Abstract
Untargeted metabolomics provides a comprehensive platform for identifying metabolites whose levels are altered between two or more populations. By using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS), hundreds to thousands of peaks with a unique m/z ratio and retention time are routinely detected from most biological samples in an untargeted profiling experiment. Each peak, termed a metabolomic feature, can be characterized on the basis of its accurate mass, retention time and tandem mass spectral fragmentation pattern. Here a seven-step protocol is suggested for such a characterization by using the METLIN metabolite database. The protocol starts from untargeted metabolomic LC-Q-TOF-MS data that have been analyzed with the bioinformatics program XCMS, and it describes a strategy for selecting interesting features as well as performing subsequent targeted tandem MS. The seven steps described will require 2–4 h to complete per feature, depending on the compound.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Comprehensive analysis of metabolome and transcriptome reveals the mechanism of color formation in different leave of Loropetalum Chinense var. Rubrum
BMC Plant Biology Open Access 08 March 2023
-
Transcriptomic and metabolomic changes triggered by Macrosiphum rosivorum in rose (Rosa longicuspis)
BMC Genomics Open Access 09 December 2021
-
The effects of tea plants-soybean intercropping on the secondary metabolites of tea plants by metabolomics analysis
BMC Plant Biology Open Access 22 October 2021
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Get just this article for as long as you need it
$39.95
Prices may be subject to local taxes which are calculated during checkout







References
Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).
Wikoff, W.R., Gangoiti, J.A., Barshop, B.A. & Siuzdak, G. Metabolomics identifies perturbations in human disorders of propionate metabolism. Clin. Chem. 53, 2169–2176 (2007).
Wikoff, W.R. et al. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc. Natl Acad. Sci. USA. 106, 3698–3703 (2009).
Vinayavekhin, N. & Saghatelian, A. Regulation of alkyl-dihydrothiazole-carboxylates (ATCs) by iron and the Pyochelin gene cluster in Pseudomonas aeruginosa. ACS Chem. Biol. 4, 617–623 (2009).
Kalisiak, J. et al. Identification of a new endogenous metabolite and the characterization of its protein interactions through an immobilization approach. J. Am. Chem. Soc. 131, 378–386 (2008).
Leiss, K.A., Maltese, F., Choi, Y.H., Verpoorte, R. & Klinkhamer, P.G.L. Identification of chlorogenic acid as a resistance factor for thrips in Chrysanthemum. Plant Physiol. 150, 1567–1575 (2009).
Allen, J. et al. Discrimination of modes of action of antifungal substances by use of metabolic footprinting. Appl. Environ. Microbiol. 70, 6157–6165 (2004).
Clayton, T.A., Baker, D., Lindon, J.C., Everett, J.R. & Nicholson, J.K. Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism. Proc. Natl Acad. Sci. USA 106, 14728–14733 (2009).
Ludwig, C. & Viant, M.R. Two-dimensional J-resolved NMR spectroscopy: review of a key methodology in the metabolomics toolbox. Phytochem. Anal. 21, 22–32 (2010).
Powers, R. NMR metabolomics and drug discovery. Magn. Reson. Chem. 47, S2–S11 (2009).
Dettmer, K., Aronov, P.A. & Hammock, B.D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 26, 51–78 (2007).
Lei, Z., Huhman, D. & Sumner, L.W. Mass spectrometry strategies in metabolomics. J. Biol. Chem. 286, 25435–25442 (2011).
Smart, K.F., Aggio, R.B.M., Van Houtte, J.R. & Villas-Boas, S.G. Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry. Nat. Protoc. 5, 1709–1729 (2010).
Dunn, W.B. et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).
Chan, E.C.Y., Pasikanti, K.K. & Nicholson, J.K. Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry. Nat. Protoc. 6, 1483–1499 (2011).
Fiehn, O. et al. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18, 1157–1161 (2000).
Babushok, V.I. et al. Development of a database of gas chromatographic retention properties of organic compounds. J. Chromatogr. A 1157, 414–421 (2007).
Kind, T. et al. FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81, 10038–10048 (2009).
Xu, F., Zou, L. & Ong, C.N. Multiorigination of chromatographic peaks in derivatized GC/MS metabolomics: a confounder that influences metabolic pathway interpretation. J. Proteome Res. 8, 5657–5665 (2009).
Nordstrom, A., Want, E., Northen, T., Lehtio, J. & Siuzdak, G. Multiple ionization mass spectrometry strategy used to reveal the complexity of metabolomics. Anal. Chem. 80, 421–429 (2007).
Wishart, D.S. et al. The human cerebrospinal fluid metabolome. J. Chromatogr. B 871, 164–173 (2008).
Lu, W., Bennett, B.D. & Rabinowitz, J.D. Analytical strategies for LC–MS-based targeted metabolomics. J. Chromatogr. B 871, 236–242 (2008).
Kaddurah-Daouk, R. et al. Lipidomic analysis of variation in response to simvastatin in the Cholesterol and Pharmacogenetics Study. Metabolomics 6, 191–201 (2010).
Vinayavekhin, N. & Saghatelian, A. Untargeted metabolomics. Curr. Protoc. Mol. Biol. 90, 30.1.1–30.1.24 (2001).
Johnson, C.H. et al. Radiation metabolomics. 4. UPLC-ESI-QTOFMS–based metabolomics for urinary biomarker discovery in γ-irradiated rats. Radiat. Res. 175, 473–484 (2011).
Trupp, M. et al. Metabolomics reveals amino acids contribute to variation in response to simvastatin treatment. PLoS ONE 7, e38386 (2012).
Wikoff, W.R., Kalisak, E., Trauger, S., Manchester, M. & Siuzdak, G. Response and recovery in the plasma metabolome tracks the acute LCMV-induced immune response. J. Proteome Res. 8, 3578–3587 (2009).
Panopoulos, A.D. et al. The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming. Cell Res. 22, 168–177 (2012).
Yanes, O. et al. Metabolic oxidation regulates embryonic stem cell differentiation. Nat. Chem. Biol. 6, 411–417 (2010).
Marshall, A.G. & Hendrickson, C.L. High-resolution mass spectrometers. Annu. Rev. Anal. Chem. 1, 579–599 (2008).
Verhoeven, H.A., Ric de Vos, C.H., Bino, R.J. & Hall, R.D. Plant metabolomics strategies based upon quadrupole time-of-flight mass spectrometry (QTOF-MS). Plant Metabolomics 57, 33–48 (2006).
Kamleh, A. et al. Metabolomic profiling using Orbitrap Fourier transform mass spectrometry with hydrophilic interaction chromatography: a method with wide applicability to analysis of biomolecules. Rapid Commun. Mass Spectrom. 22, 1912–1918 (2008).
Breitling, R., Pitt, A.R. & Barrett, M.P. Precision mapping of the metabolome. Trends Biotechnol. 24, 543–548 (2006).
Brown, S.C., Kruppa, G. & Dasseux, J.-L. Metabolomics applications of FT-ICR mass spectrometry. Mass Spectrom. Rev. 24, 223–231 (2005).
Smith, C.A. et al. METLIN: a metabolite mass spectral database. Ther. Drug Monit. 27, 747–751 (2005).
Patti, G.J. et al. Metabolomics implicates altered sphingolipids in chronic pain of neuropathic origin. Nature Chem. Biol. 8, 232–234 (2012).
Psychogios, N. et al. The human serum metabolome. PLoS One 6, e16957 (2011).
Chen, L., Zhou, L., Chan, E.C.Y., Neo, J. & Beuerman, R.W. Characterization of the human tear metabolome by LC–MS/MS. J. Proteome Res. 10, 4876–4882 (2011).
Want, E.J. et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat. Protoc. 5, 1005–1018 (2010).
Nebert, D.W., Zhang, G. & Vesell, E.S. From human genetics and genomics to pharmacogenetics and pharmacogenomics: past lessons, future directions. Drug Metab. Rev. 40, 187–224 (2008).
Brown, M. et al. A metabolome pipeline: from concept to data to knowledge. Metabolomics 1, 39–51 (2005).
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. Anal. Chem. 78, 779–787 (2006).
Tautenhahn, R., Patti, G.J., Rinehart, D. & Siuzdak, G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal. Chem. 84, 5035–5039 (2012).
Kiefer, P., Delmotte, N.l. & Vorholt, J.A. Nanoscale ion-pair reversed-phase HPLC-MS for sensitive metabolome analysis. Anal. Chem. 83, 850–855 (2010).
Castro-Perez, J. et al. Localization of fatty acyl and double bond positions in phosphatidylcholines using a dual-stage CID fragmentation coupled with ion mobility mass spectrometry. J. Am. Soc. Mass Spectrom. 22, 1552–1567 (2011).
Hsu, F.-F. & Turk, J. Elucidation of the double-bond position of long-chain unsaturated fatty acids by multiple-stage linear ion-trap mass spectrometry with electrospray ionization. J. Am. Soc. Mass Spectrom. 19, 1673–1680 (2008).
Thomas, M.C. et al. Ozone-induced dissociation: elucidation of double bond position within mass-selected lipid ions. Anal. Chem. 80, 303–311 (2007).
Gian Luigi, R. Dietary n-6 and n-3 polyunsaturated fatty acids: From biochemistry to clinical implications in cardiovascular prevention. Biochem. Pharmacol. 77, 937–946 (2009).
Ding, J. et al. Capillary LC coupled with high-mass measurement accuracy mass spectrometry for metabolic profiling. Anal. Chem. 79, 6081–6093 (2007).
Lindon, J.C. & Nicholson, J.K. Spectroscopic and statistical techniques for information recovery in metabonomics and metabolomics. Annu. Rev. Anal. Chem. 1, 45–69 (2008).
Cravatt, B. et al. Chemical characterization of a family of brain lipids that induce sleep. Science 268, 1506–1509 (1995).
Yanes, O., Tautenhahn, R., Patti, G.J. & Siuzdak, G. Expanding coverage of the metabolome for global metabolite profiling. Anal. Chem. 83, 2152–2161 (2011).
Tautenhahn, R. et al. An accelerated workflow for untargeted metabolomics using the METLIN database. Nat. Biotechnol. 30, 826–828 (2012).
Acknowledgements
This work was supported by the California Institute of Regenerative Medicine (no. TR1-01219) (G.S.), the US National Institutes of Health (nos. R01 CA170737 (G.S.), R24 EY017540 (G.S.), P30 MH062261 (G.S.), RC1 HL101034(G.S.), P01 DA026146 (G.S.), and 1R01 ES022181-01) (G.J.P.) and the US National Institutes of Health-National Institute on Aging (no. L30 AG0 038036) (G.J.P.). Financial support was also received from the US Department of Energy (grant nos. FG02-07ER64325 and DE-AC0205CH11231) (G.S.).
Author information
Authors and Affiliations
Contributions
Z.-J.Z., A.W.S. and J.W. and contributed equally to the work described. G.J.P. and G.S. supervised the work. A.W.S., J.W. and G.J.P. performed the experiments. Z.-J.Z., A.W.S., J.W. and C.H.J. wrote the manuscript. Z.-J.Z., S.M.Y., G.J.P. and G.S. read and revised the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Supplementary information
Supplementary Figure 1
Example of in-source fragmentation. Two species, m/z 480.3084 and m/z 339.2892 are observed to coelute (A), with both species observed in MS (B). Comparison of the high resolution parent ion and the MS/MS fragment (D) supports the characterization of m/z 480.3084 as a lysoPE(18:1). Note the prominent fragment at m/z 339.2892. m/z 339.2892 is observed in the MS scan (D). Fragmentation of this species provides the MS/MS spectrum (C) which is characteristic of a dehydrated oleoyl (18:1) glycerol, a major fragment of lysoPE(18:1). (PDF 411 kb)
Rights and permissions
About this article
Cite this article
Zhu, ZJ., Schultz, A., Wang, J. et al. Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nat Protoc 8, 451–460 (2013). https://doi.org/10.1038/nprot.2013.004
Published:
Issue Date:
DOI: https://doi.org/10.1038/nprot.2013.004
This article is cited by
-
Comprehensive analysis of metabolome and transcriptome reveals the mechanism of color formation in different leave of Loropetalum Chinense var. Rubrum
BMC Plant Biology (2023)
-
Penicillium chrysogenum as a fungal factory for feruloyl esterases
Applied Microbiology and Biotechnology (2023)
-
Integrated transcriptomic and metabolomic analyses of Caucasian clover (Trifolium ambiguum Bieb.) in response to freezing stress
Brazilian Journal of Botany (2022)
-
Transcriptome and metabolome profiling in different stages of infestation of Eucalyptus urophylla clones by Ralstonia solanacearum
Molecular Genetics and Genomics (2022)
-
Effects of Clostridium butyricum on growth performance, metabonomics and intestinal microbial differences of weaned piglets
BMC Microbiology (2021)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.