Advertisement

Metabolomics

, Volume 11, Issue 3, pp 518–528 | Cite as

Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample

  • Joanna Godzien
  • Vanesa Alonso-Herranz
  • Coral Barbas
  • Emily Grace ArmitageEmail author
Original Article

Abstract

The type and use of quality control (QC) samples is a ‘hot topic’ in metabolomics. QCs are not novel in analytical chemistry; however since the evolution of using QCs to control the quality of data in large scale metabolomics studies (first described in 2011), the need for detailed knowledge of how to use QCs and the effects they can have on data treatment is growing. A controlled experiment has been designed to illustrate the most advantageous uses of QCs in metabolomics experiments. For this, samples were formed from a pool of plasma whereby different metabolites were spiked into two groups in order to simulate biological biomarkers. Three different QCs were compared: QCs pooled from all samples, QCs pooled from each experimental group of samples separately and QCs provided by an external source (QC surrogate). On the experimentation of different data treatment strategies, it was revealed that QCs collected separately for groups offers the closest matrix to the samples and improves the statistical outcome, especially for biomarkers unique to one group. A novel quality assurance plus procedure has also been proposed that builds on previously published methods and has the ability to improve statistical results for QC pool. For this dataset, the best option to work with QC surrogate was to filter data based only on group presence. Finally, a novel use of recursive analysis is portrayed that allows the improvement of statistical analyses with respect to the ratio between true and false positives.

Keywords

Quality control samples Quality assurance procedure False positives Recursive analysis In silico QC surrogate 

Notes

Acknowledgments

Authors would like to acknowledge funding from the Ministry of Science and Technology (MCIT CTQ2011-23562).

Supplementary material

11306_2014_712_MOESM1_ESM.doc (1.4 mb)
Supplementary material 1 (DOC 1394 kb)

References

  1. Ciborowski, M., Lipska, A., Godzien, J., Ferrarini, A., Korsak, J., Radziwon, P., et al. (2012a). Combination of LC–MS- and GC–MS-based metabolomics to study the effect of ozonated autohemotherapy on human blood. Journal of Proteome Research, 11, 6231–6241. doi: 10.1021/pr3008946.PubMedGoogle Scholar
  2. Ciborowski, M., Teul, J., Martin-Ventura, J. L., Egido, J., & Barbas, C. (2012b). Metabolomics with LC–QTOF–MS permits the prediction of disease stage in aortic abdominal aneurysm based on plasma metabolic fingerprint. PLoS ONE, 7, e31982. doi: 10.1371/journal.pone.0031982.CrossRefPubMedCentralPubMedGoogle Scholar
  3. Ciborowski, M., Ruperez, J., Martinez-Alcazar, M. P., Angulo, S., Radziwon, P., Olszanski, R., et al. (2010). Metabolomic approach with LC–MS reveals significant effect of pressure on diver’s plasma. Journal of Proteome Research, 9, 4131–4137. doi: 10.1021/pr100331j.CrossRefPubMedGoogle Scholar
  4. Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., et al. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6, 1060–1083. doi: 10.1038/nprot.2011.335.CrossRefPubMedGoogle Scholar
  5. Gika, H. G., Theodoridis, G. A., Earll, M., & Wilson, I. D. (2012). A QC approach to the determination of day-to-day reproducibility and robustness of LC–MS methods for global metabolite profiling in metabonomics/metabolomics. Bioanalysis, 4, 2239–2247. doi: 10.4155/bio.12.212.CrossRefPubMedGoogle Scholar
  6. Gika, H. G., Theodoridis, G. A., Wingate, J. E., & Wilson, I. D. (2007). Within-day reproducibility of an HPLC–MS-based method for metabonomic analysis: Application to human urine. Journal of Proteome Research, 6, 3291–3303. doi: 10.1021/pr070183p.CrossRefPubMedGoogle Scholar
  7. Godzien, J., Ciborowski, M., Angulo, S., & Barbas, C. (2013a). From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC–MS. Electrophoresis, 34, 2812–2826. doi: 10.1002/elps.201300053.PubMedGoogle Scholar
  8. Godzien, J., Ciborowski, M., Whiley, L., Legido-Quigley, C., Ruperez, F. J., Barbas, C., et al. (2013b). In-vial dual extraction liquid chromatography coupled to mass spectrometry applied to streptozotocin-treated diabetic rats. Tips and pitfalls of the method. Journal of Chromatography A, 1304, 52–60. doi: 10.1016/j.chroma.2013.07.029.CrossRefPubMedGoogle Scholar
  9. Godzien, J., et al. (2010). Metabolomic approach with LC-QTOF to study the effect of a nutraceutical treatment on urine of diabetic rats. Journal of Proteome Research, 10, 837–844. doi: 10.1021/pr100993x.CrossRefPubMedGoogle Scholar
  10. Guy, P. A., Tavazzi, I., Bruce, S. J., Ramadan, Z., & Kochhar, S. (2008). Global metabolic profiling analysis on human urine by UPLC-TOFMS: Issues and method validation in nutritional metabolomics. Journal of Chromatography B, 871, 253–260. doi: 10.1016/j.jchromb.2008.04.034.CrossRefGoogle Scholar
  11. Kamleh, M. A., Ebbels, T. M. D., Spagou, K., Masson, P., & Want, E. J. (2012). Optimizing the use of quality control samples for signal drift correction in large-scale urine metabolic profiling studies. Analytical Chemistry, 84, 2670–2677. doi: 10.1021/ac202733q.CrossRefPubMedGoogle Scholar
  12. Llorach, R., Urpi-Sarda, M., Jauregui, O., Monagas, M., & Andres-Lacueva, C. (2009). An LC–MS-based metabolomics approach for exploring urinary metabolome modifications after cocoa consumption. Journal of Proteome Research, 8, 5060–5068. doi: 10.1021/pr900470a.CrossRefPubMedGoogle Scholar
  13. Sangster, T., Major, H., Plumb, R., Wilson, A. J., & Wilson, I. D. (2006). A pragmatic and readily implemented quality control strategy for HPLC–MS and GC–MS-based metabonomic analysis. Analyst, 131, 1075–1078. doi: 10.1039/b604498k.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Joanna Godzien
    • 1
  • Vanesa Alonso-Herranz
    • 1
  • Coral Barbas
    • 1
  • Emily Grace Armitage
    • 1
    Email author
  1. 1.Centre for Metabolomics and Bioanalysis (CEMBIO), Facultad de FarmaciaUniversidad CEU San PabloMadridSpain

Personalised recommendations