, 12:183 | Cite as

Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD)

  • Seth D. Rhoades
  • Aalim M. Weljie
Original Article



Both reverse-phase and HILIC chemistries are deployed for liquid-chromatography mass spectrometry (LC–MS) metabolomics analyses, however HILIC methods lag behind reverse-phase methods in reproducibility and versatility. Comprehensive metabolomics analysis is additionally complicated by the physiochemical diversity of metabolites and array of tunable analytical parameters.


Our aim was to rationally and efficiently design complementary HILIC-based polar metabolomics methods on multiple instruments using design of experiments (DoE).


We iteratively tuned LC and MS conditions on ion-switching triple quadrupole (QqQ) and quadrupole-time-of-flight (qTOF) mass spectrometers through multiple rounds of a workflow we term Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD). Multivariate statistical analysis guided our decision process in the method optimizations.


LC–MS/MS tuning for the QqQ method on serum metabolites yielded a median response increase of 161.5 % (p < 0.0001) over initial conditions with a 13.3 % increase in metabolite coverage. The COLMeD output was benchmarked against two widely used polar metabolomics methods, demonstrating total ion current increases of 105.8 and 57.3 %, with median metabolite response increases of 106.1 and 10.3 % (p < 0.0001 and p < 0.05 respectively). For our optimized qTOF method, 22 solvent systems were compared on a standard mix of physiochemically diverse metabolites, followed by COLMeD optimization, yielding a median 29.8 % response increase (p < 0.0001) over initial conditions.


The COLMeD process elucidated response tradeoffs, facilitating improved chromatography and MS response without compromising separation of isobars. COLMeD is efficient, requiring no more than 20 injections in a given DoE round, and flexible, capable of class-specific optimization as demonstrated through acylcarnitine optimization within the QqQ method.


Design of experiments HILIC LC–MS Method development Multivariate statistical analysis 



Funding was provided by National Institute of Health (Grant No. T32 GM008076), and [National Center for Research Resources (Grant No.UL1RR024134)].The authors would also like to thank Saikumari Krishnaiah for assistance with qTOF data acquisition and Barry Slaff for fruitful discussions regarding qTOF data analysis. S.D.R. is supported through a Pharmacology T32 Training Grant (T32 GM008076). Supported in part by the Institute for Translational Medicine and Therapeutics (ITMAT) Transdisciplinary Program in Translational Medicine and Therapeutics

Compliance with ethical standard

Conflict of interest

The authors declare no competing financial interest.

Ethical approval

This article does not contain any experiments with human participants or animals as performed by the authors.

Supplementary material

11306_2016_1132_MOESM1_ESM.doc (5.8 mb)
Supplementary material 1 (DOC 5891 kb)


  1. Bligh, E. G., Dyer, W. J., & Can, J. (1959). A rapid method of total lipid extraction and purification. Biochemistry and Physiology, 37(8), 911–917.Google Scholar
  2. Bruce, S. J., Tavazzi, I., Parisod, V., Rezzi, S., Kockhar, S., & Guy, P. A. (2009). Investigation of human blood plasma sample preparation for performing metabolomics using ultrahigh performance liquid chromatography/mass spectrometry. Analytical Chemistry, 81(9), 3285–3296.CrossRefPubMedGoogle Scholar
  3. Correa, E., & Goodacre, R. (2011). A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: Application to the rapid identification of Bacillus spores and classification of Bacillus species. BMC Bioinformatics, 12(33), 1–17.Google Scholar
  4. Eliasson, M., Rännar, S., Madsen, R., Donten, M. A., Marsden-Edwards, E., Moritz, T., et al. (2012). Strategy for optimizing LC–MS data processing in metabolomics: A design of experiments approach. Analytical Chemistry, 84(15), 6869–6876.CrossRefPubMedGoogle Scholar
  5. Eriksson, L., Johansson, E., Kettaneh-Wold, N., Wikström, C., & Wold, S. (2006). Design of Experiments, Principles and Applications (2nd ed.). Umeå: Umetrics Academy.Google Scholar
  6. Gika, H. G., Theodoridis, G. A., Vrhovsek, U., & Mattivi, F. J. (2012). Quantitative profiling of polar primary metabolites using hydrophilic interaction ultrahigh performance liquid chromatography-tandem mass spectrometry. Chromatograpy A, 1259, 121–127.CrossRefGoogle Scholar
  7. Gika, H. G., Wilson, I. D., & Theodoridis, G. A. (2014). LC–MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives. Journal of Chromatography B, 966, 1–6.CrossRefGoogle Scholar
  8. Hao, Z., Xiao, B., & Weng, N. J. (2008). Impact of column temperature and mobile phase components on selectivity of hydrophilic interaction chromatography (HILIC). Journal of Separation Science, 31(9), 1449–1464.CrossRefPubMedGoogle Scholar
  9. Ivanisevic, J., Zhu, Z., Plate, L., Tautenhahn, R., Chen, S., O’Brien, P. J., et al. (2013). Toward ‘omic scale metabolite profiling: A dual separation-mass spectrometry approach for coverage of lipid and central carbon metabolism. Analytical Chemistry, 85(14), 6876–6884.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Kivilompolo, M., Òhnrberg, L., Orešič, M., & HyÖtyläinen, T. J. (2013). Rapid quantitative analysis of carnitine and acylcarnitines by ultra-high performance-hydrophilic interaction liquid chromatography-tandem mass spectrometry. Chromatography A, 1292, 189–194.CrossRefGoogle Scholar
  11. Kostić, N., Dotsikas, Y., Malenović, A., Stojanović, J. B., Rakić, T., Ivanović, D., et al. (2013). Stepwise optimization approach for improving LC–MS/MS analysis of zwitterionic antiepileptic drugs with implementation of experimental design. Journal of Mass Spectrometry, 48(7), 875–884.CrossRefPubMedGoogle Scholar
  12. Lv, H., Palacios, G., Hartil, K., & Kurland, I. J. (2011). Advantages of tandem LC–MS for the rapid assessment of tissue-specific metabolic complexity using a pentafluorophenylpropyl stationary phase. Journal of Proteome Research, 10(4), 2104–2112.CrossRefPubMedPubMedCentralGoogle Scholar
  13. Napoles, M. O., & Steenbergen, R. D. J. M. (2014). Analysis of axle and vehicle load properties through Bayesian networks based on weigh-in-motion data. Reliability Engineering & System Safety, 125, 153–164.CrossRefGoogle Scholar
  14. New, L., & Chan, E. C. Y. (2008). Evaluation of BEH C18, BEH HILIC, and HSS T3 (C18) column chemistries for the UPLC-MS-MS analysis of glutathione, glutathione disulfide, and ophthalmic acid in mouse liver and human plasma. Journal of Chromatographic Science, 46, 209–214.CrossRefPubMedGoogle Scholar
  15. Nguyen, H. P., & Schug, K. A. (2008). The advantages of ESI-MS detection in conjunction with HILIC mode separations: Fundamentals and applications. Journal of Separation Science, 31(9), 1465–1480.CrossRefPubMedGoogle Scholar
  16. Paglia, G., Hrafnsdóttir, S., Magnúsdóttir, M., Fleming, R. M., Thorlacious, S., Palsson, B. Ø., et al. (2012). Monitoring metabolites consumption and secretion in cultured cells using ultra-performance liquid chromatography quadrupole-time of flight mass spectrometry (UPLC-Q-ToF-MS). Analytical and Bioanalytical Chemistry, 402(3), 1183–1198.CrossRefPubMedGoogle Scholar
  17. Riter, L. S., Vitek, O., Gooding, K. M., Hodge, B. D., & Julian, R. K. (2005). Statistical design of experiments as a tool in mass spectrometry. Journal of Mass Spectrometry, 40(5), 565–579.CrossRefPubMedGoogle Scholar
  18. Sampsonidis, I., Witting, M., Koch, W., Virgillou, C., Gika, H. G., Schmitt-Kopplin, P., et al. (2015). Computational analysis and ratiometric comparison approaches aimed to assist column selection in hydrophilic interaction liquid chromatography-tandem mass spectrometry targeted metabolomics. Journal of Chromatography A, 1406, 145–155.CrossRefPubMedGoogle Scholar
  19. Smith, C. A., O’Maille, G., Want, E. J., Qin, C., Trauger, S. A., Brandon, T. R., et al. (2005). METLIN: A metabolite mass spectral database. Therapeutic Drug Monitoring, 27(6), 747–751.CrossRefPubMedGoogle Scholar
  20. Székely, G. Y., Henriques, B., Gil, M., Ramos, A., & Alvarez, C. J. (2012). Design of experiments as a tool for LC–MS/MS method development for the trace analysis of the potentially genotoxic 4-dimethylaminopyridine impurity in glucocorticoids. Journal of Pharmaceutical and Biomedical Analysis, 70, 251–258.CrossRefPubMedGoogle Scholar
  21. Trygg, J., Gullberg, J., Johansson, A. I., Jonsson, P., Antti, H., Marklund, S. L., et al. (2005). Extraction and GC/MS analysis of the human blood plasma metabolome. Analytical Chemistry, 77(24), 8086–8094.CrossRefPubMedGoogle Scholar
  22. Want, E. J., Wilson, I. D., Gika, H., Theodoridis, G., Plumb, R. S., Shockor, J., et al. (2010). Global metabolic profiling procedures for urine using UPLC–MS. Nature Protocols, 5(6), 1005–1018.CrossRefPubMedGoogle Scholar
  23. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2013). HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Research, 41, D801–D807.CrossRefPubMedGoogle Scholar
  24. Yuan, M., Breitkopf, S. B., Yang, X., & Asara, J. M. (2012). A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nature Protocols, 7(5), 872–881.CrossRefPubMedPubMedCentralGoogle Scholar
  25. Zheng, H., Clausen, M. R., Dalsgaard, K. T., Mortensen, G., & Bertram, C. H. (2013). Time-saving design of experiment protocol for optimization of LC–MS data processing in metabolomic approaches. Analytical Chemistry, 85(15), 7109–7116.CrossRefPubMedGoogle Scholar
  26. Zhou, G., Pang, H., Tang, Y., Yao, X., Mo, X., Zhu, S., et al. (2013). Hydrophilic interaction ultra-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry for highly rapid and sensitive analysis of underivatized amino acids in functional foods. Amino Acids, 44(5), 1293–1305.CrossRefPubMedGoogle Scholar
  27. Zhou, Y., Song, J., Choi, F. F., Wu, H., Qiao, C., Ding, L., et al. (2009). An experimental design approach using response surface techniques to obtain optimal liquid chromatography and mass spectrometry conditions to determine the alkaloids in Meconopsi species. Journal of Chromatography A, 1216(42), 7013–7023.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Systems Pharmacology and Translational Therapeutics, Institute for Translational Medicine and TherapeuticsUniversity of PennsylvaniaPhiladelphiaUSA

Personalised recommendations