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>

<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. D., Givskov, M., Tolker-Nielsen, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pseudomonas+aeruginosa+biofilm+infections:+community+structure,+antimicrobial+tolerance+and+immune+response.">Pseudomonas aeruginosa biofilm infections: community structure, antimicrobial tolerance and immune response.</a> Journal of Molecular Biology. 427, 3628-3645 (2015).</li><li>Wendel, A. F., Ressina, S., Kolbe-Busch, S., Pfeffer, K., MacKenzie, C. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Species+diversity+of+environmental+GIM-1-producing+bacteria+collected+during+a+long-term+outbreak.">Species diversity of environmental GIM-1-producing bacteria collected during a long-term outbreak.</a> Applied and Environmental Microbiology. 82, 3605-3610 (2016).</li><li>Costerton, J. W., Stewart, P. S., Greenberg, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bacterial+biofilms:+a+common+cause+of+persistent+infections.">Bacterial biofilms: a common cause of persistent infections.</a> Science. 284, 1318-1322 (1999).</li><li>Powell, L. 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. M., An, D., Hupp, J. T., Singh, P. K., Parsek, M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mucin-Pseudomonas+aeruginosa+interactions+promote+biofilm+formation+and+antibiotic+resistance.">Mucin-Pseudomonas aeruginosa interactions promote biofilm formation and antibiotic resistance.</a> Molecular Microbiology. 59, 142-151 (2006).</li><li>Beaudoin, T., 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=Staphylococcus+aureus+interaction+with+Pseudomonas+aeruginosa+biofilm+enhances+tobramycin+resistance.">Staphylococcus aureus interaction with Pseudomonas aeruginosa biofilm enhances tobramycin resistance.</a> NPJ Biofilms and Microbiomes. 3, 1-9 (2017).</li><li>Rojo-Molinero, E., 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=Sequential+treatment+of+biofilms+with+aztreonam+and+tobramycin+is+a+novel+strategy+for+combating+Pseudomonas+aeruginosa+chronic+respiratory+infections.">Sequential treatment of biofilms with aztreonam and tobramycin is a novel strategy for combating Pseudomonas aeruginosa chronic respiratory infections.</a> Antimicrobial Agents and Chemotherapy. 60, 2912-2922 (2016).</li><li>Hentzer, 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=Alginate+overproduction+affects+Pseudomonas+aeruginosa+biofilm+structure+and+function.">Alginate overproduction affects Pseudomonas aeruginosa biofilm structure and function.</a> Journal of Bacteriology. 183, 5395-5401 (2001).</li><li>Tolker-Nielsen, T., Sternberg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growing+and+analyzing+biofilms+in+flow+chambers.">Growing and analyzing biofilms in flow chambers.</a> Current Protocols in Microbiology. 21, 1-17 (2011).</li><li>Luo, T. L., 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=A+Sensitive+thresholding+method+for+confocal+laser+scanning+microscope+image+stacks+of+microbial+biofilms.">A Sensitive thresholding method for confocal laser scanning microscope image stacks of microbial biofilms.</a> Scientific Reports. 8, 1-14 (2018).</li><li>Beaudoin, T., Kennedy, S., Yau, Y., Waters, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualizing+the+effects+of+sputum+on+biofilm+development+using+a+chambered+coverglass+model.">Visualizing the effects of sputum on biofilm development using a chambered coverglass model.</a> Journal of Visualized Experiments. (118), e54819 (2016).</li><li>DiGiandomenico, 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=Identification+of+broadly+protective+human+antibodies+to+Pseudomonas+aeruginosa+exopolysaccharide+Psl+by+phenotypic+screening.">Identification of broadly protective human antibodies to Pseudomonas aeruginosa exopolysaccharide Psl by phenotypic screening.</a> Journal of Experimental Medicine. 209, 1273-1287 (2012).</li><li>Heydorn, 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=Quantification+of+biofilm+structures+by+the+novel+computer+program+COMSTAT.">Quantification of biofilm structures by the novel computer program COMSTAT.</a> Microbiology. 146, 2395-2407 (2000).</li><li>Vorregaard, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comstat2+-+a+modern+3D+image+analysis+environment+for+biofilms,+in+Informatics+and+Mathematical+Modelling.">Comstat2 - a modern 3D image analysis environment for biofilms, in Informatics and Mathematical Modelling.</a> Technical University of Denmark. , (2008).</li><li>Hashemi, M. A., Khaddour, G., Fran&#231;ois, B., Massart, T. J., Salager, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+tomographic+imagery+segmentation+methodology+for+three-phase+geomaterials+based+on+simultaneous+region+growing.">A tomographic imagery segmentation methodology for three-phase geomaterials based on simultaneous region growing.</a> Acta Geotechnica. 9, 831-846 (2014).</li><li>Rogowska, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Overview+and+fundamentals+of+medical+image+segmentation.">Overview and fundamentals of medical image segmentation.</a> Handbook of Medical Image Processing and Analysis. , 73-90 (2009).</li><li>Webb, 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=Assessing+technician+effects+when+extracting+quantities+from+microscope+images.">Assessing technician effects when extracting quantities from microscope images.</a> Journal of Microbiological Methods. 53, 97-106 (2003).</li><li>Azeredo, J., 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=Critical+review+on+biofilm+methods.">Critical review on biofilm methods.</a> Critical Reviews in Microbiology. 43, 313-351 (2017).</li><li>Xavier, J. B., 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=Objective+threshold+selection+procedure+(OTS)+for+segmentation+of+scanning+laser+confocal+microscope+images.">Objective threshold selection procedure (OTS) for segmentation of scanning laser confocal microscope images.</a> Journal of Microbiological Methods. 47, 169-180 (2001).</li><li>Arena, E. T., 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=Quantitating+the+cell:+turning+images+into+numbers+with+ImageJ.">Quantitating the cell: turning images into numbers with ImageJ.</a> Wiley Interdisciplinary Reviews: Developmental Biology. 6, 260 (2017).</li><li>Daims, H., Wagner, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantification+of+uncultured+microorganisms+by+fluorescence+microscopy+and+digital+image+analysis.">Quantification of uncultured microorganisms by fluorescence microscopy and digital image analysis.</a> Applied Microbiology and Biotechnology. 75, 237-248 (2007).</li><li>Yerly, J., Hu, Y., Jones, S. M., Martinuzzi, R. 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+two-step+procedure+for+automatic+and+accurate+segmentation+of+volumetric+CLSM+biofilm+images.">A two-step procedure for automatic and accurate segmentation of volumetric CLSM biofilm images.</a> Journal of Microbiological Methods. 70, 424-433 (2007).</li><li>Lee, B., 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=Heterogeneity+of+biofilms+formed+by+nonmucoid+Pseudomonas+aeruginosa+isolates+from+patients+with+cystic+fibrosis.">Heterogeneity of biofilms formed by nonmucoid Pseudomonas aeruginosa isolates from patients with cystic fibrosis.</a> Journal of Clinical Microbiology. 43, 5247-5255 (2005).</li><li>Stapper, A. 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. 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=Quantification+of+confocal+images+of+biofilms+grown+on+irregular+surfaces.">Quantification of confocal images of biofilms grown on irregular surfaces.</a> Journal of Microbiological Methods. 100, 111-120 (2014).</li><li>Ma, L., 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=Assembly+and+development+of+the+Pseudomonas+aeruginosa+biofilm+matrix.">Assembly and development of the Pseudomonas aeruginosa biofilm matrix.</a> PLoS Pathogens. 5, (2009).</li><li>Mah, T. F., 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=A+genetic+basis+for+Pseudomonas+aeruginosa+biofilm+antibiotic+resistance.">A genetic basis for Pseudomonas aeruginosa biofilm antibiotic resistance.</a> Nature. 426, 306-310 (2003).</li><li>Srinandan, C. S., Jadav, V., Cecilia, D., Nerurkar, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nutrients+determine+the+spatial+architecture+of+Paracoccus+sp.+biofilm.">Nutrients determine the spatial architecture of Paracoccus sp. biofilm.</a> Biofouling. 26, 449-459 (2010).</li><li>Ramos, I., Dietrich, L. E., Price-Whelan, A., Newman, D. 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. 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 ...

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

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JoVE Journal

Immunology and Infection

3.1K Views.  Hospital for Sick Children. 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.

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

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. ...

Investigating the Effects of Probiotics on Pneumococcal Colonization Using an In Vitro Adherence Assay

Adherence of Streptococcus pneumoniae (the pneumococcus) to the epithelial lining of the nasopharynx can result in colonization and is considered a prerequisite for pneumococcal infections such as pneumonia and otitis media. In vitro adherence assays can be used to study the attachment of pneumococci to epithelial cell monolayers and to investigate potential interventions, such as the use of probiotics, to inhibit pneumococcal&#160;colonization.&#160;The protocol described here is used to investigate the effects of the probiotic Streptococcus salivarius on the adherence of pneumococci to the human epithelial cell line CCL-23 (sometimes referred to as HEp-2 cells). The assay involves three main steps: 1) preparation of epithelial and bacterial cells, 2) addition of bacteria to epithelial cell monolayers, and 3) detection of adherent pneumococci by viable counts (serial dilution and plating) or quantitative real-time PCR (qPCR). This technique is relatively straightforward and does not require specialized equipment other than a tissue culture setup. The assay can be used to test other probiotic species and/or potential inhibitors of pneumococcal colonization and can be easily modified to address other scientific questions regarding pneumococcal adherence and invasion.

Investigating the Effects of Probiotics on Pneumococcal Colonization Using an In Vitro Adherence Assay

In vitro adherence assays can be used to study the attachment of Streptococcus pneumoniae to epithelial cell monolayers and to investigate potential interventions such as the use of probiotics for inhibiting pneumococcal colonization.

Adherence of Streptococcus pneumoniae (the pneumococcus) to the epithelial lining of the nasopharynx can result in colonization and is considered a ...

Analyzing the Effects of Stromal Cells on the Recruitment of Leukocytes from Flow

Stromal cells regulate the recruitment of circulating leukocytes during inflammation through cross-talk with neighboring endothelial cells. Here we describe two in vitro &#8220;vascular&#8221; models for studying the recruitment of circulating neutrophils from flow by inflamed endothelial cells. A major advantage of these models is the ability to analyze each step in the leukocyte adhesion cascade in order, as would occur in vivo. We also describe how both models can be adapted to study the role of stromal cells, in this case mesenchymal stem cells (MSC), in regulating leukocyte recruitment.
Primary endothelial cells were cultured alone or together with human MSC in direct contact on Ibidi microslides or on opposite sides of a Transwell filter for 24 hr. Cultures were stimulated with tumor necrosis factor alpha (TNF&#945;) for 4 hr and incorporated into a flow-based adhesion assay. A bolus of neutrophils was perfused over the endothelium for 4 min. The capture of flowing neutrophils and their interactions with the endothelium was visualized by phase-contrast microscopy.
In both models, cytokine-stimulation increased endothelial recruitment of flowing neutrophils in a dose-dependent manner. Analysis of the behavior of recruited neutrophils showed a dose-dependent decrease in rolling and a dose-dependent increase in transmigration through the endothelium. In co-culture, MSC suppressed neutrophil adhesion to TNF&#945;-stimulated endothelium.
Our flow based-adhesion models mimic the initial phases of leukocyte recruitment from the circulation. In addition to leukocytes, they can be used to examine the recruitment of other cell types, such as therapeutically administered MSC or circulating tumor cells. Our multi-layered co-culture models have shown that MSC communicate with endothelium to modify their response to pro-inflammatory cytokines, altering the recruitment of neutrophils. Further research using such models is required to fully understand how stromal cells from different tissues and conditions (inflammatory disorders or cancer) influence the recruitment of leukocytes during inflammation.

The ability of inflamed endothelium to recruit leukocytes from flow is regulated by mesenchymal stromal cells. We describe two in vitro models incorporating primary human cells that can be used to assess neutrophil recruitment from flow and examine the role that mesenchymal stromal cells play in regulating this process.

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 to screen for target enzymes prior to investing greater research effort and resources. The goal of this protocol is to demonstrate two complementary assays for conducting these initial evaluations. The microslide diffusion assay provides an initial or simple detection screen to enable the qualitative and rapid assessment of proteolytic activity against an array of both viable and heat-killed bacterial target substrates. As a counterpart, the increased sensitivity and reproducibility of the dye-release assay provides a quantitative platform for evaluating and comparing environmental influences affecting the hydrolytic activity of protein antimicrobials. The ability to label specific heat-killed cell culture substrates with Remazol brilliant blue R dye expands this capability to tailor the dye-release assay to characterize enzymatic activity of interest.

Here, we present protocols to rapidly screen and characterize enzymes for antimicrobial activity. The microslide diffusion assay and the dye-release assay utilize target bacterial substrates for qualitative and quantitative enzymatic activity evaluation.

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

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 model experimental autoimmune encephalomyelitis (EAE). This process is characterized by the transmigration of activated T cells across brain endothelial cells (ECs), the main constituents of the BBB. However, the consequences on brain EC function upon interaction with such T cells are largely unknown. Here we describe an assay that allows for the evaluation of primary mouse brain microvascular EC (MBMEC) function and barrier integrity during the interaction with T cells over time. The assay makes use of impedance cell spectroscopy, a powerful tool for studying EC monolayer integrity and permeability, by measuring changes in transendothelial electrical resistance (TEER) and cell layer capacitance (Ccl). In direct contact with ECs, stimulated but not na&#239;ve T cells are capable of inducing EC monolayer dysfunction, as visualized by a decrease in TEER and an increase in Ccl. The assay records changes in EC monolayer integrity in a continuous and automated fashion. It is sensitive enough to distinguish between different strengths of stimuli and levels of T cell activation and it enables the investigation of the consequences of a targeted modulation of T cell-EC interaction using a wide range of substances such as antibodies, pharmacological reagents and cytokines. The technique can also be used as a quality control for EC integrity in in vitro T-cell transmigration assays. These applications make it a versatile tool for studying BBB properties under physiological and pathophysiological conditions.

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

Here, we describe an in vitro murine model of the blood-brain barrier that makes use of impedance cell spectroscopy, with a focus on the consequences on endothelial cell integrity and permeability upon interaction with activated T cells.

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 diameter. In addition to a characterization of forward and sideward scatter properties, it enables the use of fluorescent labeled markers like antibodies to detect respective structures. Using indirect antibody staining, flow cytometry is employed here to quantify birch pollen allergen (precisely Bet v 1)-loaded particles of 0.5 to 10 &#181;m in diameter in inhalable particulate matter (PM10, particle size &#8804;10 &#181;m in diameter). PM10 particles may act as carriers of adsorbed allergens possibly transporting them to the lower respiratory tract, where they could trigger allergic reactions.
So far the allergen content of PM10 has been studied by means of enzyme linked immunosorbent assays (ELISAs) and scanning electron microscopy. ELISA measures the dissolved and not the particle-bound allergen. Compared to scanning electron microscopy, which can visualize allergen-loaded particles, flow cytometry may additionally quantify them. As allergen content of ambient air can deviate from birch pollen count, allergic symptoms might perhaps correlate better with allergen exposure than with pollen count. In conjunction with clinical data, the presented method offers the opportunity to test in future experiments whether allergic reactions to birch pollen antigens are associated with the Bet v 1 allergen content of PM10 particles &#62;0.5 &#181;m.

Here, we present a protocol to quantify allergen-loaded particles by flow cytometry. Ambient particulate matter particles may act as carriers of adsorbed allergens. We show here that flow cytometry, a method widely used to characterize suspended solids &#62;0.5 &#181;m in diameter, can be used to measure these allergen-loaded particles.

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 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 ...

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 temporal specificity2. Chemotaxis is a critical function of lymphocytes and other immune cells that can be quantitatively assessed in vitro. This manuscript describes methods that permit the evaluation of chemotaxis, both in vitro and in vivo, for diverse cell types including cell lines and native cells. The in vitro, plate-based format permits the comparison of several conditions simultaneously in real-time, and can be completed within 1-4 h. In vitro assay conditions can be manipulated to introduce agonists and antagonists, as well as differentiate chemotaxis from chemokinesis, which is random movement. For in vivo trafficking assessments, immune cells can be labeled with multiple fluorescent dyes and used for adoptive transfer. The differential labeling of cells allows for mixed cell populations to be introduced into the same animal, thereby decreasing variance and reducing the number of animals required for an adequately powered experiment. Migration into lymphoid tissue occurs in as little as 1 h, and multiple tissue compartments can be sampled. Flow cytometry following tissue harvest allows for a rapid and quantitative analysis of the migratory patterns of multiple cell types.

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

Protocols for quantitative assessment of lymphocyte chemotaxis and migration are important tools for immunology research. Here, an in vitro protocol is described that permits real-time, multiplexed evaluation of cell migration, as well as a complementary in vivo technique enabling tracking of native cells to spleen.

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

Assessment of the Cytotoxic and Immunomodulatory Effects of Substances in Human Precision-cut Lung Slices

Respiratory diseases in their broad diversity need appropriate model systems to understand the underlying mechanisms and enable development of new therapeutics. Additionally, registration of new substances requires appropriate risk assessment with adequate testing systems to avoid the risk of individuals being harmed, for example, in the working environment. Such risk assessments are usually conducted in animal studies. In view of the 3Rs principle and public skepticism against animal experiments, human alternative methods, such as precision-cut lung slices (PCLS), have been evolving. The present paper describes the ex vivo technique of human PCLS to study the immunomodulatory potential of low-molecular-weight substances, such as ammonium hexachloroplatinate (HClPt). Measured endpoints include viability and local respiratory inflammation, marked by altered secretion of cytokines and chemokines. Pro-inflammatory cytokines, tumor necrosis factor alpha (TNF-&#945;), and interleukin 1 alpha (IL-1&#945;) were significantly increased in human PCLS after exposure to a sub-toxic concentration of HClPt. Even though the technique of PCLS has been substantially optimized over the past decades, its applicability for the testing of immunomodulation is still in development. Therefore, the results presented here are preliminary, even though they show the potential of human PCLS as a valuable tool in respiratory research.

In view of the 3Rs principle, respiratory models as alternatives to animal studies are evolving. Especially for risk assessment of respiratory substances, there is a lack of appropriate assays. Here, we describe the use of human precision-cut lung slices for the assessment of airborne substances.

Respiratory diseases in their broad diversity need appropriate model systems to understand the underlying mechanisms and enable development of new ...

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota

The emerging role of the gut microbiome in several human diseases demands a breakthrough of new tools, techniques and technologies. Such improvements are needed to decipher the utilization of microbiome modulators for human health benefits. However, the large-scale screening and optimization of modulators to validate microbiome modulation and predict related health benefits may be practically difficult due to the need for large number of animals and/or human subjects. To this end, in vitro or ex vivo models can facilitate preliminary screening of microbiome modulators. Herein, it is optimized and demonstrated an ex vivo fecal microbiota culture system that can be used for examining the effects of various interventions of gut microbiome modulators including probiotics, prebiotics and other food ingredients, aside from nutraceuticals and drugs, on the diversity and composition of the human gut microbiota. Inulin, one of the most widely studied prebiotic compounds and microbiome modulators, is used as an example here to examine its effect on the healthy fecal microbiota composition and its metabolic activities, such as fecal pH and the fecal levels of organic acids including lactate and short-chain fatty acids (SCFAs). The protocol may be useful for studies aimed at estimating the effects of different interventions of modulators on fecal microbiota profiles and at predicting their health impacts.

This protocol describes an in vitro batch-culture fermentation system of human fecal microbiota, using inulin (a well-known prebiotic and one of the most widely studied microbiota modulators) to demonstrate the use of this system in estimating effects of specific interventions on fecal microbiota composition and metabolic activities.

The emerging role of the gut microbiome in several human diseases demands a breakthrough of new tools, techniques and technologies. Such improvements are ...