A Protocol to Compare Methods for Untargeted Metabolomics

  • Lingjue Wang
  • Fuad J. Naser
  • Jonathan L. Spalding
  • Gary J. PattiEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1862)


There are thousands of published methods for profiling metabolites with liquid chromatography/mass spectrometry (LC/MS). While many have been evaluated and optimized for a small number of select metabolites, very few have been assessed on the basis of global metabolite coverage. Thus, when performing untargeted metabolomics, researchers often question which combination of extraction techniques, chromatographic separations, and mass spectrometers is best for global profiling. Method comparisons are complicated because thousands of LC/MS signals (so-called features) in a typical untargeted metabolomic experiment cannot be readily identified with current resources. It is therefore challenging to distinguish methods that increase signal number due to improved metabolite coverage from methods that increase signal number due to contamination and artifacts. Here, we present the credentialing protocol to remove the latter from untargeted metabolomic datasets without having to identify metabolite structures. This protocol can be used to compare or optimize methods pertaining to any step of the untargeted metabolomic workflow (e.g., extraction, chromatography, mass spectrometer, informatic software, etc.).

Key words

Untargeted metabolomics Metabolite profiling Metabolism Credentialing Liquid chromatography Mass spectrometry 



This work was supported by NIH grants R35ES028365 and R21CA191097 as well as support from the Pew Scholars Program in the Biomedical Sciences, the Edward Mallinckrodt, Jr., Foundation, and Agilent Technologies.


  1. 1.
    Roberts LD, Souza AL, Gerszten RE, Clish CB (2012) Targeted metabolomics. Curr Protoc Mol Biol 30:Unit 30 32 31–Unit 30 32 24. CrossRefGoogle Scholar
  2. 2.
    Nikolskiy I, Mahieu NG, Chen YJ et al (2013) An untargeted metabolomic workflow to improve structural characterization of metabolites. Anal Chem 85(16):7713–7719. CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Milne SB, Mathews TP, Myers DS et al (2013) Sum of the parts: mass spectrometry-based metabolomics. Biochemistry 52(22):3829–3840. CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Benton HP, Ivanisevic J, Mahieu NG et al (2015) Autonomous metabolomics for rapid metabolite identification in global profiling. Anal Chem 87(2):884–891. CrossRefPubMedGoogle Scholar
  5. 5.
    Mahieu NG, Huang X, Chen YJ, Patti GJ (2014) Credentialing features: a platform to benchmark and optimize untargeted metabolomic methods. Anal Chem 86(19):9583–9589. CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Mahieu NG, Patti GJ (2017) Systems-level annotation of a metabolomics data set reduces 25000 features to fewer than 1000 unique metabolites. Anal Chem 89(19):10397–10406. CrossRefPubMedGoogle Scholar
  7. 7.
    Lindahl A, Saaf S, Lehtio J, Nordstrom A (2017) Tuning Metabolome coverage in reversed phase LC-MS metabolomics of MeOH extracted samples using the reconstitution solvent composition. Anal Chem 89(14):7356–7364. CrossRefPubMedGoogle Scholar
  8. 8.
    Vinayavekhin N, Saghatelian A (2010) Untargeted metabolomics. Curr Protoc Mol Biol Chapter 30:Unit 30 1.1–Unit 30 124. CrossRefGoogle Scholar
  9. 9.
    De Vos RC, Moco S, Lommen A et al (2007) Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat Protoc 2(4):778–791. CrossRefPubMedGoogle Scholar
  10. 10.
    Weber RJM, Lawson TN, Salek RM et al (2017) Computational tools and workflows in metabolomics: an international survey highlights the opportunity for harmonisation through galaxy. Metabolomics 13(2):12. CrossRefPubMedGoogle Scholar
  11. 11.
    Patti GJ (2011) Separation strategies for untargeted metabolomics. J Sep Sci 34(24):3460–3469. CrossRefPubMedGoogle Scholar
  12. 12.
    Naser FJ, Mahieu NG, Wang L et al (2018) Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome. Anal Bioanal Chem 410(4):1287–1297. CrossRefPubMedGoogle Scholar
  13. 13.
    Ivanisevic J, Zhu ZJ, Plate L et al (2013) Toward 'omic scale metabolite profiling: a dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal Chem 85(14):6876–6884. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Mahieu NG, Genenbacher JL, Patti GJ (2016) A roadmap for the XCMS family of software solutions in metabolomics. Curr Opin Chem Biol 30:87–93. CrossRefPubMedGoogle Scholar
  15. 15.
    Libiseller G, Dvorzak M, Kleb U et al (2015) IPO: a tool for automated optimization of XCMS parameters. BMC Bioinformatics 16:118. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Patti GJ, Tautenhahn R, Siuzdak G (2012) Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nat Protoc 7(3):508–516. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Lingjue Wang
    • 1
  • Fuad J. Naser
    • 1
  • Jonathan L. Spalding
    • 1
    • 2
  • Gary J. Patti
    • 1
    • 2
    Email author
  1. 1.Department of ChemistryWashington UniversitySt. LouisUSA
  2. 2.Department of MedicineWashington University School of MedicineSt. LouisUSA

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