Analytical and Bioanalytical Chemistry

, Volume 410, Issue 29, pp 7783–7792 | Cite as

Balancing metabolome coverage and reproducibility for untargeted NMR-based metabolic profiling in tissue samples through mixture design methods

  • Hong Zheng
  • Zhitao Ni
  • Aimin Cai
  • Xi Zhang
  • Jiuxia Chen
  • Qi Shu
  • Hongchang GaoEmail author
Research Paper


Untargeted metabolomics attempts to acquire a comprehensive and reproducible set of small-molecule metabolites in biological systems. However, metabolite extraction method significantly affects the quality of metabolomics data. In the present study, we calculated the number of peaks (NP) and coefficient of variation (CV) to reflect metabolome coverage and reproducibility in untargeted NMR-based metabolic profiling of tissue samples in rats under different methanol/chloroform/water (MCW) extraction conditions. Different MCW extractions expectedly generated diverse characteristics of metabolome. Moreover, the classic MCW method revealed tissue-specific differences in the NP and CV values. To obtain high-quality metabolomics data, therefore, we used mixture design methods to optimize the MCW extraction strategy by maximizing the NP value and minimizing the CV value in each tissue sample. Results show that the optimal formulations of MCW extraction were 2:2:8 (ml/mg tissue) for brain sample, 2:4:6 (ml/mg tissue) for heart sample, 1.3:2:8.7 (ml/mg tissue) for liver sample, 4:2:6 (ml/mg tissue) for kidney sample, 2:3:7 (ml/mg tissue) for muscle sample, and 2:4:6 (ml/mg tissue) for pancreas sample. Therefore, these findings demonstrate that different tissue samples need a specific optimal extraction condition for balancing metabolome coverage and reproducibility in the untargeted metabolomics study. Mixture design method is an effective tool to optimize metabolite extraction strategy for tissue samples.

Graphical abstract


Metabolomics Metabolite extraction Metabolome reproducibility Optimization Tissue-specific 



The Laboratory Animal Center of Wenzhou Medical University is acknowledged for technical services.

Author contributions

HCG and HZ contributed to the experimental design. ZTN, AMC, XZ, JXC, and QX contributed to the sample collection and NMR metabolomics analysis. HZ and HCG contributed to the data analysis, result interpretation, and writing. All authors have read, revised, and approved the final manuscript.

Funding Information

This study was supported by the National Natural Science Foundation of China (Nos. 21605115, 81771386, and 21575105) and the Public Welfare Technology Application Research Foundation of Zhejiang Province (No. 2017C33066).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study were in accordance with the Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee of Wenzhou Medical University.

Supplementary material

216_2018_1396_MOESM1_ESM.pdf (2.6 mb)
ESM 1 (PDF 2646 kb)


  1. 1.
    Patti GJ, Yanes O, Siuzdak G. Innovation: metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol. 2012;13(4):263–9.CrossRefGoogle Scholar
  2. 2.
    Doerr A. Global metabolomics. Nat Methods. 2017;14:32.CrossRefGoogle Scholar
  3. 3.
    Mushtaq MY, Choi YH, Verpoorte R, Wilson EG. Extraction for metabolomics: access to the metabolome. Phytochem Anal. 2014;25(4):291–306.CrossRefGoogle Scholar
  4. 4.
    Choi YH, Verpoorte R. Metabolomics: what you see is what you extract. Phytochem Anal. 2014;25(4):289–90.CrossRefGoogle Scholar
  5. 5.
    Kim S, Lee DY, Wohlgemuth G, Park HS, Fiehn O, Kim KH. Evaluation and optimization of metabolome sample preparation methods for Saccharomyces cerevisiae. Anal Chem. 2013;85(4):2169–76.CrossRefGoogle Scholar
  6. 6.
    Tulipani S, Llorach R, Urpi-Sarda M, Andres-Lacueva C. Comparative analysis of sample preparation methods to handle the complexity of the blood fluid metabolome: when less is more. Anal Chem. 2012;85(1):341–8.CrossRefGoogle Scholar
  7. 7.
    Sitnikov DG, Monnin CS, Vuckovic D. Systematic assessment of seven solvent and solid-phase extraction methods for metabolomics analysis of human plasma by LC-MS. Sci Rep. 2016;6:38885.CrossRefGoogle Scholar
  8. 8.
    García-Cañaveras JC, López S, Castell JV, Donato MT, Lahoz A. Extending metabolome coverage for untargeted metabolite profiling of adherent cultured hepatic cells. Anal Bioanal Chem. 2016;408(4):1217–30.CrossRefGoogle Scholar
  9. 9.
    Ibáñez C, Simó C, Palazoglu M, Cifuentes A. GC-MS based metabolomics of colon cancer cells using different extraction solvents. Anal Chim Acta. 2017;986:48–56.CrossRefGoogle Scholar
  10. 10.
    Römisch-Margl W, Prehn C, Bogumil R, Röhring C, Suhre K, Adamski J. Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics. Metabolomics. 2012;8(1):133–42.CrossRefGoogle Scholar
  11. 11.
    Want EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G, Plumb RS, et al. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc. 2013;8(1):17–32.CrossRefGoogle Scholar
  12. 12.
    Wang H, Xu J, Chen Y, Zhang R, He J, Wang Z, et al. Optimization and evaluation strategy of esophageal tissue preparation protocols for metabolomics by LC–MS. Anal Chem. 2016;88(7):3459–64.CrossRefGoogle Scholar
  13. 13.
    Naz S, García A, Barbas C. Multiplatform analytical methodology for metabolic fingerprinting of lung tissue. Anal Chem. 2013;85(22):10941–8.CrossRefGoogle Scholar
  14. 14.
    Diémé B, Lefèvre A, Nadal-Desbarats L, Galineau L, Hounoum BM, Montigny F, et al. Workflow methodology for rat brain metabolome exploration using NMR, LC-MS and GC-MS analytical platforms. J Pharm Biomed Anal. 2017;142:270–8.CrossRefGoogle Scholar
  15. 15.
    Wu H, Southam AD, Hines A, Viant MR. High-throughput tissue extraction protocol for NMR-and MS-based metabolomics. Anal Biochem. 2008;372(2):204–12.CrossRefGoogle Scholar
  16. 16.
    Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol. 1959;37(8):911–7.CrossRefGoogle Scholar
  17. 17.
    Folch J, Lees M, Sloane Stanley GH. A simple method for the isolation and purification of total lipids from animal tissues. J Biol Chem. 1957;226(1):497–509.PubMedPubMedCentralGoogle Scholar
  18. 18.
    Zheng H, Clausen MR, Dalsgaard TK, Mortensen G, Bertram HC. Time-saving design of experiment protocol for optimization of LC-MS data processing in metabolomic approaches. Anal Chem. 2013;85(15):7109–16.CrossRefGoogle Scholar
  19. 19.
    Montgomery DC. Design and analysis of experiments. John Wiley & Sons; 2017.Google Scholar
  20. 20.
    Savorani F, Tomasi G, Engelsen SB. icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson. 2010;202:190–202.CrossRefGoogle Scholar
  21. 21.
    Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0-the human metabolome database in 2013. Nucl Acids Res. 2012;41(D1):801–7.CrossRefGoogle Scholar
  22. 22.
    Xia J, Sinelnikov IV, Han B, Wishart DS. (2015). MetaboAnalyst 3.0-making metabolomics more meaningful. Nucl Acids Res. 2015;43:251–7.CrossRefGoogle Scholar
  23. 23.
    Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell. 2008;134(5):714–7.CrossRefGoogle Scholar
  24. 24.
    Vinayavekhin N, Homan EA, Saghatelian A. Exploring disease through metabolomics. ACS Chem Biol. 2009;5(1):91–103.CrossRefGoogle Scholar
  25. 25.
    Suhre K. Metabolic profiling in diabetes. J Endocrinol. 2014;221(3):75–85.CrossRefGoogle Scholar
  26. 26.
    Lindahl A, Sääf S, Lehtiö J, Nordström A. Tuning metabolome coverage in reversed phase LC–MS metabolomics of MeOH extracted samples using the reconstitution solvent composition. Anal Chem. 2017;89(14):7356–64.CrossRefGoogle Scholar
  27. 27.
    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(6):1684–95.CrossRefGoogle Scholar
  28. 28.
    Yang W, Chen Y, Xi C, Zhang R, Song Y, Zhan Q, et al. Liquid chromatography–tandem mass spectrometry-based plasma metabonomics delineate the effect of metabolites’ stability on reliability of potential biomarkers. Anal Chem. 2013;85(5):2606–10.CrossRefGoogle Scholar
  29. 29.
    Eriksson L, Johansson E, Wikström C. Mixture design-design generation, PLS analysis, and model usage. Chemom Intell Lab Syst. 1998;43(1–2):1–24.CrossRefGoogle Scholar
  30. 30.
    Lin CY, Wu H, Tjeerdema RS, Viant MR. Evaluation of metabolite extraction strategies from tissue samples using NMR metabolomics. Metabolomics. 2007;3(1):55–67.CrossRefGoogle Scholar
  31. 31.
    Robert O, Sabatier J, Desoubzdanne D, Lalande J, Balayssac S. Gilard V, et al. pH optimization for a reliable quantification of brain tumor cell and tissue extracts with 1H NMR: focus on choline-containing compounds and taurine. Anal Bioanal Chem. 2011;399(2):987–99.CrossRefGoogle Scholar
  32. 32.
    Anwar MA, Vorkas PA, Li JV, Shalhoub J, Want EJ, Davies AH, et al. Optimization of metabolite extraction of human vein tissue for ultra performance liquid chromatography-mass spectrometry and nuclear magnetic resonance-based untargeted metabolic profiling. Analyst. 2015;140(22):7586–97.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Hong Zheng
    • 1
  • Zhitao Ni
    • 1
  • Aimin Cai
    • 1
  • Xi Zhang
    • 1
  • Jiuxia Chen
    • 1
  • Qi Shu
    • 1
  • Hongchang Gao
    • 1
    Email author
  1. 1.Institute of Metabonomics & Medical NMR, School of Pharmaceutical ScienceWenzhou Medical UniversityWenzhouChina

Personalised recommendations