Influence of collection tubes during quantitative targeted metabolomics studies in human blood samples.

BACKGROUND Plasma and serum are the most widely used matrices in clinical studies. However, some variability in absolute concentrations of metabolites are likely to be observed in these collection tubes matrices. METHODS We analyzed 189 metabolites using the same protocol for quantitative targeted metabolomics (LC-MS/MS AbsoluteIDQ p180 Kit Biocrates) in three types of samples, serum, plasma EDTA and citrate, of 80 subjects from the Cooperative Health Research In South Tyrol cohort (40 healthy elderly and 40 healthy young). RESULTS The concentration levels were higher in serum than citrate and EDTA, in particular for amino acids and biogenic amines. The average Pearson's correlation coefficients were however always higher than 0.7 for these two classes of metabolites. We could also demonstrate that blank EDTA vacutainer tubes contain a significant amount of sarcosine. Finally, we compared the metabolome of young people against elderly subjects and found that the highest number of metabolites significantly changing with age was detected in serum. CONCLUSION Serum samples provide higher sensitivity for biomarker discovery studies. Due to the presence of spurious amount of sarcosine in vacutainer EDTA tubes, plasma EDTA is not suitable for studies requiring accurate quantification of sarcosine.

[1]  Jerzy Adamski,et al.  Interlaboratory Reproducibility of a Targeted Metabolomics Platform for Analysis of Human Serum and Plasma. , 2017, Analytical chemistry.

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

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

[4]  Giuseppe Lippi,et al.  Preanalytical variables for liquid chromatography-mass spectrometry (LC-MS) analysis of human blood specimens. , 2017, Clinical biochemistry.

[5]  Ronan M. T. Fleming,et al.  Prediction of intracellular metabolic states from extracellular metabolomic data , 2014, Metabolomics.

[6]  M. Keller,et al.  Unbiased Metabolomic Investigation of Alzheimer's Disease Brain Points to Dysregulation of Mitochondrial Aspartate Metabolism. , 2016, Journal of Proteome Research.

[7]  Romanas Chaleckis,et al.  Individual variability in human blood metabolites identifies age-related differences , 2016, Proceedings of the National Academy of Sciences.

[8]  Matej Oresic,et al.  Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950–Metabolites in Frozen Human Plasma[S] , 2017, Journal of Lipid Research.

[9]  Peter P. Pramstaller,et al.  Sequential recruitment of study participants may inflate genetic heritability estimates , 2017, Human Genetics.

[10]  G. Corso,et al.  A powerful couple in the future of clinical biochemistry: in situ analysis of dried blood spots by ambient mass spectrometry. , 2010, Bioanalysis.

[11]  Douglas B. Kell,et al.  Molecular phenotyping of a UK population: defining the human serum metabolome , 2014, Metabolomics.

[12]  Nora Nikolac,et al.  Preanalytical quality improvement: in quality we trust , 2013, Clinical chemistry and laboratory medicine.

[13]  B. Palsson,et al.  Biomarkers defining the metabolic age of red blood cells during cold storage. , 2016, Blood.

[14]  R. Nijveldt,et al.  Determination of arginine, asymmetric dimethylarginine, and symmetric dimethylarginine in human plasma and other biological samples by high-performance liquid chromatography. , 2002, Analytical biochemistry.

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

[16]  A. Hicks,et al.  Pre-analytic evaluation of volumetric absorptive microsampling and integration in a mass spectrometry-based metabolomics workflow , 2017, Analytical and Bioanalytical Chemistry.

[17]  Jianguo Xia,et al.  Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis , 2016, Current protocols in bioinformatics.

[18]  Thomas Hankemeier,et al.  The influence of citrate, EDTA, and heparin anticoagulants to human plasma LC–MS lipidomic profiling , 2012, Metabolomics.

[19]  M. Giera,et al.  Analytical pitfalls and challenges in clinical metabolomics. , 2016, Bioanalysis.

[20]  B. Palsson,et al.  Comprehensive metabolomic study of platelets reveals the expression of discrete metabolic phenotypes during storage , 2014, Transfusion.

[21]  Christian Gieger,et al.  Metabolomic markers reveal novel pathways of ageing and early development in human populations , 2013, International journal of epidemiology.

[22]  Erik Peter,et al.  Impact of Prolonged Blood Incubation and Extended Serum Storage at Room Temperature on the Human Serum Metabolome , 2018, Metabolites.

[23]  M. Panteghini,et al.  Total laboratory automation: Do stat tests still matter? , 2017, Clinical biochemistry.

[24]  B. Palsson,et al.  Metabolomic analysis of platelets during storage: a comparison between apheresis‐ and buffy coat–derived platelet concentrates , 2015, Transfusion.

[25]  Thaer Barri,et al.  UPLC-ESI-QTOF/MS and multivariate data analysis for blood plasma and serum metabolomics: effect of experimental artefacts and anticoagulant. , 2013, Analytica chimica acta.

[26]  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.

[27]  Christian Fuchsberger,et al.  The Cooperative Health Research in South Tyrol (CHRIS) study: rationale, objectives, and preliminary results , 2015, Journal of Translational Medicine.

[28]  John T. Wei,et al.  Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression , 2009, Nature.

[29]  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.

[30]  J. Xu,et al.  Systematic evaluation of serum and plasma collection on the endogenous metabolome. , 2017, Bioanalysis.

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

[32]  F. Mannello Serum or plasma samples? The "Cinderella" role of blood collection procedures: preanalytical methodological issues influence the release and activity of circulating matrix metalloproteinases and their tissue inhibitors, hampering diagnostic trueness and leading to misinterpretation. , 2008, Arteriosclerosis, thrombosis, and vascular biology.

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

[34]  R. Bowen,et al.  Blood collection tubes as medical devices: The potential to affect assays and proposed verification and validation processes for the clinical laboratory. , 2016, Clinical biochemistry.

[35]  T. Pischon,et al.  Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts , 2017, European Journal of Epidemiology.