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

Journal name:
Nature Protocols
Volume:
5,
Pages:
1709–1729
Year published:
DOI:
doi:10.1038/nprot.2010.108
Published online

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).

At a glance

Figures

  1. Flowchart for the analytical platform of metabolome analysis of microbial cells using GC-MS.
    Figure 1: Flowchart for the analytical platform of metabolome analysis of microbial cells using GC-MS.

    This figure summarizes the main steps in preparing samples (for both intra- and extracellular metabolites) before GC-MS analysis. Technical details for each step are described in the protocol procedure.

  2. General mechanism of the MCF reaction.
    Figure 2: General mechanism of the MCF reaction.

    The MCF alkylation reaction converts amino and nonamino organic acids into volatile esters and carbamates, allowing them to be analyzed by GC-MS.

  3. Flowchart describing the AMDIS peak intensity correction.
    Figure 3: Flowchart describing the AMDIS peak intensity correction.

    The process is based on the AMDIS report, R software, R-XCMS package and the in-house reference ion library. The script and reference ion library we developed are available as Supplementary Data 3 and 4, respectively.

  4. Example of the recommended data structure.
    Figure 4: Example of the recommended data structure.

    (a) Although it is common sense, we strongly recommend organizing and structuring raw GC-MS data by creating a main folder for each experimental condition, and then distributing the respective sample folders copied from the GC-MS computer system into each main folder. This way, it becomes easier to track all the data generated from each study. (b) In addition, we also recommend creating an extra folder to store the reports generated with AMDIS, as described in Step 18.

  5. PCA for checking the quality of GC-MS raw data as discussed in Step 16.
    Figure 5: PCA for checking the quality of GC-MS raw data as discussed in Step 16.

    This PCA graph shows samples of intracellular metabolites from bacterial cells quenched using cold glycerol-saline. One sample clustered far apart from its replicates. This is likely due to technical problems during analysis; therefore, this sample can be eliminated from the data analysis. Note that if the reproducibility between replicates is poor, it is difficult to eliminate technically discrepant samples.

  6. Generating a report in AMDIS (Step 20A).
    Figure 6: Generating a report in AMDIS (Step 20A).

    In the main screen, click 'FILE' right arrow 'Generate Report...'. A new window called 'Generate Report' will open, and the destination folder and file name should be inserted in the field 'Report file:'. Then click 'Generate' and a flat (.txt) file will be generated in the defined folder.

  7. Analyzing and generating a report in AMDIS in batch mode (Step 20B).
    Figure 7: Analyzing and generating a report in AMDIS in batch mode (Step 20B).

    (a) In the main screen, click 'FILE' right arrow 'Batch Job' right arrow 'Create and Run Job...'. A new window called 'Build Job File:' will open. (b) Click 'Create/Open' and select the folder in which the job file should be saved. Note that this folder will also be the destination of the future AMDIS report. While still in the window 'Build Job File:', click 'Add...' and select the samples to be analyzed. Finally, activate the option 'Generate report', choose the analysis type 'Simple' and press 'Run'. After all samples are analyzed, a unique flat file (.txt) will be generated in the same folder in which the job file was saved.

  8. Manual cleaning process of AMDIS report as referred to in Step 21A(vii).
    Figure 8: Manual cleaning process of AMDIS report as referred to in Step 21A(vii).

    RT should be used as the main reference; after sorting data by RT, follow this flowchart. The process ends when there is just one compound detected for each RT.

  9. Combined spreadsheet containing compounds identified by AMDIS and their intensities in different samples (Step 21A(xv)).
    Figure 9: Combined spreadsheet containing compounds identified by AMDIS and their intensities in different samples (Step 21A(xv)).

    In a new data sheet, combine all AMDIS reports using one column for the name of all compounds detected and one column for the abundance or intensity detected in each experimental condition.

  10. Opening a script in R as discussed in Step 21B(iii).
    Figure 10: Opening a script in R as discussed in Step 21B(iii).

    With the script file available as Supplementary Data 3 downloaded, click 'File' and 'Open script' in the R main screen. A new window called 'Open script' will open; select the script file and click on 'Open'.

  11. Procedure timeline.
    Figure 11: Procedure timeline.

    Estimated timing for a standard procedure of intracellular and extracellular metabolite analysis; timing is estimated on the basis of 45 intracellular and 27 extracellular samples.

  12. GC-MS chromatograms of MCF derivatives.
    Figure 12: GC-MS chromatograms of MCF derivatives.

    (a) Chromatogram of a sample of extracellular metabolites produced by a filamentous fungus grown on a complex culture medium. (b) Chromatogram of a sample of extracellular metabolites produced by a strain of Pseudomonas aeruginosa grown on a minimal mineral culture medium. (c) Chromatogram of a sample of intracellular metabolites extracted from filamentous fungus biomass quenched by fast filtration and cold methanol-water. (d) Chromatogram of a sample of intracellular metabolites extracted from a bacterium quenched by cold glycerol-saline solution. (e) Chromatogram of a sample of intracellular metabolites extracted from Lactobacillus sp. cells (low concentration of biomass per sample) quenched by cold glycerol-saline solution. (f) Chromatograph of a sample that has not been analyzed properly by GC-MS because of injection problems or a bad column.

  13. GeneSpring-generated mass tree.
    Figure 13: GeneSpring-generated mass tree.

    This mass tree represents statistically significant mass fragments obtained by GC-MS analysis of intracellular metabolite samples of four different organisms grown under the same environmental condition and quenched using cold glycerol-saline. Each color represents a data class (organism), in which Conditions 1, 2, 3 and 4 represent micro-organisms A, B, C and D, respectively. Each column represents an individual sample and each row a mass fragment ion generated by GC-MS. In the rows, dark color indicates high abundance of a specific mass fragment and light color the absence of a specific mass fragment. Note that samples from the same data class (organism) cluster together and that the mass fragments characterizing that class are highlighted in black. Therefore, by simply analyzing the GC-MS raw data, it is possible to establish preliminary information on data trends and patterns that characterize each data class.

  14. Three-dimensional projection of samples from PCA of four different organisms.
    Figure 14: Three-dimensional projection of samples from PCA of four different organisms.

    Organisms were grown under the same environmental conditions based on mass fragment profile. Each color represents a data class (organism) in which Conditions 1, 2, 3 and 4 represent micro-organisms A, B, C and D, respectively. The majority of technical replicates (dots) cluster close to each other and clearly distinguish the different data classes (organism). This result emphasizes the reproducibility of this protocol. However, it is essential to ensure rapid quenching of the cell metabolism and to maintain samples at low temperatures at all time periods.

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Author information

  1. These authors contributed equally to the preparation of this protocol.

    • Kathleen F Smart &
    • Raphael B M Aggio

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

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

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Author details

Supplementary information

Text files

  1. Supplementary Data 1 (13K)

    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.

  2. Supplementary Data 2 (141K)

    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.

  3. Supplementary Data 3 (47K)

    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.

Other

  1. Supplementary Data 4 (15K)

    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.

Movies

  1. Supplementary Video 1 (3M)

    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.

  2. Supplementary Video 2 (4M)

    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”.

  3. Supplementary Video 3 (10M)

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

  4. Supplementary Video 4 (2M)

    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.

Additional data