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dc.contributor.authorThompson, JW
dc.contributor.authorAdams, KJ
dc.contributor.authorAdamski, J
dc.contributor.authorAsad, Y
dc.contributor.authorBorts, D
dc.contributor.authorBowden, JA
dc.contributor.authorByram, G
dc.contributor.authorDang, V
dc.contributor.authorDunn, WB
dc.contributor.authorFernandez, F
dc.contributor.authorFiehn, O
dc.contributor.authorGaul, DA
dc.contributor.authorHühmer, AF
dc.contributor.authorKalli, A
dc.contributor.authorKoal, T
dc.contributor.authorKoeniger, S
dc.contributor.authorMandal, R
dc.contributor.authorMeier, F
dc.contributor.authorNaser, FJ
dc.contributor.authorO'Neil, D
dc.contributor.authorPal, A
dc.contributor.authorPatti, GJ
dc.contributor.authorPham-Tuan, H
dc.contributor.authorPrehn, C
dc.contributor.authorRaynaud, FI
dc.contributor.authorShen, T
dc.contributor.authorSoutham, AD
dc.contributor.authorSt John-Williams, L
dc.contributor.authorSulek, K
dc.contributor.authorVasilopoulou, CG
dc.contributor.authorViant, M
dc.contributor.authorWinder, CL
dc.contributor.authorWishart, D
dc.contributor.authorZhang, L
dc.contributor.authorZheng, J
dc.contributor.authorMoseley, MA
dc.date.accessioned2020-09-30T09:24:34Z
dc.date.issued2019-10-22
dc.identifier.citationAnalytical chemistry, 2019, 91 (22), pp. 14407 - 14416
dc.identifier.issn0003-2700
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4081
dc.identifier.eissn1520-6882
dc.identifier.doi10.1021/acs.analchem.9b02908
dc.description.abstractA challenge facing metabolomics in the analysis of large human cohorts is the cross-laboratory comparability of quantitative metabolomics measurements. In this study, 14 laboratories analyzed various blood specimens using a common experimental protocol provided with the Biocrates AbsoluteIDQ p400HR kit, to quantify up to 408 metabolites. The specimens included human plasma and serum from male and female donors, mouse and rat plasma, as well as NIST SRM 1950 reference plasma. The metabolite classes covered range from polar (e.g., amino acids and biogenic amines) to nonpolar (e.g., diacyl- and triacyl-glycerols), and they span 11 common metabolite classes. The manuscript describes a strict system suitability testing (SST) criteria used to evaluate each laboratory's readiness to perform the assay, and provides the SST Skyline documents for public dissemination. The study found approximately 250 metabolites were routinely quantified in the sample types tested, using Orbitrap instruments. Interlaboratory variance for the NIST SRM-1950 has a median of 10% for amino acids, 24% for biogenic amines, 38% for acylcarnitines, 25% for glycerolipids, 23% for glycerophospholipids, 16% for cholesteryl esters, 15% for sphingolipids, and 9% for hexoses. Comparing to consensus values for NIST SRM-1950, nearly 80% of comparable analytes demonstrated bias of <50% from the reference value. The findings of this study result in recommendations of best practices for system suitability, quality control, and calibration. We demonstrate that with appropriate controls, high-resolution metabolomics can provide accurate results with good precision across laboratories, and the p400HR therefore is a reliable approach for generating consistent and comparable metabolomics data.
dc.formatPrint-Electronic
dc.format.extent14407 - 14416
dc.languageeng
dc.language.isoeng
dc.publisherAMER CHEMICAL SOC
dc.subjectAnimals
dc.subjectHumans
dc.subjectMice
dc.subjectRats
dc.subjectBiogenic Amines
dc.subjectLipids
dc.subjectAmino Acids
dc.subjectBlood Chemical Analysis
dc.subjectChromatography, High Pressure Liquid
dc.subjectAnalysis of Variance
dc.subjectReproducibility of Results
dc.subjectFemale
dc.subjectMale
dc.subjectMass Spectrometry
dc.subjectMetabolomics
dc.subjectMetabolome
dc.subjectLimit of Detection
dc.subjectData Aggregation
dc.subjectLipidomics
dc.titleInternational Ring Trial of a High Resolution Targeted Metabolomics and Lipidomics Platform for Serum and Plasma Analysis.
dc.typeJournal Article
dcterms.dateAccepted2019-10-22
rioxxterms.versionofrecord10.1021/acs.analchem.9b02908
rioxxterms.licenseref.startdate2019-11-08
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfAnalytical chemistry
pubs.issue22
pubs.notesNo embargo
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Clinical Pharmacology & Trials (including Drug Metabolism & Pharmacokinetics Group)
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Clinical Pharmacology & Trials (including Drug Metabolism & Pharmacokinetics Group)
pubs.publication-statusPublished
pubs.volume91
pubs.embargo.termsNo embargo
icr.researchteamClinical Pharmacology & Trials (including Drug Metabolism & Pharmacokinetics Group)
dc.contributor.icrauthorPal, Akos


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