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Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function

Nature Protocols volume 8, pages 190–202 (2013)Cite this article

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Abstract

Functions of complex natural microbial communities are realized by single cells that contribute differently to the overall performance of a community. Usually, molecular and, more recently, deep-sequencing techniques are used for detailed but resource-consuming phylogenetic or functional analyses of microbial communities. Here we present a method for analyzing dynamic community structures that rapidly detects functional (rather than phylogenetic) coherent subcommunities by monitoring changes in cell-specific and abiotic microenvironmental parameters. The protocol involves the use of flow cytometry to analyze elastic light scattering and fluorescent cell labeling, with subsequent determination of cell gate abundance and finally the creation of a cytometric community fingerprint. Abiotic parameter analysis data are correlated with the dynamic cytometric fingerprint to obtain a time-bound functional heat map. The map facilitates the identification of activity hot spots in communities, which can be further resolved by subsequent cell sorting of key subcommunities and concurrent phylogenetic analysis (terminal restriction fragment length polymorphism, tRFLP). The cytometric fingerprint information is based on gate template settings and the functional heat maps are created using an R script. Cytometric fingerprinting and evaluation can be accomplished in 1 d, and additional subcommunity composition information can be obtained in a further 6 d.

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Figure 1: Workflow for identifying dynamics in community structure and correlations in abundances of subsets of cells because of changes in abiotic parameter quantities.
Figure 2: Exemplary cytometric patterns after DAPI staining.
Figure 3: Gate template example.
Figure 4: Similarity analysis.
Figure 5: Gate cell number variation for ten measurements determined using cytometric fingerprinting.
Figure 6: Recognizing pitfalls in bar code interpretation.
Figure 7: Functional correlation analysis.
Figure 8: Cell sorting and tRFLP analysis of selected subcommunities.

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Acknowledgements

We thank C. Süring, A. Schwarzer, D. Schlosser and G. Weichert for technical assistance. We thank I. Fetzer for helpful discussions regarding R. This work is integrated in the internal research and development program of the UFZ and the CITE (Chemicals In The Environment) program.

Author information

Authors and Affiliations

  1. Department of Bioenergy, Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany

    Christin Koch

  2. Department of Environmental Microbiology, UFZ, Leipzig, Germany

    Susanne Günther, Thomas Hübschmann & Susann Müller

  3. Department of Biology, Addis Ababa University, Addis Ababa, Ethiopia

    Adey F Desta

Authors
  1. Christin Koch
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  2. Susanne Günther
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  3. Adey F Desta
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  4. Thomas Hübschmann
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  5. Susann Müller
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Contributions

C.K., S.G. and A.F.D. performed the experiments with contributions of T.H.; C.K. and S.G. developed the bar code and correlation tools; C.K., S.G., T.H. and S.M. designed and performed the research, and interpreted the data; C.K. and S.M. wrote the manuscript.

Corresponding author

Correspondence to Susann Müller.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Figure 1

Effects of duration of DAPI staining. The duration of the staining period has a profound impact on cytometric community analyses. The use of low DAPI concentrations and appropriate incubation times allows for very specific and reproducible DNA staining. The increasing abundance of cells with enhanced DAPI fluorescence after 30 and 150 minutes is shown. The pattern generated after 150 min incubation remained stable for several hours. (PDF 2480 kb)

Supplementary Table 1

Method reproducibility. The error introduced during sample preparation and measurement was tested. Two samples from a natural community were taken, treated identically and stained for DNA analysis with DAPI. For each sample three cytometric measurements were performed. A gate template with four representative gates covering the most dominant sub-communities was applied to all measurements and the number of DAPI stained cells determined (values given in % of all stained cells). It was found that the methodical variation of the cytometric measurement itself introduces a variation between 0.82 and 3.26 %. The sample preparation bias causes only variation up to 2.23 %. From these results it can be stated that a variation found above 3.26 % was due to a real variation in the community structure. Community variations below this value were covered by methodical variations. (PDF 262 kb)

Supplementary Table 2

Original data for batches 1 to 10 of SBR cultivation of a microbial community obtained from Ethiopian tannery wastewater. The table summarizes all cytometric and abiotic data that were used within the ANTICIPATED RESULTS section. (PDF 271 kb)

Supplementary Methods

Experimental setup of the SBR. The general setup of the SBR is presented including details about the determination of abiotic parameters and the tanning agents. (PDF 377 kb)

Supplementary Data 1

barcode.r This R-script can be used to perform step 12 of the procedure. It creates a heat map that displays the normalized cell abundance values for each gate using a color code with 21 gradations (blue, white, red). A box plot visualizes the actual cell number per gate as percentage of all measured cells. (TXT 2 kb)

Supplementary Data 2

nmds.r This R-script can be used to perform step 13 of the procedure. It visualizes the distance between samples based on their cytometrically measured cell abundance information using nonmetric multidimensional scaling (NMDS). (TXT 1 kb)

Supplementary Data 3

correlation.r This R-script can be used to perform step 15 of the procedure. It determines relationships between biotic and abiotic parameters based on Spearman's rank-order correlation coefficient and visualizes them as heat map with positive correlations in red, negative correlations in blue and neutral correlations in white. (TXT 2 kb)

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Koch, C., Günther, S., Desta, A. et al. Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function. Nat Protoc 8, 190–202 (2013). https://doi.org/10.1038/nprot.2012.149

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