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.
