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Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography–mass spectrometry

Nature Protocols volume 5, pages 1709–1729 (2010)Cite this article

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Abstract

This protocol describes an analytical platform for the analysis of intra- and extracellular metabolites of microbial cells (yeast, filamentous fungi and bacteria) using gas chromatography–mass spectrometry (GC-MS). The protocol is subdivided into sampling, sample preparation, chemical derivatization of metabolites, GC-MS analysis and data processing and analysis. This protocol uses two robust quenching methods for microbial cultures, the first of which, cold glycerol-saline quenching, causes reduced leakage of intracellular metabolites, thus allowing a more reliable separation of intra- and extracellular metabolites with simultaneous stopping of cell metabolism. The second, fast filtration, is specifically designed for quenching filamentous micro-organisms. These sampling techniques are combined with an easy sample-preparation procedure and a fast chemical derivatization reaction using methyl chloroformate. This reaction takes place at room temperature, in aqueous medium, and is less prone to matrix effect compared with other derivatizations. This protocol takes an average of 10 d to complete and enables the simultaneous analysis of hundreds of metabolites from the central carbon metabolism (amino and nonamino organic acids, phosphorylated organic acids and fatty acid intermediates) using an in-house MS library and a data analysis pipeline consisting of two free software programs (Automated Mass Deconvolution and Identification System (AMDIS) and R).

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Figure 1: Flowchart for the analytical platform of metabolome analysis of microbial cells using GC-MS.
Figure 2: General mechanism of the MCF reaction.
Figure 3: Flowchart describing the AMDIS peak intensity correction.
Figure 4: Example of the recommended data structure.
Figure 5: PCA for checking the quality of GC-MS raw data as discussed in Step 16.
Figure 6: Generating a report in AMDIS (Step 20A).
Figure 7: Analyzing and generating a report in AMDIS in batch mode (Step 20B).
Figure 8: Manual cleaning process of AMDIS report as referred to in Step 21A(vii).
Figure 9: Combined spreadsheet containing compounds identified by AMDIS and their intensities in different samples (Step 21A(xv)).
Figure 10: Opening a script in R as discussed in Step 21B(iii).
Figure 11: Procedure timeline.
Figure 12: GC-MS chromatograms of MCF derivatives.
Figure 13: GeneSpring-generated mass tree.
Figure 14: Three-dimensional projection of samples from PCA of four different organisms.

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References

  1. Castoldi, M., Schmidt, S., Benes, V., Hentze, M.W. & Muckenthaler, M.U. miChip: an array-based method for microRNA expression profiling using locked nucleic acid capture probes. Nat. Protoc. 3, 321–329 (2008).

    Article  CAS  Google Scholar 

  2. Harsha, H.C., Molina, H. & Pandey, A. Quantitative proteomics using stable isotope labeling with amino acids in cell culture. Nat. Protoc. 3, 505–516 (2008).

    Article  CAS  Google Scholar 

  3. Villas-Bôas, S.G., Mas, S., Åkesson, M., Smedsgaard, J. & Nielsen, J. Mass spectrometry in metabolome analysis. Mass Spectrom. Rev. 24, 613–646 (2005).

    Article  Google Scholar 

  4. Kell, D.B. Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol. 7, 296–307 (2004).

    Article  CAS  Google Scholar 

  5. Dunn, W.B., Bailey, N.J.C. & Johnson, H.E. Measuring the metabolome: current analytical technologies. Analyst 130, 606–625 (2005).

    Article  CAS  Google Scholar 

  6. Wang, Q., Wu, C., Chen, T., Chen, X. & Zhao, X. Integrating metabolomics into systems biology framework to exploit metabolic complexity: strategies and applications in microorganisms. Appl. Microbiol. Biotechnol. 70, 151–161 (2006).

    Article  CAS  Google Scholar 

  7. Villas-Bôas, S.G. & Bruheim, P. Cold glycerol-saline: the promising quenching solution for accurate intracellular metabolite analysis of microbial cells. Anal. Biochem. 370, 87–97 (2007).

    Article  Google Scholar 

  8. van der Werf, M.J., Overkamp, K.M., Muilwijk, B., Coulier, L. & Hankemeier, T. Microbial metabolomics: toward a platform with full metabolome coverage. Anal. Biochem. 370, 17–25 (2007).

    Article  CAS  Google Scholar 

  9. Schauer, N. et al. GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS Lett. 579, 1332–1337 (2005).

    Article  CAS  Google Scholar 

  10. Villas-Bôas, S.G. Sampling and sample preparation. In Metabolome Analysis: An Introduction (eds. Villas-Bôas, S.G., Roessner, U., Hansen, M.A.E., Smedsgaard, J. & Nielsen, J) 39–82 (John Wiley & Sons, Hoboken, New Jersey, USA, 2007).

  11. Lin, Y., Schiavo, S., Orjala, J., Vouros, P. & Kautz, R. Microscale LC-MS-NMR platform applied to the identification of active cyanobacterial metabolites. Anal. Chem. 80, 8045–8054 (2008).

    Article  CAS  Google Scholar 

  12. Bundy, J.G., Willey, T.L., Castell, R.S., Ellar, D.J. & Brindle, K.M. Discrimination of pathogenic clinical isolates and laboratory strains of Bacillus cereus by NMR-based metabolomic profiling. FEMS Microbiol. Lett. 242, 127–136 (2005).

    Article  CAS  Google Scholar 

  13. Koek, M.M., Muilwijk, B., van der Werf, M.J. & Hankemeier, T. Microbial metabolomics with gas chromatography/mass spectrometry. Anal. Chem. 78, 1272–1281 (2006).

    Article  CAS  Google Scholar 

  14. Roessner, U. et al. Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant. J. 23, 131–142 (2000).

    Article  CAS  Google Scholar 

  15. Smedsgaard, J. Analytical tools. In Metabolome Analysis: An Introduction (eds. Villas-Bôas, S.G., Roessner, U., Hansen, M.A.E., Smedsgaard, J. & Nielsen, J) 83–145 (John Wiley & Sons, Hoboken, New Jersey, USA, 2007).

  16. Villas-Bôas, S.G., Noel, S., Lane, G.A., Attwood, G. & Cookson, A. Extracellular metabolomics: a metabolic footprinting approach to assess fiber degradation in complex media. Anal. Biochem. 349, 297–305 (2006).

    Article  Google Scholar 

  17. Kanani, H.H. & Klapa, M.I. Data correlation strategy for metabolomics analysis using gas chromatography-mass spectrometry. Metab. Eng. 9, 39–51 (2007).

    Article  CAS  Google Scholar 

  18. Winder, C.L. et al. Global metabolic profiling of Escherichia coli cultures: an evaluation of methods for quenching and extraction of intracellular metabolites. Anal. Chem. 80, 2939–2948 (2008).

    Article  CAS  Google Scholar 

  19. Villas-Bôas, S.G., Delicado, D.G., Åkesson, M. & Nielsen, J. Simultaneous analysis of amino and nonamino organic acids as methyl chloroformate derivatives using gas chromatography-mass spectrometry. Anal. Biochem. 322, 134–138 (2003).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  21. Park, S.J. et al. Global physiological understanding and metabolic engineering of microorganisms based on omics studies. Appl. Microbiol. Biotechnol. 68, 567–579 (2005).

    Article  CAS  Google Scholar 

  22. Bro, C. & Nielsen, J. Impact of 'ome' analyses on inverse metabolic engineering. Metab. Eng. 6, 204–211 (2004).

    Article  CAS  Google Scholar 

  23. Lee, S.Y., Lee, D. & Kim, T.Y. Systems biotechnology for strain improvement. Trends Biotechnol. 23, 349–358 (2005).

    Article  CAS  Google Scholar 

  24. Styczynski, M.P. et al. Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery. Anal. Chem. 79, 966–973 (2007).

    Article  CAS  Google Scholar 

  25. Mapelli, V., Olsson, L. & Nielsen, J. Metabolic footprinting in microbiology: methods and applications in functional genomics and biotechnology. Trends Biotechnol. 26, 490–497 (2008).

    Article  CAS  Google Scholar 

  26. Villas-Bôas, S.G., Åkesson, M. & Nielsen, J. Biosynthesis of glyoxylate from glycine in Saccharomyces cerevisiae. FEMS Yeast Res. 5, 703–709 (2005).

    Article  Google Scholar 

  27. Villas-Bôas, S.G., Moxley, J.F., Åkesson, M., Stephanopoulos, G. & Nielsen, J. High-throughput metabolic state analysis: the missing link in integrated functional genomics of yeasts. Biochem. J. 388, 669–677 (2005).

    Article  Google Scholar 

  28. Büscher, J.M., Czernik, D., Ewald, J.C., Sauer, U. & Zamboni, N. Cross-platform comparison of methods for quantitative metabolomics of primary metabolism. Anal. Chem. 81, 2135–2143 (2009).

    Article  Google Scholar 

  29. Ewald, J.C., Heux, S. & Zamboni, N. High-throughput quantitative metabolomics: workflow for cultivation, quenching, and analysis of yeast in a multiwell format. Anal. Chem. 81, 3623–3629 (2009).

    Article  CAS  Google Scholar 

  30. Villas-Bôas, S.G., Højer-Pedersen, J., Åkesson, M., Smedsgaard, J. & Nielsen, J. Global metabolite analysis of yeasts: evaluation of sample preparation methods. Yeast 22, 1155–1169 (2005).

    Article  Google Scholar 

  31. Mas, S., Villas-Bôas, S.G., Hansen, M.E., Åkesson, M. & Nielsen, J. A comparison of direct infusion MS with GC-MS for metabolic footprinting of yeast mutants. Biotechnol. Bioeng. 96, 1014–1022 (2007).

    Article  CAS  Google Scholar 

  32. Panagiotou, G., Villas-Bôas, S.G., Christakopoulos, P., Nielsen, J. & Olsson, L. Intracellular metabolite profiling of Fusarium oxysporum converting glucose to ethanol. J. Biotechnol. 115, 425–434 (2005).

    Article  CAS  Google Scholar 

  33. Villas-Bôas, S.G. et al. Phenotypic characterization of transposon-inserted mutants of Clostridium proteoclasticum B316T using extracellular metabolomics. J. Biotechnol. 134, 55–63 (2008).

    Article  Google Scholar 

  34. Mashego, M.R. et al. MIRACLE: Mass isotopomer ratio analysis of U-13C-labeled extracts. A new method for accurate quantification of changes in concentrations of intracellular metabolites. Biotechnol. Bioeng. 85, 620–628 (2004).

    Article  CAS  Google Scholar 

  35. Villas-Bôas, S.G., Koulman, A. & Lane, G.A. Analytical methods from the perspective of method standardization. In Topics in Current Genetics—Metabolomics (eds. Nielsen, J. & Jewett, M.C.) 11–52 (Springer-Verlag, Berlin Heidelberg, Germany, 2007).

  36. Bolten, C.J., Kiefer, P., Letisse, F., Portais, J.C. & Wittmann, C. Sampling for metabolome analysis of microorganisms. Anal. Chem. 79, 3843–3849 (2007).

    Article  CAS  Google Scholar 

  37. Brauer, M.J. et al. Conservation of the metabolomic response to starvation across two divergent microbes. Proc. Natl. Acad. Sci. USA 103, 19302–19307 (2006).

    Article  CAS  Google Scholar 

  38. Wittmann, C., Krömer, J.O., Kiefer, P., Binz, T. & Heinzle, E. Impact of the cold shock phenomenon on quantification of intracellular metabolites in bacteria. Anal. Biochem. 327, 135–139 (2004).

    Article  CAS  Google Scholar 

  39. Förster, J., Famili, I., Fu, P., Palsson, B.Ø. & Nielsen, J. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. Genome Res. 13, 244–253 (2003).

    Article  Google Scholar 

  40. Stein, S.E. An integrated method for spectrum extraction and compound identification from gas chromatography/mass spectrometry. J. Am. Soc. Mass Spectrom. 10, 770–781 (1999).

    Article  CAS  Google Scholar 

  41. Freisleben, A., Schieberle, P. & Rychlik, M. Specific and sensitive quantification of folate vitamers by stable isotope dilution assays using high-performance liquid chromatography-tandem mass spectrometry. Anal. Bioanal. Chem. 376, 149–156 (2003).

    Article  CAS  Google Scholar 

  42. Bennett, B.D., Yuan, J., Kimball, E.H. & Rabinowitz, J.D. Absolute quantitation of intracellular metabolite concentrations by an isotope ratio-based approach. Nat. Protoc. 3, 1299–1311 (2008).

    Article  CAS  Google Scholar 

  43. 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).

    Article  CAS  Google Scholar 

  44. Kopka, J. et al. GMD@CSB.DB: the Golm metabolome database. Bioinformatics 21, 1635–1638 (2005).

    Article  CAS  Google Scholar 

  45. Tayrac, M., Lê, S., Aubry, M., Mosser, J. & Husson, F. Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: multiple factor analysis approach. BMC Genomics 10, 32 (2009).

    Article  Google Scholar 

  46. Mendes, P., Camacho, D. & de la Fuente, A. Modelling and simulation for metabolomics data analysis. Biochem. Soc. Trans. 33, 1427–1429 (2005).

    Article  CAS  Google Scholar 

  47. Jansen, J.J., Hoefsloot, H.C.J., Boelens, H.F.M., van der Greef, J. & Smilde, A.K. Analysis of longitudinal metabolomics data. Bioinformatics 20, 2438–2446 (2004).

    Article  CAS  Google Scholar 

  48. Bradford, M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).

    Article  CAS  Google Scholar 

  49. Devantier, R., Scheithauer, B., Villas-Bôas, S.G., Pedersen, S. & Olsson, L. Metabolite profiling for analysis of yeast stress response during very high gravity ethanol fermentations. Biotechnol. Bioeng. 90, 703–714 (2005).

    Article  CAS  Google Scholar 

  50. Moxley, J.F. et al. Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p. Proc. Natl Acad. Sci. USA 106, 6477–6482 (2009).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank the contribution of all people involved at different stages of the development of the methods summarized here, in particular, J. Nielsen, M. Åkesson, J.F. Moxley, P. Bruheim and G. Lane. We also thank the following institutions: Technical University of Denmark, The Norwegian University of Science and Technology, SINTEF Materials and Chemistry and AgResearch Limited.

Author information

Author notes

  1. Kathleen F Smart and Raphael B M Aggio: These authors contributed equally to the preparation of this protocol.

Authors and Affiliations

  1. School of Biological Sciences, The University of Auckland, Auckland, New Zealand

    Kathleen F Smart, Raphael B M Aggio, Jeremy R Van Houtte & Silas G Villas-Bôas

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Contributions

K.S. co-wrote the paper and optimized the protocol and videos; R.B.M.A. co-wrote the paper and generated the scripts for data mining, figures and videos; J.V.H. edited the MCF library and videos; and S.V.-B. supervised writing and developed the analytical platform.

Corresponding author

Correspondence to Silas G Villas-Bôas.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Data 1

MCFMS.CID file from MCF MS Library. Our in house library consists of two files: MCFMS.CID and MCFMS.MSL (supplemented material 2). Box 1 presents all steps and necessary instructions to load our in-house library in AMDIS. Download the files and follow the instruction in Box 1. (TXT 141 kb)

Supplementary Data 2

MCFMS.MSL file from MCF MS Library. Our in house library consists of two files: MCFMS.CID (supplemented material 1) and MCFMS.MSL. Box 1 presents all steps and necessary instructions to load our in-house library in AMDIS. Download the files and follow the instruction in Box 1. (TXT 141 kb)

Supplementary Data 3

Step 21(B) (i). Our in-house script consists of one R scrip file called ‘mining_script’ and all requirements and instructions to use it are described in the first rows of the script. (TXT 47 kb)

Supplementary Data 4

Step 21(B) (i). The reference ion library consists of one comma separated file (.csv) called ‘ref_ion_library.csv’. This file is complementary to the in-house script (mining_script) and both files have to be stored in the same folder. (CSV 15 kb)

Supplementary Video 1

Step 2 (A) (i), (ii). Quenching using cold glycerol-saline. Rapid transfer microbial culture suspension to a cold solution of glycerol-saline at −23°C. Note that the volume of sample transferred can be roughly controlled by level marks in the sampling flasks. This is not accurate and, therefore, the quantification of biomass in each sample must be carried out after extraction. (WMV 2559 kb)

Supplementary Video 2

Step 14 (A) (iii)-(v). MCF reaction after the samples have been resuspended in NaOH and mixed with methanol and pyridine. Although it is not clear in this video, the vortex agitation must be “vigorous”. (WMV 3788 kb)

Supplementary Video 3

Step 14 (A) (vii). Removal of aqueous phase. The top aqueous layer should be totally removed. (WMV 1043 kb)

Supplementary Video 4

Step 14 (A) (ix). End of derivatization. Sodium sulphate-dried chloroform solution containing the MCF derivatives is transferred to the GC-MS vial. Avoid transferring granules of sodium sulphate together with the sample. (WMV 2399 kb)

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Smart, K., Aggio, R., Van Houtte, J. et al. Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography–mass spectrometry. Nat Protoc 5, 1709–1729 (2010). https://doi.org/10.1038/nprot.2010.108

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