Metabolomics

, 13:50 | Cite as

Quality assurance and quality control processes: summary of a metabolomics community questionnaire

  • Warwick B. Dunn
  • David I. Broadhurst
  • Arthur Edison
  • Claude Guillou
  • Mark R. Viant
  • Daniel W. Bearden
  • Richard D. Beger
Short Communication

Abstract

Introduction

The Metabolomics Society Data Quality Task Group (DQTG) developed a questionnaire regarding quality assurance (QA) and quality control (QC) to provide baseline information about current QA and QC practices applied in the international metabolomics community.

Objectives

The DQTG has a long-term goal of promoting robust QA and QC in the metabolomics community through increased awareness via communication, outreach and education, and through the promotion of best working practices. An assessment of current QA and QC practices will serve as a foundation for future activities and development of appropriate guidelines.

Method

QA was defined as the set of procedures that are performed in advance of analysis of samples and that are used to improve data quality. QC was defined as the set of activities that a laboratory does during or immediately after analysis that are applied to demonstrate the quality of project data. A questionnaire was developed that included 70 questions covering demographic information, QA approaches and QC approaches and allowed all respondents to answer a subset or all of the questions.

Result

The DQTG questionnaire received 97 individual responses from 84 institutions in all fields of metabolomics covering NMR, LC-MS, GC-MS, and other analytical technologies.

Conclusion

There was a vast range of responses concerning the use of QA and QC approaches that indicated the limited availability of suitable training, lack of Standard Operating Procedures (SOPs) to review and make decisions on quality, and limited use of standard reference materials (SRMs) as QC materials. The DQTG QA/QC questionnaire has for the first time demonstrated that QA and QC usage is not uniform across metabolomics laboratories. Here we present recommendations on how to address the issues concerning QA and QC measurements and reporting in metabolomics.

Keywords

Metabolomics Quality assurance Quality control Questionnaire Metabolomics Society 

Supplementary material

11306_2017_1188_MOESM1_ESM.docx (123 kb)
Supplementary material 1 (DOCX 123 KB)

References

  1. Bearden, D. W., Beger, R. D., Broadhurst, D., Dunn, W., Edison, A., Guillou, C., Trengove, R., Viant, M., & Wilson, I. (2014). The New Data Quality Task Group (DQTG): ensuring high quality data today and in the future. Metabolomics, 10(4), 539–540.CrossRefGoogle Scholar
  2. Bernini, P., Bertini, I., Luchinat, C., Nincheri, P., Staderini, S., & Turano, P. (2011). Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. Journal of biomolecular NMR, 49(3–4), 231–243.CrossRefPubMedGoogle Scholar
  3. Booth, B., Arnold, M. E., DeSilva, B., Amaravadi, L., Dudal, S., Fluhler, E., Gorovits, B., Haidar, S. H., Kadavil, J., Lowes, S., & Nicholson, R. (2015). Workshop report: Crystal City V—quantitative bioanalytical method validation and implementation: the 2013 revised FDA guidance. The AAPS Journal, 17(2), 277–288.CrossRefPubMedGoogle Scholar
  4. Brown, M., Dunn, W. B., Ellis, D. I., Goodacre, R., Handl, J., Knowles, J. D., O’Hagan, S., Spasić, I., & Kell, D. B. (2005). A metabolome pipeline: from concept to data to knowledge. Metabolomics, 1(1), 39–51.CrossRefGoogle Scholar
  5. Cheng, S., Larson, M. G., McCabe, E. L., Murabito, J. M., Rhee, E. P., Ho, J. E., Jacques, P. F., Ghorbani, A., Magnusson, M., Souza, A. L., & Deik, A. A. (2015). Distinct metabolomic signatures are associated with longevity in humans. Nature Communications, 6, 6791.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Data Quality Task Group. (2016). Available from: http://metabolomicssociety.org/board/scientific-task-groups/data-quality-task-group. Retrieved from 29 August 2016.
  7. Dunn, W.B., Broadhurst, D.I., Atherton, H.J., Goodacre, R. and Griffin, J.L., Dunn, W.B. (2011). Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chemical Society Reviews, 40(1), 387–426.CrossRefPubMedGoogle Scholar
  8. Dunn, W.B., Wilson, I.D., Nicholls, A.W. and Broadhurst, D. (2012). The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis, 4(18), 2249–2264.CrossRefPubMedGoogle Scholar
  9. Furusawa, Y., Obata, Y., Fukuda, S., Endo, T. A., Nakato, G., Takahashi, D., Nakanishi, Y., Uetake, C., Kato, K., Kato, T., & Takahashi, M. (2013). Commensal microbe-derived butyrate induces the differentiation of colonic regulatory T cells. Nature, 504(7480), 446–450.CrossRefPubMedGoogle Scholar
  10. Garfield, F. M. (2000). Quality Assurance Principles for Analytical Laboratories. Washington, DC: Association of Official Analytical Chemists.Google Scholar
  11. Gika, H.G., Theodoridis, G.A. and Wilson, I.D. (2008). Liquid chromatography and ultra-performance liquid chromatography–mass spectrometry fingerprinting of human urine: sample stability under different handling and storage conditions for metabonomics studies. Journal of Chromatography A, 1189(1), 314–322.CrossRefPubMedGoogle Scholar
  12. Guidance for Industry: Bioanalytical Method Validation U.S. Department of Health and Human Services; Food and Drug Administration; Center for Drug Evaluation and Research (CDER); Center for Veterinary Medicine (CVM): http://www.fda.gov/downloads/Drugs/Guidance/ucm070107.pdf.
  13. Hibbert, D. B. (2007). Quality Assurance for the Analytical Chemistry Laboratory. Oxford: Oxford University Press.Google Scholar
  14. Kamlage, B., Maldonado, S. G., Bethan, B., Peter, E., Schmitz, O., Liebenberg, V., & Schatz, P. (2014). Quality markers addressing preanalytical variations of blood and plasma processing identified by broad and targeted metabolite profiling. Clinical Chemistry, 60(2), 399–412.CrossRefPubMedGoogle Scholar
  15. Kusano, M., Yang, Z., Okazaki, Y., Nakabayashi, R., Fukushima, A. and Saito, K. (2015). Using metabolomic approaches to explore chemical diversity in Rice. Molecular Plant, 8(1), 58–67.CrossRefPubMedGoogle Scholar
  16. Metabolomics Society. (2016). Available from: http://metabolomicssociety.org/. Retrieved from 29 Aug 2016.
  17. Oliver, S. G., Winson, M. K., Kell, D. B., & Baganz, F. (1998). Systematic functional analysis of the yeast genome. Trends in Biotechnology, 16(9), 373–378.CrossRefPubMedGoogle Scholar
  18. Pauling, L., Robinson, A. B., Teranishi, R., & Cary, P. (1971). Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proceedings of the National Academy of Sciences USA, 68(10), 2374–2376.CrossRefGoogle Scholar
  19. Westgard, J.O. (2008). Basic method validation: Training in analytical quality management for healthcare laboratories. 3rd edn, Madison: Westgard Quality Corporation.Google Scholar

Copyright information

© Springer Science+Business Media New York (outside the USA) 2017

Authors and Affiliations

  • Warwick B. Dunn
    • 1
  • David I. Broadhurst
    • 2
  • Arthur Edison
    • 3
  • Claude Guillou
    • 4
  • Mark R. Viant
    • 1
  • Daniel W. Bearden
    • 5
  • Richard D. Beger
    • 6
  1. 1.School of Biosciences and Phenome Centre BirminghamUniversity of BirminghamBirminghamUK
  2. 2.School of ScienceEdith Cowan UniversityJoondalupAustralia
  3. 3.Department of GeneticsUniversity of GeorgiaAthensUSA
  4. 4.Institute for Health and Consumer Protection, Systems Toxicology UnitEuropean Commission - Joint Research CentreIspraItaly
  5. 5.Chemical Sciences Division, Hollings Marine LaboratoryNational Institute of Standards and TechnologyCharlestonUSA
  6. 6.National Center for Toxicological ResearchUS Food and Drug AdministrationJeffersonUSA

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