International ring trial of a high resolution targeted metabolomics and lipidomics platform for serum and plasma analysis

J. Will Thompson, Kendra J. Adams, Jerzy Adamski, Yasmin Asad, David Borts, John A. Bowden, Gregory Byram, Viet Dang, Warwick Dunn, Facundo Fernandez, Oliver Fiehn, David A. Gaul, Andreas FR Hühmer, Anastasia Kalli, Therese Koal, Stormy Koeniger, Rupasri Mandal, Florian Meier, Naser Fuad J., Donna O'NeilAkos Pal, Gary J. Patti, Hai Pham-Tuan, Cornelia Prehn, Florence I. Raynaud, Tong Shen, Andrew Southam, Lisa St. John-Williams, Karolina Sulek, Catherine G. Vasilopoulou, Mark Viant, Cate Winder, David Wishart, Lun Zhang, Jiamin Zheng, M. Arthur Moseley

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21 Citations (Scopus)
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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.

Original languageEnglish
Article number9b02908
Pages (from-to)14407-14416
Number of pages10
JournalAnalytical Chemistry
Issue number22
Early online date22 Oct 2019
Publication statusPublished - 19 Nov 2019

ASJC Scopus subject areas

  • Analytical Chemistry


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