Name: quantifying the effects of antimicrobials on in vitro biofilm architecture using comstat software
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Description: Watch this Scientific Journal Video about Quantifying the Effects of Antimicrobials on In vitro Biofilm Architecture using COMSTAT Software at JoVE.com

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 modific...

<ol>
	<li>Flemming, H. C., Wingender, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+biofilm+matrix.">The biofilm matrix.</a> Nature Reviews Microbiology. 8, 623-633 (2010).</li><li>Flemming, H. C., 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=Biofilms:+an+emergent+form+of+bacterial+life.">Biofilms: an emergent form of bacterial life.</a> Nature Reviews Microbiology. 14, 563-575 (2016).</li><li>Rybtke, M., Hultqvist, L. 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C., 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=Targeted+disruption+of+the+extracellular+polymeric+network+of+Pseudomonas+aeruginosa+biofilms+by+alginate+oligosaccharides.">Targeted disruption of the extracellular polymeric network of Pseudomonas aeruginosa biofilms by alginate oligosaccharides.</a> NPJ Biofilms and Microbiomes. 4, 1-10 (2018).</li><li>Ciofu, O., Tolker-Nielsen, T., Jensen, P. &. #. 2. 1. 6. ;., Wang, H., H&#248;iby, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Antimicrobial+resistance,+respiratory+tract+infections+and+role+of+biofilms+in+lung+infections+in+cystic+fibrosis+patients.">Antimicrobial resistance, respiratory tract infections and role of biofilms in lung infections in cystic fibrosis patients.</a> Advanced Drug Delivery Reviews. 85, 7-23 (2015).</li><li>Landry, R. 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P., 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=Alginate+production+affects+Pseudomonas+aeruginosa+biofilm+development+and+architecture,+but+is+not+essential+for+biofilm+formation.">Alginate production affects Pseudomonas aeruginosa biofilm development and architecture, but is not essential for biofilm formation.</a> Journal of Medical Microbiology. 53, 679-690 (2004).</li><li>Reichhardt, C., Parsek, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Confocal+laser+scanning+microscopy+for+analysis+of+Pseudomonas+aeruginosa+biofilm+architecture+and+matrix+localization.">Confocal laser scanning microscopy for analysis of Pseudomonas aeruginosa biofilm architecture and matrix localization.</a> Frontiers in Microbiology. 10, 677 (2019).</li><li>Yang, X., Beyenal, H., Harkin, G., Lewandowski, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+biofilm+structure+using+image+analysis.">Quantifying biofilm structure using image analysis.</a> Journal of Microbiological Methods. 39, 109-119 (2000).</li><li>Yang, X., Beyenal, H., Harkin, G., Lewandowski, Z. <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+biofilm+image+thresholding+methods.">Evaluation of biofilm image thresholding methods.</a> Water Research. 35, 1149-1158 (2001).</li><li>Ross, S. 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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. aeru...

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 &#181;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, 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 ...

The significance of this protocol is that it offers a simple and reproducible framework for quantifying in vitro biofilm structures under varying antimicrobial conditions. The main advantage of this technique is that it helps eliminate subjective variations associated with the manual operation of COMSTAT, which ultimately facilitates the standardization and transparency of experiments across research centers. To image the Pseudomonas aeruginosa cultures, place the plate onto the stage of a confocal microscope, equipped with the appropriate laser excitation wavelengths and filter sets.And use a 20 to 25x water immersion lens to capture at least six layered z-stack images from the substratum to the top of each biofilm segment in 0.3 micrometer increments. Then save the images as OME-TIFFs for COMSTAT analysis. When all the images have been acquired, open the COMSTAT2 package in ImageJ and create a source folder on the desktop.Add a single OME-TIFF to the folder and open OME-TIFF from the source folder. Delete any empty layers containing no biomass. Select File, Import, and Image Sequence to import the file into ImageJ.Locate and highlight the source folder without opening it and click Select. A sequence options window will appear, click OK.Select Image, Transform, and Flip Z to flip the orientation of the biofilm so that the substratum is at the top of the stack. To define the image properties, select Image and Properties, a source window will appear.Specify the unit of length as micron, define the voxel depth as 0.3, and click OK.Select Image, Adjust, and Threshold. To adjust the image threshold in the threshold window, position the lower slider to the far right to manually set the maximum threshold value and use the upper slider to set the minimum threshold. Then use the slider in the image window to scroll through each layer to ensure that the background noise is sufficiently removed throughout the stack, and select Set to fix the lower threshold value.In the set threshold levels window, select OK, and set and fix the maximum threshold value similarly. Select Apply, click OK in the convert stack to binary window that appears, and exit the threshold window. Select Plugins, Bio-Formats, and Bio-Formats Exporter to save the file with a new file name in the source folder.A Bio-Formats Exporter multiple files window will appear, select OK.A Bio-Formats Exporter options window will appear, select OK again. To run COMSTAT, select plugins and COMSTAT2, an about window will appear, select OK.In the Observed Directories window, select Add, locate and highlight the source folder, highlight and click Choose. An Images in Directories window will appear that lists the OME-TIFFs to be analyzed.On the COMSTAT2.1 window, deselect Automatic Thresholding Otsu's Method and Connected Volume Filtering to ensure that the software uses preset threshold values for individual OME-TIFFs. In the COMSTAT2.1 window, select Biomass, Thickness Distribution and Surface Area, and click Go to run the program. The output data will be shown in the log window.The measurements will be automatically saved as TXT files in the source folder. In this representative analysis, a Pseudomonas aeruginosa isolate, cultured from an infected cystic fibrosis patient, was used to demonstrate the strengths of this approach, inaccurately quantifying antimicrobial induced changes in in vitro biofilm architecture. Live cell imaging can also visualize changes in Pseudomonas aeruginosa biofilm structure before and after biofilm specific monoclonal and tobramycin treatment.COMSTAT data analysis revealed significant differences between treated biofilm structures compared to control cultures. In tobramycin-treated cultures, a clear reduction in the average thickness and biomass was observed. However, in the presence of antibodies, the P.aeruginosa biofilm was resistant to these tobramycin-induced structural changes.When examining the surface to bio volume ratio, a significant reduction was observed in the presence of antibodies in both tobramycin-exposed and unexposed biofilms, indicating the formation of aggregates in antibody treated cultures. This procedure can be performed to quantitatively assess or compare the effects of different antimicrobials used to disrupt biofilm matrices. Using this technique as a general framework, researchers can explore different ways to refine the protocol to include new mathematical models and algorithmic plugins in ImageJ to better depict biofilm heterogenecy.

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Stromal cells regulate the recruitment of circulating leukocytes during inflammation through cross-talk with neighboring endothelial cells. Here we ...

Qualitative and Quantitative Assays for Detection and Characterization of Protein Antimicrobials

Initial evaluations of large microbial libraries for potential producers of novel antimicrobial proteins require both qualitative and quantitative methods ...

An In Vitro Model of the Blood-brain Barrier Using Impedance Spectroscopy: A Focus on T Cell-endothelial Cell Interaction

An In Vitro Model of the Blood-brain Barrier Using Impedance Spectroscopy: A Focus on T Cell-endothelial Cell Interaction

Breakdown of the blood-brain barrier (BBB) is a critical step in the development of autoimmune diseases such as multiple sclerosis (MS) and its animal ...

Flow Cytometric Analysis of Particle-bound Bet v 1 Allergen in PM10

Flow cytometry is a method widely used to quantify suspended solids such as cells or bacteria in a size range from 0.5 to several tens of micrometers in ...

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 ...

Quantifying Human Monocyte Chemotaxis In Vitro and Murine Lymphocyte Trafficking In Vivo

Quantifying Human Monocyte Chemotaxis In Vitro and Murine Lymphocyte Trafficking In Vivo

Chemotaxis is migration along a specific chemical gradient1. Chemokines are chemotactic cytokines that promote cellular trafficking with anatomic and ...

Visualization of Biofilm Formation in Candida albicans Using an Automated Microfluidic Device

Visualization of Biofilm Formation in Candida albicans Using an Automated Microfluidic Device

Candida albicans is the most common fungal pathogen of humans, causing about 15% of hospital-acquired sepsis cases. A major virulence attribute of C. ...