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Metabolomics. 2015;11(4):807-821. Epub 2014 Oct 14.

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

Author information

1
INRA UMR1260, "Nutrition, Obésité et Risque Thrombotique", 13385 Marseille, France ; Faculté de Médecine, Aix-Marseille Université, 13385 Marseille, France ; INSERM, UMR1062 "Nutrition, Obésité et Risque Thrombotique", 13385 Marseille, France.
2
INRA, UMR 1083 SPO, INRA Campus SupAgro, Plateforme Polyphénols, 2 Place Viala, 34060 Montpellier Cedex 02, France.
3
BRUKER, 4 allée Hendrick Lorentz, 77447 Marne La Vallée Cedex 2, France.
4
INRA, UMR 1019, UNH, CRNH Auvergne, 63000 Clermond-Ferrand, France ; INRA, UMR 1019, Plateforme d'Exploration du Métabolisme, UNH, 63000 Clermond-Ferrand, France.
5
INRA, UMR 1331 TOXALIM (Research Center in Food Toxicology), Axiom-Metatoul, 31027 Toulouse, France.
6
Laboratoire d'Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI, CEA-Saclay, 91191 Gif-sur-Yvette Cedex, France.
7
European Commission, Joint Research Centre, Institute for Health and Consumer Protection, Via Enrico Fermi 2749, 21027 Ispra, Italy.
8
INRA, UMR1332 Fruit Biology and Pathology, Centre INRA de Bordeaux, 33140 Villenave d'Ornon, France.
9
INRA, UMR 1145 Ingénierie Procédés Aliments, 75005 Paris, France ; AgroParisTech, UMR 1145 Ingénierie Procédés Aliments, 75005 Paris, France.
10
INRA, UMR 1331 TOXALIM (Research Center in Food Toxicology), Metabolism of Xenobiotics (MeX), 31027 Toulouse, France.
11
LUNAM Université, Oniris, Laboratoire d'Etude des Résidus et Contaminants dans les Aliments (LABERCA), USC INRA 1329, BP 50707, 44307 Nantes Cedex 3, France.
12
Institute of Food Research, Norwich Research Park, Norwich, NR4 7UA UK.
13
UPMC, Institut Parisien de Chimie Moléculaire, UMR-CNRS 7201, 4 Place Jussieu, Paris Cédex 05, France.
14
Aix-Marseille Université, ISM2, Campus Scientifique Saint Jérôme, 13397 Marseille Cedex 20, France.
15
Université Paris 13, Sorbonne Paris Cité, Laboratoire CSPBAT, CNRS (UMR 7244), 93017 Bobigny, France.
16
LCH, Laboratoire des Courses Hippiques, 91370 Verrières-le-Buisson, France.
17
AP-HM, Hôpital Timone, Laboratoire de Biochimie, 13385 Marseille, France.
18
Université de Picardie Jules Verne, EA 3900 BIOPI Biologie des plantes innovation, UFR de Pharmacie, 1 rue des Louvels, 80000 Amiens, France.
19
MetaboMer, FR2424, CNRS/UPMC, Station Biologique de Roscoff, Place Georges Tessier, 29680 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.

KEYWORDS:

Inter-laboratory; Mass spectrometry; Metabolic fingerprinting; Nuclear magnetic resonance; Untargeted metabolomics

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