| Title |
Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study
|
|---|---|
| Published in |
Metabolomics, October 2014
|
| DOI | 10.1007/s11306-014-0740-0 |
| Pubmed ID | |
| Authors |
Jean-Charles Martin, Matthieu Maillot, Gérard Mazerolles, Alexandre Verdu, Bernard Lyan, Carole Migné, Catherine Defoort, Cecile Canlet, Christophe Junot, Claude Guillou, Claudine Manach, Daniel Jabob, Delphine Jouan-Rimbaud Bouveresse, Estelle Paris, Estelle Pujos-Guillot, Fabien Jourdan, Franck Giacomoni, Frédérique Courant, Gaëlle Favé, Gwenaëlle Le Gall, Hubert Chassaigne, Jean-Claude Tabet, Jean-Francois Martin, Jean-Philippe Antignac, Laetitia Shintu, Marianne Defernez, Mark Philo, Marie-Cécile Alexandre-Gouaubau, Marie-Josephe Amiot-Carlin, Mathilde Bossis, Mohamed N. Triba, Natali Stojilkovic, Nathalie Banzet, Roland Molinié, Romain Bott, Sophie Goulitquer, Stefano Caldarelli, Douglas N. Rutledge |
| 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. |
X Demographics
The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 10% |
| Spain | 2 | 10% |
| Qatar | 1 | 5% |
| Germany | 1 | 5% |
| France | 1 | 5% |
| United States | 1 | 5% |
| United Kingdom | 1 | 5% |
| Australia | 1 | 5% |
| Unknown | 10 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 9 | 45% |
| Members of the public | 8 | 40% |
| Science communicators (journalists, bloggers, editors) | 2 | 10% |
| Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 222 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Colombia | 2 | <1% |
| Germany | 1 | <1% |
| Malaysia | 1 | <1% |
| Italy | 1 | <1% |
| Ecuador | 1 | <1% |
| Brazil | 1 | <1% |
| Sweden | 1 | <1% |
| South Africa | 1 | <1% |
| Czechia | 1 | <1% |
| Other | 3 | 1% |
| Unknown | 209 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 58 | 26% |
| Student > Ph. D. Student | 51 | 23% |
| Student > Master | 24 | 11% |
| Student > Doctoral Student | 17 | 8% |
| Student > Bachelor | 12 | 5% |
| Other | 33 | 15% |
| Unknown | 27 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 56 | 25% |
| Agricultural and Biological Sciences | 43 | 19% |
| Biochemistry, Genetics and Molecular Biology | 29 | 13% |
| Medicine and Dentistry | 19 | 9% |
| Computer Science | 7 | 3% |
| Other | 24 | 11% |
| Unknown | 44 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 July 2016.
All research outputs
#3,101,767
of 25,654,806 outputs
Outputs from Metabolomics
#138
of 1,397 outputs
Outputs of similar age
#33,910
of 268,811 outputs
Outputs of similar age from Metabolomics
#3
of 24 outputs
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