The authors would like to acknowledge Cystic Fibrosis Foundation for providing funding for this research.

1. Bacterial isolate collection
<ol>
	<li>Obtain P. aeruginosa isolates from a cohort of pediatric patients with CF undergoing eradication treatment with inhaled tobramycin at SickKids (Toronto). Freeze isolates at -80 &#176;C in glycerol citrate and sub-culture at least three times prior to use.</li>
</ol>
2. In vitro biofilm formation
NOTE: Use a chambered coverglass method1 for in vitro biofilm formation with modifications. The overall workflow of this model is shown in Figure 1.
<ol>
	<li>Grow P. aeruginosa isolate overnight at 37 &#176;C on blood agar prepared with tryptic soy agar and 5% sheep blood (see Table of Materials).</li>
	<li>Inoculate 1&#8211;2 bacterial colonies from the blood agar into 4 mL of lysogeny broth (LB). Grow overnight at 37 &#176;C on a shaker set to 225 rpm.</li>
	<li>Prepare a 1:100 dilution of overnight inoculum by adding 40 &#956;L of the culture in 4 mL of fresh LB. Grow for 3&#8211;4 h at 37 &#176;C on a shaker set to 225 rpm, to achieve an optical density of approximately 0.1 at 600 nm (OD600) (early log phase).</li>
	<li>Transfer 220 &#956;L of the inoculum to each well of an 8-chambered coverglass slide. Incubate undisturbed at 37 &#176;C for 24 h.</li>
	<li>Slowly remove the medium from each well to prevent the biofilms from detaching at the base. 
	NOTE: Tilt the slide forward at a 45&#176; angle and aspirate the medium from the bottom corners of each chambered well without touching the base with pipette tip.</li>
	<li>Prepare and slowly add 100 &#956;L of 56 &#956;g/mL fluorescent&#160;labelled monoclonal antibody (mAb) (see Table of Materials) to the side of the designated chambered wells. Incubate at room temperature (RT) for 1 h to allow antibody attachment to the bacterial antigen epitope. 
	NOTE: Prior to use, dilute the fluorescent&#160;labelled (red) mAb, Psl0096, in LB to obtain a final concentration of 56 &#956;g/mL. Psl0096 is an anti-Psl mAb (optimized affinity derivative of Cam003), which binds to the class I Psl epitope&#8212;a key EPS matrix component of P. aeruginosa biofilms involved in initial cell attachment and structural integrity15.</li>
	<li>Prepare and slowly add 100 &#956;L of a 1000 &#956;g/mL antibiotic solution (see Table of Materials) to the side of the designated chambered wells. Incubate undisturbed at 37 &#176;C for 24 h. 
	NOTE: Prior to use, dilute a 50 mg/mL stock of tobramycin antibiotic in LB to obtain a final concentration of 1000 &#956;g/mL.</li>
</ol>
3. Biofilm fluorescent staining
<ol>
	<li>Prepare a 0.01 mM solution of a live-cell-staining fluorescent dye. Slowly remove the medium from the chambered wells and add 200 &#956;L of the dye mixture to each well. Incubate at RT in the dark for 45 min. 
	NOTE: Prior to use, prepare the live cell staining (green) fluorescent dye by adding 4 &#956;L of a 5 mM stock to 2 mL of LB.</li>
	<li>Slowly remove the medium from each well, and wash 2x with 200 &#956;L of fresh LB.</li>
	<li>Add 200 &#956;L of fresh LB to each well and proceed to examination via confocal microscopy.</li>
</ol>
4. Image acquisition by confocal microscopy
NOTE: The image-processing and COMSTAT analysis procedure is presented in Figure 2. Acquire&#160;images of&#160;wells on the same day of biofilm staining. If delay in visualization exceeds 1 h, refrigerate the chambered coverglass in the dark until further processing.
<ol>
	<li>Acquire images of wells using a confocal microscope system (see Table of Materials) with appropriate laser excitation wavelengths and filter sets for acquisition. 
	NOTE: Here, excite the fluorescent&#160;labeled (red) mAb and live cell (green) stain using 561 and 491 nm excitation wavelengths, respectively.</li>
	<li>Capture layered z-stack images (from the substratum to the top of each biofilm segment) in increments of 0.3 &#956;m with a 20&#8211;25x water immersion lens. Take a at least 6 image stacks per well. 
	NOTE: Here, visualize the images using a high-resolution camera with a 25x water immersion lens and processed using image analysis software (see Table of Materials). Keep software setup and digital imaging parameters (i.e., brightness and sensitivity) constant for all acquisitions in a single experiment.</li>
	<li>Save images as OME-TIFFs for COMSTAT analysis. 
	NOTE: Ensure that OME-TIFFs are saved separately for each channel (i.e., red and green). This step varies depending on the image analysis software used.</li>
	<li>Repeat steps 2.1&#8211;4.3&#160;to capture images from a total of 3 biological replicates (i.e., 3 independent experiments) per bacterial isolate.</li>
</ol>
5. COMSTAT analysis
NOTE: Analyze images quantitatively using the freely available computer program, COMSTAT16,17, rewritten as a plugin (Comstat2) to ImageJ. Read the general instructions for analyzing image stacks of biofilms within the downloaded package. This contribution provides a summarized protocol, with selected ImageJ processing steps and COMSTAT features recommended for quantifying the effects of antimicrobials on biofilm formation.
<ol>
	<li>Download the Comstat2 package from http://www.comstat.dk/. Within the installed folder, locate ImageJ and run it.</li>
	<li>Create a source folder on desktop and add a single OME-TIFF to the folder.</li>
	<li>Open OME-TIFF from the source folder, and delete any empty layers containing no biomass. These layers will be either the first or last few layers of the z-stack. 
	NOTE: Microscope-defined z-stack boundaries are sometimes overestimated by users. Deleting these empty layers establishes a more refined z-stack boundary for COMSTAT analysis.</li>
	<li>Import OME-TIFF in ImageJ by selecting File | Import | Image Sequence. Locate the source folder, highlight without opening it, and click Select. A &#8216;Sequence Options&#8217; window will appear. Select OK. 
	NOTE: To import additional images in ImageJ, first remove the previous OME-TIFF from the source folder, then add the new OME-TIFF to the folder, and repeat steps 5.3 and 5.4.</li>
	<li>Flip the orientation of the biofilm by selecting Image | Transform | Flip Z&#160;to position the substratum as the first (topmost) stack. 
	NOTE: COMSTAT algorithms read biofilms in the z-direction from top (stack 1) to bottom. Depending on the confocal microscope system used, the OME-TIFF output can be inverted. Thus, it&#160;is important to reverse the order of slices by positioning the substratum as image stack 1 to prevent output data from becoming flawed.</li>
	<li>Define image properties by selecting Image | Properties. A &#8216;Source&#8217; window will appear.
	<ol>
		<li>Specify &#8216;Unit of Length&#8217; as &#8216;micron&#8217;.</li>
		<li>Mathematically determine &#8216;Pixel width&#8217; and &#8216;Pixel height&#8217; using the following equation: 
		<img alt="Equation 1" src="/files/ftp_upload/61759/61759equ01.jpg" /> (1) 
		Here, &#8216;Pixel width&#8217; and &#8216;Pixel height&#8217; are defined as 0.427, where the physical length of a pixel on the charge-coupled device (CCD) is 16 &#956;m; total magnification is 25x; and magnifier position is 1.5x. 
		NOTE: The equation used to calculate cell size per pixel may vary depending on the microscope camera manufacturer. Alternately, &#8216;Pixel width&#8217; and &#8216;Pixel height&#8217; can be defined by spatial calibration (refer to https://imagej.net/Spatial_Calibration).</li>
		<li>Define &#8216;Voxel depth&#8217; as 0.3 (i.e., incremental space between each z-stack layer). Select OK in the &#8216;Source&#8217; window.</li>
	</ol>
	</li>
	<li>Adjust the image threshold by selecting Image | Adjust | Threshold. A &#8216;Threshold&#8217; window will appear. 
	NOTE: Objects in the image will appear red with a greyscale background. Alternatively,&#160;threshold can be adjusted in black-and-white, by using&#160;the drop-down menu in the &#8216;Threshold&#8217; window to change &#8216;Red&#8217; to &#8216;B&#38;W&#8217;.
	<ol>
		<li>In the image window, adjust the slider to the far right (i.e., topmost layer of the biofilm). To remove background noise, use the &#8216;Threshold&#8217; window, which displays a histogram of the image, to manually set the maximum and minimum threshold values. First, set the maximum threshold value by adjusting the lower slider as far right as possible. Second, use the upper slider to adjust the minimum threshold value, which segments the image into two separate phases: red biomass and greyscale&#160;background (Figure 3). 
		NOTE: The in vitro biofilm formation and fluorescent microscopy procedure described herein generates OME-TIFFs in the ideal case, allowing for images to be segmented into two distinct phases by means of a simple histogram threshold method. However, in some cases, the histogram distinction between the different phases is not as clear. This can be due to the presence of extensive background noise, varying background intensities, or a low intensity contrast between biomass and background. In such cases, users should adopt an enhanced segmentation procedure18,19.
		<ol>
			<li>Alternatively, adjust thresholds algorithmically for individual images using the left drop-down menu set as &#8216;Default&#8217; in the &#8216;Threshold&#8217; window. This feature provides 17 different algorithmic threshold options to choose from (refer to https://imagej.net/Auto_Threshold). Select the most applicable option, then &#8216;Auto&#8217; to set threshold.</li>
		</ol>
		</li>
		<li>When threshold values are adjusted, use the slider in the image window to scroll through each layer to ensure that background noise is sufficiently removed throughout.</li>
		<li>Select Set in the &#8216;Threshold&#8217; window to first fix the lower threshold value. A &#8216;Set Threshold Levels&#8217; window will appear. Select OK. Select Set again and repeat this step to fix the maximum threshold value. 
		NOTE: Each time &#8216;Set&#8217; is selected, the lower slider may adjust automatically. In such cases, manually re-adjust the slider (or select &#8216;Auto&#8217; if using one of the algorithmic thresholds) and repeat steps 5.6.2&#8211;5.6.3. The main idea is that whenever the sliders are re-adjusted automatically, &#8216;Set&#8217; should be selected an additional two times afterwards to ensure that both the upper and lower thresholds are fixed.</li>
		<li>Select Apply and a &#8216;Convert Stack to Binary&#8217; window will appear. Select OK, and then exit the &#8216;Threshold&#8217; window.</li>
		<li>Save the newly adjusted OME-TIFF by selecting Plugins | Bio-Formats | Bio-Formats Exporter|. Enter a new file name and save as OME-TIFF in the source folder. A &#8216;Bio-Formats Exporter &#8211; Multiple Files&#8217; window will appear. Select OK. A &#8216;Bio-Formats Exporter Options&#8217; window will appear. Select OK. 
		NOTE: Ensure that only the newly adjusted, black-and-white OME-TIFF(s) are saved in the source folder before proceeding to COMSTAT analysis. Remove all original OME-TIFFs from the folder.</li>
	</ol>
	</li>
	<li>Run COMSTAT by selecting Plugins | Comstat2 |. An &#8216;About&#8217; window will appear. Select OK. Three windows will appear.
	<ol>
		<li>In the &#8216;Observed Directories&#8217; window (top right), select Add. Locate the source folder, highlight without opening it, and select&#160;Choose. An &#8216;Images in Directories&#8217; window will appear (top left) that lists the OME-TIFFs to be analyzed via COMSTAT.</li>
		<li>On the &#8216;Comstat 2.1&#8217; window (bottom right), de-select &#8216;Automatic thresholding (Otsu&#8217;s method)&#8217; to ensure that the software uses threshold values previously set up for individual OME-TIFFs. Also, de-select Connected Volume Filtering (CVF) to ensure that very thin parts of the biofilm as well as free floating cells or biomass found within voids of the biofilm structure are included in the analysis. 
		NOTE: Here, de-select CVF because COMSTAT analysis is performed on very early biofilms (24 h initial growth) and remaining planktonic cells/colonies post antimicrobial treatment. For mature biofilms, select CVF to ensure that only biomass connected to the biofilm structure is quantified.</li>
		<li>On the &#8216;Comstat 2.1&#8217; window (bottom right), select desired features for quantitative analysis. Here, select Bio Mass, Thickness Distribution, and Surface Area. Select Go to run the program. In the &#8216;Log&#8217; window (bottom left), the output data are shown processing until &#8216;Done with selected functions/images!&#8217; appears. Record the COMSTAT measurements. These measurements are also automatically saved as TXT files in the source folder.</li>
	</ol>
	</li>
</ol>

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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phenazines+affect+biofilm+formation+by+Pseudomonas+aeruginosa+in+similar+ways+at+various+scales.">Phenazines affect biofilm formation by Pseudomonas aeruginosa in similar ways at various scales.</a> Research in Microbiology. 161, 187-191 (2010).</li><li>Ma, L., Jackson, K. D., Landry, R. M., Parsek, M. R., Wozniak, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Pseudomonas+aeruginosa+conditional+psl+variants+reveals+roles+for+the+psl+polysaccharide+in+adhesion+and+maintaining+biofilm+structure+postattachment.">Analysis of Pseudomonas aeruginosa conditional psl variants reveals roles for the psl polysaccharide in adhesion and maintaining biofilm structure postattachment.</a> Journal of Bacteriology. 188, 8213-8221 (2006).</li></ol>

There is no prescribed method for quantitatively comparing three-dimensional images of in vitro biofilm structures, and procedures described in this context are often difficult to standardize due to inter-operator variability20. Thus, this protocol offers a simple and reproducible framework for COMSTAT applications seeking to quantify changes in in vitro biofilm architecture under varying antimicrobial conditions. The strengths of this technique are modeled using a CF P. aeruginosa isolate, grown in the form of biofilm replicates, and exposed to tobramycin and the anti-Psl mAb, Psl0096.
One of the most critical steps outlined in this protocol, as well as in related work, is image thresholding13,21. The image thresholding procedure is a segmentation technique, which reduces greyscale images into a readable black-and-white format for COMSTAT algorithms. During this process, it is important to effectively differentiate which greyscale pixels should be considered biomass (black) versus background noise (white), otherwise the threshold value will change the volume and morphology assigned to an individual biofilm image22. Thresholding can be performed in many different ways in ImageJ: manually or algorithmically by one of 17 options; set for individual images or by applying a fixed value (determined by the user) to a group of images17; applied to the entire image (global); or algorithmically applied to specific regions (local)23,24 of an 8-bit image. The chosen method for thresholding and how it is performed is up to the discretion of the operator, which can introduce significant errors in biofilm image analysis and lead to vastly different results.
In the present protocol, global thresholding was performed manually to segment individual, 16-bit z-stack images. Although algorithmic methods have advantages over manual, including elimination of user intervention13,22 and reduced time spent on image-processing (especially for large data sets)25, they do not account for the wide characteristic variability of images from heterogenous samples, as observed among P. aeruginosa isolates26,27,28. Previous work suggests that to numerically correlate biofilm activity with architectural changes, it is necessary to threshold images in a way that emulate biofilm heterogeneity29,30. Available algorithms in ImageJ for thresholding are considered less suitable given their inconsistencies with expert-determined values30, and technological limitations, including an inability to segment discrete geometric properties22. These algorithms are also limited because very few studies have evaluated their strengths and weaknesses with respect to manual operations. Without a consensus on the best algorithm for thresholding, manual eye-based methods in ImageJ remain most suitable13,17.
Apart from thresholding, CVF is another segmentation process that can be used to remove additional background noise from three-dimensional biofilm images (e.g., planktonic elements that are not attached to the biofilm structure). However, when analyzing early biofilms, utilizing CVF runs the risk of generating null or unexpectedly low COMSTAT outputs because the function assumes that all biofilms grow in a connected fashion from the substratum31. A biofilm may consist of spatial structures, such as voids or channels filled with fluids and floating cells32,33, which CVF inaccurately excludes from the analysis. In the study described herein, de-selection of CVF is important, especially because COMSTAT is performed to quantify the remaining planktonic cells/colonies post antimicrobial treatment. Similarly, under certain conditions, different bacterial strains produce very thin areas within the biofilm that are undetected by the CVF feature due to the set voxel depth16 (in this case, 0.3 &#956;m), and are subsequently removed from the analysis. Therefore, the present study de-selects CVF to prevent null outputs or undercalculations of biomass.
A final critical step of the protocol involves selecting quantitative analysis functions that best represent morphological changes to biofilm architecture. Here, biomass, thickness distribution, and surface area are selected. COMSTAT parameters that quantify biomass and thickness are widely used in studies that describe structural changes to P. aeruginosa biofilms8,9,10,11,14,27. Although less frequently interpreted, surface-to-biovolume ratio (among the surface area outputs) is found herein to be most effective in quantifying aggregation&#8212;an important mechanism of antimicrobial resistance, preventing anti-infective agents from penetrating the full depth of biofilms5. This observation is consistent with previous work, which determined a negative correlation of surface-to-biovolume ratio to be indicative of densely clustered bacterial cells34,35. Furthermore, a closely related study reported a 2.6-fold higher surface-to-biovolume ratio among flat formed, undifferentiated PA01 biofilms compared to an overproducing-Psl strain, which visually formed aggregates described as hyper-biofilm structures36. Notably, because of the inherent complexity of biofilms, it is typical to generate variable COMSTAT results, regardless of chosen analysis function and even if experimental conditions are kept constant. Therefore, all quantitative measurements of CLSM images should be performed with a minimum of 3 biological replicates and a consistent number of z-stack images per condition, accompanied by statistical analysis.
Overall, a key approach in quantifying CLSM images via COMSTAT is in the development of a standardized framework for software operation and analysis. To our knowledge, such an integration does not exist, but rather there is a surplus of biofilm cultivation frameworks, image segmentation techniques, and operational parameters applied to different studies. In the absence of a gold standard, this protocol offers an important step toward transparency across laboratories involved in determining the effects of antimicrobials against in vitro biofilm formation. A limitation of this technique is that it does not include a dual fluorescent staining procedure to differentiate live cells from dead cells. Thus, future applications of this method may wish to incorporate a viability assay, as an internal control to determine viable cell numbers. Likewise, a crystal violet assay may be useful for correlating relative cell density with microscopy results. With the rise of new mathematical modeling and algorithmic plugins for image segmentation, it may also be worth investigating different ways to refine this protocol to better depict the heterogenicity of biofilms. This may include scripts for capturing the irregularities of biofilm matrices, spatial coherence of segmented voxels and channels, and localized thresholds. In terms of clinical application, this protocol may be useful for measuring the therapeutic success of different antimicrobials used to treat biofilm infections. It may also be valuable for testing various strategies used to disrupt biofilms to make them more susceptible to antimicrobial treatment.

Biofilms are aggregates of microorganisms oriented in a matrix of self-produced extracellular polymeric substances (EPS). The EPS matrix is very complex, consisting primarily of bacterial cells, water, proteins, polysaccharides, lipids, and nucleic acids1, all of which make biofilms distinctly different from free-living planktonic cells. Biofilm EPS are adherent to each other and various surfaces. The EPS matrix has properties that mediate cell-to-cell exchange of metabolites, genetic material, and compounds used for intercellular signaling and defense2. These properties collectively provide biofilms structural integrity and protection against external stressors, contributing to immune evasion and antimicrobial resistance3.
Pseudomonas aeruginosa is a well-recognized nosocomial pathogen, known to adopt an evasive biofilm growth strategy in response to antimicrobials. A prime example of this occurs in patients with the recessive genetic disorder, cystic fibrosis (CF). Biofilms play a pivotal role in the development of antimicrobial-resistant P. aeruginosa4 and permit the establishment of chronic pulmonary infection in patients with CF, causing accelerated decline in lung function and premature mortality5. Hence, in vitro biofilm studies are performed to test the efficacy of antibiotics and new anti-infective agents against P. aeruginosa isolates obtained from patients with CF6,7. Following biofilm formation, antimicrobials are applied externally to the structure, and confocal laser scanning microscopy (CLSM) is used to generate high-resolution, three-dimensional reconstructions of biofilm segments. It is common practice to then use the computer software, COMSTAT, as a plugin to ImageJ, to quantify changes in biofilm architecture8,9,10,11.
Although COMSTAT is&#160;useful&#160;for quantifying biofilm structure, the reproducibility and standardization of image analysis&#160;is less commonly addressed. For example, the image-processing procedure, performed prior to running COMSTAT, is objective, but contains an element of subjectivity when setting image thresholds12,13. In a similar manner, the COMSTAT program allows the operator to choose from basic to advanced conditions and parameters for image segmentation as well as ten quantitative analysis functions (e.g., thickness distribution, surface area, biomass, dimensionless roughness coefficient). The multitude of user options, compounded with varying operator expertise levels, may result in misguided reporting of biofilm behavior.
Thus, the goal of this protocol is to present a relatively simple method for the quantitative comparison of in vitro biofilm structures using COMSTAT. Herein, three-dimensional images of biofilm segments from a CF P. aeruginosa isolate are captured via CLSM using the chambered coverglass model14&#8212;an established technique used to perform reproducible in vitro biofilm experiments. Utilizing COMSTAT as a plugin to ImageJ, this method allows for researchers to quantitatively identify changes in biofilm architecture in the presence of antimicrobials under varying conditions. Overall, this method aims to eliminate subjective variations associated with the manual operation of COMSTAT, thereby facilitating the standardization of protocols across centers.

<table><tbody><tr><td>Anti-Psl mAb, Psl0096</td><td>Medimmune</td><td></td><td></td></tr><tr><td>Blood Agar (TSA with 5 % Sheep Blood) Medium</td><td>Fisher Scientific</td><td>R01200</td><td></td></tr><tr><td>Eight-well Chambered Coverglass w/ non-removable wells</td><td>Thermo Fisher Scientific</td><td>155411</td><td></td></tr><tr><td>Invitrogen SYTO 9 Green Fluorescent Nucleic Acid Stain</td><td>Thermo Fisher Scientific</td><td>S34854</td><td></td></tr><tr><td>LB BROTH (LENNOX), Liquid Autoclave Sterilized</td><td>BioShop Canada</td><td>LBL666</td><td></td></tr><tr><td>Tobramycin, 900 &amp;micro;g/mg</td><td>Alfa Aesar by Thermo Fisher Scientific</td><td>J66040</td><td>It is recommended to perform a minimal inhibitory concentration (MIC) test for every batch made to ensure quality control of antimicrobial potency</td></tr><tr><td>Quorum Volocity 6.3</td><td>Quorum Technologies</td><td></td><td>Image analysis software</td></tr></tbody></table>

quantifying the effects of antimicrobials on in vitro biofilm architecture using comstat software

Biofilms are aggregates of microorganisms that rely on a self-produced matrix of extracellular polymeric substance for protection and structural integrity. The nosocomial pathogen, Pseudomonas aeruginosa, is known to adopt a biofilm mode of growth, causing chronic pulmonary infection in patients with cystic fibrosis (CF). The computer program, COMSTAT, is a useful tool for quantifying antimicrobial-induced changes in P. aeruginosa biofilm architecture by extracting data from three-dimensional confocal images. However, standardized operation of the software is less commonly addressed, which is important for optimal reporting of biofilm behavior and cross-center comparison. Thus, the aim of this protocol is to provide a simple and reproducible framework for quantifying in vitro biofilm structures under varying antimicrobial conditions via COMSTAT. The technique is modeled using a CF P. aeruginosa isolate, grown in the form of biofilm replicates, and exposed to tobramycin and the anti-Psl monoclonal antibody, Psl0096. The step-by-step approach aims to reduce user ambiguity and minimize the chance of overlooking crucial image-processing steps. Specifically, the protocol emphasizes the elimination of subjective variations associated with the manual operation of COMSTAT, including image segmentation and the selection of appropriate quantitative analysis functions. Although this method requires users to spend additional time processing confocal images prior to running COMSTAT, it helps minimize misrepresented biofilm heterogenicity in automated outputs.

A P. aeruginosa isolate cultured from an infected&#160;patient with CF is used to demonstrate the strengths of this approach in accurately quantifying antimicrobial-induced changes in in vitro biofilm architecture. The overall workflow of this model is represented in Figure 1. The image-processing and COMSTAT analysis procedure&#160;in ImageJ is shown in&#160;Figure 2. A simple histogram thresholding approach for image segmentation in ImageJ, applied to a&#160;CLSM z-stack image (saved as OME-TIFF), is shown in Figure 3. Biofilm structural changes caused by tobramycin and anti-Psl mAb, Psl0096, is shown in Figure 4. Representative confocal images of biofilm segments separated into live cell (green) and antibody (red) channels are shown in Figure 4A. Corresponding data of three COMSTAT parameters, including average biofilm thickness, biomass, and surface-to-biovolume ratio are shown in Figure 4B-D. Overall, COMSTAT data demonstrate significant differences among biofilm structures compared to control wells. With tobramycin, a clear reduction in average thickness and biomass can be observed in Figure 4B and Figure 4C, respectively. However, in the presence of the anti-Psl mAb, Psl0096, the P. aeruginosa biofilm is resistant to tobramycin. When examining surface-to-biovolume ratio (Figure 4D), a significant reduction is observed with anti-Psl mAb, Psl0096 (p &#60; 0.0001), in both tobramycin-exposed and unexposed biofilms, representing the formation of aggregates (Figure 4A).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61759/61759fig1v4.jpg" /> 
Figure 1: In vitro biofilm formation.&#160;A Pseudomonas aeruginosa isolate is grown on blood agar and inoculated in LB overnight. The overnight culture is diluted 1:100 and grown to early log phase (3&#8211;4 h incubation) to obtain a final OD600 of approximately 0.1. The OD-adjusted inoculum is seeded into each well of an 8-chambered coverglass and grown for 24 h, allowing biofilms to attach and mature at the base of the wells. Media is carefully removed from the wells, and a fluorescent labelled (red) mAb is added to the designated wells. The chambered coverglass is incubated for 1 h, allowing the antibody to bind to the bacterial antigen epitope. The antibiotic is added to designated wells, and biofilms are grown for 24 h. Medium is removed from the wells, and a live-cell fluorescent (green) stain is added. Following 45 min of incubation, the wells are washed 2x with fresh LB, and confocal imaging is performed in LB.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61759/61759fig02.jpg" /> 
Figure 2: Image-processing and COMSTAT analysis.&#160;Comstat2, as a plugin to ImageJ, is used for the quantitative analysis of in vitro biofilm architecture. (A) Initial setup is performed by first opening the ImageJ program found within the downloaded Comstat2 package. It is then recommended to create a source folder, from which a single OME-TIFF can be added and imported into ImageJ. Once imported, the OME-TIFF is deleted from the source folder. * These steps can be repeated to add additional OME-TIFFs, allowing the operator to process multiple images at a time. (B) Image-processing is performed entirely in ImageJ. The orientation of OME-TIFF is flipped in the z-direction, assigning the first layer of biofilm substratum as stack 1. ** This step is only necessary if the OME-TIFF output from the confocal microscope is inverted. Image properties are defined, and the threshold of OME-TIFF is adjusted manually. The Bio-formats Exporter plugin is used to save the newly adjusted OME-TIFF in source folder. (C) COMSTAT analysis is performed using the Comstat2 plugin. The source folder containing the OME-TIFF(s) is added. Specific features that may flaw results are de-selected, while desired features for quantitative analysis are selected. &#8216;Run&#8217; is selected to initiate analysis. Once completed, measurements are recoded from TXT files automatically saved in the source folder. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61759/61759fig02large.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/61759/61759fig3v4.jpg" /> 
Figure 3: Simple histogram thresholding of a 16-bit CLSM OME-TIFF in ImageJ.&#160;(A) OME-TIFF before thresholding with background noise and two distinct phases: red biomass and greyscale&#160;background. (B) Threshold window used to remove background noise and set optimal threshold by converting lower intensity pixels (values less than the minimum set threshold) into black background. Maximum threshold value set to establish gray level range to be converted to red biomass. (C) Quality segmented z-stack image generated, and&#160;(D) converted stack to a binary black-and-white OME-TIFF&#160;that is readable to COMSTAT algorithms. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61759/61759fig3v4large.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/61759/61759fig4v2.jpg" /> 
Figure 4: Representative CLSM images and COMSTAT comparison of fluorescent&#160;labelled CF Pseudomonas aeruginosa biofilms under varying antimicrobial conditions.&#160;(A) Three-dimensional images of 48 h biofilms grown in chambered coverglass. Live-cell (green) channel of biofilm grown in LB media alone (top left) and with 1000 &#956;g/mL tobramycin in LB (bottom left), representing antibiotic effect on control wells. Live-cell channel of biofilm exposed to anti-Psl mAb, Psl0096, in the absence (top middle) and presence (bottom middle) of tobramycin, showing the formation of aggregates and tobramycin resistance. Corresponding Psl0096 (red) channel of identical biofilm is shown in the absence (top right) and presence (bottom right) of tobramycin. White arrows indicate high Psl-antibody binding localized in the same region of aggregates shown in the live cell channel. Quantitative comparison of COMSTAT parameters are displayed in a series of bar graphs, including (B) average thickness (entire well), (C) biomass, and (D) surface-to-biovolume ratio of the isolate. One scale unit is equivalent to 19.68 &#956;m. Each bar represents average of multiple images (n=18) from 3 biologically independent experiments, in the absence (grey bars) and presence (black bars) of tobramycin, with standard error bars of the mean. *p &#60;0.01 **p &#60;0.001 ***p &#60; 0.0001 by Mann-Whitney test. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61759/61759fig4v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Quantifying the Effects of Antimicrobials on In vitro Biofilm Architecture using COMSTAT Software at JoVE.com

Quantifying the Effects of Antimicrobials on In vitro Biofilm Architecture using COMSTAT Software

Antimicrobial-induced changes to Pseudomonas aeruginosa biofilm architecture differ among clinical isolates cultured from patients with cystic fibrosis and chronic pulmonary infection. Following confocal microscopy, COMSTAT software can be utilized to quantify variations in biofilm architecture (e.g., surface area, thickness, biomass) for individual isolates to assess the efficacy of anti-infective agents.

Quantifying the Effects of Antimicrobials on In vitro Biofilm Architecture using COMSTAT Software

Biofilms are aggregates of microorganisms that rely on a self-produced matrix of extracellular polymeric substance for protection and structural ...

quantifying-effects-antimicrobials-on-vitro-biofilm-architecture

Nanyang Technological University

Research

JoVE Journal

Immunology and Infection

3.3K Views.  Hospital for Sick Children. Door antimicrobiële stoffen geïnduceerde veranderingen in de biofilmarchitectuur van Pseudomonas aeruginosa verschillen tussen klinische isolaten die zijn gekweekt van patiënten met cystische fibrose en chronische longinfectie. Na confocale microscopie kan COMSTAT-software worden gebruikt om variaties in biofilmarchitectuur (bijv. oppervlakte, dikte, biomassa) voor individuele isolaten te kwantificeren om de werkzaamheid van anti-infectiemiddelen te b...

Video: Quantifying the Effects of Antimicrobials on In vitro Biofilm Architecture using COMSTAT Software

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 ...

In vitro Biofilm Formation in an 8-well Chamber Slide

The chronic nature of many diseases is attributed to the formation of bacterial biofilms which are recalcitrant to traditional antibiotic therapy. Biofilms are community-associated bacteria attached to a surface and encased in a matrix. The role of the extracellular matrix is multifaceted, including facilitating nutrient acquisition, and offers significant protection against environmental stresses (e.g. host immune responses). In an effort to acquire a better understanding as to how the bacteria within a biofilm respond to environmental stresses we have used a protocol wherein we visualize bacterial biofilms which have formed in an 8-well chamber slide. The biofilms were stained with the BacLight Live/Dead stain and examined using a confocal microscope to characterize the relative biofilm size, and structure under varying incubation conditions. Z-stack images were collected via confocal microscopy and analyzed by COMSTAT. This protocol can be used to help elucidate the mechanism and kinetics by which biofilms form, as well as identify components that are important to biofilm structure and stability.

In vitro Biofilm Formation in an 8-well Chamber Slide

This article describes the procedure for the formation and visualization of a bacterial biofilm grown within an 8-well chamber slide

The chronic nature of many diseases is attributed to the formation of bacterial biofilms which are recalcitrant to traditional antibiotic therapy. ...

An Analytical Tool-box for Comprehensive Biochemical, Structural and Transcriptome Evaluation of Oral Biofilms Mediated by Mutans Streptococci

Biofilms are highly dynamic, organized and structured communities of microbial cells enmeshed in an extracellular matrix of variable density and composition 1, 2. In general, biofilms develop from initial microbial attachment on a surface followed by formation of cell clusters (or microcolonies) and further development and stabilization of the microcolonies, which occur in a complex extracellular matrix. The majority of biofilm matrices harbor exopolysaccharides (EPS), and dental biofilms are no exception; especially those associated with caries disease, which are mostly mediated by mutans streptococci 3. The EPS are synthesized by microorganisms (S. mutans, a key contributor) by means of extracellular enzymes, such as glucosyltransferases using sucrose primarily as substrate 3.
Studies of biofilms formed on tooth surfaces are particularly challenging owing to their constant exposure to environmental challenges associated with complex diet-host-microbial interactions occurring in the oral cavity. Better understanding of the dynamic changes of the structural organization and composition of the matrix, physiology and transcriptome/proteome profile of biofilm-cells in response to these complex interactions would further advance the current knowledge of how oral biofilms modulate pathogenicity. Therefore, we have developed an analytical tool-box to facilitate biofilm analysis at structural, biochemical and molecular levels by combining commonly available and novel techniques with custom-made software for data analysis. Standard analytical (colorimetric assays, RT-qPCR and microarrays) and novel fluorescence techniques (for simultaneous labeling of bacteria and EPS) were integrated with specific software for data analysis to address the complex nature of oral biofilm research.
The tool-box is comprised of 4 distinct but interconnected steps (Figure 1): 1) Bioassays, 2) Raw Data Input, 3) Data Processing, and 4) Data Analysis. We used our in vitro biofilm model and specific experimental conditions to demonstrate the usefulness and flexibility of the tool-box. The biofilm model is simple, reproducible and multiple replicates of a single experiment can be done simultaneously 4, 5. Moreover, it allows temporal evaluation, inclusion of various microbial species 5 and assessment of the effects of distinct experimental conditions (e.g. treatments 6; comparison of knockout mutants vs. parental strain 5; carbohydrates availability 7). Here, we describe two specific components of the tool-box, including (i) new software for microarray data mining/organization (MDV) and fluorescence imaging analysis (DUOSTAT), and (ii) in situ EPS-labeling. We also provide an experimental case showing how the tool-box can assist with biofilms analysis, data organization, integration and interpretation.

Biofilms formed on tooth surfaces are highly complex and exposed to constant innate and exogenous environmental challenges, which modulate their architecture, physiology and transcriptome. We developed a toolbox to examine the composition, structural organization and gene expression of oral biofilms, which can be adapted to other areas of biofilm research.

Biofilms are highly dynamic, organized and structured communities of microbial cells enmeshed in an extracellular matrix of variable density and ...

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 ...

Use of a High-throughput In Vitro Microfluidic System to Develop Oral Multi-species Biofilms

There are few high-throughput in vitro systems which facilitate the development of multi-species biofilms that contain numerous species commonly detected within in vivo oral biofilms. Furthermore, a system that uses natural human saliva as the nutrient source, instead of artificial media, is particularly desirable in order to support the expression of cellular and biofilm-specific properties that mimic the in vivo communities. We describe a method for the development of multi-species oral biofilms that are comparable, with respect to species composition, to supragingival dental plaque, under conditions similar to the human oral cavity. Specifically, this methods article will describe how a commercially available microfluidic system can be adapted to facilitate the development of multi-species oral biofilms derived from and grown within pooled saliva. Furthermore, a description of how the system can be used in conjunction with a confocal laser scanning microscope to generate 3-D biofilm reconstructions for architectural and viability analyses will be presented. Given the broad diversity of microorganisms that grow within biofilms in the microfluidic system (including Streptococcus, Neisseria, Veillonella, Gemella, and Porphyromonas), a protocol will also be presented describing how to harvest the biofilm cells for further subculture&#160;or DNA extraction and analysis. The limits of both the microfluidic biofilm system and the current state-of-the-art data analyses will be addressed. Ultimately, it is envisioned that this article will provide a baseline technique that will improve the study of oral biofilms and aid in the development of additional technologies that can be integrated with the microfluidic platform.

Use of a High-throughput In Vitro Microfluidic System to Develop Oral Multi-species Biofilms

The goal of this methods paper is to describe the use of a microfluidic system for the development of multi-species biofilms that contain species typically identified in human supragingival dental plaque.&#160;Methods to describe biofilm architecture, biofilm viability, and an approach to harvest biofilm for culture-dependent or culture-independent analyses are highlighted.

There are few high-throughput in vitro systems which facilitate the development of multi-species biofilms that contain numerous species commonly detected ...

Bioengineering

Anti-virulent Disruption of Pathogenic Biofilms using Engineered Quorum-quenching Lactonases

The rapid emergence of multi-drug resistant bacteria has accelerated the need for novel therapeutic approaches to counter life-threatening infections. The persistence of bacterial infection is often associated with quorum-sensing-mediated biofilm formation. Thus, the disruption of this signaling circuit presents an attractive anti-virulence strategy. Quorum-quenching lactonases have been reported to be effective disrupters of quorum-sensing circuits. However, there have been very few reports of the effective use of these enzymes in disrupting bacterial biofilm formation. This protocol describes a method to disrupt biofilm formation in a clinically relevant A. baumannii S1 strain through the use of an engineered quorum-quenching lactonase. Acinetobacter baumannii is a major human pathogen implicated in serious hospital-acquired infections globally and its virulence is attributed predominantly to its biofilm's tenacity. The engineered lactonase treatment achieved significant A. baumannii S1 biofilm reduction. This study also showed the possibility of using engineered quorum-quenching enzymes in future treatment of biofilm-mediated bacterial diseases. Lastly, the method may be used to evaluate the competency of promising quorum-quenching enzymes.

Quorum-quenching enzymes are anti-virulent and anti-bacterial options that can mitigate pathogenesis without risk of incurring resistance, by preventing the expression of virulence factors and genes associated with antibiotic resistance and biofilm formation. In this study, we report a method that demonstrates the efficacy of quorum-quenching enzymes in bacterial biofilm disruption.

The rapid emergence of multi-drug resistant bacteria has accelerated the need for novel therapeutic approaches to counter life-threatening infections. The ...

Methodologies for Studying B. subtilis Biofilms as a Model for Characterizing Small Molecule Biofilm Inhibitors

This work assesses different methodologies to study the impact of small molecule biofilm inhibitors, such as D-amino acids, on the development and resilience of Bacillus subtilis biofilms. First, methods are presented that select for small molecule inhibitors with biofilm-specific targets in order to separate the effect of the small molecule inhibitors on planktonic growth from their effect on biofilm formation. Next, we focus on how inoculation conditions affect the sensitivity of multicellular, floating B. subtilis cultures to small molecule inhibitors. The results suggest that discrepancies in the reported effects of such inhibitors such as D-amino acids are due to inconsistent pre-culture conditions. Furthermore, a recently developed protocol is described for evaluating the contribution of small molecule treatments towards biofilm resistance to antibacterial substances. Lastly, scanning electron microscopy (SEM) techniques are presented to analyze the three-dimensional spatial arrangement of cells and their surrounding extracellular matrix in a B. subtilis biofilm. SEM facilitates insight into the three-dimensional biofilm architecture and the matrix texture. A combination of the methods described here can greatly assist the study of biofilm development in the presence and absence of biofilm inhibitors, and shed light on the mechanism of action of these inhibitors.

Methodologies for Studying B. subtilis Biofilms as a Model for Characterizing Small Molecule Biofilm Inhibitors

This study presents the development of reproducible methodologies to study biofilm inhibitors and their effects on Bacillus subtilis multicellularity.

This work assesses different methodologies to study the impact of small molecule biofilm inhibitors, such as D-amino acids, on the development and ...

Visualizing the Effects of Sputum on Biofilm Development Using a Chambered Coverglass Model

Biofilms consist of groups of bacteria encased in a self-secreted matrix. They play an important role in industrial contamination as well as in the development and persistence of many health related infections. One of the most well described and studied biofilms in human disease occurs in chronic pulmonary infection of cystic fibrosis patients. When studying biofilms in the context of the host, many factors can impact biofilm formation and development. In order to identify how host factors may affect biofilm formation and development, we used a static chambered coverglass method to grow biofilms in the presence of host-derived factors in the form of sputum supernatants. Bacteria are seeded into chambers and exposed to sputum filtrates. Following 48 hr of growth, biofilms are stained with a commercial biofilm viability kit prior to confocal microscopy and analysis. Following image acquisition, biofilm properties can be assessed using different software platforms. This method allows us to visualize key properties of biofilm growth in presence of different substances including antibiotics.

This protocol describes the visualization of biofilm development following exposure to host-factors using a slide chamber model. This model allows for direct visualization of biofilm development as well as analysis of biofilm parameters using computer software programs.

Biofilms consist of groups of bacteria encased in a self-secreted matrix. They play an important role in industrial contamination as well as in the ...

A Platform of Anti-biofilm Assays Suited to the Exploration of Natural Compound Libraries

Biofilms are regarded as one of the most challenging topics of modern biomedicine, and they are potentially responsible for over 80% of antibiotic-tolerant infections. Biofilms have displayed an exceptionally high tolerance for chemotherapy, which is thought to be multifactorial. For instance, the matrix provides a physical barrier that decreases the penetration of antibiotics into the biofilm. Also, cells within the biofilms are phenotypically diverse. Likely, biofilm resilience arises from a combination of these and other, yet unknown, mechanisms. All of the currently existing antibiotics have been developed against single-cells (planktonic) bacteria. Therefore, so far, a very limited repertoire of molecules exists that can selectively act on mature biofilms. This situation has driven a progressive paradigm shift in drug discovery, in which searching for anti-biofilms has been urged to occupy a more prominent place. An additional challenge is that there are a very limited number of standardized methods for biofilm research, especially those that can be used for large-throughput screening of chemical libraries. Here, an experimental anti-biofilm platform for chemical screening is presented. It uses three assays to measure biofilm viability (with resazurin staining), total biomass (with crystal violet staining), and biofilm matrix (using a wheat germ agglutinin, WGA-fluorescence-based staining of the poly-N-acetyl-glucosamine, PNAG, fraction). All the assays were developed using Staphylococcus aureus as the model bacteria. Examples of how the platform can be used for primary screening as well as for functional characterization of identified anti-biofilm hits are presented. This experimental sequence further allows for the classification of the hits based upon the measured end-points. It also provides information on their mode of action, especially on long-term versus short-term chemotherapeutic effects. Thus, it is very advantageous for the quick identification of high-quality hit compounds that can serve as starting points for various biomedical applications.

Biofilm infections show high tolerance towards chemotherapy. No single assay captures the complexity of biofilms. Instead, complementary assays are needed. We present a screening platform (developed for S. aureus) that combines assays for viability, biomass, and biofilm matrix. It allows anti-biofilm drug discovery, including the assessment of long-term chemotherapeutic effects.

Biofilms are regarded as one of the most challenging topics of modern biomedicine, and they are potentially responsible for over 80% of ...

Adaptable CF Model for Antimicrobial Testing and Microbial Dynamics Analysis

Development of a Polymicrobial Colony Biofilm Model to Test Antimicrobials in Cystic Fibrosis

A range of bacteria biofilm models exist for the testing of antibiotics. However, many of these are limited to a single experimental output, such as colony-forming units or metabolic activity. Furthermore, many biofilm models do not reflect the biological and physiochemical properties of the human host environment. This is an important issue in many conditions, but most noticeably in cystic fibrosis (CF). A large proportion of people with CF suffer from both chronic and intermittent infections, and in vitro, antibiotic susceptibility testing poorly correlates with patient treatment outcomes. Some biofilm models incorporate CF lung-relevant media, including synthetic sputum mimics, but do not consider the polymicrobial nature of the environment, which alters biofilm architecture, physiology, and the way microbes respond to treatment. The solid-air interface colony biofilm model described here is highly adaptable and incorporates both CF-relevant media and a polymicrobial context. This model can also be used for mid-throughput screening of antimicrobials and to study their effect on polymicrobial dynamics. Output measurements from the model can be colony-forming units, metabolic activity, and confocal microscopy analysis. The model can easily be adapted to different microorganisms, media, temperatures, and variable oxygen conditions and can be used to test a wide range of chemical, biological, and physical treatments.

This methodology allows for the establishment of a polymicrobial biofilm model in cystic fibrosis for antimicrobial sensitivity testing in research and clinical laboratories. This model provides accurate and reliable results over a range of outputs.

A range of bacteria biofilm models exist for the testing of antibiotics. However, many of these are limited to a single experimental output, such as ...

Quantifying the Effects of Antimicrobials on In vitro Biofilm Architecture using COMSTAT Software

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