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>
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The authors have nothing to disclose. 
&#160;

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 border="0" cellpadding="0" cellspacing="0" style="width:985px;" width="985">
	<tbody>
		<tr height="21">
			<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 &#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

Research

JoVE Journal

Environment

11.8K Views.  Helmholtz Centre for Environmental Research - UFZ. 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.

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

Helminth Collection and Identification from Wildlife

Wild animals are commonly parasitized by a wide range of helminths. The four major types of helminths are &#34;roundworms&#34; (nematodes), &#34;thorny-headed worms&#34; (acanthocephalans), &#34;flukes&#34; (trematodes), and &#34;tapeworms&#34; (cestodes). The optimum method for collecting helminths is to examine a host that has been dead less than 4-6 hr since most helminths will still be alive. A thorough necropsy should be conducted and all major organs examined. Organs are washed over a 106 &#956;m sieve under running water and contents examined under a stereo microscope. All helminths are counted and a representative number are fixed (either in 70% ethanol, 10% buffered formalin, or alcohol-formalin-acetic acid). For species identification, helminths are either cleared in lactophenol (nematodes and small acanthocephalans) or stained (trematodes, cestodes, and large acanthocephalans) using Harris&#39; hematoxylin or Semichon&#39;s carmine. Helminths are keyed to species by examining different structures (e.g. male spicules in nematodes or the rostellum in cestodes). The protocols outlined here can be applied to any vertebrate animal. They require some expertise on recognizing the different organs and being able to differentiate helminths from other tissue debris or gut contents. Collection, preservation, and staining are straightforward techniques that require minimal equipment and reagents. Taxonomic identification, especially to species, can be very time consuming and might require the submission of specimens to an expert or DNA analysis.

Wild animals are commonly parasitized by a wide range of helminths.&#160; The four major types of helminths are &#8220;roundworms&#8221; (nematodes), &#8220;thorny-headed worms&#8221; (acanthocephalans), &#8220;flukes&#8221; (trematodes), and &#8220;tapeworms&#8221; (cestodes).&#160; Here we describe how helminths are collected from a vertebrate animal and how they are preserved and taxonomically identified.&#160;

Wild animals are commonly parasitized by a wide range of helminths. The four major types of helminths are &#34;roundworms&#34; (nematodes), ...

A Technical Perspective in Modern Tree-ring Research - How to Overcome Dendroecological and Wood Anatomical Challenges

Dendroecological research uses information stored in tree rings to understand how single trees and even entire forest ecosystems responded to environmental changes and to finally reconstruct such changes. This is done by analyzing growth variations back in time and correlating various plant-specific parameters to (for example) temperature records. Integrating wood anatomical parameters in these analyses would strengthen reconstructions, even down to intra-annual resolution. We therefore present a protocol on how to sample, prepare, and analyze wooden specimen for common macroscopic analyses, but also for subsequent microscopic analyses. Furthermore we introduce a potential solution for analyzing digital images generated from common small and large specimens to support time-series analyses. The protocol presents the basic steps as they currently can be used. Beyond this, there is an ongoing need for the improvement of existing techniques, and development of new techniques, to record and quantify past and ongoing environmental processes. Traditional wood anatomical research needs to be expanded to include ecological information to this field of research. This would support dendro-scientists who intend to analyze new parameters and develop new methodologies to understand the short and long term effects of specific environmental factors on the anatomy of woody plants.

Here we present a protocol outlining how to sample wooden specimens for the overall assessment of their growth structures. Macro- and microscopic preparation and visualization techniques necessary to generate well-replicated and highly resolved wood anatomical and dendroecological dataset, are described are described.

Dendroecological research uses information stored in tree rings to understand how single trees and even entire forest ecosystems responded to ...

Simultaneous DNA-RNA Extraction from Coastal Sediments and Quantification of 16S rRNA Genes and Transcripts by Real-time PCR

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of taxonomic and functional groups in environmental samples. Used in combination with a reverse transcriptase reaction (RT-q-PCR), it can also be employed to quantify gene transcripts. q-PCR makes use of highly sensitive fluorescent detection chemistries that allow quantification of PCR amplicons during the exponential phase of the reaction. Therefore, the biases associated with &#39;end-point&#39; PCR detected in the plateau phase of the PCR reaction are avoided. A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. First, a method for the co-extraction of DNA and RNA from coastal sediments, including the additional steps required for the preparation of DNA-free RNA, is outlined. Second, a step-by-step guide for the quantification of 16S rRNA genes and transcripts from the extracted nucleic acids via q-PCR and RT-q-PCR is outlined. This includes details for the construction of DNA and RNA standard curves. Key considerations for the use of RT-q-PCR assays in microbial ecology are included.

A protocol to quantify bacterial 16S rRNA genes and transcripts from coastal sediments via real-time PCR is provided. The methodology includes the co-extraction of DNA and RNA; preparation of DNA-free RNA; and 16S rRNA gene and transcript quantification via RT-q-PCR, including standard curve construction.

Real Time Polymerase Chain Reaction also known as quantitative PCR (q-PCR) is a widely used tool in microbial ecology to quantify gene abundances of ...

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 ...

A Video Surveillance System to Monitor Breeding Colonies of Common Terns (Sterna Hirundo)

Many waterbird populations have faced declines over the last century, including the common tern (Sterna hirundo), a waterbird species with a widespread breeding distribution, that has been recently listed as endangered in some habitats of its range. Waterbird monitoring programs exist to track populations through time; however, some of the more intensive approaches require entering colonies and can be disruptive to nesting populations. This paper describes a protocol that utilizes a minimally invasive surveillance system to continuously monitor common tern nesting behavior in typical ground-nesting colonies. The video monitoring system utilizes wireless cameras focused on individual nests as well as over the colony as a whole, and allows for observation without entering the colony. The video system is powered with several 12 V car batteries that are continuously recharged using solar panels. Footage is recorded using a digital video recorder (DVR) connected to a hard drive, which can be replaced when full. The DVR may be placed outside of the colony to reduce disturbance. In this study, 3,624 h of footage recorded over 63 days in weather conditions ranging from 12.8 &#176;C to 35.0 &#176;C produced 3,006 h (83%) of usable behavioral data. The types of data retrieved from the recorded video can vary; we used it to detect external disturbances and measure nesting behavior during incubation. Although the protocol detailed here was designed for ground-nesting waterbirds, the principal system could easily be modified to accommodate alternative scenarios, such as colonial arboreal nesting species, making it widely applicable to a variety of research needs.

A Video Surveillance System to Monitor Breeding Colonies of Common Terns Sterna Hirundo

This paper describes a protocol that uses a remote video monitoring surveillance system to continuously monitor breeding colonies of ground-nesting waterbirds. The system includes five cameras monitoring individual nests and one camera monitoring the colony as a whole, and is powered by car batteries that are recharged via solar panels.

Many waterbird populations have faced declines over the last century, including the common tern (Sterna hirundo), a waterbird species with a widespread ...

Necropsy-based Wild Fish Health Assessment

Anthropogenic influences from increased nutrients and chemical contaminants, to habitat alterations and climate change, can have significant effects on fish populations. Adverse effects monitoring, utilizing biomarkers from the organismal to the molecular level, can be used to assess the cumulative effects on fishes and other organisms. Fish health has been used worldwide as an indicator of aquatic ecosystem health. The necropsy-based fish health assessment provides data on visible abnormalities and lesions, parasites, condition and organosomatic indices. These can be compared by site, season and sex, as well as temporally, to document change over time. Severity ratings can be assigned to various observations to calculate a fish health index for more quantitative assessment. A drawback of the necropsy-based assessment is that it is based on visual observations and condition factors, which are not as sensitive as tissue and subcellular biomarkers for sublethal effects. Additionally, it is rarely possible to identify causes or risk factors associated with observed abnormalities. So, for instance a raised lesion or "tumor" on the fins, lips or body surface may be a neoplasm. However, it could also be a response to a parasite, chronic inflammation or hyperplasia of normal cells in response to an irritant. Conversely, neoplasms, certain parasites, other infectious agents and many tissue changes are not visible and so may be underestimated. However, during the necropsy-based assessment, blood (plasma), tissues for histopathology (microscopic pathology), genomics and other molecular analyses, and otoliths for aging can be collected. These downstream analyses, together with geospatial analyses, habitat assessments, water quality and contaminant analyses can all be important in comprehensive ecosystem evaluations.

The health of wild fishes can be used as an indicator of aquatic ecosystem health. Necropsy-based fish health assessments provide documentation of visible lesions or abnormalities, data used to calculate condition indices as well as the opportunity to collect tissues for microscopic evaluation, gene expression and other more in-depth analyses.

Anthropogenic influences from increased nutrients and chemical contaminants, to habitat alterations and climate change, can have significant effects on ...

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive harvest or expensive equipment. We present a customizable and affordable rhizobox method that allows the non-destructive visualization of root growth over time and is particularly well-suited to studying root plasticity in response to localized resource patches. The method was validated by assessing maize genotypic variation in plasticity responses to patches containing 15N-labeled legume residue. Methods are described to obtain representative developmental measurements over time, measure root length density in resource-containing and control patches, calculate root growth rates, and determine 15N recovery by plant roots and shoots. Advantages, caveats, and potential future applications of the method are also discussed. Although care must be taken to ensure that experimental conditions do not bias root growth data, the rhizobox protocol presented here yields reliable results if carried out with sufficient attention to detail.

Visualizing and measuring root growth in situ is extremely challenging. We present a customizable rhizobox method to track root development and proliferation over time in response to nutrient enrichment. This method is used to analyze maize genotypic differences in root plasticity in response to an organic nitrogen source.

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Accurate estimations of leaf area index (LAI), defined as half of the total leaf surface area per unit of horizontal ground surface area, are crucial for describing the vegetation structure in the fields of ecology, forestry, and agriculture. Therefore, procedures of three commercially used methods (litter traps, needle technique, and a plant canopy analyzer) for performing LAI estimation were presented step-by-step. Specific methodological approaches were compared, and their current advantages, controversies, challenges, and future perspectives were discussed in this protocol. Litter traps are usually deemed as the reference level. Both the needle technique and the plant canopy analyzer (e.g., LAI-2000) frequently underestimate LAI values in comparison with the reference. The needle technique is easy to use in deciduous stands where the litter completely decomposes each year (e.g., oak and beech stands). However, calibration based on litter traps or direct destructive methods is necessary. The plant canopy analyzer is a commonly used device for performing LAI estimation in ecology, forestry, and agriculture, but is subject to potential error due to foliage clumping and the contribution of woody elements in the field of view (FOV) of the sensor. Eliminating these potential error sources was discussed. The plant canopy analyzer is a very suitable device for performing LAI estimations at the high spatial level, observing a seasonal LAI dynamic, and for long-term monitoring of LAI.

An accurate estimation of leaf area index (LAI) is crucial for many models of material and energy fluxes within plant ecosystems and between an ecosystem and the atmospheric boundary layer. Therefore, three methods (litter traps, needle technique, and PCA) for taking precise LAI measurements were in the presented protocol.

Accurate estimations of leaf area index (LAI), defined as half of the total leaf surface area per unit of horizontal ground surface area, are crucial for ...

A Guide to Concentration Alternating Frequency Response Analysis of Fuel Cells

An experimental setup capable of generating a periodic concentration input perturbation of oxygen was used to perform concentration-alternating frequency response analysis (cFRA) on proton-exchange membrane (PEM) fuel cells. During cFRA experiments, the modulated concentration feed was sent to the cathode of the cell at different frequencies. The electric response, which can be cell potential or current depending on the control applied on the cell, was registered in order to formulate a frequency response transfer function. Unlike traditional electrochemical impedance spectroscopy (EIS), the novel cFRA methodology makes it possible to separate the contribution of different mass transport phenomena from the kinetic charge transfer processes in the frequency response spectra of the cell. Moreover, cFRA is able to differentiate between varying humidification states of the cathode. In this protocol, the focus is on the detailed description of the procedure to perform cFRA experiments. The most critical steps of the measurements and future improvements to the technique are discussed.

We present a protocol for concentration alternating frequency response analysis of fuel cells, a promising new method of studying fuel cell dynamics.

An experimental setup capable of generating a periodic concentration input perturbation of oxygen was used to perform concentration-alternating frequency ...