Bioanalytical Challenge

Validation of a two-step quality control approach for a large-scale human urine metabolomic study conducted in seven experimental batches with LC/QTOF-MS

*Author for correspondence:

E-mail Address: info@foodtech.uni-kiel.de

Institute for Human Nutrition & Food Science, Department of Food Technology, University of Kiel, Germany

Search for more papers by this author

Beate Petersen

Institute for Human Nutrition & Food Science, Department of Food Technology, University of Kiel, Germany

Search for more papers by this author

Julia K Keppler

Institute for Human Nutrition & Food Science, Department of Food Technology, University of Kiel, Germany

Search for more papers by this author

Andreas Koch

Institute for Experimental Medicine, Department of Maritime Physiology, University of Kiel, Germany

Search for more papers by this author

Stefan Schreiber

Department of Internal Medicine I, University of Kiel, Germany

Search for more papers by this author

Matthias Laudes

Department of Internal Medicine I, University of Kiel, Germany

Search for more papers by this author

Karin Schwarz

Institute for Human Nutrition & Food Science, Department of Food Technology, University of Kiel, Germany

Search for more papers by this author

Published Online:5 Jan 2015https://doi.org/10.4155/bio.14.270

View article

Abstract

After his study of food science at the Rheinische Friedrich-Wilhelms University of Bonn, Tobias J Demetrowitsch obtained his doctoral degree in the research field of metabolomics at the Christian-Albrechts-University of Kiel. The present paper is part of his doctoral thesis and describes an extended strategy to evaluate and verify complex or large-scale experiments and data sets.

Large-scale studies result in high sample numbers, requiring the analysis of samples in different batches. So far, the verification of such LC–MS-based metabolomics studies is difficult. Common approaches have not provided a reliable validation procedure to date. This article shows a novel verification process for a large-scale human urine study (analyzed by a LC/QToF-MS system) using a two-step validation procedure. The first step comprises a targeted approach that aims to examine and exclude statistical outliers. The second step consists of a principle component analysis, with the aim of a tight cluster of all quality controls and a second for all volunteer samples. The applied study design provides a reliable two-step validation procedure for large-scale studies and additionally contains an inhouse verification procedure.

Keywords:

References

1 Kemperman RF, Ramses FJ, Horvatovich PL et al. Comparative urine analysis by liquid chromatography−mass spectrometry and multivariate statistics: method development, evaluation, and application to proteinuria. J. Proteome Res. 6(1), 194–206 (2007).
Medline CASGoogle Scholar
2 Spagou K, Wilson ID, Masson P et al. HILIC-UPLC–MS for exploratory urinary metabolic profiling in toxicological studies. Anal. Chem. 83(1), 382–390 (2011).
Medline CASGoogle Scholar
3 Gika HG, Theodoridis GA, Earll M, Wilson ID. 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(18), 2239–2247 (2012).
CASGoogle Scholar
4 Gika HG, Theodoridis GA, Wingate JE, Wilson ID. Within-day reproducibility of an HPLC–MS-based method for metabonomic analysis: application to human urine. J. Proteome Res. 6(8), 3291–3303 (2007).
Medline CASGoogle Scholar
5 Gika HG, Macpherson E, Theodoridis GA, Wilson ID. Evaluation of the repeatability of ultra-performance liquid chromatography–TOF-MS for global metabolic profiling of human urine samples. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 871(2), 299–305 (2008).
Medline CASGoogle Scholar
6 Zelena E, Dunn WB, Broadhurst D et al. Development of a robust and repeatable UPLC−MS method for the long-term metabolomic study of human serum. Anal. Chem. 81(4), 1357–1364 (2009).
Medline CASGoogle Scholar
7 Dunn WB, Wilson ID, Nicholls AW, Broadhurst D. The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis 4(18), 2249–2264 (2012).
CASGoogle Scholar
8 Kuhnert N, Jaiswal R, Eravuchira P et al. Scope and limitations of principal component analysis of high resolution LC-TOF-MS data: the analysis of the chlorogenic acid fraction in green coffee beans as a case study. Anal. Methods 3(1), 144–155 (2011).
Medline CASGoogle Scholar
9 Warrack BM, Hnatyshyn S, Ott K et al. Normalization strategies for metabonomic analysis of urine samples. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 877(5–6??), 547–552 (2009).
Medline CASGoogle Scholar
10 Committee for Medicinal Products for Human Use. Guideline on Bioanalytical Method Validation (2011). www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2011/08/WC500109686.pdf.
Google Scholar
11 US Department of Health and Human Services FDA. Guidance for Industry: Bioanalytical Method Validation (2013). www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM368107.pdf.
Google Scholar
12 Rümelin A, Fauth U, Halmágyi M. Determination of ascorbic acid in plasma and urine by high performance liquid chromatography with ultraviolet detection. Clin. Chem. Lab. Med. 37(5), 533–536 (1999).
MedlineGoogle Scholar
13 Keltz FR, Kies C, Fox HM. Urinary ascorbic acid excretion in the human as affected by dietary fiber and zinc. Am. J. Clin. Nutr. 31(7), 1167–1171 (1978).
Medline CASGoogle Scholar
14 Sangster T, Major H, Plumb R, Wilson AJ, Wilson ID. A pragmatic and readily implemented quality control strategy for HPLC–MS and GC-MS-based metabonomic analysis. Analyst 131(10), 1075–1078 (2006).
Medline CASGoogle Scholar
15 Sangster TP, Wingate JE, Burton L, Teichert F, Wilson ID. Investigation of analytical variation in metabonomic analysis using liquid chromatography/mass spectrometry. Rapid Commun. Mass Spectrom. 21(18), 2965–2970 (2007).
Medline CASGoogle Scholar
16 Jansen R, Lachatre G, Marquet P.LC–MS/MS systematic toxicological analysis: comparison of MS/MS spectra obtained with different instruments and settings. Clin. Biochem. 38(4), 362–372 (2005).
Medline CASGoogle Scholar
17 Barnett V, Lewis T. Outliers in Outliers in Statistical Data (3rd Edition). Wiley, NY, USA (1994).
Google Scholar
18 Wishart DS, Tzur D, Knox C et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 35, D521–D526 (2007).
Medline CASGoogle Scholar
19 Wishart DS, Knox C, Guo AC et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37, D603–D610 (2009).
Medline CASGoogle Scholar
20 Wishart DS, Jewison T, Guo AC et al. HMDB 3.0 – The Human Metabolome Database in 2013. Nucleic Acids Res. 41(D1), D801–D807 (2012).
MedlineGoogle Scholar
21 Boes T. Effects of low-level analysis of the results of gene expression data from Affymetrix. Medical Faculty of the University of Duisburg-Essen. 1–90 (2007).
Google Scholar
22 Bolstad BM, Irizarry R, Astrand M, Speed TP. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2), 185–193 (2003).
Medline CASGoogle Scholar
23 Ivosev G, Burton L, Bonner R. Dimensionality reduction and visualization in principal component analysis. Anal. Chem. 80(13), 4933–4944 (2008).
Medline CASGoogle Scholar
24 Wagner S, Scholz K, Donegan M, Burton L, Wingate J, Völkel W. Metabonomics and biomarker discovery: LC–MS metabolic profiling and constant neutral loss scanning combined with multivariate data analysis for mercapturic acid analysis. Anal. Chem. 78(4), 1296–1305 (2006).
Medline CASGoogle Scholar
25 Medina S, Ferreres F, García-Viguera C et al. Non-targeted metabolomic approach reveals urinary metabolites linked to steroid biosynthesis pathway after ingestion of citrus juice. Food Chem. 136(2), 938–946 (2013).
Medline CASGoogle Scholar
26 Kiss A, Lucio M, Fildier A, Buisson C, Schmitt-Kopplin P, Cren-Olivé C. Doping control using high and ultra-high resolution mass spectrometry based non-targeted metabolomics-a case study of salbutamol and budesonide abuse. PLoS ONE 8(9), e74584 (2013).
Medline CASGoogle Scholar

Vol. 7, No. 1

Metrics

Downloaded 257 times

History

Published online 5 January 2015

Published in print January 2015

Information

Keywords

Financial & competing interests disclosure

This study was part of the Food Chain Plus Study and funded by the Federal Ministry of Education and Research (BMBF, Germany), number: FKZ 0315539A. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Ethical conduct of research

The authors state that they have obtained appropriate insti-tutional review board approval or have followed the princi-ples outlined in the Declaration of Helsinki for all human or animal experimental investigations. In addition, for investi-gations involving human subjects, informed consent has been obtained from the participants involved.

PDF download