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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Acknowledgments
Authors would like to acknowledge funding from the Ministry of Science and Technology (MCIT CTQ2011-23562).
Author information
Authors and Affiliations
Corresponding author
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Godzien, J., Alonso-Herranz, V., Barbas, C. et al. Controlling the quality of metabolomics data: new strategies to get the best out of the QC sample. Metabolomics 11, 518–528 (2015). https://doi.org/10.1007/s11306-014-0712-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11306-014-0712-4