High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry


Flow injection electrospray–mass spectrometry (FIE–MS) is finding utility as a first-pass metabolite fingerprinting tool in many research fields. We provide a protocol that has proved reliable in large-scale research projects involving diverse sample matrices originating from plants, microbes and mammalian biofluids. Using Brachypodium leaf material as an example matrix all steps are summarized from sample extraction to data quality assessment. Alternative procedures for dealing with other common matrices such as bloods and urine are included. With little sample pretreatment, no chromatography and instrument cycle times of <5 min it is feasible to analyze >1,000 samples per week. Analysis of a typical batch of 240 samples (including first-pass data analysis) can be accomplished within 48 h.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Ideal flow-infusion chromatogram of Brachypodium-infected leaf extract acquired on the LTQ mass spectrometer.
Figure 2: Applying background subtraction for signal processing.
Figure 3: Averaged mass spectra of a typical infusion peak.
Figure 4: Example plot of a nominal-mass raw-intensity matrix after signal processing.
Figure 5
Figure 6: Work flow in flow injection electrospray–mass spectrometry (FIE–MS) high-throughput analysis.
Figure 7: Use of principal components analysis (PCA) and principal component linear discriminant analysis (PC-LDA) for initial investigation of data quality.
Figure 8: Reanalysis of data after removal of sample outliers.
Figure 9: Score plots obtained after instrument troubleshooting and sample reanalysis.
Figure 10: PC2 loading plot revealing a mass binning error.
Figure 11: Final score plots obtained after repeating data signal processing using adjusted binning limit.


  1. 1

    Martin, D.B. & Nelson, P.S. From genomics to proteomics: techniques and applications in cancer research. Trends Cell Biol. 11, S60–S65 (2001).

    CAS  Article  Google Scholar 

  2. 2

    Saghatelian, A. & Cravatt, B.F. Global strategies to integrate the proteome and metabolome. Curr. Opin. Chem. Biol. 9, 62–68 (2005).

    CAS  Article  Google Scholar 

  3. 3

    Murray, D.B., Beckmann, M. & Kitano, H. Regulation of yeast oscillatory dynamics. Proc. Natl. Acad. Sci. USA 104, 2241–2246 (2007).

    CAS  Article  Google Scholar 

  4. 4

    Catchpole, G.S. et al. Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. Proc. Natl. Acad. Sci. USA 102, 14458–14462 (2005).

    CAS  Article  Google Scholar 

  5. 5

    Bino, R.J. et al. Potential of metabolomics as a functional genomics tool. Trends Plant Sci. 9, 418–425 (2004).

    CAS  Article  Google Scholar 

  6. 6

    Fiehn, O. Metabolomics–the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 (2002).

    CAS  Google Scholar 

  7. 7

    Lisec, J., Schauer, N., Kopka, J., Willmitzer, L. & Fernie, A.R. Gas chromatography mass spectrometry–based metabolite profiling in plants. Nat. Protoc. 1, 387–396 (2006).

    CAS  Article  Google Scholar 

  8. 8

    Kaderbhai, N.N., Broadhurst, D.I., Ellis, D.I., Goodacre, R. & Kell, D.B. Functional genomics via metabolic footprinting: monitoring metabolite secretion by Escherichia coli tryptophan metabolism mutants using FT-IR and direct injection electrospray mass spectrometry. Comp. Funct. Genomics 4, 376–391 (2003).

    CAS  Article  Google Scholar 

  9. 9

    Enot, D.P., Beckmann, M., Overy, D. & Draper, J. Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals. Proc. Natl. Acad. Sci. USA 103, 14865–14870 (2006).

    CAS  Article  Google Scholar 

  10. 10

    Ward, J.L., Harris, C., Lewis, J. & Beale, M.H. Assessment of H-1 NMR spectroscopy and multivariate analysis as a technique for metabolite fingerprinting of Arabidopsis thaliana. Phytochemistry 62, 949–957 (2003).

    CAS  Article  Google Scholar 

  11. 11

    Roessner, U., Wagner, C., Kopka, J., Trethewey, R.N. & Willmitzer, L. Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J. 23, 131–142 (2000).

    CAS  Article  Google Scholar 

  12. 12

    Taylor, J., King, R.D., Altmann, T. & Fiehn, O. Application of metabolomics to plant genotype discrimination using statistics and machine learning. Bioinformatics 18, S241–S248 (2002).

    Article  Google Scholar 

  13. 13

    Wagner, C., Sefkow, M. & Kopka, J. Construction and application of a mass spectral and retention time index database generated from plant GC/EI-TOF-MS metabolite profiles. Phytochemistry 62, 887–900 (2003).

    CAS  Article  Google Scholar 

  14. 14

    Tolstikov, V.V., Lommen, A., Nakanishi, K., Tanaka, N. & Fiehn, O. Monolithic silica-based capillary reversed-phase liquid chromatography/electrospray mass spectrometry for plant metabolomics. Anal. Chem. 75, 6737–6740 (2003).

    CAS  Article  Google Scholar 

  15. 15

    Tolstikov, V.V. & Fiehn, O. Analysis of highly polar compounds of plant origin: combination of hydrophilic interaction chromatography and electrospray ion trap mass spectrometry. Anal. Biochem. 301, 298–307 (2002).

    CAS  Article  Google Scholar 

  16. 16

    Goodacre, R., Vaidyanathan, S., Dunn, W.B., Harrigan, G.G. & Kell, D.B. Metabolomics by numbers: acquiring and understanding global metabolite data. Trends Biotechnol. 22, 245–252 (2004).

    CAS  Article  Google Scholar 

  17. 17

    Sumner, L.W., Mendes, P. & Dixon, R.A. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62, 817–836 (2003).

    CAS  Article  Google Scholar 

  18. 18

    Fiehn, O. et al. Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18, 1157–1161 (2000).

    CAS  Article  Google Scholar 

  19. 19

    Dear, G.J., James, A.D. & Sarda, S. Ultra-performance liquid chromatography coupled to linear ion trap mass spectrometry for the identification of drug metabolites in biological samples. Rapid Commun. Mass Spectrom. 20, 1351–1360 (2006).

    CAS  Article  Google Scholar 

  20. 20

    Startin, J.R., Hird, S.J. & Sykes, M.D. Determination of ethylenethiourea (ETU) and propylenethiourea (PTU) in foods by high performance liquid chromatography-atmospheric pressure chemical ionisation-medium-resolution mass spectrometry. Food Addit. Contam. 22, 245–250 (2005).

    CAS  Article  Google Scholar 

  21. 21

    Wang, W. et al. Quantification of proteins and metabolites by mass spectrometry without isotopic labelling or spiked standards. Anal. Chem. 75, 4818–4826 (2003).

    CAS  Article  Google Scholar 

  22. 22

    Smedsgaard, J., Hansen, M.E. & Frisvad, J.C. Classification of terverticillate Penicillia by electrospray mass spectrometric profiling. Stud. Mycol. 49, 243–251 (2004).

    Google Scholar 

  23. 23

    Goodacre, R. et al. Detection of the dipicolinic acid biomarker in Bacillus spores using Curie-point pyrolysis mass spectrometry and fourier transform infrared spectroscopy. Anal. Chem. 72, 119–127 (2000).

    CAS  Article  Google Scholar 

  24. 24

    Nicholson, J.K. & Wilson, I.D. Opinion: understanding 'global' systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2, 668–676 (2003).

    CAS  Article  Google Scholar 

  25. 25

    Allen, J. et al. High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 (2003).

    CAS  Article  Google Scholar 

  26. 26

    Scholz, M., Gatzek, S., Sterling, A., Fiehn, O. & Selbig, J. Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 20, 2447–2454 (2004).

    CAS  Article  Google Scholar 

  27. 27

    Aharoni, A. et al. Nontargeted metabolome analysis by use of Fourier transform ion cyclotron mass spectrometry. OMICS 6, 217–234 (2002).

    CAS  Article  Google Scholar 

  28. 28

    Smedsgaard, J. & Frisvad, J.C. Using direct electrospray mass spectrometry in taxonomy and secondary metabolite profiling of crude fungal extracts. J. Microbiol. Methods 25, 5–17 (1996).

    CAS  Article  Google Scholar 

  29. 29

    Hopfgartner, G. et al. Triple quadrupole linear ion trap mass spectrometer for the analysis of small molecules and macromolecules. J. Mass Spectrom. 39, 845–855 (2004).

    CAS  Article  Google Scholar 

  30. 30

    Zamfir, A.D. et al. Thin chip microsprayer system coupled to quadrupole time-of-flight mass spectrometer for glycoconjugate analysis. Lab Chip 5, 298–307 (2005).

    CAS  Article  Google Scholar 

  31. 31

    Nielsen, K.F. & Smedsgaard, J. Fungal metabolite screening: database of 474 mycotoxins and fungal metabolites for dereplication by standardised liquid chromatography-UV-mass spectrometry methodology. J. Chromatogr. A 1002, 111–136 (2003).

    CAS  Article  Google Scholar 

  32. 32

    Gorlach, E. & Richmond, R. Discovery of quasi-molecular ions in electrospray spectra by automated searching for simultaneous adduct mass differences. Anal. Chem. 71, 5557–5562 (1999).

    Article  Google Scholar 

  33. 33

    Overy, D.P. et al. Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints. Nat. Protoc. 3, 471–485 (2008).

    CAS  Article  Google Scholar 

  34. 34

    Antignac, J.P. et al. The ion suppression phenomenon in liquid chromatography-mass spectrometry and its consequences in the field of residue analysis. Anal. Chim. Acta 529, 129–136 (2005).

    CAS  Article  Google Scholar 

  35. 35

    van Hout, M.W., Niederländer, H.A., de Zeeuw, R.A. & de Jong, G.J. Ion suppression in the determination of clenbuterol in urine by solid-phase extraction atmospheric pressure chemical ionisation ion-trap mass spectrometry. Rapid Commun. Mass Spectrom. 17, 245–250 (2003).

    CAS  Article  Google Scholar 

  36. 36

    Müller, C., Schäfer, P., Störtzel, M., Vogt, S. & Weinmann, W. Ion suppression effects in liquid chromatography-electrospray-ionisation transport-region collision induced dissociation mass spectrometry with different serum extraction methods for systematic toxicological analysis with mass spectra libraries. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 773, 47–52 (2002).

    Article  Google Scholar 

  37. 37

    Annesley, T.M. Ion suppression in mass spectrometry. Clin. Chem. 49, 1041–1044 (2003).

    CAS  Article  Google Scholar 

  38. 38

    Vaidyanathan, S., Kell, D.B. & Goodacre, R. Flow-injection electrospray ionization mass spectrometry of crude cell extracts for high-throughput bacterial identification. J. Am. Soc. Mass Spectrom. 13, 118–128 (2002).

    CAS  Article  Google Scholar 

  39. 39

    Mazzella, N. et al. Use of electrospray ionization mass spectrometry for profiling of crude oil effects on the phospholipid molecular species of two marine bacteria. Rapid Commun. Mass Spectrom. 19, 3579–3588 (2005).

    CAS  Article  Google Scholar 

  40. 40

    Favretto, D., Piovan, A., Filippini, R. & Caniato, R. Monitoring the production yields of vincristine and vinblastine in Catharanthus roseus from somatic embryogenesis. Semiquantitative determination by flow-injection electrospray ionization mass spectrometry. Rapid Commun. Mass Spectrom. 15, 364–369 (2001).

    CAS  Article  Google Scholar 

  41. 41

    Zahn, J.A., Higgs, R.E. & Hilton, M.D. Use of direct-infusion electrospray mass spectrometry to guide empirical development of improved conditions for expression of secondary metabolites from actinomycetes. Appl. Environ. Microbiol. 67, 377–386 (2001).

    CAS  Article  Google Scholar 

  42. 42

    Goodacre, R., York, E.V., Heald, J.K. & Scott, I.M. Chemometric discrimination of unfractionated plant extracts analyzed by electrospray mass spectrometry. Phytochemistry 62, 859–863 (2003).

    CAS  Article  Google Scholar 

  43. 43

    Overy, S.A. et al. Application of metabolite profiling to the identification of traits in a population of tomato introgression lines. J. Exp. Bot. 56, 287–296 (2005).

    CAS  Article  Google Scholar 

  44. 44

    Koulman, A. et al. High-throughput direct-infusion ion trap mass spectrometry: a new method for metabolomics. Rapid Commun. Mass Spectrom. 21, 421–428 (2007).

    CAS  Article  Google Scholar 

  45. 45

    Rashed, M.S., Al-Ahaidib, L.Y., Aboul-Enein, H.Y., Al-Amoudi, M. & Jacob, M. Determination of L-pipecolic acid in plasma using chiral liquid chromatography-electrospray tandem mass spectrometry. Clin. Chem. 47, 2124–2130 (2001).

    CAS  PubMed  Google Scholar 

  46. 46

    Kell, D.B., Darby, R.M. & Draper, J. Genomic computing. Explanatory analysis of plant expression profiling data using machine learning. Plant Physiol. 126, 943–951 (2001).

    CAS  Article  Google Scholar 

  47. 47

    Kay, C.D., Mazza, G., Holub, B.J. & Wang, J. Anthocyanin metabolites in human urine and serum. Br. J. Nutr. 91, 933–942 (2004).

    CAS  Article  Google Scholar 

  48. 48

    Beckmann, M. & Enot, D.P. Overy, D & Draper, J. Representation, comparison and interpretation of metabolome fingerprint data for total composition analysis and quality trait investigation in potato plants. J. Agric. Food. Chem. 55, 3444–3451 (2007).

    CAS  Article  Google Scholar 

  49. 49

    Parker, D. et al. Rice blast infection of Brachypodium distachyon as a model system to study dynamic host/pathogen interactions. Nat. Protoc. 3, 435–445 (2008).

    CAS  Article  Google Scholar 

  50. 50

    Enot, D. et al. Preprocessing, classification modeling and feature selection using flow injection electrospray mass spectrometry metabolite fingerprint data. Nat. Protoc. 3, 446–470 (2008).

    CAS  Article  Google Scholar 

  51. 51

    Jenkins, H. et al. A proposed framework for the description of plant metabolomics experiments and their results. Nat. Biotechnol. 22, 1601–1606 (2004).

    CAS  Article  Google Scholar 

  52. 52

    Castle, A.L., Fiehn, O., Kaddurah-Daouk, R. & Lindon, J.C. Metabolomics Standards Workshop and the development of international standards for reporting metabolomics experimental results. Brief. Bioinform. 7, 159–165 (2006).

    CAS  Article  Google Scholar 

  53. 53

    Broadhurst, D.I. & Kell, D.B. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2, 171–196 (2006).

    CAS  Article  Google Scholar 

  54. 54

    Bligh, E.G. & Dyer, W.J. A rapid method of total lipid extraction and purification. Can. J. Biochem. Physiol. 37, 911–917 (1959).

    CAS  Article  Google Scholar 

  55. 55

    Enot, D.P., Beckmann, M. & Draper, J. Detecting a difference–assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified plants. Metabolomics 3, 335–347 (2007).

    Article  Google Scholar 

  56. 56

    Chernushevich, I.V., Loboda, A.V. & Thomson, B.A. An introduction to quadrupole-time-of-flight mass spectrometry. J. Mass Spectrom. 36, 849–865 (2001).

    CAS  Article  Google Scholar 

  57. 57

    Nieuwoudt, H.H., Prior, B.A., Pretorius, I.S., Manley, M. & Bauer, F.F. Principal component analysis applied to Fourier transform infrared spectroscopy for the design of calibration sets for glycerol prediction models in wine and for the detection and classification of outlier samples. J. Agric. Food Chem. 52, 3726–3735 (2004).

    CAS  Article  Google Scholar 

  58. 58

    Gullberg, J., Jonsson, P., Nordstrom, A., Sjostrom, M. & Moritz, T. Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Anal. Biochem. 331, 283–295 (2004).

    CAS  Article  Google Scholar 

  59. 59

    R_Development_Core_Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2006, http://www.R-project.org).

Download references


We thank all collaborators who provided valuable samples to develop this protocol and, in particular, Rob Darby for overarching support with laboratory infrastructure, equipment and materials. Metabolite analysis and statistical work (M.B. and D.E.) was partly funded by the Food Standards Agency (London) as part of its G02006 and G03012 projects and biological materials used in example data were generated as part of the UK Biotechnology and Biological Sciences Research council grant BBD0069531 (D.P. and E.D.).

Author information



Corresponding author

Correspondence to Manfred Beckmann.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Beckmann, M., Parker, D., Enot, D. et al. High-throughput, nontargeted metabolite fingerprinting using nominal mass flow injection electrospray mass spectrometry. Nat Protoc 3, 486–504 (2008). https://doi.org/10.1038/nprot.2007.500

Download citation

Further reading


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.


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing