Advertisement

Analytical and Bioanalytical Chemistry

, Volume 410, Issue 4, pp 1287–1297 | Cite as

Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome

  • Fuad J. Naser
  • Nathaniel G. Mahieu
  • Lingjue Wang
  • Jonathan L. Spalding
  • Stephen L. Johnson
  • Gary J. Patti
Paper in Forefront

Abstract

Although it is common in untargeted metabolomics to apply reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) methods that have been systematically optimized for lipids and central carbon metabolites, here we show that these established protocols provide poor coverage of semipolar metabolites because of inadequate retention. Our objective was to develop an RPLC approach that improved detection of these metabolites without sacrificing lipid coverage. We initially evaluated columns recently released by Waters under the CORTECS line by analyzing 47 small-molecule standards that evenly span the nonpolar and semipolar ranges. An RPLC method commonly used in untargeted metabolomics was considered a benchmarking reference. We found that highly nonpolar and semipolar metabolites cannot be reliably profiled with any single method because of retention and solubility limitations of the injection solvent. Instead, we optimized a multiplexed approach using the CORTECS T3 column to analyze semipolar compounds and the CORTECS C8 column to analyze lipids. Strikingly, we determined that combining these methods allowed detection of 41 of the total 47 standards, whereas our reference RPLC method detected only 10 of the 47 standards. We then applied credentialing to compare method performance at the comprehensive scale. The tandem method showed more than a fivefold increase in credentialing coverage relative to our RPLC benchmark. Our results demonstrate that comprehensive coverage of metabolites amenable to reversed-phase separation necessitates two reconstitution solvents and chromatographic methods. Thus, we suggest complementing HILIC methods with a dual T3 and C8 RPLC approach to increase coverage of semipolar metabolites and lipids for untargeted metabolomics.

Graphical abstract

Analysis of semipolar and nonpolar metabolites necessitates two reversed-phase chromatography (RPLC) methods, which extend metabolome coverage more than fivefold for untargeted profiling. HILIC hydrophilic interaction liquid chromatography

Keywords

Metabolomics Untargeted profiling Mass spectrometry Global coverage Semipolar metabolome Reversed-phase 

Notes

Acknowledgements

This work was supported by the National Institutes of Health (grants R35 ES028365, R21 CA191097, R01 GM05698), the Alfred P. Sloan Foundation, the Camille & Henry Dreyfus Foundation, the Edward Mallinckrodt, Jr. Foundation, and the Pew Scholars Program in the Biomedical Sciences.

Compliance with ethical standards

Conflict of interest

GJP is a scientific advisory board member for Cambridge Isotope Laboratories and a recipient of the Agilent Early Career Professor Award. The other authors declare that they no have competing financial interests. The authors declare that they have no nonfinancial conflicts of interest.

Supplementary material

216_2017_768_MOESM1_ESM.pdf (1.7 mb)
ESM 1 (PDF 1.72 MB)

References

  1. 1.
    Haggarty J, Burgess KEV. Recent advances in liquid and gas chromatography methodology for extending coverage of the metabolome. Curr Opin Biotechnol. 2017;43:77–85.  https://doi.org/10.1016/j.copbio.2016.09.006.CrossRefGoogle Scholar
  2. 2.
    Vinayavekhin N, Homan EA, Saghatelian A. Exploring disease through metabolomics. ACS Chem Biol. 2010;5:91–103.  https://doi.org/10.1021/cb900271r.CrossRefGoogle Scholar
  3. 3.
    Weckwerth W. Metabolomics in systems biology. Annu Rev Plant Biol. 2003;54:669–89.  https://doi.org/10.1146/annurev.arplant.54.031902.135014.CrossRefGoogle Scholar
  4. 4.
    Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, et al. The human serum metabolome. PLoS One. 2011;6:e16957.  https://doi.org/10.1371/journal.pone.0016957.CrossRefGoogle Scholar
  5. 5.
    Masson P, Alves AC, Ebbels TMD, Nicholson JK, Want EJ. Optimization and evaluation of metabolite extraction protocols for untargeted metabolic profiling of liver samples by UPLC-MS. Anal Chem. 2010;82:7779–86.  https://doi.org/10.1021/ac101722e.CrossRefGoogle Scholar
  6. 6.
    Stanstrup J, Gerlich M, Dragsted LO, Neumann S. Metabolite profiling and beyond: approaches for the rapid processing and annotation of human blood serum mass spectrometry data. Anal Bioanal Chem. 2013;405:5037–48.  https://doi.org/10.1007/s00216-013-6954-6.CrossRefGoogle Scholar
  7. 7.
    Benton HP, Ivanisevic J, Mahieu NG, Kurczy ME, Johnson CH, Franco L, et al. Autonomous metabolomics for rapid metabolite identification in global profiling. Anal Chem. 2015;87:884–91.  https://doi.org/10.1021/ac5025649.CrossRefGoogle Scholar
  8. 8.
    Nordström A, Want E, Northen T, Lehtiö J, Siuzdak G. Multiple ionization mass spectrometry strategy used To reveal the complexity of metabolomics. Anal Chem. 2008;80:421–9.  https://doi.org/10.1021/ac701982e.CrossRefGoogle Scholar
  9. 9.
    Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012;84:5035–9.  https://doi.org/10.1021/ac300698c.CrossRefGoogle Scholar
  10. 10.
    Zeng Z, Liu X, Dai W, Yin P, Zhou L, Huang Q, et al. Ion fusion of high-resolution LC–MS-based metabolomics data to discover more reliable biomarkers. Anal Chem. 2014;86:3793–800.  https://doi.org/10.1021/ac500878x.CrossRefGoogle Scholar
  11. 11.
    Kuhl C, Tautenhahn R, Böttcher C, Larson TR, Neumann S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2012;84:283–9.  https://doi.org/10.1021/ac202450g.CrossRefGoogle Scholar
  12. 12.
    Daly R, Rogers S, Wandy J, Jankevics A, Burgess KEV, Breitling R. MetAssign: probabilistic annotation of metabolites from LC–MS data using a Bayesian clustering approach. Bioinformatics. 2014;30:2764–71.  https://doi.org/10.1093/bioinformatics/btu370.CrossRefGoogle Scholar
  13. 13.
    Zhang W, Chang J, Lei Z, Huhman D, Sumner LW, Zhao PX. MET-COFEA: a liquid chromatography/mass spectrometry data processing platform for metabolite compound feature extraction and annotation. Anal Chem. 2014;86:6245–53.  https://doi.org/10.1021/ac501162k.CrossRefGoogle Scholar
  14. 14.
    Broeckling CD, Afsar FA, Neumann S, Ben-Hur A, Prenni JE. RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal Chem. 2014;86:6812–7.  https://doi.org/10.1021/ac501530d.CrossRefGoogle Scholar
  15. 15.
    Yao C-H, Liu G-Y, Yang K, Gross RW, Patti GJ. Inaccurate quantitation of palmitate in metabolomics and isotope tracer studies due to plastics. Metabolomics. 2016;12:143.  https://doi.org/10.1007/s11306-016-1081-y.CrossRefGoogle Scholar
  16. 16.
    Mahieu NG, Huang X, Chen Y-J, Patti GJ. Credentialing features: a platform to benchmark and optimize untargeted metabolomic methods. Anal Chem. 2014;86:9583–9.  https://doi.org/10.1021/ac503092d.CrossRefGoogle Scholar
  17. 17.
    Bajad SU, Lu W, Kimball EH, Yuan J, Peterson C, Rabinowitz JD. Separation and quantitation of water soluble cellular metabolites by hydrophilic interaction chromatography-tandem mass spectrometry. J Chromatogr A. 2006;1125:76–88.  https://doi.org/10.1016/j.chroma.2006.05.019.CrossRefGoogle Scholar
  18. 18.
    Contrepois K, Jiang L, Snyder M. Optimized analytical procedures for the untargeted metabolomic profiling of human urine and plasma by combining hydrophilic interaction (HILIC) and reverse-phase liquid chromatography (RPLC)–mass spectrometry. Mol Cell Proteomics. 2015;14:1684–95.  https://doi.org/10.1074/mcp.M114.046508.CrossRefGoogle Scholar
  19. 19.
    Zhang R, Watson DG, Wang L, Westrop GD, Coombs GH, Zhang T. Evaluation of mobile phase characteristics on three zwitterionic columns in hydrophilic interaction liquid chromatography mode for liquid chromatography-high resolution mass spectrometry based untargeted metabolite profiling of Leishmania parasites. J Chromatogr A. 2014;1362:168–79.  https://doi.org/10.1016/j.chroma.2014.08.039.CrossRefGoogle Scholar
  20. 20.
    Patti GJ. Separation strategies for untargeted metabolomics. J Sep Sci. 2011;34:3460–9.  https://doi.org/10.1002/jssc.201100532.CrossRefGoogle Scholar
  21. 21.
    Nikolskiy I, Mahieu NG, Chen Y-J, Tautenhahn R, Patti GJ. An untargeted metabolomic workflow to improve structural characterization of metabolites. Anal Chem. 2013;85:7713–9.  https://doi.org/10.1021/ac400751j.CrossRefGoogle Scholar
  22. 22.
    Mahieu NG, Spalding JL, Patti GJ. Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis. Bioinformatics. 2016;32:268–75.  https://doi.org/10.1093/bioinformatics/btv564.Google Scholar
  23. 23.
    Yu T, Park Y, Johnson JM, Jones DP. apLCMS—adaptive processing of high-resolution LC/MS data. Bioinformatics. 2009;25  https://doi.org/10.1093/bioinformatics/btp291.
  24. 24.
    Conley CJ, Smith R, Torgrip RJO, Taylor RM, Tautenhahn R, Prince JT. Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection. Bioinformatics. 2014;30:2636–43.  https://doi.org/10.1093/bioinformatics/btu359.CrossRefGoogle Scholar
  25. 25.
    Ivanisevic J, Zhu Z-J, Plate L, Tautenhahn R, Chen S, O’Brien PJ, et al. Toward ‘omic scale metabolite profiling: a dual separation–mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal Chem. 2013;85:6876–84.  https://doi.org/10.1021/ac401140h.CrossRefGoogle Scholar
  26. 26.
    Yanes O, Tautenhahn R, Patti GJ, Siuzdak G. Expanding coverage of the metabolome for global metabolite profiling. Anal Chem. 2011;83:2152–61.  https://doi.org/10.1021/ac102981k.CrossRefGoogle Scholar
  27. 27.
    Tufi S, Lamoree M, de Boer J, Leonards P. Simultaneous analysis of multiple neurotransmitters by hydrophilic interaction liquid chromatography coupled to tandem mass spectrometry. J Chromatogr A. 2015;1395:79–87.  https://doi.org/10.1016/j.chroma.2015.03.056.CrossRefGoogle Scholar
  28. 28.
    Keunchkarian S, Reta M, Romero L, Castells C. Effect of sample solvent on the chromatographic peak shape of analytes eluted under reversed-phase liquid chromatogaphic conditions. J Chromatogr A. 2006;1119:20–8.  https://doi.org/10.1016/j.chroma.2006.02.006.CrossRefGoogle Scholar
  29. 29.
    Tautenhahn R, Böttcher C, Neumann S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics. 2008;9:504.  https://doi.org/10.1186/1471-2105-9-504.CrossRefGoogle Scholar
  30. 30.
    Fekete S, Oláh E, Fekete J. Fast liquid chromatography: the domination of core–shell and very fine particles. J Chromatogr A. 2012;1228:57–71.  https://doi.org/10.1016/j.chroma.2011.09.050.CrossRefGoogle Scholar
  31. 31.
    Cajka T, Fiehn O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal Chem. 2016;88:524–45.  https://doi.org/10.1021/acs.analchem.5b04491.CrossRefGoogle Scholar
  32. 32.
    Cajka T, Fiehn O. Increasing lipidomic coverage by selecting optimal mobile-phase modifiers in LC–MS of blood plasma. Metabolomics. 2016;12:34.  https://doi.org/10.1007/s11306-015-0929-x.CrossRefGoogle Scholar
  33. 33.
    Cappiello A, Famiglini G, Rossi L, Magnani M. Use of nonvolatile buffers in liquid chromatography/mass spectrometry: advantages of capillary-scale particle beam interfacing. Anal Chem. 1997;69:5136–41.  https://doi.org/10.1021/ac970765y.CrossRefGoogle Scholar
  34. 34.
    Johnson CH, Dejea CM, Edler D, Hoang LT, Santidrian AF, Felding BH, et al. Metabolism links bacterial biofilms and colon carcinogenesis. Cell Metab. 2015;21:891–7.  https://doi.org/10.1016/j.cmet.2015.04.011.CrossRefGoogle Scholar
  35. 35.
    Cotter DG, Ercal B, Huang X, Leid JM, d’Avignon DA, Graham MJ, et al. Ketogenesis prevents diet-induced fatty liver injury and hyperglycemia. J Clin Investig. 2014;124:5175–90.  https://doi.org/10.1172/JCI76388.CrossRefGoogle Scholar
  36. 36.
    Ivanisevic J, Epstein AA, Kurczy ME, Benton PH, Uritboonthai W, Fox HS, et al. Brain region mapping using global metabolomics. Chem Biol. 2014;21:1575–84.  https://doi.org/10.1016/j.chembiol.2014.09.016.CrossRefGoogle Scholar
  37. 37.
    Zamboni N, Saghatelian A, Patti GJ. Defining the metabolome: size, flux, and regulation. Mol Cell. 2015;58:699–706.  https://doi.org/10.1016/j.molcel.2015.04.021.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Fuad J. Naser
    • 1
  • Nathaniel G. Mahieu
    • 1
  • Lingjue Wang
    • 1
  • Jonathan L. Spalding
    • 2
  • Stephen L. Johnson
    • 2
  • Gary J. Patti
    • 1
  1. 1.Department of ChemistryWashington University in St. LouisSt. LouisUSA
  2. 2.Department of GeneticsWashington University in St. LouisSt. LouisUSA

Personalised recommendations