The presented work was supported by a SNF Ambizione Fellowship (PZ00P2_142533) and a Velux Research Grant (Amplebig). We would like to thank Bettina Wagner for help with the experimental work.

1. Ecosystem Selection and Biofilm Sampling
<ol>
	<li>Select an aquatic ecosystem of interest and find sampling sites where biofilms grow. Shallow parts of a stream with slow to medium water flow and a stony streambed for biofilm attachment are appropriate17,18.</li>
	<li>Optional: If the site does not have enough surfaces for biofilm attachment, or to decrease the biofilms variability within a site, place artificial substrates for biofilm attachment (e.g. glass slides, ceramic tiles) into the site, while making sure that they are placed in the same direction with respect to the water flow and at similar depth and light intensity19.</li>
	<li>To determine the environmental conditions at the sampling site, use portable instruments to measure light intensity, water temperature, conductivity, oxygen and pH right above the surface biofilm is to be sampled from. Over the course of the sampling, take repeated measures (3&#8211;5) in order to calculate mean values.</li>
	<li>Optional: Take water samples in order to determine relevant water chemistry parameters (dissolved organic carbon (DOC), K+, Na+, Ca2+, Mg2+, Cl-, F-, SO42-, PO42-, NO32-, SiO44-).</li>
	<li>Using protective gloves, collect comparable stones (in size, shape, and type) from each site (or artificial substrates). The stones should be exposed to similar flow and light conditions18.</li>
	<li>Filter enough stream water from the site through 0.22 &#956;m filters, so that it is ready for all collected samples (e.g., 15 mL per sample).</li>
	<li>Use a soft toothbrush to remove the biofilm from the substrate into a flask (brush until all biofilm is removed) with 15 mL of filtered stream water20.</li>
	<li>Fix the samples in 0.01% paraformaldehyde and 0.1% glutaraldehyde21.</li>
</ol>
2. Determination of Optimal Flow-cytometric (FC) Parameters
<ol>
	<li>Transfer subsamples to a light microscope (using a 40X/0.75 objective; if necessary use a 100 &#215; 1.3 Oil objective ) and identify the species present in the samples22,23.</li>
	<li>Order identified single species from appropriate culture collections (e.g. the Experimental Phycology and Culture Collection of Algae at the University of Goettingen [EPSAG] or Culture Collection of Algae at the University of Cologne [CCAC] if monitoring is performed in central European ecosystems).</li>
	<li>Grow the single species continuously in similar environmental conditions (e.g., light, temperature, pH) to the environment the biofilm samples were taken from. Staying within 1 &#176;C and 0.5 pH point from the environmental samples is recommended.</li>
	<li>To detach/disperse the cells, put 15 mL of single species samples into centrifuge tubes and put the tubes in the center of a water bath and submerge, so that the liquid surface of the sample is at the same level as the surface of the bath. Sonicate the samples for 1 min at 45 kHz24,25.</li>
	<li>Record the absorbance and fluorescence spectra of the single species using a plate reader. Please refer to plate reader of choice for instructions. In this example, 96-well flat bottom transparent polystyrol plates were loaded with 200 &#181;L sample volume and absorbance was measured. (Settings used: (a) fluorescence scan mode; emission wavelength step size 10 nm, emission scan number 30, bandwidth 20 nm, gain 100, 25 flashes, 20 us integration time or (b) absorbance scan mode; 25 flashes, bandwidth 9 nm).</li>
	<li>Set up the flow cytometer with lasers, dichroic splitters and filters that in combination cover all those fluorescent ranges in which different species have specific properties. For central European streams, a 405 nm, 488 nm and 638 nm laser produce useful results.</li>
	<li>Adjust the voltage setting of the photomultipliers (not applicable to all flow cytometers) and the range of the measured fluorescences to include at least 99% of all events of the samples.</li>
	<li>Alternatively, if knowledge on species present in the biofilms is available, start with step 2.2. If fluorescent properties are known, start with Step 2.6.</li>
</ol>
3. Optional: Preparation of a Flow-cytometric Reference Database
NOTE: FC does not allow taxonomic identification of cells present in the biofilm. A reference database of FC measurements of single species known to be present in the biofilms, grown in similar conditions as the biofilms, can help with the interpretation of the data.
<ol>
	<li>Reference: single species
	<ol>
		<li>Using the optimized FC parameters set up in 2.5&#8211;7, measure the optical and fluorescent properties of single species (fixed in paraformaldehyde and glutaraldehyde and filtered through filters of appropriate size for the flow cytometer).</li>
		<li>Normalize the data from single species in the same way as the data from the biofilms (see step 6.1.2).</li>
	</ol>
	</li>
	<li>Reference: damaged and decaying cells 
	One of the most important indicators of biofilm health is the fraction of damaged/dying cells present in the biofilm. To quantify these cells, a reference of decaying cells can be prepared.
	<ol>
		<li>Take 1 mL subsamples of diluted biofilms and centrifuge them at room temperature at 8,000 x g for 10 minutes in a tabletop centrifuge. Suspend the resulting pellet in 1 mL 90% ethanol and store the suspension overnight at 4 &#176;C. Repeat on the next day.</li>
		<li>Using the optimized FC settings, measure the optical and fluorescent properties of the damaged and decaying cells (fixed and filtered). &#160;</li>
	</ol>
	</li>
	<li>Reference: microplastics 
	NOTE: If interested in monitoring microplastic particles in the biofilms, check whether they differ enough from biotic particles in optical and fluorescence properties for identification.
	<ol>
		<li>Suspend the microplastic particles according to the instructions of the microplastics producer and measure their optical and fluorescent properties (using FC parameters from 2.7). &#160;</li>
		<li>Add the microplastic particles to the biofilm sample and leave over night at 4 &#176;C.</li>
		<li>Filter the suspension and measure with the same FC parameters as above.</li>
	</ol>
	</li>
</ol>
4. Sample Preparation and Storage
<ol>
	<li>Once the FC has been set up and the FC reference database is ready, one can proceed with the evaluation of fresh biofilm samples, collected according to Steps 1.1&#8211;7. To detach/disperse the biofilm, transfer 15 mL of the samples from the flasks (see Step 1.7) into centrifuge tubes.</li>
	<li>Put each tube in the center of a water bath and submerge so that the liquid surface of the sample is at the same level as the surface of the bath. Sonicate the samples for 1 min at 45 kHz.</li>
	<li>Fix the samples in 0.01% paraformaldehyde and 0.1% glutaraldehyde (stock in nanopure water). Store at 4 &#176;C until ready for analysis.</li>
	<li>Important: Use half of the samples for flow-cytometry analysis and keep half in storage for safety and for optional validation with fluorescence-activated cell sorting and/or light microscopy (see Section 6). 
	NOTE: Stored in the fridge, the fixed biofilm samples should keep their optical and fluorescent properties for up to three weeks.</li>
</ol>
5. Running the Sample Through the FC
<ol>
	<li>If the samples were stored in the fridge, sonicate them quickly or pipette several times to separate the particles. Filter the subsamples with 50 &#956;m filters (filter size depends on the width of the FC capillary) before measuring.</li>
	<li>Load the individual samples (1&#8211;2 mL, this depends on the flow cytometer used) into the flow cytometer and measure the optical and fluorescent properties of each particle. Repeat the measurement three times per each biological replicate (three technical replicates).</li>
	<li>Export the data from the FC in .csv format. &#160;</li>
</ol>
6. Data Preparation and Correlation Analysis
FC can analyze several parameters (e.g., signal area, height, width) for each measured optical property and fluorescence range. Run a correlation analysis on the measured variables and only keep those measurements that are not highly correlated. This usually removes all but one FC parameter (e.g., signal area) and can also remove highly correlated fluorescent measurements (usually stemming from the same laser and neighboring filters).
<ol>
	<li>Run CYT as a toolbox in Matlab:
	<ol>
		<li>Import the reduced FC data in .csv format from all the samples that are to be analyzed into the CYT software16, including all technical and biological replicates. (Click +, File filter *.csv, select files for import).</li>
		<li>Transform the fluorescent channels of all the samples using the hyperbolic arcsine transformation. The value of the cofactor needs to be selected; 150 works for most phototrophic biofilms and flow cytometers, but optimization might be necessary for particular experimental setups and heterotrophic biofilms. (Right-Click/Transform/Enter Cofactor 150)</li>
		<li>For quality control, visually compare (in CYT) the histograms of individual samples and individual optical and fluorescent channels to make sure there are no outliers at the technical and biological level of replication. Outliers will manifest in significantly shifted fluorescent/optical distributions between the replicates. (Select Data, Select Channel, Plot)
		<ol>
			<li>Alternative: run a principal component analysis (PCA) on the median values of fluorescent distributions and make sure that technical and biological replicates cluster together.</li>
		</ol>
		</li>
		<li>Merge the technical replicates in each biological replicate, and subsample the biological replicates, so that each is represented by the same number of particles (cells, cell fragment and abiotic particles) and so that the total number of particles analyzed by Barnes-Hut stochastic neighbor embedding (bh-SNE) is around 150,000 (for best visualization results26). (Select Data, Right-Click, Merge/Subsample)</li>
		<li>For comparison between samples, it is important to select only those samples that are to be compared. Select the samples and run bh-SNE27. Once the algorithm is finished, new channels, named bh-SNE1 and bh-SNE2 appear. These are the SNE coordinates of all analyzed particles, which can be used to visualize the data in a scatter plot (viSNE map). (Select Data, Select Channels, Right-Click, bh-SNE, Yes normalized data)</li>
		<li>In the viSNE map, particles are positioned according to similarity and grouped into visually separable clusters. Inspect the optical/fluorescent properties of the clusters and mark and name those that are well separated and have different optical/fluorescence properties by using the drawing tools available in CYT. (Select Channel bh-SNE1, bh-SNE2)</li>
		<li>Optional: If a reference database of single microbial species (measured with the same flow-cytometry setting and grown under similar conditions!) is available, export the viSNE maps into Matlab and project the reference database onto the maps. In this way, particular clusters can be connected to particular species/taxonomic groups (scripts available for download at https://github.com/anzezupanic/FC_analysis). (Session/Save)</li>
		<li>Troubleshooting: If the bh-SNE analysis does not return visually separable clusters, but a single large one, too many particles were used in the bh-SNE. Try again with fewer particles per sample. If there are no clusters, but rather sparse individual points, increase the number of particles used.</li>
	</ol>
	</li>
	<li>Optional: Instead of relying on bh-SNE for the clustering, other clustering methods (e.g., hierarchical, centroid-based or density based clustering) can be used on the normalized data and then superimposed on the viSNE map (https://github.com/anzezupanic/FC_analysis).</li>
	<li>Once the clusters have been identified, count the number of particles in each cluster belonging to each biological replicate in each sample. Assess differences between the samples, using ANOVA and Tukey&#8217;s test with appropriate (e.g., Holmes) correction for multiple comparison.</li>
</ol>
7. Optional: Validation of Cluster Interpretation Using Fluorescence-activated Cell Sorting
<ol>
	<li>Once the clusters have been identified, use fluorescence-activated cell sorting (FACS) to sort them out of the samples if desired, provided that the FACS has a similar (or identical) configuration of lasers and fluorescent filters.</li>
	<li>For each identified cluster, CYT provides optical and fluorescent gates that define the cluster. Use these gates to sort out the particles in each cluster with the FACS and then identify the sorted cells using light microscopy.</li>
</ol>

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The authors have nothing to disclose.

The protocol described above is relatively simple to implement. However, while the presented default settings have been shown to be suitable for all phototrophic biofilm tested thus far, optimization (as described in the protocol) is necessary to maximize the information obtained from the method. Indeed, the optical and fluorescent properties of biofilms can vary, depending on the environmental conditions (season, temperature, chemical composition of the water)1. Therefore, it is important to take...

Biofilms are dynamic consortia of microorganism that play a key role in freshwater ecosystems, ranging from primary production, nutrient cycling, and water purification to influencing the distribution of microorganisms and their biodiversity in the ecosystem1. When biofilms are exposed to changing environmental conditions or to stressors, such as chemicals, their community structure quickly shifts towards more tolerant species2,3. Their high sensitivity turns biofilms into attractive model systems for environmental monitoring4, however none of the current methods is perfectly suited to actually trace the dynamic of a biofilm community in a fast and easy way.
The commonly used set of methods to characterize biofilms consists of the measurement of functional and structural endpoints. At the level of the entire community, photosynthetic and respiratory activity as well as the activity of extracellular enzymes provides information on the functional state of the biofilm5,6,7,8,9. Biomass accrual is used as an indicator for overall biofilm growth. Structural changes are currently measured either by using traditional species identification with light microscopy or with nucleotide-based techniques (e.g., denaturing gradient gel electrophoresis (DGGE), automated ribosomal intergenic spacer analysis (ARISA), metagenomics)10,11,12. These methods provide information but are time-consuming to perform, or require specific knowledge, or are still under development. Finally, new methods for the evaluation of the extracellular polymeric substances (EPS)13,14 and the biofilm architecture1, while sensitive, are low-throughput and have not yet been developed towards monitoring purposes.
It is evident that for a complete characterization of freshwater biofilms, it is necessary to combine several different methods, which provide insight into the biofilm function, composition and architecture. For environmental monitoring, on the other hand, a fast and sensitive method that is able to detect changes in the biofilm and enable basic biological interpretation of the shifts at the functional and structural level is required.
We have developed a new method for microbial community characterization of the phototrophic component of stream biofilms (periphyton), which is fast enough for monitoring purposes, and at the same time provides sufficient information on the biofilm community structure to allow biological interpretation15. It is based on single-cell flow cytometry (FC) of biofilm samples and coupled with computational visualization and provides information on optical and fluorescent properties of the biofilm on the single cell level.
The workflow after biofilm sampling consists of sample preparation in the form of sonication, fixation and size-based filtering of the samples followed by assessing the sample by flow cytometry. The acquired data provide information on several cellular traits in a single measurement: particle size, density, pigment content, abiotic content (e.g. microplastics) in the biofilm. This set of data is than analyzed via computational visualization using visual stochastic neighbor embedding (viSNE)16, which enables fast and easy interpretation of the data. Although a few weeks are required to set up and optimize the method, once set up, it takes only a few hours from collecting the biofilm samples to interpreting the results.
The main advantages of the presented method over others are the speed of analysis and high-information-content. Moreover, the samples can be stored for several weeks after collection without loss of their optical and fluorescence properties. This can be very useful when characterization of a large number of samples is required, such as large sampling studies or biomonitoring programs, but can also provide a substantial amount of information in smaller exploratory studies.
The presented protocol is based on flow-cytometric analysis of phototrophic biofilms (periphyton) collected from different locations of a stream. Many steps of the protocol, e.g. the selection of sites appropriate for monitoring purposes, depend on the goals of the research and therefore cannot be prescribed. Others allow less freedom and require that the protocol is followed closely, this is made clear in the detailed protocol below.
The protocol starts with the selection of sites for biofilm-based environmental monitoring. The next step is to set-up the flow-cytometer (FC) so that its measurements enable discrimination between different phototrophic organisms living in biofilms in the monitored environment. This is performed by collecting biofilm samples from the sites, identifying the fluorescent properties of different species present in the biofilm and setting up the flow-cytometer with lasers and filters that enable measurement at the appropriate optical wavelengths. Once the FC has been set-up, biofilms can be collected from the sites periodically, the optical and fluorescent properties of the single particles present in the biofilm measured by flow-cytometry, and the data analyzed by visual clustering. For better interpretation of the results, it is possible to build a FC reference database of local biofilm-living species and their phenotypes and use the database to identify different taxonomies in the FC data. Validation is possible through fluorescence-based sorting of the identified clusters of single cells using FACS and microscopy-based taxonomic identification of the present species. A schematic of the protocol is given in Figure 1.

<table><tbody><tr><td>Multimeter</td><td>WTW</td><td>MultiLine 3620 IDS</td><td>For measuring temperature, pH, dissolved oxygen</td></tr><tr><td>Ultrasonic cleaner</td><td>VWR International</td><td>97043-986</td><td>Tank dimesions: 15*14*10 cm</td></tr><tr><td>Flow cytometer</td><td>Beckman Coulter</td><td>Gallios</td><td>Lasers: 405, 488 and 638 nm. Filters bands in Supplementary Table 11. Sgier et, Nat Comm, 2016.&amp;nbsp;</td></tr><tr><td>Plate reader</td><td>Tecan</td><td>Infinite M200</td><td>used for selecting appropriate setting of the FC</td></tr><tr><td>Cell sorter</td><td>Beckman Coulter</td><td>MoFlo Astrios</td><td>Settings in Supplementary Table 12. Sgier et, Nat Comm, 2016.&amp;nbsp;</td></tr><tr><td>Fluorescence microscope</td><td>Zeiss</td><td>Axiovert 135</td><td>Zeiss EC Plan-Neofluar 40x/0.75 objective</td></tr><tr><td>SOFTWARE</td><td></td><td></td><td></td></tr><tr><td>Matlab</td><td>MathWorks</td><td>R2013a</td><td>software for numerical computing</td></tr><tr><td>CYT</td><td>Dana Pe&#039;er Lab</td><td>Version 1.1</td><td>free interactive visualization tool for analysis of cytometry data</td></tr></tbody></table>

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.

Using the procedure presented here (Figure 1), samples taken from several sites of a local stream in Switzerland were analyzed. At each site, three similar sized stones (10&#8211;12 cm) were taken, and biofilms were brushed off the stones. The samples were then sonicated, fixed, filtered and later analyzed by flow cytometry. The setup of the flow cytometer and the reference database used were the same as described in a previous paper15...

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Characterization of Aquatic Biofilms with Flow Cytometry

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 ...

characterization-of-aquatic-biofilms-with-flow-cytometry

Research

JoVE Journal

Environment

8.9K Views.  Eawag - Swiss Federal Institute for Aquatic Science and Technology. 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.

Video: Characterization of Aquatic Biofilms with Flow Cytometry

Unraveling the Unseen Players in the Ocean - A Field Guide to Water Chemistry and Marine Microbiology

Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling protocols include the assessment of resources available to the microbial community (dissolved organic carbon, particulate organic matter, inorganic nutrients), and a comprehensive description of the viral and bacterial communities (via direct viral and microbial counts, enumeration of autofluorescent microbes, and construction of viral and microbial metagenomes). We use a combination of methods, which represent a dispersed field of scientific disciplines comprising already established protocols and some of the most recent techniques developed. Especially metagenomic sequencing techniques used for viral and bacterial community characterization, have been established only in recent years, and are thus still subjected to constant improvement. This has led to a variety of sampling and sample processing procedures currently in use. The set of methods presented here provides an up to date approach to collect and process environmental samples. Parameters addressed with these protocols yield the minimum on information essential to characterize and understand the underlying mechanisms of viral and microbial community dynamics. It gives easy to follow guidelines to conduct comprehensive surveys and discusses critical steps and potential caveats pertinent to each technique.

Here, we present a comprehensive protocol to assess the organic and inorganic nutrient availability and the abundance and structure of microbial and viral communities in remote marine environments.

Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling ...

Pseudomonas aeruginosa and Saccharomyces cerevisiae Biofilm in Flow Cells

Many microbial cells have the ability to form sessile microbial communities defined as biofilms that have altered physiological and pathological properties compared to free living microorganisms. Biofilms in nature are often difficult to investigate and reside under poorly defined conditions1. Using a transparent substratum it is possible to device a system where simple biofilms can be examined in a non-destructive way in real-time: here we demonstrate the assembly and operation of a flow cell model system, for in vitro 3D studies of microbial biofilms generating high reproducibility under well-defined conditions2,3.
The system consists of a flow cell that serves as growth chamber for the biofilm. The flow cell is supplied with nutrients and oxygen from a medium flask via a peristaltic pump and spent medium is collected in a waste container. This construction of the flow system allows a continuous supply of nutrients and administration of e.g. antibiotics with minimal disturbance of the cells grown in the flow chamber. Moreover, the flow conditions within the flow cell allow studies of biofilm exposed to shear stress. A bubble trapping device confines air bubbles from the tubing which otherwise could disrupt the biofilm structure in the flow cell.
The flow cell system is compatible with Confocal Laser Scanning Microscopy (CLSM) and can thereby provide highly detailed 3D information about developing microbial biofilms. Cells in the biofilm can be labeled with fluorescent probes or proteins compatible with CLSM analysis. This enables online visualization and allows investigation of niches in the developing biofilm. Microbial interrelationship, investigation of antimicrobial agents or the expression of specific genes, are of the many experimental setups that can be investigated in the flow cell system.

Pseudomonas aeruginosa and Saccharomyces cerevisiae Biofilm in Flow Cells

Protocol describing the application of a flow cell system for growing and analyzing microbial biofilms for Confocal Laser Scanning Microscopy (CLSM).

Many microbial cells have the ability to form sessile microbial communities defined as biofilms that have altered physiological and pathological ...

Immunology and Infection

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 ...

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.

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 ...

Fluorescently Labeled Bacteria as a Tracer to Reveal Novel Pathways of Organic Carbon Flow in Aquatic Ecosystems

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial communities has many limitations, and determining a predator, prey, and predatory rates is often difficult. Presented here is an optimized method based on the addition of fluorescently labelled prey as a tracer, which allows for reliable quantitation of the grazing rates in aquatic predatory eukaryotes and estimation of nutrient transfer to higher trophic levels.

Presented here is a protocol for a single-cell, epifluorescence microscopy-based technique to quantify grazing rates in aquatic predatory eukaryotes with high precision and taxonomic resolution.

Elucidating trophic interactions, such as predation and its effects, is a frequent task for many researchers in ecology. The study of microbial ...

Concurrent Quantification of Cellular and Extracellular Components of Biofilms

Confocal laser scanning microscopy (CLSM) is a powerful tool for investigation of biofilms. Very few investigations have successfully quantified concurrent distribution of more than two components within biofilms because: 1)&#160;selection of fluorescent dyes having minimal spectral overlap is complicated, and 2)&#160;quantification of multiple fluorochromes poses a multifactorial problem. Objectives: Report a methodology to quantify and compare concurrent 3-dimensional distributions of three cellular/extracellular components of biofilms grown on relevant substrates. Methods: The method consists of distinct, interconnected steps involving biofilm growth, staining, CLSM imaging, biofilm structural analysis and visualization, and statistical analysis of structural parameters. Biofilms of Streptococcus mutans (strain UA159) were grown for 48 hr on sterile specimens of Point 4 and TPH3 resin composites. Specimens were subsequently immersed for 60 sec in either Biot&#232;ne PBF (BIO) or Listerine Total Care (LTO) mouthwashes, or water (control group; n=5/group). Biofilms were stained with fluorochromes for extracellular polymeric substances, proteins and nucleic acids before imaging with CLSM. Biofilm structural parameters calculated using ISA3D image analysis software were biovolume and mean biofilm thickness. Mixed models statistical analyses compared structural parameters between mouthwash and control groups (SAS software; &#945;=0.05). Volocity software permitted visualization of 3D distributions of overlaid biofilm components (fluorochromes). Results: Mouthwash BIO produced biofilm structures that differed significantly from the control (p&#60;0.05) on both resin composites, whereas LTO did not produce differences (p&#62;0.05) on either product. Conclusions: This methodology efficiently and successfully quantified and compared concurrent 3D distributions of three major components within S. mutans biofilms on relevant substrates, thus overcoming two challenges to simultaneous assessment of biofilm components. This method can also be used to determine the efficacy of antibacterial/antifouling agents against multiple biofilm components, as shown using mouthwashes. Furthermore, this method has broad application because it facilitates comparison of 3D structures/architecture of biofilms in a variety of disciplines.

A protocol for the concurrent quantification and comparison of three cellular and extracellular components within biofilms is presented. The methodology involves the use of confocal laser scanning microscopy, biofilm structural analysis and visualization software, and statistical analysis software.

Confocal laser scanning microscopy (CLSM) is a powerful tool for investigation of biofilms. Very few investigations have successfully quantified ...

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

A New Method for Qualitative Multi-scale Analysis of Bacterial Biofilms on Filamentous Fungal Colonies Using Confocal and Electron Microscopy

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic cooperation, competition, or predation. The study of biofilms is important in many biological fields, including environmental science, food production, and medicine. However, few studies have focused on such bacterial biofilms, partially due to the difficulty of investigating them. Most of the methods for qualitative and quantitative biofilm analyses described in the literature are only suitable for biofilms forming on abiotic surfaces or on homogeneous and thin biotic surfaces, such as a monolayer of epithelial cells.
While laser scanning confocal microscopy (LSCM) is often used to analyze in situ and in vivo biofilms, this technology becomes very challenging when applied to bacterial biofilms on fungal hyphae, due to the thickness and the three dimensions of the hyphal networks. To overcome this shortcoming, we developed a protocol combining microscopy with a method to limit the accumulation of hyphal layers in fungal colonies. Using this method, we were able to investigate the development of bacterial biofilms on fungal hyphae at multiple scales using both LSCM and scanning electron microscopy (SEM). This report describes the protocol, including microorganism cultures, bacterial biofilm formation conditions, biofilm staining, and LSCM and SEM visualizations.

This protocol describes a new method to grow and qualitatively analyze bacterial biofilms on fungal hyphae by confocal and electron microscopy.

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic ...

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

Real-time Imaging and Quantification of Fungal Biofilm Development Using a Two-Phase Recirculating Flow System

In oropharyngeal candidiasis, members of the genus Candida must adhere to and grow on the oral mucosal surface while under the effects of salivary flow. While models for the growth under flow have been developed, many of these systems are expensive, or do not allow imaging while the cells are under flow. We have developed a novel apparatus that allows us to image the growth and development of Candida albicans cells under flow and in real-time. Here, we detail the protocol for the assembly and use of this flow apparatus, as well as the quantification of data that are generated. We are able to quantify the rates that the cells attach to and detach from the slide, as well as to determine a measure of the biomass on the slide over time. This system is both economical and versatile, working with many types of light microscopes, including inexpensive benchtop microscopes, and is capable of extended imaging times compared to other flow systems. Overall, this is a low-throughput system that can provide highly detailed real-time information on the biofilm growth of fungal species under flow.

We describe the assembly, operation, and cleaning of a flow apparatus designed to image fungal biofilm formation in real time while under flow. We also provide and discuss quantitative algorithms to be used on the acquired images.

In oropharyngeal candidiasis, members of the genus Candida must adhere to and grow on the oral mucosal surface while under the effects of salivary flow. ...

Characterization of Aquatic Biofilms with Flow Cytometry

Summary

Explore More Videos

Chapters in this video

Unraveling the Unseen Players in the Ocean - A Field Guide to Water Chemistry and Marine Microbiology

Pseudomonas aeruginosa and Saccharomyces cerevisiae Biofilm in Flow Cells

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

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

Fluorescently Labeled Bacteria as a Tracer to Reveal Novel Pathways of Organic Carbon Flow in Aquatic Ecosystems

Concurrent Quantification of Cellular and Extracellular Components of Biofilms

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

A New Method for Qualitative Multi-scale Analysis of Bacterial Biofilms on Filamentous Fungal Colonies Using Confocal and Electron Microscopy

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

Real-time Imaging and Quantification of Fungal Biofilm Development Using a Two-Phase Recirculating Flow System