and LCMS) and across methods (NMR vs. LCMS) was highly convergent (from 64 to 91 % on average). No effect of the LCMS instrumentation (TOF, QTOF, LTQ-Orbitrap) was noted. The outlier individual was best detected and characterised by LCMS instruments. In conclusion, untargeted metabolomics analyses report consistent information within and across instruments of various technologies, even without prior standardisation.

Item Type: Article
Uncontrolled Keywords: inter-laboratory,mass spectrometry,metabolic fingerprinting,nuclear magnetic resonance,untargeted metabolomics,endocrinology, diabetes and metabolism,biochemistry,clinical biochemistry ,/dk/atira/pure/subjectarea/asjc/2700/2712
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Depositing User: LivePure Connector
Date Deposited: 26 Oct 2018 15:30
Last Modified: 22 Apr 2020 07:08
URI: https://ueaeprints.uea.ac.uk/id/eprint/68667
DOI: 10.1007/s11306-014-0740-0

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