Bacterial mock communities as standards for reproducible cytometric microbiome analysis.


Journal

Nature protocols
ISSN: 1750-2799
Titre abrégé: Nat Protoc
Pays: England
ID NLM: 101284307

Informations de publication

Date de publication:
09 2020
Historique:
received: 09 01 2020
accepted: 27 05 2020
pubmed: 10 8 2020
medline: 21 10 2020
entrez: 10 8 2020
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 32770154
doi: 10.1038/s41596-020-0362-0
pii: 10.1038/s41596-020-0362-0
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2788-2812

Références

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).
doi: 10.1111/j.1574-6976.2010.00214.x pubmed: 20337722
Günther, S. et al. Species-sorting and mass-transfer paradigms control managed natural metacommunities. Environ. Microbiol. 18, 4862–4877 (2016).
doi: 10.1111/1462-2920.13402 pubmed: 27338005
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).
doi: 10.1111/2041-210X.12607
Liu, Z. et al. Ecological stability properties of microbial communities assessed by flow cytometry. mSphere 3, e00564–17 (2018).
doi: 10.1128/mSphere.00564-17 pubmed: 29359193 pmcid: 5770544
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).
doi: 10.1111/1462-2920.14437 pubmed: 30289191
De Vrieze, J., Boon, N. & Verstrate, W. Taking the technical microbiome into the next decade. Environ. Microbiol. 20, 1991–2000 (2018).
doi: 10.1111/1462-2920.14269 pubmed: 29745026
Koch, C. et al. Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function. Nat. Protoc. 8, 190–202 (2013).
doi: 10.1038/nprot.2012.149 pubmed: 23288319
Mage, L. M. et al. Shape-based separation of synthetic microparticles. Nat. Mater. 18, 82–89 (2019).
doi: 10.1038/s41563-018-0244-9 pubmed: 30542094
Müller, S. Modes of cytometric bacterial DNA pattern: a tool for pursuing growth. Cell Prolif. 40, 621–639 (2007).
doi: 10.1111/j.1365-2184.2007.00465.x pubmed: 17877606 pmcid: 6496216
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).
doi: 10.1186/s12859-019-3152-3
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).
doi: 10.1002/cyto.a.22286
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)
Aghaeepour, N. et al. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 228–238 (2013).
doi: 10.1038/nmeth.2365 pubmed: 23396282 pmcid: 3906045
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).
doi: 10.5858/2010-0387-RAR.1 pubmed: 21204710
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).
doi: 10.1126/science.1198704 pubmed: 21551058 pmcid: 3273988
Spitzer, H. M. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).
doi: 10.1016/j.cell.2016.04.019 pubmed: 27153492 pmcid: 4860251
Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annu. Rev. Microbiol. 8, 711–730 (2017).
doi: 10.1146/annurev-micro-090816-093449
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).
doi: 10.1038/s41586-019-1058-x pubmed: 30867587 pmcid: 6784871
Roesch, L. F. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).
doi: 10.1038/ismej.2007.53 pubmed: 18043639
Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).
doi: 10.1038/sdata.2016.81 pubmed: 27673566 pmcid: 5037974
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).
doi: 10.1038/s41598-018-24573-y pubmed: 29674641 pmcid: 5908915
Hardwick, S. A. et al. Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis. Nat. Commun. 9, 3096 (2018).
doi: 10.1038/s41467-018-05555-0 pubmed: 30082706 pmcid: 6078961
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).
doi: 10.1093/femsec/fiz045 pubmed: 30997495 pmcid: 6469980
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).
doi: 10.1128/mSphere.00163-19 pubmed: 31118299 pmcid: 6531881
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).
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).
doi: 10.1038/s41467-017-00128-z pubmed: 28729688 pmcid: 5519541
De Bruin, O. M. & Birnboim, H. C. A method for assessing efficiency of bacterial cell disruption and DNA release. BMC Microbiol. 16, 197 (2016).
doi: 10.1186/s12866-016-0815-3 pubmed: 27566276 pmcid: 5002184
Mie, G. Beiträge zur optik trüber medien, speziell kolloidaler metallösungen. Ann. Phys. 25, 377–445 (1908).
doi: 10.1002/andp.19083300302
Woyke, T., Doud, D. F. R. & Schulz, F. The trajectory of microbial single-cell sequencing. Nat. Methods 14, 1045–1054 (2017).
doi: 10.1038/nmeth.4469 pubmed: 29088131
Jahn, M. et al. Subpopulation-proteomics in prokaryotic populations. Curr. Opin. Biotech. 24, 79–87 (2013).
doi: 10.1016/j.copbio.2012.10.017 pubmed: 23153572
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).
doi: 10.1128/AEM.01541-09 pubmed: 19801464 pmcid: 2786419
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).
doi: 10.1093/bioinformatics/btr381 pubmed: 21700674 pmcid: 3150044
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Lane, D. J. in Nucleic Acid Techniques in Bacterial Systematics (eds. Stackebrandt, E. & Goodfellow, M.) 115–175 (Wiley, 1991).
Lambrecht, J. et al. Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F420 autofluorescence. Microb. Cell Fact. 16, 180 (2017).
doi: 10.1186/s12934-017-0793-7 pubmed: 29084543 pmcid: 5663091
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).
doi: 10.3389/fmicb.2014.00265 pubmed: 24917858 pmcid: 4040452
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).
doi: 10.1371/journal.pone.0105592 pubmed: 25144201 pmcid: 4140814
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).
doi: 10.1038/ismej.2011.41 pubmed: 21472016 pmcid: 3176514

Auteurs

Nicolas Cichocki (N)

Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.

Thomas Hübschmann (T)

Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.

Florian Schattenberg (F)

Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.

Frederiek-Maarten Kerckhof (FM)

Center for Microbial Ecology and Technology, Faculty of Bioscience Engineering, Gent University, Gent, Belgium.

Jörg Overmann (J)

Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany.
Institut for Microbiology, Braunschweig University of Technology, Braunschweig, Germany.

Susann Müller (S)

Department of Environmental Microbiology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany. susann.mueller@ufz.de.

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