Published August 4, 2025 | Version v2
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Interlaboratory comparison of metabolomics & lipidomics analyses in human and rodent blood using the MxP® Quant 500 kit

Authors/Creators

  • 1. Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
  • 2. Department of Biostatistics, Erciyes University School of Medicine, Kayseri, Turkey
  • 3. Drug Application and Research Center (ERFARMA), Erciyes University, Kayseri, Turkey
  • 4. Metabolomics and Proteomics Core, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
  • 5. Metabolomics Platform, Berlin Institute of Health at Charité, Berlin, Germany
  • 6. Metabolomics Core Technology Platform, Heidelberg University, Germany
  • 7. Fraunhofer-Institute for Toxicology and Experimental Medicine, Hannover, Germany
  • 8. Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
  • 9. Laboratory of Biomedical and Analytical Sciences, Faculty of Pharma-Science, Teikyo University, Japan
  • 10. Department of Biological Sciences, University of Alberta, Alberta, Canada
  • 11. Department of Computing Sciences, University of Alberta, Alberta, Canada
  • 12. Duke University, Durham, NC. USA
  • 13. Stanford University, Stanford, CA, USA
  • 14. Beaumont Health System, Royal Oak, MI, USA
  • 15. Indiana University, Indianapolis, IN, USA
  • 16. Center of Metabolomics, Institute of Metabolic Disease, Baylor Scott & White Research Institute, 3434 Live Oak St, Dallas, TX 75204, USA
  • 17. biocrates life sciences ag, Eduard-Bodem-Gasse 8, 6020 Innsbruck, Austria
  • 18. Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
  • 19. Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597, Singapore
  • 20. Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia

Description

Metabolomics and lipidomics are pivotal in understanding phenotypic variations beyond genomics. However, quantification and comparability of mass spectrometry (MS)-derived data are challenging. Standardised assays can enhance data comparability, enabling applications in multi-center epidemiological and clinical studies.

Here we evaluated the performance and reproducibility of the MxP® Quant 500 kit across 14 laboratories. The kit allows quantification of 634 different metabolites from 26 compound classes using triple quadrupole MS. Each laboratory analysed twelve samples, including human plasma and serum, lipaemic plasma, NIST SRM 1950, and mouse and rat plasma, in triplicates. 505 out of the 634 metabolites were measurable above the limit of detection in all laboratories, while eight metabolites were undetectable in our study.

Out of the 505 metabolites, 412 were observed in both human and rodent samples. Overall, the kit exhibited high reproducibility with a median coefficient of variation (CV) of 14.3%. CVs in NIST SRM 1950 reference plasma were below 25% and 10% for 494 and 138 metabolites, respectively. To facilitate further inspection of reproducibility for any compound, we provide detailed results from the in-depth evaluation of reproducibility across concentration ranges using Deming regression.

Interlaboratory reproducibility was similar across sample types, with some species-, matrix-, and phenotype-specific differences due to variations in concentration ranges. Comparisons with previous studies on the performance of MS-based kits revealed good concordance of reproducibility results and measured absolute concentrations in NIST SRM 1950 for most metabolites, making the MxP® Quant 500 kit a relevant tool for metabolomics and lipidomics in multi-center studies.

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Software

Programming language
R