Name: characterization of aquatic biofilms with flow cytometry
Uploaded: 2018-06-06T13:30:00
Description: Watch this Scientific Journal Video about Characterization of Aquatic Biofilms with Flow Cytometry at JoVE.com

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.<...

<ol>
	<li>Battin, T. J., Besemer, K., Bengtsson, M. M., Romani, A. M., Packmann, A. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+ecology+and+biogeochemistry+of+stream+biofilms.">The ecology and biogeochemistry of stream biofilms.</a> Nat Rev Microbiol. 14 (4), 251-263 (2016).</li><li>Rotter, S., Heilmeier, H., Altenburger, R., Schmitt-Jansen, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple+stressors+in+periphyton+-+comparison+of+observed+and+predicted+tolerance+responses+to+high+ionic+loads+and+herbicide+exposure.">Multiple stressors in periphyton - comparison of observed and predicted tolerance responses to high ionic loads and herbicide exposure.</a> J Appl Ecol. 50 (6), 1459-1468 (2013).</li><li>Tlili, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pollution-induced+community+tolerance+(PICT):+towards+an+ecologically+relevant+risk+assessment+of+chemicals+in+aquatic+systems.">Pollution-induced community tolerance (PICT): towards an ecologically relevant risk assessment of chemicals in aquatic systems.</a> Freshw Biol. 61 (12), 2141-2151 (2016).</li><li>Lavoie, I., Lavoie, M., Fortin, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+mine+of+information:+Benthic+algal+communities+as+biomonitors+of+metal+contamination+from+abandoned+tailings.">A mine of information: Benthic algal communities as biomonitors of metal contamination from abandoned tailings.</a> Science of the Total Environment. 425, 231-241 (2012).</li><li>Tlili, A., Montuelle, B., Berard, A., Bouchez, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+chronic+and+acute+pesticide+exposures+on+periphyton+communities.">Impact of chronic and acute pesticide exposures on periphyton communities.</a> Sci Total Environ. 409 (11), 2102-2113 (2011).</li><li>Dorigo, U., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lotic+biofilm+community+structure+and+pesticide+tolerance+along+a+contamination+gradient+in+a+vineyard+area.">Lotic biofilm community structure and pesticide tolerance along a contamination gradient in a vineyard area.</a> Aquat Microb Ecol. 50 (1), 91-102 (2007).</li><li>Pesce, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+single+and+joint+toxic+effects+of+diuron+and+its+main+metabolites+on+natural+phototrophic+biofilms+using+a+pollution-induced+community+tolerance+(PICT)+approach.">Evaluation of single and joint toxic effects of diuron and its main metabolites on natural phototrophic biofilms using a pollution-induced community tolerance (PICT) approach.</a> Aquat Toxicol. 99 (4), 492-499 (2010).</li><li>Ricart, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+low+concentrations+of+the+phenylurea+herbicide+diuron+on+biofilm+algae+and+bacteria.">Effects of low concentrations of the phenylurea herbicide diuron on biofilm algae and bacteria.</a> Chemosphere. 76 (10), 1392-1401 (2009).</li><li>Martinez, A., Kominoski, J. 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J., Traber, J., Kroll, A., Behra, R., Sigg, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+extracellular+polymeric+substances+(EPS)+from+periphyton+using+liquid+chromatography-organic+carbon+detection-organic+nitrogen+detection+(LC-OCD-OND).">Characterization of extracellular polymeric substances (EPS) from periphyton using liquid chromatography-organic carbon detection-organic nitrogen detection (LC-OCD-OND).</a> Environ Sci Pollut R. 20 (5), 3214-3223 (2013).</li><li>Kroll, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mixed+messages+from+benthic+microbial+communities+exposed+to+nanoparticulate+and+ionic+silver:+3D+structure+picks+up+nano-specific+effects,+while+EPS+and+traditional+endpoints+indicate+a+concentration-dependent+impact+of+silver+ions.">Mixed messages from benthic microbial communities exposed to nanoparticulate and ionic silver: 3D structure picks up nano-specific effects, while EPS and traditional endpoints indicate a concentration-dependent impact of silver ions.</a> Environ Sci Pollut R. 23 (5), 4218-4234 (2016).</li><li>Sgier, L., Freimann, R., Zupanic, A., Kroll, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flow+cytometry+combined+with+viSNE+for+the+analysis+of+microbial+biofilms+and+detection+of+microplastics.">Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics.</a> Nat Commun. 7, 11587 (2016).</li><li>Amir el, A. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=viSNE+enables+visualization+of+high+dimensional+single-cell+data+and+reveals+phenotypic+heterogeneity+of+leukemia.">viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia.</a> Nat Biotechnol. 31 (6), 545-552 (2013).</li><li>Mora-Gomez, J., Freixa, A., Perujo, N., Barral-Fraga, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Limits+of+the+biofilm+concept+and+types+of+aquatic+biofilms.">Limits of the biofilm concept and types of aquatic biofilms.</a> Aquatic Biofilms: Ecology, Water Quality and Wastewater Treatment. , 3-27 (2016).</li><li>Matthaei, C. D., Guggelberger, C., Huber, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Local+disturbance+history+affects+patchiness+of+benthic+river+algae.">Local disturbance history affects patchiness of benthic river algae.</a> Freshw Biol. 48 (9), 1514-1526 (2003).</li><li>Tlili, A., Hollender, J., Kienle, C., Behra, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Micropollutant-induced+tolerance+of+in+situ+periphyton:+Establishing+causality+in+wastewater-impacted+streams.">Micropollutant-induced tolerance of in situ periphyton: Establishing causality in wastewater-impacted streams.</a> Water Res. 111, 185-194 (2017).</li><li>Robinson, C. T., Rushforth, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+physical+disturbance+and+canopy+cover+on+attached+diatom+community+structure+in+an+idaho+stream.">Effects of physical disturbance and canopy cover on attached diatom community structure in an idaho stream.</a> Hydrobiologia. 154, 49-59 (1987).</li><li>Pomati, F., Nizzetto, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Assessing+triclosan-induced+ecological+and+trans-generational+effects+in+natural+phytoplankton+communities:+A+trait-based+field+method.">Assessing triclosan-induced ecological and trans-generational effects in natural phytoplankton communities: A trait-based field method.</a> Ecotoxicology. 22 (5), 779-794 (2013).</li><li>Hulliger, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Methoden zur Untersuchung und Beurteilung der Fliessgew&#228;sser. , (2007).</li><li>Tlili, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+situ+spatio-temporal+changes+in+pollution-induced+community+tolerance+to+zinc+in+autotrophic+and+heterotrophic+biofilm+communities.">In situ spatio-temporal changes in pollution-induced community tolerance to zinc in autotrophic and heterotrophic biofilm communities.</a> Ecotoxicology. 20 (8), 1823-1839 (2011).</li><li>Foladori, P., Laura, B., Gianni, A., Giuliano, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+sonication+on+bacteria+viability+in+wastewater+treatment+plants+evaluated+by+flow+cytometry+-+Fecal+indicators,+wastewater+and+activated+sludge.">Effects of sonication on bacteria viability in wastewater treatment plants evaluated by flow cytometry - Fecal indicators, wastewater and activated sludge.</a> Water Res. 41 (1), 235-243 (2007).</li><li>Buesing, N., Gessner, M. 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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 border="0" cellpadding="0" cellspacing="0" width="1095">
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Multimeter</td>
			<td style="width:123px;">WTW</td>
			<td style="width:344px;">MultiLine 3620 IDS</td>
			<td style="width:413px;">For measuring temperature, pH, dissolved oxygen</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Ultrasonic cleaner</td>
			<td>VWR International</td>
			<td>97043-986</td>
			<td>Tank dimesions: 15*14*10 cm</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Flow cytometer</td>
			<td>Beckman Coulter</td>
			<td>Gallios</td>
			<td style="width:413px;">Lasers: 405, 488 and 638 nm. Filters bands in Supplementary Table 11. Sgier et, Nat Comm, 2016.&#160;</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Plate reader</td>
			<td>Tecan</td>
			<td>Infinite M200</td>
			<td>used for selecting appropriate setting of the FC</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Cell sorter</td>
			<td style="width:123px;">Beckman Coulter</td>
			<td style="width:344px;">MoFlo Astrios</td>
			<td style="width:413px;">Settings in Supplementary Table 12. Sgier et, Nat Comm, 2016.&#160;</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Fluorescence microscope</td>
			<td style="width:123px;">Zeiss</td>
			<td style="width:344px;">Axiovert 135</td>
			<td style="width:413px;">Zeiss EC Plan-Neofluar 40x/0.75 objective</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">SOFTWARE</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">Matlab</td>
			<td>MathWorks</td>
			<td>R2013a</td>
			<td>software for numerical computing</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;">CYT</td>
			<td>Dana Pe&#39;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...

Watch this Scientific Journal Video about Characterization of Aquatic Biofilms with Flow Cytometry at JoVE.com

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 ...

This method can help to answer key questions in the field of environmental sciences, such as impacts of chemical pollution or climate change in aquatic ecosystems. The main advantage of this technique is that all the individual steps are simple and fast to perform. Although the focus of the procedure is to characterize autotrophic biofilms, it can also be used to characterize heterotrophic biofilms and detect particle pollutants such as microplastics.To begin, select an aquatic sampling site where biofilm grow, these are typically shallow parts of streams that have slow to intermediate water flow and stony streambeds. If the site doesn't have enough surface for biofilm attachment, you can use artificial substrates where biofilms can grow on. Measure the environmental conditions at the sampling site using portable instruments to measure conductivity, pH, water temperature and light intensity right above the surface of the biofilm to be sampled.Using protective gloves, collect stones that are similar in size, shape, and type from each site. The stones should be collected from areas that are exposed to similar flow and light conditions. In addition to biofilm sample collection and the measurement of the growth conditions on site, obtain water samples to measure water chemistry parameters.To process the biofilm samples, first filter 15 milliliters of stream water from the site through a 0.22-micrometer filter. Then, use a soft toothbrush to remove the biofilm from the substrate until it is completely suspended in a flask. Fix the biofilm samples using 0.01%paraformaldehyde and 0.1%glutaraldehyde.Transfer subsamples of the biofilm to a light microscope. Turn to the appropriate magnification for your sample and identify the species present in the biofilm samples. Prepare cultures for the individual species.Grow them continuously using similar environmental conditions to the environment where the biofilm samples were taken from. Once grown, disperse the cells by sonicating them for five to 10 seconds at 45 kilohertz. Place the dispersed cells into a 96-well plate.Once dispersed, record the absorbance and fluorescence spectra of the single species using a plate reader. Next, set up the flow cytometer with dichroic splitters and filters to cover the fluorescent ranges in which the different species were found to have specific properties. To prepare a single-species reference, use the optimized flow cytometry parameters to measure the optical and fluorescent properties of single species.Next, prepare damaged and decaying cells by taking a one milliliter subsample of diluted biofilm, centrifuging it at 8, 000 g for 10 minutes, and then re-suspending the pellet in one milliliter of 90%ethanol. Store the suspension overnight at four degrees Celsius, and repeat the process again the next day. Using the same optimized flow cytometry settings, measure the optical and fluorescent properties of the damaged and decaying cells.After setting the flow cytometer parameters and constructing the reference database, it's time to start collecting samples for the monitoring campaign. The samples should be collected and processed as was shown in previous steps, with the exception that sonication should take one minute at 45 kilohertz. Once ready to analyze, take samples from storage.To avoid blocking the flow cytometer, filter the subsamples through 50-micrometer pore filters, then load one to two milliliters of the individual samples into the flow cytometer and measure the optical and fluorescent properties of each particle. Repeat the measurement three times for each sample. Open MATLAB and run CYT as a toolbox.Then, import the reduced flow cytometry data as a csv file into the CYT software. Next, use the hyperbolic arcsine transformation to transform the fluorescent channels of all the samples. Enter 150 as the value of the co-factor.This value works for most phototrophic biofilms and flow cytometers, but optimization might be necessary. For quality control, plot and visually compare 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.Next, merge the technical replicates in each biological replicate and subsample the biological replicates so that each is represented by the same number of particles. The ideal number of particles analyzed by Barnes-Hut Stochastic Neighbor Embedding is around 150, 000. For comparison between samples, select only those samples that are to be compared and run Barnes-Hut Stochastic Neighbor Embedding.Once the algorithm is finished, new channels, named bh-SNE1 and bh-SNE2, appear. Visualize the data from the resulting channels as a scatter plot to show all of the Stochastic Neighbor Embedding coordinates containing all of the analyzed particles. If the clusters are not visually separable, too many or not enough particles were probably used in the analysis.Adjust the number of particles and repeat the analysis. 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 properties using the drawing tools.For subsequent statistical analysis, export the viSNE clusters into MATLAB. Using the procedure presented here, samples taken from several sites of a local stream in Switzerland were analyzed. Using viSNE maps, it is possible to distinguish between different environmental biofilms.Here six different samples feature different densities in various parts of the viSNE map. The viSNE maps shown here are populated by particles with different optical and fluorescence properties that give clues to the biotic/abiotic origin of the particles and to which taxonomic group to which the biotic particles belong. Additionally, projection of the reference database onto the viSNE map can help interpret which parts of the map are populated by which taxa and/or abiotic particles.It can also establish which subpopulations are different between the samples. Here, the number of particles for each subpopulation are shown for each of the different biological replicates. Once mastered, this technique can be done in two hours from sample collection to finished analysis.Following this procedure, other methods such as fluorescence-activated cell sorting can be used to validate the predictions of the visual clustering. After watching this video, you should have a good understanding how to use flow cytometry and visual clustering to analyze aquatic biofilms.

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JoVE Journal

Environment

Waste Water Derived Electroactive Microbial Biofilms: Growth, Maintenance, and Basic Characterization

The growth of anodic electroactive microbial biofilms from waste water inocula in a fed-batch reactor is demonstrated using a three-electrode setup ...

Characterization and Application of Passive Samplers for Monitoring of Pesticides in Water

Five different water passive samplers were calibrated under laboratory conditions for measurement of 124 legacy and current used pesticides. This study ...

Alternative Method of Removing Otoliths from Sturgeon

Extracting the otoliths (ear bones) from fish that have very thick skulls can be difficult and very time consuming. The common practice of making a ...

A Flow-through Exposure System for Evaluating Suspended Sediments Effects on Aquatic Life

This paper describes the Fish Larvae and Egg Exposure System (FLEES). The flow-through exposure system is used to investigate the effects of suspended ...

Identification of Pharmaceuticals in The Aquatic Environment Using HPLC-ESI-Q-TOF-MS and Elimination of Erythromycin Through Photo-Induced Degradation

Monitoring pharmaceuticals throughout the water cycle is becoming increasingly important for the aquatic environment and eventually for human health. ...

Improving Infrared Spectroscopy Characterization of Soil Organic Matter with Spectral Subtractions

Soil organic matter (SOM) underlies numerous soil processes and functions. Fourier transform infrared (FTIR) spectroscopy detects infrared-active organic ...

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine

Fish models and behaviors are increasingly used in the biomedical sciences; however, fish have long been the subject of ecological, physiological and ...

Sampling, Sorting, and Characterizing Microplastics in Aquatic Environments with High Suspended Sediment Loads and Large Floating Debris

The ubiquitous presence of plastic debris in the ocean is widely recognized by the public, scientific communities, and government agencies. However, only ...

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 ...