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Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC–MS based untargeted metabolomics practitioners

Metabolomics volume 16, Article number: 113 (2020) Cite this article

Abstract

Introduction

The metabolomics quality assurance and quality control consortium (mQACC) evolved from the recognized need for a community-wide consensus on improving and systematizing quality assurance (QA) and quality control (QC) practices for untargeted metabolomics.

Objectives

In this work, we sought to identify and share the common and divergent QA and QC practices amongst mQACC members and collaborators who use liquid chromatography-mass spectrometry (LC–MS) in untargeted metabolomics.

Methods

All authors voluntarily participated in this collaborative research project by providing the details of and insights into the QA and QC practices used in their laboratories. This sharing was enabled via a six-page questionnaire composed of over 120 questions and comment fields which was developed as part of this work and has proved the basis for ongoing mQACC outreach.

Results

For QA, many laboratories reported documenting maintenance, calibration and tuning (82%); having established data storage and archival processes (71%); depositing data in public repositories (55%); having standard operating procedures (SOPs) in place for all laboratory processes (68%) and training staff on laboratory processes (55%). For QC, universal practices included using system suitability procedures (100%) and using a robust system of identification (Metabolomics Standards Initiative level 1 identification standards) for at least some of the detected compounds. Most laboratories used QC samples (>86%); used internal standards (91%); used a designated analytical acquisition template with randomized experimental samples (91%); and manually reviewed peak integration following data acquisition (86%). A minority of laboratories included technical replicates of experimental samples in their workflows (36%).

Conclusions

Although the 23 contributors were researchers with diverse and international backgrounds from academia, industry and government, they are not necessarily representative of the worldwide pool of practitioners due to the recruitment method for participants and its voluntary nature. However, both questionnaire and the findings presented here have already informed and led other data gathering efforts by mQACC at conferences and other outreach activities and will continue to evolve in order to guide discussions for recommendations of best practices within the community and to establish internationally agreed upon reporting standards. We very much welcome further feedback from readers of this article.

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Data availability

The questionnaire data reported in this paper has been organized and blinded into an excel spreadsheet and are available in Supplementary Material 2.

References

  • Beger, R. D., Dunn, W. B., Bandukwala, A., Bethan, B., Broadhurst, D., Clish, C. B., et al. (2019). Towards quality assurance and quality control in untargeted metabolomics studies. Metabolomics, 15, 4.

    Google Scholar 

  • Bijlsma, S., Bobeldijk, I., Verheij, E. R., Ramaker, R., Kochhar, S., Macdonald, I. A., et al. (2006). Large-scale human metabolomics studies: a strategy for data (pre-) processing and validation. Analytical Chemistry, 78, 567–574.

    CAS  Google Scholar 

  • Blacher, E., Bashiardes, S., Shapiro, H., Rothschild, D., Mor, U., Dori-Bachash, M., et al. (2019). Potential roles of gut microbiome and metabolites in modulating ALS in mice. Nature, 572, 474–480.

    CAS  Google Scholar 

  • Bouhifd, M., Beger, R., Flynn, T., Guo, L., Harris, G., Hogberg, H., et al. (2015). Quality assurance of metabolomics. Altex, 32, 319–326.

    Google Scholar 

  • Bouhifd, M., Hartung, T., Hogberg, H. T., Kleensang, A., & Zhao, L. (2013). Review: toxicometabolomics. Journal of Applied Toxicology, 33, 1365–1383.

    CAS  Google Scholar 

  • Broadhurst, D., Goodacre, R., Reinke, S. N., Kuligowski, J., Wilson, I. D., Lewis, M. R., et al. (2018). Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics, 14, 72.

    Google Scholar 

  • Burrage, L. C., Thistlethwaite, L., Stroup, B. M., Sun, Q., Miller, M. J., Nagamani, S. C. S., Craigen, W., Scaglia, F., Sutton, V. R., Graham, B., Kennedy, A. D., Members of the, U., Milosavljevic, A., Lee, B. H. & Elsea, S. H. (2019). Untargeted metabolomic profiling reveals multiple pathway perturbations and new clinical biomarkers in urea cycle disorders. Genet Med, 21, 1977–1986.

    Google Scholar 

  • Cao, Z., Miller, M. S., Lubet, R. A., Grubbs, C. J., & Beger, R. D. (2019). Pharmacometabolomic pathway response of effective anticancer agents on different diets in rats with induced mammary tumors. Metabolites, 9(7), 149.

    CAS  Google Scholar 

  • Chen, L., He, F. J., Dong, Y., Huang, Y., Harshfield, G. A., & Zhu, H. (2019). Sodium reduction, metabolomic profiling, and cardiovascular disease risk in untreated black hypertensives. Hypertension, 74, 194–200.

    CAS  Google Scholar 

  • Cirulli, E. T., Guo, L., Leon Swisher, C., Shah, N., Huang, L., Napier, L. A., et al. (2019). Profound perturbation of the metabolome in obesity is associated with health risk. Cell Metabolism, 29(488–500), e2.

    Google Scholar 

  • Crestani, E., Harb, H., Charbonnier, L. M., Leirer, J., Motsinger-Reif, A., Rachid, R., et al. (2019). Untargeted metabolomic profiling identifies disease-specific signatures in food allergy and asthma. The Journal of Allergy and Clinical Immunology, 145(3), 897–906.

    Google Scholar 

  • Dudzik, D., Barbas-Bernardos, C., Garcia, A., & Barbas, C. (2018). Quality assurance procedures for mass spectrometry untargeted metabolomics. a review. Journal of Pharmaceutical and Biomedical Analysis, 147, 149–173.

    CAS  Google Scholar 

  • Dunn, W. B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N., et al. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6, 1060–1083.

    CAS  Google Scholar 

  • Dunn, W. B., Broadhurst, D., Edison, A., Guillou, C., Viant, M. R., Bearden, D. W., et al. (2017). Quality assurance and quality control processes: summary of a metabolomics community questionnaire. Metabolomics. https://doi.org/10.1007/s11306-017-1188-9.

    Article  Google Scholar 

  • Dunn, W. B., Lin, W., Broadhurst, D., Begley, P., Brown, M., Zelena, E., et al. (2015). Molecular phenotyping of a UK population: defining the human serum metabolome. Metabolomics, 11, 9–26.

    CAS  Google Scholar 

  • Dunn, W. B., Wilson, I. D., Nicholls, A. W., & Broadhurst, D. (2012). The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis, 4, 2249–2264.

    CAS  Google Scholar 

  • Eurachem/CITAC Guide (2016). Guide to quality in analytical chemistry: An aid to accreditation. In: BARWICK, V. (ed.) 3rd ed.

  • Gafson, A. R., Savva, C., Thorne, T., David, M., Gomez-Romero, M., Lewis, M. R., et al. (2019). Breaking the cycle: Reversal of flux in the tricarboxylic acid cycle by dimethyl fumarate. Neurol Neuroimmunol Neuroinflamm, 6, e562.

    Google Scholar 

  • Gangler, S., Waldenberger, M., Artati, A., Adamski, J., van Bolhuis, J. N., Sorgjerd, E. P., et al. (2019). Exposure to disinfection byproducts and risk of type 2 diabetes: A nested case-control study in the HUNT and Lifelines cohorts. Metabolomics, 15, 60.

    Google Scholar 

  • Gika, H. G., Macpherson, E., Theodoridis, G. A., & Wilson, I. D. (2008). Evaluation of the repeatability of ultra-performance liquid chromatography-TOF-MS for global metabolic profiling of human urine samples. J Chromatogr B Analyt Technol Biomed Life Sci, 871, 299–305.

    CAS  Google Scholar 

  • Gika, H. G., Theodoridis, G. A., Wingate, J. E., & Wilson, I. D. (2007). Within-day reproducibility of an HPLC-MS-based method for metabonomic analysis: application to human urine. Journal of Proteome Research, 6, 3291–3303.

    CAS  Google Scholar 

  • de Groot, P., Scheithauer, T., Bakker, G. J., Prodan, A., Levin, E., Khan, M. T., et al. (2019). Donor metabolic characteristics drive effects of faecalmicrobiota transplantation on recipient insulin sensitivity, energy expenditure and intestinal transit time. Gut, 69(3), 502–512.

    Google Scholar 

  • Hollister, E. B., Oezguen, N., Chumpitazi, B. P., Luna, R. A., Weidler, E. M., Rubio-Gonzales, M., et al. (2019). Leveraging human microbiome features to diagnose and stratify children with irritable bowel syndrome. The Journal of Molecular Diagnostics, 21, 449–461.

    Google Scholar 

  • Hu, J. R., Grams, M. E., Coresh, J., Hwang, S., Kovesdy, C. P., Guallar, E., et al. (2019). Serum metabolites and cardiac death in patients on hemodialysis. Clinical Journal of the American Society of Nephrology, 14, 747–749.

    Google Scholar 

  • Ilhan, Z. E., Laniewski, P., Thomas, N., Roe, D. J., Chase, D. M., & Herbst-Kralovetz, M. M. (2019). Deciphering the complex interplay between microbiota, HPV, inflammation and cancer through cervicovaginal metabolic profiling. EBioMedicine, 44, 675–690.

    Google Scholar 

  • ISO 9000: (2015). Quality management systems-fundamentals and vocabulary, Switzerland, International Organization for Standardization.

  • Isganaitis, E., Venditti, S., Matthews, T. J., Lerin, C., Demerath, E. W., & Fields, D. A. (2019). Maternal obesity and the human milk metabolome: associations with infant body composition and postnatal weight gain. American Journal of Clinical Nutrition, 110(1), 111–120.

    Google Scholar 

  • Kauffmann, H. M., Kamp, H., Fuchs, R., Chorley, B. N., Deferme, L., Ebbels, T., et al. (2017). Framework for the quality assurance of 'omics technologies considering GLP requirements. Regulatory Toxicology and Pharmacology, 91(Suppl 1), S27–S35.

    Google Scholar 

  • Kelly, R. S., Boulin, A., Laranjo, N., Lee-Sarwar, K., Chu, S. H., Yadama, A. P., et al. (2019). Metabolomics and Communication Skills Development in Children (p. 9). Metabolites: Evidence from the Ages and Stages Questionnaire.

    Google Scholar 

  • Kirwan, J. A., Brennan, L., Broadhurst, D., Fiehn, O., Cascante, M., Dunn, W. B., et al. (2018). Preanalytical processing and biobanking procedures of biological samples for metabolomics research: A white paper, community perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative). Clinical Chemistry, 64, 1158–1182.

    CAS  Google Scholar 

  • Lains, I., Chung, W., Kelly, R. S., Gil, J., Marques, M., Barreto, P., et al. (2019). Human plasma metabolomics in age-related macular degeneration: Meta-analysis of two cohorts. Metabolites, 9(7), 127.

    CAS  Google Scholar 

  • Manghani, K. (2011). Quality assurance: Importance of systems and standard operating procedures. Perspect Clin Res, 2, 34–37.

    Google Scholar 

  • McCullough, M. L., Maliniak, M. L., Stevens, V. L., Carter, B. D., Hodge, R. A., & Wang, Y. (2019). Metabolomic markers of healthy dietary patterns in US postmenopausal women. American Journal of Clinical Nutrition, 109, 1439–1451.

    Google Scholar 

  • Olson, C. A., Vuong, H. E., Yano, J. M., Liang, Q. Y., Nusbaum, D. J., & Hsiao, E. Y. (2018). The gut microbiota mediates the anti-seizure effects of the ketogenic diet. Cell, 174, 497.

    CAS  Google Scholar 

  • Playdon, M. C., Joshi, A. D., Tabung, F. K., Cheng, S., Henglin, M., Kim, A., et al. (2019). Metabolomics analytics workflow for epidemiological research: Perspectives from the consortium of metabolomics studies (COMETS). Metabolites, 9(7), 145.

    CAS  Google Scholar 

  • Plaza-Diaz, J., Alvarez-Mercado, A. I., Ruiz-Marin, C. M., Reina-Perez, I., Perez-Alonso, A. J., Sanchez-Andujar, M. B., et al. (2019). Association of breast and gut microbiota dysbiosis and the risk of breast cancer: a case-control clinical study. BMC Cancer, 19, 495.

    Google Scholar 

  • Ramirez, T., Daneshian, M., Kamp, H., Bois, F. Y., Clench, M. R., Coen, M., et al. (2013). Metabolomics in toxicology and preclinical research. Altex, 30, 209–225.

    Google Scholar 

  • Rangel-Huerta, O. D., Gomez-Fernandez, A., de la Torre-Aguilar, M. J., Gil, A., Perez-Navero, J. L., Flores-Rojas, K., et al. (2019). Metabolic profiling in children with autism spectrum disorder with and without mental regression: preliminary results from a cross-sectional case-control study. Metabolomics, 15, 99.

    CAS  Google Scholar 

  • Rebholz, C. M., Surapaneni, A., Levey, A. S., Sarnak, M. J., Inker, L. A., Appel, L. J., et al. (2019). The serum metabolome identifies biomarkers of dietary acid load in 2 studies of adults with chronic kidney disease. Journal of Nutrition, 149, 578–585.

    Google Scholar 

  • Sangster, T., Major, H., Plumb, R., Wilson, A. J., & Wilson, I. D. (2006). A pragmatic and readily implemented quality control strategy for HPLC-MS and GC-MS-based metabonomic analysis. Analyst, 131, 1075–1078.

    CAS  Google Scholar 

  • Sato, S., Basse, A. L., Schonke, M., Chen, S., Samad, M., Altintas, A., et al. (2019). Time of exercise specifies the impact on muscle metabolic pathways and systemic energy homeostasis. Cell Metabolism, 30(92–110), e4.

    Google Scholar 

  • Shi, M., Bazzano, L. A., He, J., Gu, X., Li, C., Li, S., et al. (2019). Novel serum metabolites associate with cognition phenotypes among bogalusa heart study participants. Aging (Albany NY), 11, 5124–5139.

    CAS  Google Scholar 

  • Shin, S. Y., Fauman, E. B., Petersen, A. K., Krumsiek, J., Santos, R., Huang, J., et al. (2014). An atlas of genetic influences on human blood metabolites. Nature Genetics, 46, 543–550.

    CAS  Google Scholar 

  • Simon-Manso, Y., Lowenthal, M. S., Kilpatrick, L. E., Sampson, M. L., Telu, K. H., Rudnick, P. A., et al. (2013). Metabolite profiling of a NIST standard reference material for human plasma (SRM 1950): GC-MS, LC-MS, NMR, and clinical laboratory analyses, libraries, and web-based resources. Analytical Chemistry, 85, 11725–11731.

    CAS  Google Scholar 

  • Smilde, A. K., van der Werf, M. J., Bijlsma, S., & van derWerffJellema, B. J. R. H. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77, 6729–6736.

    CAS  Google Scholar 

  • Sumner, L. W., Amberg, A., Barrett, D., Beale, M. H., Beger, R., Daykin, C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics, 3, 211–221.

    CAS  Google Scholar 

  • Tang, W., Putluri, V., Ambati, C. R., Dorsey, T. H., Putluri, N., & Ambs, S. (2019). Liver- and microbiome-derived bile acids accumulate in human breast tumors and inhibit growth and improve patient survival. Clinical Cancer Research, 25, 5972–5983.

    CAS  Google Scholar 

  • Tziotzios, C., Petridis, C., Dand, N., Ainali, C., Saklatvala, J. R., Pullabhatla, V., et al. (2019). Genome-wide association study in frontal fibrosing alopecia identifies four susceptibility loci including HLA-B*07:02. Nat Commun, 10, 1150.

    Google Scholar 

  • Viant, M. R., Ebbels, T. M. D., Beger, R. D., Ekman, D. R., Epps, D. J. T., Kamp, H., et al. (2019). Use cases, best practice and reporting standards for metabolomics in regulatory toxicology. Nat Commun, 10, 3041.

    Google Scholar 

  • Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Sci Data, 3, 160018.

    Google Scholar 

  • Wilmanski, T., Rappaport, N., Earls, J. C., Magis, A. T., Manor, O., Lovejoy, J., et al. (2019). Blood metabolome predicts gut microbiome alpha-diversity in humans. Nature Biotechnology, 37, 1217–1228.

    CAS  Google Scholar 

  • Wittemans, L. B. L., Lotta, L. A., Oliver-Williams, C., Stewart, I. D., Surendran, P., Karthikeyan, S., et al. (2019). Assessing the causal association of glycine with risk of cardio-metabolic diseases. Nat Commun, 10, 1060.

    Google Scholar 

  • Yu, B., Flexeder, C., McGarrah, R. W., Wyss, A., Morrison, A. C., North, K. E., et al. (2019). Metabolomics identifies novel blood biomarkers of pulmonary function and COPD in the general population. Metabolites, 9(4), 61.

    CAS  Google Scholar 

  • Zambrana, L. E., McKeen, S., Ibrahim, H., Zarei, I., Borresen, E. C., Doumbia, L., et al. (2019). Rice bran supplementation modulates growth, microbiota and metabolome in weaning infants: A clinical trial in Nicaragua and Mali. Sci Rep, 9, 13919.

    Google Scholar 

  • Zelena, E., Dunn, W. B., Broadhurst, D., Francis-McIntyre, S., Carroll, K. M., Begley, P., O'Hagan, S., Knowles, J. D., Halsall, A., Consortium, H., Wilson, I. D. & Kell, D. B. (2009). Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Analytical Chemistry, 81, 1357–1364.

    Google Scholar 

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Authors and Affiliations

  1. Metabolon, Morrisville, NC, USA

    Anne M. Evans

  2. European Molecular Biology Laboratory (EMBL), The European Bioinformatics Institute, Cambridgeshire, UK

    Claire O’Donovan

  3. University of Utah, Salt Lake City, Utah, USA

    Mary Playdon

  4. IROA Technologies, Chapel Hill, NC, USA

    Chris Beecher

  5. National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR, USA

    Richard D. Beger

  6. College of Veterinary Medicine, University of Florida, Gainesville, FL, USA

    John A. Bowden

  7. Centre for Integrative Metabolomics & Computational Biology, School of Science, Edith Cowan University, Joondalup, WA, Australia

    David Broadhurst

  8. Broad Institute, Cambridge, MA, USA

    Clary B. Clish

  9. Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA

    Surendra Dasari

  10. School of Biosciences, Phenome Centre Birmingham and Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Warwick B. Dunn

  11. Department of Biochemistry, University of Cambridge, Cambridge, UK

    Julian L. Griffin

  12. Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

    Julian L. Griffin

  13. University of Arkansas for Medical Sciences, Little Rock, AR, USA

    Ping- Ching Hsu

  14. Department of Chemistry, University of British Columbia, Vancouver, Canada

    Tao Huan

  15. University Medical Center Utrecht, Utrecht, Netherlands

    Judith Jans

  16. Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, USA

    Christina M. Jones

  17. University of Michigan, Ann Arbor, MI, USA

    Maureen Kachman

  18. Center for Alternatives To Animal Testing (CAAT), Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

    Thomas Hartung & Andre Kleensang

  19. National Phenome Centre, Imperial College London, London, UK

    Matthew R. Lewis

  20. Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas Y Técnicas (CONICET), C1425FQD, Ciudad de Buenos Aires, Argentina

    María Eugenia Monge

  21. Center for Environmental Measurement and Modeling, Environmental Protection Agency, Washington, DC, USA

    Jonathan D. Mosley

  22. University of Iowa, Iowa City, Iowa, USA

    Eric Taylor

  23. Department of Internal Medicine, Metabolomics Core, The University of Iowa, Iowa City, Iowa, USA

    Fariba Tayyari

  24. Aristotle University, Thessaloniki, Greece

    Georgios Theodoridis

  25. Singapore Lipidomics Incubator, Department of Biochemistry, Life Sciences Institute and Yong Loo Lin School of Medicine, National University of Singapore, Kent Ridge, Singapore

    Federico Torta

  26. Sciex, Redwood, USA

    Baljit K. Ubhi

  27. Concordia University, Montreal, QC, Canada

    Dajana Vuckovic

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  1. Anne M. Evans
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  2. Claire O’Donovan
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Consortia

on behalf of the Metabolomics Quality Assurance, Quality Control Consortium (mQACC)

Contributions

AME, CO and MP wrote the manuscript. AME, CB, RDB, JAB, DB, CBC, SD, WBD, JG, TH, PCH, TH, JJ, CMJ, MK, AK, MRL, MEM, JDM, ET, FT, GT, FT, BKU, and DV all contributed the QA and QC procedures used by their laboratories through a questionnaire and also edited, read and approved the manuscript.

Corresponding author

Correspondence to Anne M. Evans.

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Evans, A.M., O’Donovan, C., Playdon, M. et al. Dissemination and analysis of the quality assurance (QA) and quality control (QC) practices of LC–MS based untargeted metabolomics practitioners. Metabolomics 16, 113 (2020). https://doi.org/10.1007/s11306-020-01728-5

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Keywords

  • Quality assurance
  • Quality control
  • Untargeted metabolomics
  • Metabolomics quality assurance and quality control consortium (mQACC)
  • LC-MS