Name: characterizing microbiome dynamics – flow cytometry based workflows from pure cultures to natural communities
Uploaded: 2018-07-12T15:00:00.000+00:00
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Description: Watch this Scientific Journal Video about Characterizing Microbiome Dynamics â€“ 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).

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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.

<table><tbody><tr><td>Milli-Q system Synthesis A10</td><td>Merck, Darmstadt, (GER)</td><td></td><td></td></tr><tr><td>BioPak Ultrafiltration Cartridge</td><td>Merck, Darmstadt, (GER)</td><td>CDUF BI0 01</td><td></td></tr><tr><td>MoFlo Legacy cell sorter</td><td>Beckman-Coulter, Brea, California (USA)</td><td></td><td></td></tr><tr><td>Innova 90C</td><td>Coherent, Inc , Santa Clara (USA)</td><td></td><td>334 nm - 364 nm, 100 mW</td></tr><tr><td>Innova 70C&amp;nbsp;</td><td>Coherent, Inc , Santa Clara (USA)</td><td></td><td>488 nm, 400 mW</td></tr><tr><td>Genesis MX488-500 STM OPS</td><td>Coherent, Inc , Santa Clara (USA)</td><td></td><td>488 nm, 400 mW</td></tr><tr><td>Xcyte CY-355-150</td><td>Lumentum, Milpitas, California, USA</td><td></td><td>355 nm, 150 mW</td></tr><tr><td>Photomultiplier tubes R928 and R3896</td><td>Hamamatsu Photonics, Hamamatsu City (Japan)</td><td></td><td></td></tr><tr><td>Summit 4.3 software</td><td>Beckman-Coulter, Brea, California (USA)</td><td></td><td></td></tr><tr><td>1 &amp;micro;m&amp;nbsp; FluoSpheres</td><td>Molecular Probes, Eugene, Oregon (USA)</td><td>F8815</td><td>350/440 blue fluorescent</td></tr><tr><td>2 &amp;mu;m YG FluoSpheres</td><td>Molecular Probes, Eugene, Oregon (USA)</td><td>F-8827</td><td>505/515 yellow-green fluorescent beads&amp;nbsp;</td></tr><tr><td>0.5 &amp;mu;m Fluoresbrite BB Carboxylate microspheres</td><td>PolyScience, Niles, Illinois (USA)</td><td>18339</td><td>360/407</td></tr><tr><td>1 &amp;mu;m Fluoresbrite BB Carboxylate microspheres</td><td>PolyScience, Niles, Illinois (USA)</td><td>17458</td><td>360/407 blue fluorescent</td></tr><tr><td>1 &amp;micro;mYG FluoSpheres</td><td>Molecular Probes, Eugene, Oregon (USA)</td><td>F-13081</td><td>505/515 yellow-green fluorescent beads&amp;nbsp;</td></tr><tr><td>KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;</td><td>Sigma-Aldrich, St. Louis (USA)</td><td>CAS no. 7778-77-0</td><td></td></tr><tr><td>KCl</td><td>Sigma-Aldrich, St. Louis (USA)</td><td>CAS no. 7447-40-7</td><td></td></tr><tr><td>Cyflow Space&amp;nbsp;</td><td>Sysmex Corporation, Kobe (Japan)</td><td></td><td></td></tr><tr><td>UV Laser Genesis CX,</td><td>Coherent, Inc , Santa Clara (USA)</td><td></td><td>355 nm, 150 mW</td></tr><tr><td>H6779-32 series PMTs</td><td>Hamamatsu Photonics, Hamamatsu City (Japan)</td><td></td><td></td></tr><tr><td>FloMax software</td><td>Sysmex Partec GmbH, G&amp;ouml;rlitz, (GER)</td><td></td><td></td></tr><tr><td>all optical filters</td><td>Carl Zeiss, Jena (GER)</td><td></td><td></td></tr><tr><td>KH&lt;sub&gt;2&lt;/sub&gt;PO&lt;sub&gt;4&lt;/sub&gt;</td><td>Sigma-Aldrich, St. Louis (USA)</td><td>CAS no. 7778-77-0</td><td></td></tr><tr><td>KCl</td><td>Carl Roth, Karlsruhe (GER)</td><td>6781.3</td><td></td></tr><tr><td>FlowJo 10.0.8r1</td><td>FlowJo, LLC, Ashland, Oregon (USA)</td><td></td><td>Build number: 42398</td></tr><tr><td>R-software v.3.4.3</td><td>R Core Team</td><td></td><td></td></tr><tr><td>flowCHIC</td><td>&lt;a href=&quot;http://www.bioconductor.org/packages/release/bioc/html/flowCHIC.html&quot;&gt;http://www.bioconductor.org/packages/release/bioc/html/flowCHIC.html&lt;/a&gt;</td><td></td><td></td></tr><tr><td>flowCyBar</td><td>&lt;a href=&quot;http://www.bioconductor.org/packages/devel/bioc/html/flowCyBar.html&quot;&gt;http://www.bioconductor.org/packages/devel/bioc/html/flowCyBar.html&lt;/a&gt;</td><td></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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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.remotexs.ntu.edu.sg/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 â€“ Flow Cytometry Based Workflows from Pure Cultures to Natural Communities at JoVE.com

Characterizing Microbiome Dynamics &#8211; 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.

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

characterizing-microbiome-dynamics-flow-cytometry-based-workflows

Nanyang Technological University

Research

JoVE Journal

Environment

11.8K Views.  Helmholtz Centre for Environmental Research - UFZ. Flow cytometrische analyse heeft waardevol voor het onderzoeken van zuivere culturen en controle van de microbiële Gemeenschap dynamiek bewezen. Wij presenteren drie uitgebreide werkstromen, van monsterneming tot data-analyse, voor zuivere culturen en complexe gemeenschappen in duidelijke medium zoals in uitdagende matrices, respectievelijk.

Video: Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Biofilm formation is a general attribute to almost all bacteria 1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly constituted by proteins and exopolysaccharides, among other factors 7-10. The microbial community encased within the biofilm often shows the differentiation of distinct subpopulation of specialized cells 11-17. These subpopulations coexist and often show spatial and temporal organization within the biofilm 18-21.
Biofilm formation in the model organism Bacillus subtilis requires the differentiation of distinct subpopulations of specialized cells. Among them, the subpopulation of matrix producers, responsible to produce and secrete the extracellular matrix of the biofilm is essential for biofilm formation 11,19. Hence, differentiation of matrix producers is a hallmark of biofilm formation in B. subtilis.
We have used fluorescent reporters to visualize and quantify the subpopulation of matrix producers in biofilms of B. subtilis 15,19,22-24. Concretely, we have observed that the subpopulation of matrix producers differentiates in response to the presence of self-produced extracellular signal surfactin 25. Interestingly, surfactin is produced by a subpopulation of specialized cells different from the subpopulation of matrix producers 15.
We have detailed in this report the technical approach necessary to visualize and quantify the subpopulation of matrix producers and surfactin producers within the biofilms of B. subtilis. To do this, fluorescent reporters of genes required for matrix production and surfactin production are inserted into the chromosome of B. subtilis. Reporters are expressed only in a subpopulation of specialized cells. Then, the subpopulations can be monitored using fluorescence microscopy and flow cytometry (See Fig 1).
The fact that different subpopulations of specialized cells coexist within multicellular communities of bacteria gives us a different perspective about the regulation of gene expression in prokaryotes. This protocol addresses this phenomenon experimentally and it can be easily adapted to any other working model, to elucidate the molecular mechanisms underlying phenotypic heterogeneity within a microbial community.

Single-cell Analysis of Bacillus subtilis Biofilms Using Fluorescence Microscopy and Flow Cytometry

Microbial biofilms are generally constituted by distinct subpopulations of specialized cells. Single-cell analysis of these subpopulations requires the use of fluorescent reporters. Here we describe a protocol to visualize and monitor several subpopulationswithin B. subtilis biofilms using fluorescence microscopy and flow cytometry.

Biofilm formation is a general attribute to almost all bacteria 1-6. When bacteria form biofilms, cells are encased in extracellular matrix that is mostly ...

Immunology and Infection

Characterization of Aquatic Biofilms with Flow Cytometry

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond quickly to environmental changes and can be thus used as indicators of water quality. Currently, biofilm assessment is mostly based on integrative and functional endpoints, such as photosynthetic or respiratory activity, which do not provide information on the biofilm community structure. Flow cytometry and computational visualization offer an alternative, sensitive, and easy-to-use method for assessment of the community composition, particularly of the photoautotrophic part of freshwater biofilms. It requires only basic sample preparation, after which the entire sample is run through the flow cytometer. The single-cell optical and fluorescent information is used for computational visualization and biological interpretation. Its main advantages over other methods are the speed of analysis and the high-information-content nature. Flow cytometry provides information on several cellular and biofilm traits in a single measurement: particle size, density, pigment content, abiotic content in the biofilm, and coarse taxonomic information. However, it does not provide information on biofilm composition on the species level. We see high potential in the use of the method for environmental monitoring of aquatic ecosystems and as an initial biofilm evaluation step that informs downstream detailed investigations by complementary and more detailed methods.

Flow cytometry in combination with visual clustering offers an easy-to-use and fast method for studying aquatic biofilms. It can be used for biofilm characterization, detection of changes in biofilm community structure, and detection of abiotic particles embedded in the biofilm.

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems. By changing their community structure, biofilms respond ...

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as hydrodynamics, ecology and environmental engineering. Modeling bacterial transport in porous environments at different spatial scales is critical to better predict the consequences of bacterial transport, yet current models often fail to up-scale from laboratory to field conditions. Here, we introduce experimental tools to study bacterial transport in porous media at two spatial scales. The aim of these tools is to obtain macroscopic observables (such as breakthrough curves or deposition profiles) of bacteria injected into transparent porous matrices. At the small scale (10-1000 &#181;m), microfluidic devices are combined with optical video-microscopy and image processing to obtain breakthrough curves and, at the same time, to track individual bacterial cells at the pore scale. At larger scale, flow cytometry is combined with a self-made robotic dispenser to obtain breakthrough curves. We illustrate the utility of these tools to better understand how bacteria are transported in complex porous media such as the hyporheic zone of streams. As these tools provide simultaneous measurements across scales, they pave the way for mechanism-based models, critically important for upscaling. Application of these tools may not only contribute to the development of novel bioremediation applications but also shed new light on the ecological strategies of microorganisms colonizing porous substrates.

Breakthrough curves (BTCs) are efficient tools to study the transport of bacteria in porous media. Here we introduce tools based on fluidic devices in combination with microscopy and flow cytometric counting to obtain BTCs.

Understanding the transport, dispersion and deposition of microorganisms in porous media is a complex scientific task comprising topics as diverse as ...

Methods for Characterizing the Co-development of Biofilm and Habitat Heterogeneity

Biofilms are surface-attached microbial communities that have complex structures and produce significant spatial heterogeneities. Biofilm development is strongly regulated by the surrounding flow and nutritional environment. Biofilm growth also increases the heterogeneity of the local microenvironment by generating complex flow fields and solute transport patterns. To investigate the development of heterogeneity in biofilms and interactions between biofilms and their local micro-habitat, we grew mono-species biofilms of Pseudomonas aeruginosa and dual-species biofilms of P. aeruginosa and Escherichia coli under nutritional gradients in a microfluidic flow cell. We provide detailed protocols for creating nutrient gradients within the flow cell and for growing and visualizing biofilm development under these conditions. We also present protocols for a series of optical methods to quantify spatial patterns in biofilm structure, flow distributions over biofilms, and mass transport around and within biofilm colonies. These methods support comprehensive investigations of the co-development of biofilm and habitat heterogeneity.

Biofilms have complex interactions with their surrounding environment. To comprehensively investigate biofilm-environment interactions, we present here a series of methods to create heterogeneous chemical environment for biofilm development, to quantify local flow velocity, and to analyze mass transport in and around biofilm colonies.

Biofilms are surface-attached microbial communities that have complex structures and produce significant spatial heterogeneities. Biofilm development is ...

Bioengineering

Improvement of Bacillus subtilis Spore Enumeration and Label Analysis in Flow Cytometry

The spores of Bacillus subtilis have already been proposed for different biotechnological and immunological applications; however, there is an increasing need for the development of methodologies that improve the detection of antigens immobilized on the surface of spores together with their quantification. Flow cytometry-based analyses have been previously proposed as fast, reliable, and specific approaches for detecting labeled cells of B. subtilis. Herein, we propose the use of flow cytometry to evaluate the display efficiency of a fluorescent antibody (FA) on the surface of the spore and quantify the number of spores using counting beads. 
For this, we used ethidium bromide as a DNA marker and an allophycocyanin (APC)-labeled antibody, which was coupled to the spores, as a surface marker. The quantification of spores was performed using counting beads since this technique demonstrates high accuracy in the detection of cells. The labeled spores were analyzed using a flow cytometer, which confirmed the coupling. As a result, it was demonstrated that DNA labeling improved the accuracy of quantification by flow cytometry, for the detection of germinated spores. It was observed that ethidium bromide was not able to label dormant spores; however, this technique provides a more precise determination of the number of spores with fluorescent protein coupled to their surface, thus helping in the development of studies that focus on the use of spores as a biotechnological platform in different applications.

Improvement of Bacillus subtilis Spore Enumeration and Label Analysis in Flow Cytometry

This protocol focuses on the use of flow cytometry and counting beads to quantify bacterial spores labeled with ethidium bromide. The method is also efficient for analyzing the covalent coupling of proteins on the surface of intact spores.

The spores of Bacillus subtilis have already been proposed for different biotechnological and immunological applications; however, there is an increasing ...

Biochemistry

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism (Microcystis aeruginosa) Using Flow Cytometry

Microbial subpopulations in field and laboratory studies have been shown to display high heterogeneity in morphological and physiological parameters. Determining the real time state of a microbial cell goes beyond live or dead categories, as microbes can exist in a dormant state, whereby cell division and metabolic activities are reduced. Given the need for detection and quantification of microbes, flow cytometry (FCM) with molecular probes provides a rapid and accurate method to help determine overall population viability. By using SYTOX Green and SYTOX Orange in the model cyanobacteria Microcystis aeruginosa to detect membrane integrity, we develop a transferable method for rapid indication of single cell mortality. The molecular probes used within this journal will be referred to as green or orange nucleic acid probes respectively (although there are other products with similar excitation and emission wavelengths that have a comparable modes of action, we specifically refer to the fore mentioned probes). Protocols using molecular probes vary between species, differing principally in concentration and incubation times. Following this protocol set out on M.aeruginosa the green nucleic acid probe was optimized at concentrations of 0.5 &#181;M after 30 min of incubation and the orange nucleic acid probe at 1 &#181;M after 10 min. In both probes concentrations less than the stated optimal led to an under reporting of cells with membrane damage. Conversely, 5 &#181;M concentrations and higher in both probes exhibited a type of non-specific staining, whereby &#39;live&#39; cells produced a target fluorescence, leading to an over representation of &#39;non-viable&#39; cell numbers. The positive controls (heat-killed) provided testable dead biomass, although the appropriateness of control generation remains subject to debate. By demonstrating a logical sequence of steps for optimizing the green and orange nucleic acid probes we demonstrate how to create a protocol that can be used to analyse cyanobacterial physiological state effectively.

Molecular Probe Optimization to Determine Cell Mortality in a Photosynthetic Organism Microcystis aeruginosa Using Flow Cytometry

Microbial populations contain substantial cell heterogeneity, which can dictate overall behavior. Molecular probe analysis through flow cytometry can determine physiological states of cells, however its application varies between species. This study provides a protocol to accurately determine cell mortality within a cyanobacterium population, without underestimating or recording false positive results.

Microbial subpopulations in field and laboratory studies have been shown to display high heterogeneity in morphological and physiological parameters. ...

Medicine

Quantifying Human Norovirus Virus-like Particles Binding to Commensal Bacteria Using Flow Cytometry

Commensal bacteria are well established to impact infection of eukaryotic viruses. Direct binding between the pathogen and the host microbiome is responsible for altering infection for many of these viruses. Thus, characterizing the nature of virus-bacteria binding is a foundational step needed for elucidating the mechanism(s) by which bacteria alter viral infection. For human norovirus, commensal bacteria enhance B cell infection. The virus directly binds to these bacteria, indicating that this direct interaction is involved in the mechanism of infection enhancement. A variety of techniques can be used to quantify interactions between bacteria and viruses including scintillation counting of radiolabeled viruses and polymerase chain reaction (PCR). Both methods require the use of live virus, which may need to be generated in the laboratory. Currently, none of the established in vitro culture systems available for human norovirus are robust enough to allow for generation of highly concentrated viral stocks. In lieu of live virus, virus-like particles (VLPs) have been used to characterize the interactions between norovirus and bacteria. Herein a flow cytometry method is described with uses virus specific antibodies to quantify VLP binding to gram-negative and gram-positive bacteria. Inclusion of both bacteria only and isotype controls allowed for optimization of the assay to reduce background antibody binding and accurate quantification of VLP attachment to the bacteria tested. High VLP:bacterium ratios result in VLPs binding to large percentages of the bacterial population. However, when VLP quantities are decreased, the percent of bacteria bound also decreases. Ultimately, this method can be employed in future experiments elucidating the specific conditions and structural components that regulate norovirus:bacterial interactions.

The goal of this protocol is to quantify binding of the eukaryotic pathogen human norovirus to bacteria. After performing an initial virus-bacterium attachment assay, flow cytometry is used to detect virally-bound bacteria within the population.

Commensal bacteria are well established to impact infection of eukaryotic viruses. Direct binding between the pathogen and the host microbiome is ...

Flow Cytometric Characterization of Murine B Cell Development

Extensive studies have characterized the development and differentiation of murine B cells in secondary lymphoid organs. Antibodies secreted by B cells have been isolated and developed into well-established therapeutics. Validation of murine B cell development, in the context of autoimmune prone mice, or in mice with modified immune systems, is a crucial component of developing or testing therapeutic agents in mice and is an appropriate use of flow cytometry. Well established B cell flow cytometric parameters can be used to evaluate B cell development in the murine peritoneum, bone marrow, and spleen, but a number of best practices must be adhered to. In addition, flow cytometric analysis of B cell compartments should also complement additional readouts of B cell development. Data generated using this technique can further our understanding of wild type, autoimmune prone mouse models as well as humanized mice that can be used to generate antibody or antibody-like molecules as therapeutics.

We describe herein a simple analysis of the heterogeneity of the murine immune B cell compartment in the peritoneum, spleen, and bone marrow tissues by flow cytometry. The protocol can be adapted and extended to other mouse tissues.

Extensive studies have characterized the development and differentiation of murine B cells in secondary lymphoid organs. Antibodies secreted by B cells ...

Flow Cytometry Approach to Characterize Phagocytic Properties of Acutely-Isolated Adult Microglia and Brain Macrophages In Vitro

Microglia and central nervous system (CNS)-infiltrating macrophages, collectively called CNS mononuclear phagocytes (CNS-MPs), play central roles in neurological diseases including neurodegeneration and stroke. CNS-MPs are involved in phagocytic clearance of pathological proteins, debris and neuronal synapses, each with distinct underlying molecular pathways. Characterizing these phagocytic properties can provide a functional readout that compliments molecular profiling of microglia using traditional flow cytometry, transcriptomics and proteomics approaches. Phagocytic profiling of microglia has relied on microscopic visualization and in vitro cultures of mouse neonatal microglia. The former approach suffers from limited sampling while the latter approach is inherently poorly reflective of the true in vivo state of adult CNS-MPs. This paper describes optimized protocols to phenotype phagocytic properties of acutely-isolated mouse CNS-MPs by flow cytometry. CNS-MPs are acutely isolated from adult mouse brain using mechanical dissociation followed by density gradient centrifugation, incubated with fluorescent microspheres or fluorescent A&#946; fibrils, washed, and then labeled with panels of antibodies against surface markers (CD11b, CD45). Using this approach, it is possible to compare phagocytic properties of brain-resident microglia with CNS-infiltrating macrophages and then assess the effect of aging and disease pathology on these phagocytic phenotypes. This rapid method also holds potential to functionally phenotype acutely-isolated human CNS-MPs from post-mortem or surgical brain specimens. Additionally, specific mechanisms of phagocytosis by CNS-MP subsets can be investigated by inhibiting select phagocytic pathways.

Assessment of phagocytic properties of microglia and brain macrophages can provide a valuable functional dimension to molecular profiling studies. We describe a validated protocol that uses flow cytometry to rapidly and reliably quantify phagocytosis of fluorescent microspheres and amyloid beta fibrils by acutely-isolated microglia and brain macrophages from mouse models.

Microglia and central nervous system (CNS)-infiltrating macrophages, collectively called CNS mononuclear phagocytes (CNS-MPs), play central roles in ...

Neuroscience

Protocol for Co-Culturing Sucrose-Secreting Cyanobacteria with Fungal Partners

Assembly and Quantification of Co-Cultures Combining Heterotrophic Yeast with Phototrophic Sugar-Secreting Cyanobacteria

With the increasing demand for sustainable biotechnologies, mixed consortia containing a phototrophic microbe and heterotrophic partner species are being explored as a method for solar-driven bioproduction. One approach involves the use of CO2-fixing cyanobacteria that secrete organic carbon to support the metabolism of a co-cultivated heterotroph, which in turn transforms the carbon into higher-value goods or services. In this protocol, a technical description to assist the experimentalist in the establishment of a co-culture combining a sucrose-secreting cyanobacterial strain with a fungal partner(s), as represented by model yeast species, is provided. The protocol describes the key prerequisites for co-culture establishment: Defining the media composition, monitoring the growth characteristics of individual partners, and the analysis of mixed cultures with multiple species combined in the same growth vessel. Basic laboratory techniques for co-culture monitoring, including microscopy, cell counter, and single-cell flow cytometry, are summarized, and examples of nonproprietary software to use for data analysis of raw flow cytometry standard (FCS) files in line with FAIR (Findable, Accessible, Interoperable, Reusable) principles are provided. Finally, commentary on the bottlenecks and pitfalls frequently encountered when attempting to establish a co-culture with sugar-secreting cyanobacteria and a novel heterotrophic partner is included. This protocol provides a resource for researchers attempting to establish a new pair of co-cultured microbes that includes a cyanobacterium and a heterotrophic microbe.

This protocol provides a comprehensive guideline for the setup and quantitative monitoring of co-cultures, including photoautotrophic sugar-secreting cyanobacteria and heterotrophic yeasts.

With the increasing demand for sustainable biotechnologies, mixed consortia containing a phototrophic microbe and heterotrophic partner species are being ...

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

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