JoVE Logo
Faculty Resource Center

Sign In





Representative Results






Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Published: May 20th, 2013



1Centers for Cancer Pharmacology and Excellence in Environmental Toxicology, Department of Pharmacology, University of Pennsylvania

Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first separated into polar and non-polar fractions by a liquid-liquid phase extraction, and partially purified to facilitate downstream analysis. Both aqueous (polar metabolites) and organic (non-polar metabolites) phases of the initial extraction are processed to survey a broad range of metabolites. Metabolites are separated by different liquid chromatography methods based upon their partition properties. In this method, we present microflow ultra-performance (UP)LC methods, but the protocol is scalable to higher flows and lower pressures. Introduction into the mass spectrometer can be through either general or compound optimized source conditions. Detection of a broad range of ions is carried out in full scan mode in both positive and negative mode over a broad m/z range using high resolution on a recently calibrated instrument. Label-free differential analysis is carried out on bioinformatics platforms. Applications of this approach include metabolic pathway screening, biomarker discovery, and drug development.

Due to recent technological advances in the field of HRMS, untargeted, hypothesis-generating metabolomics approaches have become a feasible approach to analysis of complex samples.1 Mass spectrometers capable of 100,000 resolution facilitating routine low part per million (ppm) mass accuracy have become widely available from multiple vendors.2,3 This mass accuracy allows greater specificity and confidence in a preliminary assignment of analyte identity, isotopic pattern recognition, and adduct identification.4 When coupled with an appropriate extraction procedure and high-performance LC or UPLC, complex mixtures can be analyzed with ad....

Log in or to access full content. Learn more about your institution’s access to JoVE content here

1. Sample Extraction from Cells

  1. For a 10 cm plate of cells: collect 1.5 ml of lifted cell suspension in media into a pre-labeled 10 ml glass centrifuge tube. For adherent lines, cells should be lifted with gentle scraping in 1.5 ml of media kept on ice. Optional: If internal standards are used, add an appropriate aliquot at this step.
    Comment: Quenching of cellular metabolism is crucial for certain metabolites. For analysis of time-sensitive metabolites, procedures such as cold metha.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

The results presented show selected data from a 6-hr treatment of SH-SY5Y glioblastoma cells with the pesticide and mitochondrial complex I inhibitor rotenone. For brevity, only the organic phase positive mode data is presented. The samples were processed and analyzed as described above (Figure 1, Table 1, Table 2) and loaded onto two differential analysis platforms for label-free quantification, SIEVE and XCMS online. Although a large number of hits (Figure 2, Figure 3).......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

Untargeted metabolomics offers a powerful tool for investigating endogenous or xenobiotic biotransformations, or capturing a metabolic profile from a sample of interest. The output of the technique scales with the resolution and sensitivity of the technology used to separate and analyze the sample, the ability to deal with the large datasets generated, and the ability to mine the dataset for useful information (e.g. accurate mass database searching). Recently, this has been facilitated by advances in high resolu.......

Log in or to access full content. Learn more about your institution’s access to JoVE content here

We acknowledge support of NIH grants P30ES013508 and 5T32GM008076. We also thank Thermo Scientific for access to SIEVE 2.0 and Drs. Eugene Ciccimaro and Mark Sanders of Thermo Scientific for useful discussions.


Log in or to access full content. Learn more about your institution’s access to JoVE content here

Name Company Catalog Number Comments
Phosphate Buffered Saline Mediatech 21-031-CM  
Water (H2O) Fisher Scientific W7-4 (optima)
Acetonitrile (CH3CN) Fisher Scientific A996-4 (optima)
Methanol (CH3OH) Fisher Scientific A454-4 (optima)
Isopropanol Fisher Scientific A464-4 (optima)
Chloroform (CH3Cl) Sigma-Aldrich 366927 Hazard
Dichloromethane (CH2Cl2) Acros Organics 61030-1000 To replace chloroform
Diethyl Ether Sigma-Aldrich 346136 To replace chloroform
Formic Acid (FA) Fisher Scientific   (optima)
NH4OH Fisher Scientific A470-250 (optima)
Ammonium formate (HCOONH4) Sigma-Aldrich 78314  
MicroSpin C18 Columns Nest Group Inc SS18V  
Pasteur Pipettes Fisher Scientific 13-678-200  
10 ml Glass Centrifuge Tubes Kimble Chase 73785-10  
10 ml Plastic Centrifuge Tubes CellTreat CLS-4301-015  
LC Vials (glass) Waters 60000751CV  
LC Inserts (glass) Waters WAT094171  
LC Vials (plastic) Waters 186002640  
0.22 μm Filters Corning 8169 nylon
2 ml Eppendorf Tubes BioExpress C-3229-1 Low Retention
High Resolution Mass Spectrometer Thermo Scientific LTQ XL-Orbitrap  
Source Michrom Thermo Advance Source  
Differential Analysis Software Thermo Scientific SIEVE 2.0  
nanoACQUITY C18 BEH130 Waters 186003546 1.7 μm particle size, 150 mm x 100 μm
Acentis Express C8 Sigma-Aldrich 54262 2.7 μm particle size, 15 cm x 200 μm

  1. Pluskal, T., Nakamura, T., Villar-Briones, A., Yanagida, M. Metabolic profiling of the fission yeast S. pombe: quantification of compounds under different temperatures and genetic perturbation. Mol. Biosyst. 6 (1), 182-198 (2010).
  2. Makarov, A., Denisov, E., et al. Performance Evaluation of a Hybrid Linear Ion Trap/Orbitrap Mass Spectrometer. Analytical Chemistry. 78 (7), 2113-2120 (2006).
  3. Timischl, B., Dettmer, K., Kaspar, H., Thieme, M., Oefner, P. J. Development of a quantitative, validated capillary electrophoresis-time of flight-mass spectrometry method with integrated high-confidence analyte identification for metabolomics. Electrophoresis. 29 (10), 2203-2214 (2008).
  4. Katajamaa, M., Oresic, M. Data processing for mass spectrometry-based metabolomics. J. Chromatogr. A. 1158 (1-2), 318-328 (2007).
  5. Katajamaa, M., Oresic, M. Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics. 6, 179 (2005).
  6. Wilson, I. D., Nicholson, J. K., et al. High resolution ultra performance liquid chromatography coupled to q-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. Journal of Proteome Research. 4 (2), 591-598 (2005).
  7. Benton, H. P., Wong, D. M., Trauger, S. A., Siuzdak, G. XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Anal. Chem. 80 (16), 6382-6389 (2008).
  8. Katajamaa, M., Miettinen, J., Oresic, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics. 22 (5), 634-636 (2006).
  9. Pluskal, T., Castillo, S., Villar-Briones, A., Oresic, M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 11 (1), 395 (2010).
  10. Smith, C. A., Want, E. J., O'Maille, G., Abagyan, R., Siuzdak, G. XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Analytical Chemistry. 78 (3), 779-787 (2006).
  11. Tautenhahn, R., Patti, G. J., Rinehart, D., Siuzdak, G. XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data. Analytical Chemistry. 84 (11), 5035-5039 (2012).
  12. Gelhaus, S. L., Mesaros, A. C., Blair, I. A. Cellular Lipid Extraction for Targeted Stable Isotope Dilution Liquid Chromatography-Mass Spectrometry Analysis. J. Vis. Exp. (57), e3399 (2011).
  13. Want, E. J., Wilson, I. D., et al. Global metabolic profiling procedures for urine using UPLCGÇôMS. Nature Protocols. 5 (6), 1005-1018 (2010).
  14. Sellick, C. A., Hansen, R., Stephens, G. M., Goodacre, R., Dickson, A. J. Metabolite extraction from suspension-cultured mammalian cells for global metabolite profiling. Nature Protocols. 6 (8), 1241-1249 (2011).
  15. Dunn, W. B., Broadhurst, D., et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature protocols. 6 (7), 1060-1083 (2011).
  16. Masson, P., Alves, A. C., Ebbels, T. M. D., Nicholson, J. K., Want, E. J. Optimization and evaluation of metabolite extraction protocols for untargeted metabolic profiling of liver samples by UPLC-MS. Analytical Chemistry. 82 (18), 7779-7786 (2010).
  17. Shaham, O., Slate, N. G., et al. A plasma signature of human mitochondrial disease revealed through metabolic profiling of spent media from cultured muscle cells. Proceedings of the National Academy of Sciences. 107 (4), 1571-1575 (2010).
  18. Cequier-Saünchez, E., Rodriüguez, C., Ravelo, A. G., Zaürate, R. Dichloromethane as a Solvent for Lipid Extraction and Assessment of Lipid Classes and Fatty Acids from Samples of Different Natures. Journal of Agricultural and Food Chemistry. 56 (12), 4297-4303 (2008).
  19. Keller, A., Eng, J., Zhang, N., Li, X. J., Aebersold, R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol. Syst. Biol. 1, (2005).
  20. Cleveland, W. S., Devlin, S. J. Locally weighted regression - an approach to regression-analysis by local fitting. J. Am. Stat. Assoc. 83 (403), 596-610 (1988).
  21. Lange, E., Tautenhahn, R., Neumann, S., Gropl, C. Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics. 9 (1), 375 (2008).
  22. Tautenhahn, R., Bottcher, C., Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics. 9 (1), 504 (2008).
  23. Kuhl, C., Tautenhahn, R., Bottcher, C., Larson, T. R., Neumann, S. CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets. Analytical Chemistry. 84 (1), 283-289 (2012).
  24. Kanehisa, M., Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28 (1), 27-30 (2000).
  25. Smith, C. A., O'Maille, G., et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27 (6), 747-751 (2005).
  26. Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., Bryant, S. H. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 37 Web Server, W623-W633 (2009).
  27. Wishart, D. S., Knox, C., et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37 Database, D603-D610 (2009).
  28. Bligh, E. G., Dyer, W. J. A rapid method of total lipid extraction and purification. Canadian Journal of Biochemistry and Physiology. 37 (8), 911-917 (1959).
  29. Folch, J. A simple method for the isolation and purification of total lipids from animal tissues. J. Biol. Chem. 226, 497-509 (1957).
  30. Avery, M. J. Quantitative characterization of differential ion suppression on liquid chromatography/atmospheric pressure ionization mass spectrometric bioanalytical methods. Rapid Communications in Mass Spectrometry. 17 (3), 197-201 (2003).

This article has been published

Video Coming Soon

JoVE Logo


Terms of Use





Copyright © 2024 MyJoVE Corporation. All rights reserved