LC-MS Untargeted Analysis

  • Elizabeth J. Want
Part of the Methods in Molecular Biology book series (MIMB, volume 1738)


LC-MS untargeted analysis is a valuable tool in the field of metabolic profiling (metabonomics/metabolomics), and the applications of this technology have grown rapidly over the past decade. LC-MS offers advantages over other analytical platforms such as speed, sensitivity, relative ease of sample preparation, and large dynamic range. As with any analytical approach, there are still drawbacks and challenges to overcome, but advances are constantly being made regarding both column chemistries and instrumentation. There are numerous untargeted LC-MS approaches which can be used in this ever-growing research field; these can be optimized depending on sample type and the nature of the study or biological question. Some of the main LC-MS approaches for the untargeted analysis of biological samples will be described in detail in the following protocol.

Key words

LC-MS Untargeted Mass spectrometry Liquid chromatography Metabolic profiling 


  1. 1.
    Holmes E, Loo RL, Stamler J et al (2008) Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453(7193):396–400. CrossRefPubMedGoogle Scholar
  2. 2.
    Newgard CB (2017) Metabolomics and metabolic diseases: where do we stand? Cell Metab 25(1):43–56. CrossRefPubMedGoogle Scholar
  3. 3.
    Brennan L (2016) Metabolomics in nutrition research-a powerful window into nutritional metabolism. Essays Biochem 60(5):451–458CrossRefGoogle Scholar
  4. 4.
    Saurina J, Sentellas S (2017) Strategies for metabolite profiling based on liquid chromatography. J Chromatogr B 1044-1045:103–111. CrossRefGoogle Scholar
  5. 5.
    Haggarty J, Burgess KE (2016) Recent advances in liquid and gas chromatography methodology for extending coverage of the metabolome. Curr Opin Biotechnol 43:77–85. CrossRefPubMedGoogle Scholar
  6. 6.
    Markley JL, Brüschweiler R, Edison AS et al (2016) The future of NMR-based metabolomics. Curr Opin Biotechnol 43:34–40. CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Chen Y, Xu J, Zhang R, Abliz Z (2016) Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS. Bioanalysis 8(9):981–997. CrossRefPubMedGoogle Scholar
  8. 8.
    Wilson ID, Nicholson JK, Castro-Perez J et al (2005) High resolution “ultra performance” liquid chromatography coupled to oa-TOF mass spectrometry as a tool for differential metabolic pathway profiling in functional genomic studies. J Proteome Res 4(2):591–598CrossRefGoogle Scholar
  9. 9.
    Nassar AF, Wu T, Nassar SF, Wisnewski AV (2017) UPLC-MS for metabolomics: a giant step forward in support of pharmaceutical research. Drug Discov Today 22(2):463–470. CrossRefPubMedGoogle Scholar
  10. 10.
    Zhao YY, Lin RC (2014) UPLC-MS(E) application in disease biomarker discovery: the discoveries in proteomics to metabolomics. Chem Biol Interact 215:7–16. CrossRefPubMedGoogle Scholar
  11. 11.
    Wang X, Sun H, Zhang A (2011) Ultra-performance liquid chromatography coupled to mass spectrometry as a sensitive and powerful technology for metabolomic studies. J Sep Sci 34(24):3451–3459. CrossRefPubMedGoogle Scholar
  12. 12.
    Siskos AP, Jain P, Römisch-Margl W et al (2016) Interlaboratory reproducibility of a targeted metabolomics platform for analysis of human serum and plasma. Anal Chem 89(1):656–665CrossRefGoogle Scholar
  13. 13.
    Michopoulos F, Whalley N, Theodoridis G et al (2014) Targeted profiling of polar intracellular metabolites using ion-pair-high performance liquid chromatography and -ultra high performance liquid chromatography coupled to tandem mass spectrometry: applications to serum, urine and tissue extracts. J Chromatogr A 1349:60–68. CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Monteiro MS, Carvalho M, Bastos ML et al (2013) Metabolomics analysis for biomarker discovery: advances and challenges. Curr Med Chem 20(2):257–271CrossRefGoogle Scholar
  15. 15.
    Dunn WB, Broadhurst D, Begley P et al (2011) Human serum metabolome (HUSERMET) consortium. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6(7):1060–1083. CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Gray N, Lewis MR, Plumb RS et al (2015) High-throughput microbore UPLC-MS metabolic phenotyping of urine for large-scale epidemiology studies. J Proteome Res 14(6):2714–2721. CrossRefPubMedGoogle Scholar
  17. 17.
    Wilson ID (2015) Metabolic phenotyping by liquid chromatography-mass spectrometry to study human health and disease. Anal Chem 87(5):2519. No abstract availableCrossRefPubMedGoogle Scholar
  18. 18.
    Vorkas PA, Isaac G, Anwar MA et al (2015) Untargeted UPLC-MS profiling pipeline to expand tissue metabolome coverage: application to cardiovascular disease. Anal Chem 87(8):4184–4193. CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Gika HG, Theodoridis GA, Plumb RS et al (2014) Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics. J Pharm Biomed Anal 87:12–25. CrossRefPubMedGoogle Scholar
  20. 20.
    Spagou K, Wilson ID, Masson P et al (2011) HILIC-UPLC-MS for exploratory urinary metabolic profiling in toxicological studies. Anal Chem 83(1):382–390. CrossRefPubMedGoogle Scholar
  21. 21.
    Virgiliou C, Sampsonidis I, Gika HG et al (2015) Development and validation of a HILIC- MS/MS multi-targeted method for metabolomics applications. Electrophoresis 36:2215–2225CrossRefGoogle Scholar
  22. 22.
    Want EJ, Wilson ID, Gika H et al (2010) Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc 5(6):1005–1018. CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Want EJ, Coen M, Masson P et al (2010) Ultra performance liquid chromatography-mass spectrometry profiling of bile acid metabolites in biofluids: application to experimental toxicology studies. Anal Chem 82(12):5282–5289. CrossRefPubMedGoogle Scholar
  24. 24.
    Want EJ, Masson P, Michopoulos F et al (2013) Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc 8(1):17–32. CrossRefPubMedGoogle Scholar
  25. 25.
    Vorkas PA, Shalhoub J, Isaac G et al (2015) Metabolic phenotyping of atherosclerotic plaques reveals latent associations between free cholesterol and ceramide metabolism in atherogenesis. J Proteome Res 14(3):1389–1399. CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Veselkov KA, Vingara LK, Masson P et al (2011) Optimized preprocessing of ultra-performance liquid chromatography/mass spectrometry urinary metabolic profiles for improved information recovery. Anal Chem 83(15):5864–5872. CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Dias DA, Jones OA, Beale DJ, (2016) Current and future perspectives on the structural identification of small molecules in biological systems. Metabolites. 6(4). pii: E46.CrossRefGoogle Scholar
  28. 28.
    Bocker S (2016) Searching molecular structure databases using tandem MS data: are we there yet? Curr Opin Chem Biol 36:1–6. CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computational and Systems MedicineImperial College LondonLondonUK

Personalised recommendations