Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study. Martin, J.; Maillot, M.; Mazerolles, G.; Verdu, A.; Lyan, B.; Migné, C.; Defoort, C.; Canlet, C.; Junot, C.; Guillou, C.; Manach, C.; Jabob, D.; Bouveresse, D.; Paris, E.; Pujos-Guillot, E.; Jourdan, F.; Giacomoni, F.; Courant, F.; Favé, G.; Le Gall, G.; Chassaigne, H.; Tabet, J.; Martin, J.; Antignac, J.; Shintu, L.; Defernez, M.; Philo, M.; Alexandre-Gouaubau, M.; Amiot-Carlin, M.; Bossis, M.; Triba, M.; Stojilkovic, N.; Banzet, N.; Molinié, R.; Bott, R.; Goulitquer, S.; Caldarelli, S.; and Rutledge, D. Metabolomics, 11(4):807-821, 2015. cited By 27
Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study [link]Paper  doi  abstract   bibtex   
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. © 2014, The Author(s).
@ARTICLE{Martin2015807,
author={Martin, J.-C. and Maillot, M. and Mazerolles, G. and Verdu, A. and Lyan, B. and Migné, C. and Defoort, C. and Canlet, C. and Junot, C. and Guillou, C. and Manach, C. and Jabob, D. and Bouveresse, D.J.-R. and Paris, E. and Pujos-Guillot, E. and Jourdan, F. and Giacomoni, F. and Courant, F. and Favé, G. and Le Gall, G. and Chassaigne, H. and Tabet, J.-C. and Martin, J.-F. and Antignac, J.-P. and Shintu, L. and Defernez, M. and Philo, M. and Alexandre-Gouaubau, M.-C. and Amiot-Carlin, M.-J. and Bossis, M. and Triba, M.N. and Stojilkovic, N. and Banzet, N. and Molinié, R. and Bott, R. and Goulitquer, S. and Caldarelli, S. and Rutledge, D.N.},
title={Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study},
journal={Metabolomics},
year={2015},
volume={11},
number={4},
pages={807-821},
doi={10.1007/s11306-014-0740-0},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84931575490&doi=10.1007%2fs11306-014-0740-0&partnerID=40&md5=75fbff8d44e52afe556e02b7c470bcc5},
affiliation={INRA UMR1260, “Nutrition, Obésité et Risque Thrombotique”, Marseille, France; Aix-Marseille Université, Marseille, France; INSERM, UMR1062 “Nutrition, Obésité et Risque Thrombotique”, Marseille, France; INRA, UMR 1083 SPO, INRA Campus SupAgro, Plateforme Polyphénols, 2 Place Viala, Montpellier Cedex 02, France; BRUKER, 4 allée Hendrick Lorentz, Marne La Vallée Cedex 2, France; INRA, UMR 1019, UNH, CRNH Auvergne, Clermond-Ferrand, France; INRA, UMR 1019, Plateforme d’Exploration du Métabolisme, UNH, Clermond-Ferrand, France; INRA, UMR 1331 TOXALIM (Research Center in Food Toxicology), Axiom-Metatoul, Toulouse, France; Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI, CEA-Saclay, Gif-sur-Yvette Cedex, France; European Commission, Joint Research Centre, Institute for Health and Consumer Protection, Via Enrico Fermi 2749, Ispra, Italy; INRA, UMR1332 Fruit Biology and Pathology, Centre INRA de Bordeaux, Villenave d’Ornon, France; INRA, UMR 1145 Ingénierie Procédés Aliments, Paris, France; AgroParisTech, UMR 1145 Ingénierie Procédés Aliments, Paris, France; UPMC, Institut Parisien de Chimie Moléculaire, UMR-CNRS 7201, 4 Place Jussieu, Paris Cédex 05, France; INRA, UMR 1331 TOXALIM (Research Center in Food Toxicology), Metabolism of Xenobiotics (MeX), Toulouse, France; LUNAM Université, Oniris, Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA), USC INRA 1329, BP 50707, Nantes Cedex 3, France; Institute of Food Research, Norwich Research Park, Norwich, United Kingdom; Aix-Marseille Université, ISM2, Campus Scientifique Saint Jérôme, Marseille Cedex 20, France; Université Paris 13, Sorbonne Paris Cité, Laboratoire CSPBAT, CNRS (UMR 7244), Bobigny, France; LCH, Laboratoire des Courses Hippiques, Verrières-le-Buisson, France; AP-HM, Hôpital Timone, Laboratoire de Biochimie, Marseille, France; Université de Picardie Jules Verne, EA 3900 BIOPI Biologie des plantes innovation, UFR de Pharmacie, 1 rue des Louvels, Amiens, France; MetaboMer, FR2424, CNRS/UPMC, Station Biologique de Roscoff, Place Georges Tessier, Roscoff, France},
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. © 2014, The Author(s).},
author_keywords={Inter-laboratory;  Mass spectrometry;  Metabolic fingerprinting;  Nuclear magnetic resonance;  Untargeted metabolomics},
document_type={Article},
source={Scopus},
}
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