| Literature DB >> 31638379 |
J Will Thompson1,2, Kendra J Adams1, Jerzy Adamski3,4,5,6, Yasmin Asad7, David Borts8,9, John A Bowden10,11, Gregory Byram12, Viet Dang8, Warwick B Dunn13, Facundo Fernandez14, Oliver Fiehn12, David A Gaul14, Andreas Fr Hühmer9, Anastasia Kalli9, Therese Koal15, Stormy Koeniger16, Rupasri Mandal17, Florian Meier18, Fuad J Naser19, Donna O'Neil13, Akos Pal7, Gary J Patti19, Hai Pham-Tuan15, Cornelia Prehn3, Florence I Raynaud7, Tong Shen12, Andrew D Southam13, Lisa St John-Williams1, Karolina Sulek20, Catherine G Vasilopoulou18, Mark Viant13, Catherine L Winder13, David Wishart17, Lun Zhang17, Jiamin Zheng17, M Arthur Moseley1.
Abstract
A 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.Entities:
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Year: 2019 PMID: 31638379 PMCID: PMC7310668 DOI: 10.1021/acs.analchem.9b02908
Source DB: PubMed Journal: Anal Chem ISSN: 0003-2700 Impact factor: 6.986