Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Protocol Extension
  • Published:

Bacterial mock communities as standards for reproducible cytometric microbiome analysis

Abstract

Flow cytometry has recently established itself as a tool to track short-term dynamics in microbial community assembly and link those dynamics with ecological parameters. However, instrumental configurations of commercial cytometers and variability introduced through differential handling of the cells and instruments frequently cause data set variability at the single-cell level. This is especially pronounced with microorganisms, which are in the lower range of optical resolution. Although alignment beads are valuable to generally minimize instrumental noise and align overall machine settings, an artificial microbial cytometric mock community (mCMC) is mandatory for validating lab workflows and enabling comparison of data between experiments, thus representing a necessary reference standard for the reproducible cytometric characterization of microbial communities, especially in long-term studies. In this study, the mock community consisted of two Gram-positive and two Gram-negative bacterial strains, which can be assembled with respective subsets of cells, including spores, in any selected ratio or concentration. The preparation of the four strains takes a maximum of 5 d, and the stains are storable with either PFA/ethanol fixation at –20 °C or drying at 4 °C for at least 6 months. Starting from this stock, an mCMC can be assembled within 1 h. Fluorescence staining methods are presented and representatively applied with two high-resolution cell sorters and three benchtop flow cytometers. Benchmarked data sets allow the use of bioinformatic evaluation procedures to decode community behavior or convey qualified cell sorting decisions for subsequent high-resolution sequencing or proteomic routines.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Workflows for the preparation of the liquid, plate and dried mCMCs.
Fig. 2: Construction of the liquid mCMC and establishment of the gate template.
Fig. 3: Construction of the plate mCMC and establishment of the gate template.
Fig. 4: Dependence of the mCMC mock community patterns on instrumental setup and instrument type.
Fig. 5: Construction of the liquid mCMC and plate mCMC at varying proportions.
Fig. 6: Influence of changes in sample treatment on the position of cells in the gate template of the plate mCMC and of microbial communities harvested from activated sludge, digester fluid and centrate.

Similar content being viewed by others

Data availability

The cytometric data sets generated during the current study are available under FlowRepository: https://flowrepository.org/, no. FR-FCM-Z2CJ. The Illumina MiSeq data sets generated during the current study are available under BioProject: https://www.ncbi.nlm.nih.gov/bioproject, accession no. PRJNA541369.

Code and software availability

All links to the R scripts used in the current study are listed in the Equipment section.

R package flowCybar: http://bioconductor.org/packages/release/bioc/html/flowCyBar.html.

R package flowCHIC: http://www.bioconductor.org/packages/release/bioc/html/flowCHIC.html.

R script for the evaluation of the microscopic pictures (https://github.com/Allerdnec/InsightPro.git, developed by C. Lepleux).

References

  1. Müller, S. & Nebe-von-Caron, G. Functional single-cell analyses: flow cytometry and cell sorting of microbial populations and communities. FEMS Microbiol. Rev. 34, 554–587 (2010).

    Article  PubMed  Google Scholar 

  2. Günther, S. et al. Species-sorting and mass-transfer paradigms control managed natural metacommunities. Environ. Microbiol. 18, 4862–4877 (2016).

    Article  PubMed  Google Scholar 

  3. Props, R., Monsieurs, P., Mysara, M., Clement, L. & Boon, N. Measuring the biodiversity of microbial communities by flow cytometry. Methods Ecol. Evol. 7, 1376–1385 (2016).

    Article  Google Scholar 

  4. Liu, Z. et al. Ecological stability properties of microbial communities assessed by flow cytometry. mSphere 3, e00564–17 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Liu, Z. et al. Neutral mechanisms and niche differentiation in steady-state insular microbial communities revealed by single cell analysis. Environ. Microbiol. 21, 164–181 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. De Vrieze, J., Boon, N. & Verstrate, W. Taking the technical microbiome into the next decade. Environ. Microbiol. 20, 1991–2000 (2018).

    Article  PubMed  Google Scholar 

  7. Koch, C. et al. Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function. Nat. Protoc. 8, 190–202 (2013).

    Article  CAS  PubMed  Google Scholar 

  8. Mage, L. M. et al. Shape-based separation of synthetic microparticles. Nat. Mater. 18, 82–89 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Müller, S. Modes of cytometric bacterial DNA pattern: a tool for pursuing growth. Cell Prolif. 40, 621–639 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ludwig, J., Höner zu Siederdissen, C., Liu, Z., Stadler, P. F. & Müller, S. flowEMMi: an automated model-based clustering tool for microbial cytometric data. BMC Bioinforma. 20, 643 (2019).

    Article  CAS  Google Scholar 

  11. Koch, C., Fetzer, I., Harms, H. & Müller, S. CHIC-an automated approach for the detection of dynamic variations in complex microbial communities. Cytom. A 83, 561–567 (2013).

    Article  Google Scholar 

  12. Liu, Z. & Müller, S. Bacterial community diversity dynamics highlight degrees of nestedness and turnover patterns. Cytom. Part A https://onlinelibrary.wiley.com/doi/10.1002/cyto.a.23965 (2020)

  13. Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 228–238 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Peters, J. M. & Ansari, M. Q. Multiparameter flow cytometry in the diagnosis and management of acute leukemia. Arch. Pathol. Lab. Med. 135, 44–54 (2011).

    Article  PubMed  Google Scholar 

  15. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Spitzer, H. M. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annu. Rev. Microbiol. 8, 711–730 (2017).

    Article  Google Scholar 

  18. Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S. & Kyrpides, N. C. New insights from uncultivated genomes of the global human gut microbiome. Nature 568, 505–510 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Roesch, L. F. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).

    Article  CAS  PubMed  Google Scholar 

  20. Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Hallmaier-Wacker, L. K., Lueert, S., Roos, C. & Knauf, S. The impact of storage buffer, DNA extraction method, and polymerase on microbial analysis. Sci. Rep. 8, 6292 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hardwick, S. A. et al. Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis. Nat. Commun. 9, 3096 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Hornung, B. V. H., Zwittink, R. D. & Kuijper, E. J. Issues and current standards of controls in microbiome research. FEMS Microbiol. Ecol. 95, fiz045 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sze, M. A. & Schloss, P. D. The impact of DNA polymerase and number of rounds of amplification in PCR on 16S rRNA gene sequence data. mSphere 4, e00163–19 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Clingenpeel, S., Clum, A., Schwientel, P., Rinke, C. & Woyke, T. Reconstructing each cell’s genome within complex communities—dream or reality? Front. Microbiol. 8, 771 (2015).

    Google Scholar 

  26. Stepanauskas, R. et al. Improved genome recovery and intergrated cell-size analyses of individual uncultured microbial cells and viral particles. Nat. Commun. 8, 84 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  27. De Bruin, O. M. & Birnboim, H. C. A method for assessing efficiency of bacterial cell disruption and DNA release. BMC Microbiol. 16, 197 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Mie, G. Beiträge zur optik trüber medien, speziell kolloidaler metallösungen. Ann. Phys. 25, 377–445 (1908).

    Article  CAS  Google Scholar 

  29. Woyke, T., Doud, D. F. R. & Schulz, F. The trajectory of microbial single-cell sequencing. Nat. Methods 14, 1045–1054 (2017).

    Article  CAS  PubMed  Google Scholar 

  30. Jahn, M. et al. Subpopulation-proteomics in prokaryotic populations. Curr. Opin. Biotech. 24, 79–87 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

  34. Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds. Stackebrandt, E. & Goodfellow, M.) 115–175 (Wiley, 1991).

  35. Lambrecht, J. et al. Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F420 autofluorescence. Microb. Cell Fact. 16, 180 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Besmer, M. D. et al. The feasibility of automated online flow cytometry for in-situ monitoring of microbial dynamics in aquatic ecosystems. Front. Microbiol 5, 265 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of bacteria and archaea using next-generation sequencing. PLoS ONE 9, e105592 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was funded by the Central Innovation Programme for SMEs (ZIM) of the Federal Ministry of Economic Affairs and Energy Germany (BMWi; INAR-ABOS,16KN043222), the European Regional Development Funds (EFRE—Europe Funds Saxony, grant 100192205) and the Helmholtz Association within RP Renewable Energies. We thank C. Lepleux for developing the script for evaluation of the microscopic data (https://github.com/Allerdnec/InsightPro.git) and Z. Liu for creating Fig. 6 and Supplementary Methods 3.

Author information

Authors and Affiliations

Authors

Contributions

N.C., T.H. and F.S. performed the experiments and prepared the samples. N.C., T.H., F.-M.K. and F.S. measured the samples. N.C. and T.H. performed the data analysis. N.C., F.-M.K., J.O. and S.M. wrote the manuscript. T.H. and S.M. conceived the ideas and designed the study. S.M. supervised the work. All authors contributed critically to the drafts and gave final approval for publication.

Corresponding author

Correspondence to Susann Müller.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Related links

Key references using this protocol

Ludwig, J., Höner zu Siederdissen, C., Liu Z, Stadler, P.F. & Müller, S. BMC Bioinformatics 20, 643 (2019): https://doi.org/10.1186/s12859-019-3152-3

Liu, Z. & Müller, S. Cytom. Part A (2020): https://doi.org/10.1002/cyto.a.23965

Protocol Extension

This protocol is an extension to Nat. Protoc. 8, 190–202 (2013): https://doi.org/10.1038/nprot.2012.149

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, Supplementary Methods 1–3 and Supplementary Tables 1–6.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cichocki, N., Hübschmann, T., Schattenberg, F. et al. Bacterial mock communities as standards for reproducible cytometric microbiome analysis. Nat Protoc 15, 2788–2812 (2020). https://doi.org/10.1038/s41596-020-0362-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-020-0362-0

This article is cited by

Comments

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.

Search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

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