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Data-handling strategies for metabonomic studies: example of the UHPLC-ESI/ToF urinary signature of tetrahydrocannabinol in humans

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Abstract

Metabonomics has become a very valuable tool and many research fields rely on results coming out from this combination of analytical techniques, chemometric strategies, and biological interpretation. Moreover, the matrices are more and more complex and the implications of the results are often of major importance. In this context, the need for pertinent validation strategies comes naturally. The choice of the appropriate chemometric method remains nevertheless a difficult task due to particularities such as: the number of measured variables, the complexity of the matrix and the purposes of the study. Consequently, this paper presents a detailed metabonomic study on human urine with a special emphasis on the importance of assessing the data's quality. It also describes, step by step, the statistical tools currently used and offers a critical view on some of their limits. In this work, 29 urine samples among which 15 samples obtained from tetrahydrocannabinol (delta-9-tetrahydrocannabinol)-consuming athletes, 5 samples provided by volunteers, and 9 samples obtained from athletes were submitted to untargeted analysis by means of ultra high-pressure liquid chromatography–electrospray ionization–time-of-flight mass spectrometry. Next, the quality of the obtained data was assessed and the results were compared to those found in databases. Then, unsupervised (principal component analysis (PCA)) and supervised (ANOVA/PCA, partial least-square–discriminant analysis (PLS-DA), orthogonal PLS-DA) univariate and multivariate statistical methods were applied.

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Abbreviations

ANOVA:

Analysis of variance

CE-MS:

Capillary electrophoresis-mass spectrometry

CV:

Coefficient of variation

GC-MS:

Gaseous chromatography–mass spectrometry

LC-MS:

Liquid chromatography–mass spectrometry

LC-ToF:

Liquid chromatography–time-of-flight mass spectrometry

NMR:

Nuclear magnetic resonance

OPLS:

Orthogonal partial least-square analysis

PCA:

Principal component analysis

PLS-DA:

Partial least-square–discriminant analysis

QC:

Quality control

THC:

Tetrahydrocannabinol delta-9-tetrahydrocannabinol; (6aR,10aR)-6,6,9-trimethyl-3-pentyl-6a,7,8,10a-tetrahydrobenzo[c]chromen-1-ol

UHPLC-ESI/ToF:

Ultra high-pressure liquid chromatography–electrospray ionization–time-of-flight mass spectrometry

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Acknowledgments

The authors would like to acknowledge the contribution of the Rhone-Alpes Regional Council and the French anti-Doping Agency for the samples necessary to this work. We would also like to thank Aurélie Fildier for her technical help for the high resolution mass spectrometry.

Author information

Authors and Affiliations

  1. Institut des Sciences Analytiques, UMR 5280 CNRS, Equipe TRACES, 5 rue de la Doua, 69100, Villeurbanne, France

    Agneta Kiss & Cécile Cren-Olivé

  2. Institut des Sciences Analytiques, UMR 5280 CNRS, Equipe CHEMO, 5 rue de la Doua, 69100, Villeurbanne, France

    Claire Bordes & Pierre Lanteri

  3. Département des Analyses, Agence Française de Lutte contre le Dopage, 143, avenue Roger Salengro, 92290, Châtenay-Malabry, France

    Corinne Buisson & Francoise Lasne

Authors
  1. Agneta Kiss
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  2. Claire Bordes
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  3. Corinne Buisson
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  4. Francoise Lasne
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  5. Pierre Lanteri
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  6. Cécile Cren-Olivé
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Corresponding author

Correspondence to Cécile Cren-Olivé.

Additional information

Published in the special issue Analytical Science in France with guest editors Christian Rolando and Philippe Garrigues.

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Kiss, A., Bordes, C., Buisson, C. et al. Data-handling strategies for metabonomic studies: example of the UHPLC-ESI/ToF urinary signature of tetrahydrocannabinol in humans. Anal Bioanal Chem 406, 1209–1219 (2014). https://doi.org/10.1007/s00216-013-7199-0

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  • DOI: https://doi.org/10.1007/s00216-013-7199-0

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