Name: characterizing microbiome dynamics &#8211; flow cytometry based workflows from pure cultures to natural communities
Uploaded: 2018-07-12T15:00:00
Duration: 9 min 57 s
Description: Watch this Scientific Journal Video about Characterizing Microbiome Dynamics Ã¢â‚¬â€œ Flow Cytometry Based Workflows from Pure Cultures to Natural Communities at JoVE.com

We gratefully acknowledge Michael Jahn and Yuting Guo for providing the procedure and datasets of the pure culture and the activated sludge community, respectively. We further thank Katrin M&#246;rters for analyzing the biogas community samples. This work was funded by the Fachagentur Nachwachsende Rohstoffe e. V. (FNR, Project Biogas-Fingerprint Nr. 22008313) on behalf of the German Federal Ministry for Food and Agriculture (BMEL), the Central Innovation Programme for SMEs (ZIM) of the federal ministry of economic affairs and energy (BMWi) (INAR-ABOS, 16KN043222), the Deutsche Bundesstiftung Umwelt (DBU, Project On-demand Produktion von Phosphatd&#252;nger aus Reststoffen von Brauerei und Kl&#228;ranlage 33960/01-32), the German Federal Ministry for Education and Research (BMBF) (FZK 03XP0041G), and the Helmholtz Association&#39;s Program-oriented funding (POF III R31 topic 3 Bioenergy).

<ol>
	<li>Xu, X., Hui, D., King, A. W., Song, X., Thornton, P. E., Zhang, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convergence+of+microbial+assimilations+of+soil+carbon,+nitrogen,+phosphorus,+and+sulfur+in+terrestrial+ecosystems.">Convergence of microbial assimilations of soil carbon, nitrogen, phosphorus, and sulfur in terrestrial ecosystems.</a> Scientific Reports. 5 (1), 17445 (2015).</li><li>Fathepure, B. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recent+studies+in+microbial+degradation+of+petroleum+hydrocarbons+in+hypersaline+environments.">Recent studies in microbial degradation of petroleum hydrocarbons in hypersaline environments.</a> Frontiers in Microbiology. 5, 173 (2014).</li><li>Alshehrei, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biodegradation+of+Synthetic+and+Natural+Plastic+by+Microorganisms.">Biodegradation of Synthetic and Natural Plastic by Microorganisms.</a> Journal of Applied &amp; Environmental Microbiology. 5 (1), 8-19 (2017).</li><li>Sarria, S., Kruyer, N. S., Peralta-Yahya, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microbial+Synthesis+of+medium-chain+chemicals+from+renewables.">Microbial Synthesis of medium-chain chemicals from renewables.</a> Nature Biotechnology. 35 (12), 1158-1166 (2017).</li><li>G&#252;nther, S., Faust, K., Schumann, J., Harms, H., Raes, J., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Species-sorting+and+mass-transfer+paradigms+control+managed+natural+metacommunities:+Species-sorting+and+mass-transfer+paradigms+in+metacommunities.">Species-sorting and mass-transfer paradigms control managed natural metacommunities: Species-sorting and mass-transfer paradigms in metacommunities.</a> Environmental Microbiology. 18 (12), 4862-4877 (2016).</li><li>Lambrecht, J., Cichocki, N., H&#252;bschmann, T., Koch, C., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometric+quantification,+sorting+and+sequencing+of+methanogenic+archaea+based+on+F420+autofluorescence.">Flow cytometric quantification, sorting and sequencing of methanogenic archaea based on F420 autofluorescence.</a> Microbial Cell Factories. 16 (1), 180 (2017).</li><li>Huttenhower, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure,+function+and+diversity+of+the+healthy+human+microbiome.">Structure, function and diversity of the healthy human microbiome.</a> Nature. 486 (7402), 207-214 (2012).</li><li>Lloyd-Price, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Strains,+functions+and+dynamics+in+the+expanded+Human+Microbiome+Project.">Strains, functions and dynamics in the expanded Human Microbiome Project.</a> Nature. 550, 61-66 (2017).</li><li>Goodwin, S., McPherson, J. D., McCombie, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coming+of+age:+ten+years+of+next-generation+sequencing+technologies.">Coming of age: ten years of next-generation sequencing technologies.</a> Nature Reviews Genetics. 17 (6), 333-351 (2016).</li><li>Liu, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ecological+Stability+Properties+of+Microbial+Communities+Assessed+by+Flow+Cytometry.">Ecological Stability Properties of Microbial Communities Assessed by Flow Cytometry.</a> mSphere. 3 (1), e00564-e00517 (2018).</li><li>Koch, C., G&#252;nther, S., Desta, A. F., H&#252;bschmann, T., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytometric+fingerprinting+for+analyzing+microbial+intracommunity+structure+variation+and+identifying+subcommunity+function.">Cytometric fingerprinting for analyzing microbial intracommunity structure variation and identifying subcommunity function.</a> Nature Protocols. 8 (1), 190-202 (2013).</li><li>Koch, C., Fetzer, I., Schmidt, T., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monitoring+Functions+in+Managed+Microbial+Systems+by+Cytometric+Bar+Coding.">Monitoring Functions in Managed Microbial Systems by Cytometric Bar Coding.</a> Environmental Science &amp; Technology. 47 (3), 1753-1760 (2013).</li><li>Lefort, T., Gasol, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Short-time+scale+coupling+of+picoplankton+community+structure+and+single-cell+heterotrophic+activity+in+winter+in+coastal+NW+Mediterranean+Sea+waters.">Short-time scale coupling of picoplankton community structure and single-cell heterotrophic activity in winter in coastal NW Mediterranean Sea waters.</a> Journal of Plankton Research. 36 (1), 243-258 (2014).</li><li>Besmer, M. D., Epting, J., Page, R. M., Sigrist, J. A., Huggenberger, P., Hammes, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Online+flow+cytometry+reveals+microbial+dynamics+influenced+by+concurrent+natural+and+operational+events+in+groundwater+used+for+drinking+water+treatment.">Online flow cytometry reveals microbial dynamics influenced by concurrent natural and operational events in groundwater used for drinking water treatment.</a> Scientific Reports. 6, 38462 (2016).</li><li>van Gelder, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+cytometric+approach+to+follow+variation+and+dynamics+of+the+salivary+microbiota.">A cytometric approach to follow variation and dynamics of the salivary microbiota.</a> Methods. 134, 67-79 (2018).</li><li>Jehmlich, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advanced+tool+for+characterization+of+microbial+cultures+by+combining+cytomics+and+proteomics.">Advanced tool for characterization of microbial cultures by combining cytomics and proteomics.</a> Applied Microbiology and Biotechnology. 88 (2), 575-584 (2010).</li><li>Jahn, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accurate+Determination+of+Plasmid+Copy+Number+of+Flow-Sorted+Cells+using+Droplet+Digital+PCR.">Accurate Determination of Plasmid Copy Number of Flow-Sorted Cells using Droplet Digital PCR.</a> Analytical Chemistry. 86 (12), 5969-5976 (2014).</li><li>Koch, C., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Personalized+microbiome+dynamics+-+Cytometric+fingerprints+for+routine+diagnostics.">Personalized microbiome dynamics - Cytometric fingerprints for routine diagnostics.</a> Molecular Aspects of Medicine. 59, 123-134 (2018).</li><li>Jahn, M., Vorpahl, C., H&#252;bschmann, T., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Copy+number+variability+of+expression+plasmids+determined+by+cell+sorting+and+Droplet+Digital+PCR.">Copy number variability of expression plasmids determined by cell sorting and Droplet Digital PCR.</a> Microbial Cell Factories. 15 (1), 211 (2016).</li><li>Koch, C., Fetzer, I., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=CHIC-an+automated+approach+for+the+detection+of+dynamic+variations+in+complex+microbial+communities.">CHIC-an automated approach for the detection of dynamic variations in complex microbial communities.</a> Cytometry Part A. 83 (6), 561-567 (2013).</li><li>G&#252;nther, S., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Facilitated+gate+setting+by+sequential+dot+plot+scanning:+Facilitated+Gate+Setting+by+Sequential+Dot+Plot+Scanning.">Facilitated gate setting by sequential dot plot scanning: Facilitated Gate Setting by Sequential Dot Plot Scanning.</a> Cytometry Part A. 87 (7), 661-664 (2015).</li><li>Guo, Y., Baumgart, S., St&#228;rk, H. -. J., Harms, H., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+Cytometry+for+Detection+of+Silver+at+the+Bacterial+Single+Cell+Level.">Mass Cytometry for Detection of Silver at the Bacterial Single Cell Level.</a> Frontiers in Microbiology. 8, 1326 (2017).</li><li>Gasol, J. M., Del Giorgio, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+flow+cytometry+for+counting+natural+planktonic+bacteria+and+understanding+the+structure+of+planktonic+bacterial+communities.">Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities.</a> Scientia Marina. 64 (2), 197-224 (2000).</li><li>Gasol, J. M., Mor&#225;n, X. A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+Cytometric+Determination+of+Microbial+Abundances+and+Its+Use+to+Obtain+Indices+of+Community+Structure+and+Relative+Activity.">Flow Cytometric Determination of Microbial Abundances and Its Use to Obtain Indices of Community Structure and Relative Activity.</a> Hydrocarbon and Lipid Microbiology Protocols. , 159-187 (2016).</li><li>Props, R., Monsieurs, P., Mysara, M., Clement, L., Boon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+the+biodiversity+of+microbial+communities+by+flow+cytometry.">Measuring the biodiversity of microbial communities by flow cytometry.</a> Methods in Ecology and Evolution. 7 (11), 1376-1385 (2016).</li><li>Kinet, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometry+community+fingerprinting+and+amplicon+sequencing+for+the+assessment+of+landfill+leachate+cellulolytic+bioaugmentation.">Flow cytometry community fingerprinting and amplicon sequencing for the assessment of landfill leachate cellulolytic bioaugmentation.</a> Bioresource Technology. 214, 450-459 (2016).</li><li>De Roy, K., Clement, L., Thas, O., Wang, Y., Boon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometry+for+fast+microbial+community+fingerprinting.">Flow cytometry for fast microbial community fingerprinting.</a> Water Research. 46 (3), 907-919 (2012).</li><li>Koch, C., Harnisch, F., Schr&#246;der, U., M&#252;ller, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytometric+fingerprints:+evaluation+of+new+tools+for+analyzing+microbial+community+dynamics.">Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics.</a> Frontiers in Microbiology. 5, 273 (2014).</li><li>Amalfitano, S., Fazi, S., Ejarque, E., Freixa, A., Roman&#237;, A. M., Butturini, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deconvolution+model+to+resolve+cytometric+microbial+community+patterns+in+flowing+waters:+Deconvolving+Cytometric+Microbial+Subgroups.">Deconvolution model to resolve cytometric microbial community patterns in flowing waters: Deconvolving Cytometric Microbial Subgroups.</a> Cytometry Part A. 93 (2), 194-200 (2017).</li><li>Buysschaert, B., Kerckhof, F. -. M., Vandamme, P., De Baets, B., Boon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometric+fingerprinting+for+microbial+strain+discrimination+and+physiological+characterization:+Flow+Cytometric+Fingerprinting.">Flow cytometric fingerprinting for microbial strain discrimination and physiological characterization: Flow Cytometric Fingerprinting.</a> Cytometry Part A. 93 (2), 201-212 (2017).</li><li>Besmer, M. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laboratory-Scale+Simulation+and+Real-Time+Tracking+of+a+Microbial+Contamination+Event+and+Subsequent+Shock-Chlorination+in+Drinking+Water.">Laboratory-Scale Simulation and Real-Time Tracking of a Microbial Contamination Event and Subsequent Shock-Chlorination in Drinking Water.</a> Frontiers in Microbiology. 8 (1900), (2017).</li><li>Zimmermann, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-resolution+microbiota+flow+cytometry+reveals+dynamic+colitis-associated+changes+in+fecal+bacterial+composition:+Technical+comment.">High-resolution microbiota flow cytometry reveals dynamic colitis-associated changes in fecal bacterial composition: Technical comment.</a> European Journal of Immunology. 46 (5), 1300-1303 (2016).</li></ol>

The authors have nothing to disclose. 
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A successful analysis of microbial populations and communities requires well-tuned cytometers, an appropriate choice of cell parameters and a reliable workflow from sampling and fixation towards the measurement and data evaluation. The selected cell parameters must consort with the available excitation wavelengths. We use and recommend DAPI, which is very sensitive at low concentrations; but needs to be excited by a UV-laser, which is usually not comprised in standard cytometer set-ups. Other dyes, such as SYBR Green I, also stain whole populations or communities but generally deliver inferior resolutions. We do not recommend using FISH procedures or viability tests in microbial communities. These approaches are impossible to quantify, verify and control because their effects on individual species in the community is uncertain. They cannot be reliably tested, as long as a substantial proportion of the typical communities is still not available as a pure culture.
Critical steps in the protocol include sampling and fixation procedures as well as cell sorting. The sampling can be complicated by the tendency of certain pure cultures and microbial communities to aggregate to flocs or adhere to particles of the sample matrix. It is essential to dissipate these aggregates and separate the cells in a sample before introducing them to flow cytometric analysis to guarantee reliable results. The presented preparation protocols were optimized to enable single cell analysis. A final 50 &#181;m filtration is performed before the measurement to remove any residual aggregates and prevent the 70 &#181;m nozzle of the cell sorter and the flow cuvette of the analyzer from clogging. Cell flocs from wastewater treatment plants are especially abundant, robust and vital to the function of the system. We investigated the planktonic and sludge based microbial communities in different tanks of a full-scale wastewater treatment plant, to test the established protocol. The sludge forming cell aggregates were clearly dissipated and the community composition remained stable. Furthermore, planktonic and sludge-based communities exhibited remarkable similarity between them in all three sampled tanks (Figure 8, Supplementary File 1-S10). These results were verified by comparative 16S rDNA amplicon sequencing of a fresh-, a formaldehyde treated-, a formaldehyde and ethanol fixated-, and a sorted sample10. Nevertheless, extraordinarily challenging environmental samples, such as strong biofilms or bacteria growing in tightly connected chains, can be impossible to dissipate. Soil samples can be particularly problematic, as the ubiquitous particles appear in the histogram and can obscure the cells by sheer abundance. In such cases, traditional sequencing methods need to be applied.
The fixation stability of every new sample set should be tested before designing the experiment to guarantee result validity. Good fixation stability also enables the pooled staining and measurement of multiple sample time points on a single day. It furthermore enables replication measurements and retrospective cell sorting of decisive time points after the conclusion and final evaluation of the experiment. The fixation stability of the pure culture was verified over 28 days (Figure 9). For on-site experiments including sample transport, the fixation stability should be tested over longer periods (in this case, 60 days for the activated sludge community, 195 days for the biogas community, Supplementary File 1-S9).
The staining is usually not problematic but needs to be controlled with a biological standard to allow application of the bioinformatic evaluation tools. We used the mock strain E. coli BL21 (DE3), which was fixated according to the ASC protocol and stockpiled for use in every staining batch.
Apart from DAPI, SYBR Green I has been successfully applied to resolve microbial communities for several years14,23,24,25,26. SYBR Green I can resolve communities into high- and low nucleic acid subsets (HNA and LNA) using live cells in an online modus. DAPI, however, is more specific in its binding to DNA and enables the distinction of over 50 subcommunities in one sample set but requires a fixation step prior to staining.
Cell sorting is an outstanding feature of this approach and can be applied if certain subcommunities are of further interest. It needs to be emphasized that neither PFA- (performed for only 30 min) nor ethanol treatment or DAPI staining10 had a negative effect on the subsequent 16S amplicon sequencing. Potential influences of the fixation and staining procedures on subcommunity metagenome analyses still need to be tested.
A lot of information on community functions and trend dynamics can be obtained independent from the sorting approach when involving bioinformatics tools that resolve community features on a virtual cell by cell level and connect these features with abiotic parameters.
Recently, a number of new bioinformatics evaluation tools, all useful for both the SYBR Green I and the DAPI approaches, have been developed to resolve cytometric community patterns. These are FlowFP27, the packages used in this study (flowCHIC20, flowCyBar28), a deconvolution model established with fresh water communities29 and a tool used to discriminate strains according to their physiological characteristics30. Additionally, cytometric diversity can be determined25 and even stability properties of microbial communities can now be followed with an online tool10.
Thus, flow cytometric analyses have a huge advantage when it comes to rapid evaluation of microbial communities and possible abiotic parameter correlations. However, there are still limitations to the method. It cannot be applied truly online with the flowCyBar tool, due to the experienced based gate setting procedure. Furthermore, the development of custom-made, easy to use flow cytometers would greatly advance the proliferation of the flow cytometric microbiome analysis approach. First steps in this direction are undertaken28.
Future applications of microbial flow cytometry can be envisioned in microbial ecology, as it allows high frequency monitoring within the range of bacterial generation times and it has been shown that ecological paradigms are applicable to cytometric data5,10. The method is very useful for routine screenings of natural environments like the mandatory drinking water controls in Switzerland31. It can also be a valuable tool for medical applications like human15 or animal32 microbiome screening. In addition, latest developments in bioinformatics may enable microbial flow cytometry to be an integral sensor in the process controls of managed microbial systems. Microbial community cytometry also provides a screening tool for cell sorting, which allows higher resolution genomic investigations of specific community subsets.

Microorganisms play a vital role in many aspects of human live. They are the main biotic drivers of the planets carbon, nitrogen, phosphorus and sulfur cycles1, and act as degrading2,3 as well as synthesizing4 biocatalysts in various industries, such as wastewater treatment5 or biotechnology6. They even constitute the human microbiome, which is directly influencing human health and metabolism7,8. Therefore, the information on structure, function and dynamic behavior of microorganisms, in response to their immediate environment, is necessary if we aim to fully understand and manipulate these systems. Next generation sequencing (NGS) is the established technology for resolving microbiome structure and function9. However, the analysis and evaluation of NGS data cannot yield quantitative information, and is still costly, time-consuming and by no means ready to provide on-site results within performance corridors. Some bacteria display generation times below 1 h and cause constantly changing community structures in certain environments10. Following such dynamics, using sequencing approaches would exceed the financial and workforce capacity of most scientific labs.
In contrast, flow cytometry can provide community fingerprints similar to NGS data, while retaining a higher time-, labor- and cost efficiency. Here, we present flow cytometric techniques able to follow microbial community dynamics at-line on the single cell level. Unlike NGS, flow cytometry does not provide phylogenetic affiliation or information on functional genes, but delivers quantitative cell numbers. Using flow cytometry, microbial communities can be resolved into subcommunities with distinct light scatter and fluorescence properties (forward scatter (FSC) and side scatter (SSC) provide indications of cell size and granularity, respectively). The presented approach utilizes predominantly cell size- and DNA amount related information that are associated to certain cell types and physiological (growth) states. The DNA content is quantified using the UV-excitable 4&#39;,6-diamidino-2&#39;-phenylindole (DAPI) dye, which binds to A-T rich regions of the DNA and can even resolve differing chromosomal levels. By combining DAPI fluorescence and FSC more than 50 subcommunities can be distinguished and monitored for changing abundances over time11. The subcommunity abundance variations can be correlated with changes in the micro-environmental surroundings, such as pH and product titers12, with global parameters, such as the weather13,14, or in case of gut or saliva microbiomes with certain medical treatments15. These correlations reveal key subcommunities responsible for particular functions in the metabolic network of the whole community. The key subcommunities can then be specifically promoted or suppressed by altering the surrounding micro-environments, or sorted for subsequent sequencing15 or proteomic investigation16.
However, microbial community flow cytometry is not yet widely established due to a rather low number of flow cytometric devices capable of resolving microbial populations or communities properly. Furthermore, the lack of experience, community analysis pipelines and effective data evaluation may pose an entry barrier for researchers facing microbiome analysis challenges. We have established comprehensive workflows to tackle these issues. To illustrate their general applicability, we will present them on three exemplary sample sets (Supplementary File 1-S1), namely i) a pure culture (PC) for biotechnological applications grown in defined clear medium (Pseudomonas putida KT 2440 on glucose), ii) a complex lab community in clear medium (activated sludge community (ASC) in synthetic wastewater) and iii) a complex community from natural environments in a dense matrix (biogas community (BC) in maize silage).
Several factors influence the protocol choice for each of these sample sets. Deep freezing forgoes the use of toxic chemicals but is mostly limited to lab environments due to the use of liquid nitrogen for shock frosting. The formaldehyde stabilization and subsequent ethanol fixation allows bulk sampling without the need for aliquot preparation and has proven to be stable over long periods. However, protein-rich samples such as human saliva15 may be problematic, because protein flocs, denaturized by the formaldehyde, may cause unfavorable signal to noise ratios. The sample drying will take the most time (approximately 1 h in total) of the procedures in this comparison, but can be performed on site without toxic chemicals. It yields stable pellets in tubes, which can be easily shipped without cooling or additional hazard precautions. Additionally, plenty of alternative fixation methods have been proposed11. We strongly advise to test the resolution and fixation stability of different protocols when introducing a new sample set.
We used two different flow cytometers to analyze the sets: i) a highly sophisticated and expensive, research centered cell sorter and ii) an application focused bench top analyzer which appears more appropriate for on-site application5. The BC was measured with the bench top analyzer, while the PC and ASC were measured with the cell sorter. The analysis of the three exemplary sample sets required different procedures for optimized reproducibility, sample stability and workflow convenience. The following protocol will specify the sections for the three different systems.

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			<td height="21" style="height:21px;width:335px;">Milli-Q system Synthesis A10</td>
			<td style="width:335px;">Merck, Darmstadt, (GER)</td>
			<td style="width:149px;"></td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">BioPak Ultrafiltration Cartridge</td>
			<td style="width:335px;">Merck, Darmstadt, (GER)</td>
			<td>CDUF BI0 01</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MoFlo Legacy cell sorter</td>
			<td style="width:335px;">Beckman-Coulter, Brea, California (USA)</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Innova 90C</td>
			<td style="width:335px;">Coherent, Inc , Santa Clara (USA)</td>
			<td style="width:149px;"></td>
			<td>334 nm - 364 nm, 100 mW</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">Innova 70C&#160;</td>
			<td style="width:335px;">Coherent, Inc , Santa Clara (USA)</td>
			<td style="width:149px;"></td>
			<td>488 nm, 400 mW</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Genesis MX488-500 STM OPS</td>
			<td style="width:335px;">Coherent, Inc , Santa Clara (USA)</td>
			<td style="width:149px;"></td>
			<td>488 nm, 400 mW</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Xcyte CY-355-150</td>
			<td style="width:335px;">Lumentum, Milpitas, California, USA</td>
			<td style="width:149px;"></td>
			<td>355 nm, 150 mW</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Photomultiplier tubes R928 and R3896</td>
			<td style="width:335px;">Hamamatsu Photonics, Hamamatsu City (Japan)</td>
			<td style="width:149px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Summit 4.3 software</td>
			<td style="width:335px;">Beckman-Coulter, Brea, California (USA)</td>
			<td style="width:149px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">1 &#181;m&#160; FluoSpheres</td>
			<td style="width:335px;">Molecular Probes, Eugene, Oregon (USA)</td>
			<td style="width:149px;">F8815</td>
			<td>350/440 blue fluorescent</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">2 &#956;m YG FluoSpheres</td>
			<td style="width:335px;">Molecular Probes, Eugene, Oregon (USA)</td>
			<td style="width:149px;">F-8827</td>
			<td>505/515 yellow-green fluorescent beads&#160;</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;">0.5 &#956;m Fluoresbrite BB Carboxylate microspheres</td>
			<td style="width:335px;">PolyScience, Niles, Illinois (USA)</td>
			<td>18339</td>
			<td>360/407</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">1 &#956;m Fluoresbrite BB Carboxylate microspheres</td>
			<td style="width:335px;">PolyScience, Niles, Illinois (USA)</td>
			<td>17458</td>
			<td>360/407 blue fluorescent</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">1 &#181;mYG FluoSpheres</td>
			<td style="width:335px;">Molecular Probes, Eugene, Oregon (USA)</td>
			<td>F-13081</td>
			<td>505/515 yellow-green fluorescent beads&#160;</td>
		</tr>
		<tr height="22">
			<td height="22" style="height:22px;width:335px;">KH2PO4</td>
			<td>Sigma-Aldrich, St. Louis (USA)</td>
			<td style="width:149px;">CAS no. 7778-77-0</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">KCl</td>
			<td>Sigma-Aldrich, St. Louis (USA)</td>
			<td style="width:149px;">CAS no. 7447-40-7</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Cyflow Space&#160;</td>
			<td>Sysmex Corporation, Kobe (Japan)</td>
			<td style="width:149px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">UV Laser Genesis CX,</td>
			<td style="width:335px;">Coherent, Inc , Santa Clara (USA)</td>
			<td style="width:149px;"></td>
			<td>355 nm, 150 mW</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">H6779-32 series PMTs</td>
			<td style="width:335px;">Hamamatsu Photonics, Hamamatsu City (Japan)</td>
			<td style="width:149px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">FloMax software</td>
			<td style="width:335px;">Sysmex Partec GmbH, G&#246;rlitz, (GER)</td>
			<td style="width:149px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">all optical filters</td>
			<td>Carl Zeiss, Jena (GER)</td>
			<td style="width:149px;"></td>
			<td></td>
		</tr>
		<tr height="25">
			<td height="25" style="height:25px;width:335px;">KH2PO4</td>
			<td>Sigma-Aldrich, St. Louis (USA)</td>
			<td>CAS no. 7778-77-0</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">KCl</td>
			<td style="width:335px;">Carl Roth, Karlsruhe (GER)</td>
			<td align="right">6781.3</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">FlowJo 10.0.8r1</td>
			<td>FlowJo, LLC, Ashland, Oregon (USA)</td>
			<td style="width:149px;"></td>
			<td>Build number: 42398</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">R-software v.3.4.3</td>
			<td>R Core Team</td>
			<td style="width:149px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">flowCHIC</td>
			<td colspan="2"><a href="http://www.bioconductor.org/packages/release/bioc/html/flowCHIC.html">http://www.bioconductor.org/packages/release/bioc/html/flowCHIC.html</a></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:335px;">flowCyBar</td>
			<td colspan="2"><a href="http://www.bioconductor.org/packages/devel/bioc/html/flowCyBar.html">http://www.bioconductor.org/packages/devel/bioc/html/flowCyBar.html</a></td>
			<td></td>
		</tr>
	</tbody>
</table>

characterizing microbiome dynamics &#8211; flow cytometry based workflows from pure cultures to natural communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical applications driven by microorganisms. Next generation sequencing methods are widely utilized to resolve microbiomes, but they are generally resource and time intensive and deliver mostly qualitative information. Flow cytometric microbiome analysis does not suffer from those disadvantages and can provide relative subcommunity abundances and absolute cell numbers at-line. Although it does not deliver direct phylogenetic information, it can enhance the analysis depth and resolution of sequencing approaches. In sharp contrast to medical applications in both research and routine settings, flow cytometry is still not widely used for microbiome analysis. Missing information on sample preparation and data analysis pipelines may create an entry barrier for the researchers facing microbiome analysis challenges that would often be textbook flow cytometry applications. Here, we present three comprehensive workflows for pure cultures, complex communities in clear medium and complex communities in challenging matrices, respectively. We describe individual sampling and fixation procedures and optimized staining protocols for the respective sample sets. We elaborate the cytometric analysis with a complex research centered and an application focused bench top device, describe the cell sorting procedure and suggest data analysis packages. We furthermore propose important experimental controls and apply the presented workflows to the respective sample sets.

The following representative results emphasize the necessity of replicates and exemplify different data visualization and analysis techniques on each of the three sample sets. They show the results of possible data evaluation pipelines suitable for answering standard research questions about the respective set. However, the presented techniques are not exclusive to the sample set which they are presented with. The flow cytometry raw data was made publically available with the FlowRepository ID FR-FCM-ZY46. Previously uploaded ASC data is available under ID FR-FCM-ZYD7.
Biological and technical replicates were taken, prepared and analyzed according to published protocols11. Biological replicates from the PC and ASC were taken from three parallel flasks. Biological replicates of the BC were taken as three subsequent samplings at one location in a pilot scale plant. Three aliquots of one sample were fixed, stained and analyzed to ensure technical reproducibility. The methods reproducibility was assessed according to previously published procedures11. A gate template for all samples of one sample set (Supplementary File 1-S6) was used for calculating the standard deviation (%) per gate.
For the PC, the maximum standard deviation of the relative abundance was 3.44%, while the average standard deviation was 2.13% (Figure 1). For the ASC and the BC, the maximum standard deviations of the relative abundances were 1.27% and 0.88%, while the averages of the standard deviations were 2.13% and 0.21% (Supplementary File 1-S8).
A standard procedure, when investigating a pure strain culture, is the preparation of a growth curve to analyze lag phase, generation times and cell density under specific conditions. We combined this procedure with the flow cytometric analysis workflow to attain a deeper understanding of the system (Figure 2). The FSC vs. DAPI fluorescence plots reveal the cell cycle states of the culture at different points of the batch culture. A master gate template (Supplementary File 1-S6) was established to quantify the proportion of cells with one (c1n), two (c2n) and multiple chromosomes (cXn, Figure 2B). The predominant proportion of inoculated cells had only one chromosome (93.7% at 0 h). This changed drastically during exponential growth, where nearly all cells contained more than one (99.6%, 4 h) and over half of the population (53.1%) even contained more than two chromosomes. This illustrates P. putidas ability to replicate its chromosomes faster than its generation time.
Flow cytometric analysis has proven very useful for monitoring the evolution of microbial communities. It can follow community dynamics much closer than the more resource intensive molecular methods5,10. We highlighted these dynamics in a movie and gained an overview of the activated sludge community shifts over time. Each one-second frame shows a FSC vs. DAPI fluorescence plot of a sample point (Supplementary File 2). A very distinct shift between day 0 and day 4 was followed by the establishment of a core community after day 7. Additional subcommunities came up at day 21. We established a master gate template (Supplementary File 1-S6) that enabled the evaluation of microbial dynamics on a subcommunity level. The dominant subcommunities in different stages of the experiment were clearly identified using the CyBar tool (Figure 3). Combining it with the frequency distribution of the relative subcommunity abundances helped to select gates that are interesting (significant change in abundance triggered at a key time point) and viable (over 5% relative abundance) for sorting and further analysis22.
Industrial scale bioreactor applications can face potential spatial heterogeneities due to agitation limitations. They are also frequently exposed to changing operational parameters, like alternations in the quality of non-synthetic substrates. The BC represents such a system and was sampled from different localities in a dynamically driven plug flow reactor. The FSC vs. DAPI fluorescence plots (Figure 4) of the exemplary sample points show only little spatial, but pronounced temporal heterogeneity. The BC was further investigated using both, the fast-automated CHIC and the more in-depth master gate template based CyBar approach.
Its automated, fast and unbiased nature, makes the CHIC particularly interesting for on-site control of bioprocesses in an industrial setting. It compares raw .fcs data of all available samples. Two of these comparisons are displayed in Figure 5. The resulting dissimilarity values confirm the previous observations concerning spatial and temporal heterogeneity. The NMDS plots generated form the CHIC tools dissimilarity matrix (Figure 6) further substantiates this result and visualizes it accordingly.
In addition, we calculated the relative abundances of 23 subcommunities of the BC using a master gate template (Supplementary File 1-S6). A correlation analysis of 48 samples from a one-year period was conducted to understand functional relations in the microbial community. This can help to identify gates of interest for further investigation (4 Cell sorting). Strong positive or negative correlations with abiotic parameters like product titers can help to understand and optimize ecosystems and biotechnological processes. The organic loading rate included in this correlation (Figure 7) exemplifies such an abiotic parameter. G5, G4 and G10 exhibit strong positive correlations and could possibly contain fermentative species, while the negative correlation in G8 might hint towards methanogenic archaea.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58033/58033fig1.jpg" /> 
Figure 1: Reproducibility of cytometric analysis of the pure culture. FSC vs. DAPI fluorescence plots with control beads (A, B) and bar plots of the 3 subcommunity abundances [%] with error bars representing &#177; one standard deviation (C, D). The relative abundances were determined with the gate template shown in Supplementary File 1-S6. Three biological replicates A, B and C were taken of the growth curve at 4 h and three technical replicates P1, P2, P3 were prepared from sample A and measured thrice, respectively: M1, M2, M3, and are given with their respective standard deviations. 50,000 events were recorded in the cell gate. <a href="https://www.jove.com/files/ftp_upload/58033/58033fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58033/58033fig2.jpg" /> 
Figure 2: Cell cycle analysis of the pure culture taken during a growth curve experiment over 12 h. (A). FSC vs. DAPI fluorescence plots of the bi-hourly samplings that exhibit a shift of the DNA content during the exponential growth phase. This measurement series does not include beads (see Supplementary&#160;File 1-S3). (B). Optical density-based growth curve with error bars representing &#177; one standard deviation and relative abundances in the subcommunities over time. <a href="https://www.jove.com/files/ftp_upload/58033/58033fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58033/58033fig3.jpg" /> 
Figure 3: Evolution of the activated sludge community structure over 24 days. (A) The flowCyBar with overlaying frequency distributions visualized in boxplots for the individual gates that are arranged by similarity of the trends, the corresponding color key (B) and a similarity analysis in an NMDS plot wit R &#60;0.001 (C). The underlying plots are shown in the film sequence in Supplementary File 2. Relative abundances were determined using the gate template in Supplementary File 1- S6. <a href="https://www.jove.com/files/ftp_upload/58033/58033fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58033/58033fig4.jpg" /> 
Figure 4: Spatial and temporal heterogeneity of the microbial community structure in an industrial scale plug flow biogas reactor. (A) The comparison of the microbial community structure of the four sampling ports I-IV on day 223. (B) The comparison of the time dependent community structure variations from day 0 to day 384 in port II. The noise has been cut off belatedly to enhance the visualization. 250,000 events were measured in the cell gate (Supplementary File 1-S6). <a href="https://www.jove.com/files/ftp_upload/58033/58033fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58033/58033fig5.jpg" /> 
Figure 5: Cytometric Histogram Image Comparison &#8212; CHIC. The principle of the CHIC algorithm is exemplified by comparing two samples representing spatial and temporal heterogeneity, respectively. (i) CHIC images are created from .fcs files after selecting two parameters to display (FSC vs. DAPI fluorescence). The greyscale (0&#8211;255) codes the event count in a pixel. (ii) CHIC subsets are generated after specifying the area containing cells (here: 200 &#60; x &#60; 4000, 200 &#60; y &#60; 2200). (iii) XOR combination image is created by comparing pixels between the subsets. No difference equals 0 and is displayed as black. Maximum difference equals 200 and is displayed as white. The corresponding XOR value is calculated by adding the grey scale values of all pixels in the XOR image. (iv) Overlay combination image is created by adding the greyscale values of the pixels of the subsets. The corresponding overlay value is calculated by counting the pixels of an overlay image that do not equal zero and therefore contain information. (v) Dissimilarity values are calculated by dividing XOR and overlay values. These are the basis for the NMDS plot in Figure 6. Both the dissimilarity values and the NMDS plot show a negligible spatial- and a pronounced temporal community heterogeneity. <a href="https://www.jove.com/files/ftp_upload/58033/58033fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58033/58033fig6.jpg" /> 
Figure 6: CHIC based similarity analysis of the biogas community samples. The 0-day sample was excluded from this plot due to its extremely different community structure distorting the analysis (Supplementary File 1-S11). The NMDS plot confirms the assumption about low spatial- and higher temporal heterogeneity made using the CHIC tool and explained in Figure 5. <a href="https://www.jove.com/files/ftp_upload/58033/58033fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/58033/58033fig7.jpg" /> 
Figure 7: Correlation analysis on the biogas community. The matrix was calculated using Spearman&#39;s rank order coefficient and is based on the relative abundances of 23 subcommunities that were determined with the master gate template in Supplementary File 1-S6. They were correlated with the organic loading rate (OLR) for 48 samples from a one-year period. <a href="https://www.jove.com/files/ftp_upload/58033/58033fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/58033/58033fig8.jpg" /> 
Figure 8: Similarity analysis of planktonic (P) and sludge based (S) communities in wastewater. Samples were taken from the primary clarifier (PCL), activated sludge basin (AS) and digester tank (DT) of a full-scale wastewater treatment plant (dot plots in Supplementary File 1-S10). Relative abundances were determined using the gate template shown in Supplementary File 1-S6. These subcommunity abundances were also analyzed with the flowCyBar tool to create a CyBar (Supplementary File 1-S10) and NMDS plot (R &#60;0.01). <a href="https://www.jove.com/files/ftp_upload/58033/58033fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/58033/58033fig9.jpg" /> 
Figure 9: Fixation stability of the pure culture. Samples from the growth curve experiment taken at 0 h were stored over 28 days at -80 &#176;C after fixation in 15% glycerol. FSC vs. DAPI fluorescence plots with control beads are shown. Measurement series does not include beads (Supplementary File 1-S3). 50,000 events were recorded in the cell gate (Supplementary File 1-S6). <a href="https://www.jove.com/files/ftp_upload/58033/58033fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary File 1: <a href="https://www.jove.com/files/ftp_upload/58033/Supplementary-File-1.pdf" target="_blank">Please click here to download this file.</a>
Supplementary File 2: <a href="https://www.jove.com/files/ftp_upload/58033/Supplementary_File_2.gif" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Characterizing Microbiome Dynamics Ã¢â‚¬â€œ Flow Cytometry Based Workflows from Pure Cultures to Natural Communities at JoVE.com

Characterizing Microbiome Dynamics ÃƒÆ’Ã‚Â¢ÃƒÂ¢Ã¢â‚¬Å¡Ã‚Â¬ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œ Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Flow cytometric analysis has proven valuable for investigating pure cultures and monitoring microbial community dynamics. We present three comprehensive workflows, from sampling to data analysis, for pure cultures and complex communities in clear medium as well as in challenging matrices, respectively.

Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

The investigation of pure cultures and monitoring of microbial community dynamics is vital to understand and control natural ecosystems and technical ...

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