Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma.

A critical question facing the field of metabolomics is whether data obtained from different centers can be effectively compared and combined. An important aspect of this is the interlaboratory precision (reproducibility) of the analytical protocols used. We analyzed human samples in six laboratories using different instrumentation but a common protocol (the AbsoluteIDQ p180 kit) for the measurement of 189 metabolites via liquid chromatography (LC) or flow injection analysis (FIA) coupled to tandem mass spectrometry (MS/MS). In spiked quality control (QC) samples 82% of metabolite measurements had an interlaboratory precision of <20%, while 83% of averaged individual laboratory measurements were accurate to within 20%. For 20 typical biological samples (serum and plasma from healthy individuals) the median interlaboratory coefficient of variation (CV) was 7.6%, with 85% of metabolites exhibiting a median interlaboratory CV of <20%. Precision was largely independent of the type of sample (serum or plasma) or the anticoagulant used but was reduced in a sample from a patient with dyslipidaemia. The median interlaboratory accuracy and precision of the assay for standard reference plasma (NIST SRM 1950) were 107% and 6.7%, respectively. Likely sources of irreproducibility were the near limit of detection (LOD) typical abundance of some metabolites and the degree of manual review and optimization of peak integration in the LC-MS/MS data after acquisition. Normalization to a reference material was crucial for the semi-quantitative FIA measurements. This is the first interlaboratory assessment of a widely used, targeted metabolomics assay illustrating the reproducibility of the protocol and how data generated on different instruments could be directly integrated in large-scale epidemiological studies.

[1]  John M. Baker,et al.  An inter-laboratory comparison demonstrates that [1H]-NMR metabolite fingerprinting is a robust technique for collaborative plant metabolomic data collection , 2010, Metabolomics.

[2]  Douglas N. Rutledge,et al.  Can we trust untargeted metabolomics? Results of the metabo-ring initiative, a large-scale, multi-instrument inter-laboratory study , 2014, Metabolomics.

[3]  Catherine A Rimmer,et al.  Development of a Standard Reference Material for metabolomics research. , 2013, Analytical chemistry.

[4]  Simone Wahl,et al.  Targeted Metabolomics Identifies Reliable and Stable Metabolites in Human Serum and Plasma Samples , 2014, PloS one.

[5]  T. Key,et al.  Plasma concentrations and intakes of amino acids in male meat-eaters, fish-eaters, vegetarians and vegans: a cross-sectional analysis in the EPIC-Oxford cohort , 2015, European Journal of Clinical Nutrition.

[6]  Fabian J Theis,et al.  Body Fat Free Mass Is Associated with the Serum Metabolite Profile in a Population-Based Study , 2012, PloS one.

[7]  Alexander Amberg,et al.  Intra- and interlaboratory reproducibility of ultra performance liquid chromatography-time-of-flight mass spectrometry for urinary metabolic profiling. , 2012, Analytical chemistry.

[8]  Stephen E. Stein,et al.  Metabolite profiling of a NIST Standard Reference Material for human plasma (SRM 1950): GC-MS, LC-MS, NMR, and clinical laboratory analyses, libraries, and web-based resources. , 2013, Analytical chemistry.

[9]  Mark R Viant,et al.  International NMR-based environmental metabolomics intercomparison exercise. , 2009, Environmental science & technology.

[10]  J. Lindon,et al.  'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. , 1999, Xenobiotica; the fate of foreign compounds in biological systems.

[11]  Fabian J Theis,et al.  Effects of smoking and smoking cessation on human serum metabolite profile: results from the KORA cohort study , 2013, BMC Medicine.

[12]  H. Boeing,et al.  Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study , 2016, BMC Medicine.

[13]  Royston Goodacre,et al.  Inter-laboratory reproducibility of fast gas chromatography–electron impact–time of flight mass spectrometry (GC–EI–TOF/MS) based plant metabolomics , 2009, Metabolomics.

[14]  Gabi Kastenmüller,et al.  Pre-Analytical Sample Quality: Metabolite Ratios as an Intrinsic Marker for Prolonged Room Temperature Exposure of Serum Samples , 2015, PloS one.

[15]  Peter Donnelly,et al.  A Genome-Wide Metabolic QTL Analysis in Europeans Implicates Two Loci Shaped by Recent Positive Selection , 2011, PLoS genetics.

[16]  B. Vanhaesebroeck,et al.  Plasma Metabolomic Changes following PI3K Inhibition as Pharmacodynamic Biomarkers: Preclinical Discovery to Phase I Trial Evaluation , 2016, Molecular Cancer Therapeutics.

[17]  A. Peters,et al.  Identification of Serum Metabolites Associated With Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach , 2013, Diabetes.

[18]  Christian Gieger,et al.  Novel biomarkers for pre-diabetes identified by metabolomics , 2012, Molecular systems biology.

[19]  T. Pischon,et al.  Reliability of Serum Metabolite Concentrations over a 4-Month Period Using a Targeted Metabolomic Approach , 2011, PloS one.

[20]  P. Vineis,et al.  Alteration of amino acid and biogenic amine metabolism in hepatobiliary cancers: Findings from a prospective cohort study , 2016, International journal of cancer.

[21]  M. Sauer,et al.  Interlaboratory comparison for quantitative primary metabolite profiling in Pichia pastoris , 2013, Analytical and Bioanalytical Chemistry.

[22]  M. Schulze,et al.  Linking diet, physical activity, cardiorespiratory fitness and obesity to serum metabolite networks: findings from a population-based study , 2014, International Journal of Obesity.

[23]  Florian Kronenberg,et al.  Differences between Human Plasma and Serum Metabolite Profiles , 2011, PloS one.

[24]  Florence I. Raynaud,et al.  Effect of sleep deprivation on the human metabolome , 2014, Proceedings of the National Academy of Sciences.

[25]  F. Schena,et al.  Robustness of NMR-based metabolomics to generate comparable data sets for olive oil cultivar classification. An inter-laboratory study on Apulian olive oils. , 2016, Food chemistry.

[26]  O. Fiehn Metabolomics – the link between genotypes and phenotypes , 2004, Plant Molecular Biology.

[27]  T. Ebbels,et al.  Analytical reproducibility in (1)H NMR-based metabonomic urinalysis. , 2002, Chemical research in toxicology.

[28]  T. Hankemeier,et al.  Integrating metabolomics profiling measurements across multiple biobanks. , 2014, Analytical chemistry.

[29]  Christian Gieger,et al.  A genome-wide perspective of genetic variation in human metabolism , 2010, Nature Genetics.

[30]  Thomas A Neubert,et al.  The ABRF Metabolomics Research Group 2013 Study: Investigation of Spiked Compound Differences in a Human Plasma Matrix. , 2015, Journal of biomolecular techniques : JBT.

[31]  P. Vineis,et al.  Reliability of Serum Metabolites over a Two-Year Period: A Targeted Metabolomic Approach in Fasting and Non-Fasting Samples from EPIC , 2015, PLoS ONE.

[32]  C. Gieger,et al.  Human serum metabolic profiles are age dependent , 2012, Aging cell.

[33]  Christian Gieger,et al.  Metabolic Footprint of Diabetes: A Multiplatform Metabolomics Study in an Epidemiological Setting , 2010, PloS one.