Biofilm Viability Kit
Biofilm Viability Kit - Related Videos
07:41
Quantification, Viability Assessment, and Visualization Strategies for Acinetobacter Biofilms
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Cited by 1 •
2023
This protocol describes the preparation of the inoculum, the biofilm quantification on microtiter plates using crystal violet dye, the viable count in biofilms, and the visualization of biofilms of Acinetobacter.
09:03
Growth of Mycobacterium tuberculosis Biofilms
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Cited by 79 •
2012
Mycobacterium tuberculosis forms drug tolerant biofilms when cultured in certain conditions. Here we describe methods for culturing M. tuberculosis biofilms and determining the frequency of drug tolerant persisters. These protocols will be useful for further studies into the mechanisms of drug tolerance in M. tuberculosis.
05:03
Visualizing the Effects of Sputum on Biofilm Development Using a Chambered Coverglass Model
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Cited by 5 •
2016
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.
06:14
In vitro Biofilm Formation in an 8-well Chamber Slide
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Cited by 62 •
2011
This article describes the procedure for the formation and visualization of a bacterial biofilm grown within an 8-well chamber...
02:32
Confocal Imaging of Bacterial Biofilms Induced by Sputum Filtrates from Cystic Fibrosis Patients
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2026
Source: Beaudoin, T., et al. Visualizing the Effects of Sputum on Biofilm Development Using a Chambered Coverglass Model. J. Vis. Exp. (2016)This video demonstrates an in vitro method to assess how patient-derived cystic fibrosis sputum influences bacterial biofilm formation. Using chamber slides, fluorescent staining, and confocal microscopy, it reveals increased thickness, altered architecture, and higher viable biomass in biofilms grown with sputum, highlighting the role of host components...
FAQs
The assay uses viability-sensitive fluorescent stains whose cellular entry differs according to membrane integrity. Cells with intact membranes and cells with damaged membranes therefore generate distinguishable signals. This contrast allows researchers to separate living and nonviable populations within the same biofilm image, rather than relying only on total microbial abundance.
The location of viable and nonviable cells can reveal uneven survival within a biofilm. A treatment may reduce viability in one region while leaving other areas less affected. Examining both signal distribution and overall proportions therefore provides more information than a single average measurement and can help identify patterns of persistence.
Viability profiles indicate how much of a biofilm remains living and how that survival is distributed between intact and damaged or dead cells. Comparing these profiles across conditions helps distinguish broad microbial loss from partial damage. That distinction is useful when evaluating whether an intervention substantially affects survival or produces a mixed response.
A basic workflow applies the viability-sensitive fluorescent stains to the biofilm, allows the cells to produce distinguishable signals according to membrane condition, and then uses imaging to examine the stained community. Quantification converts those signals into proportions or distributions of living and nonviable microorganisms, supporting comparisons between experimental conditions.
Researchers can compare viability measurements from treated and untreated biofilms to determine how an antimicrobial changes the living fraction and the pattern of survival. Imaging may show whether effects are distributed throughout the community or concentrated in particular regions. These results help characterize treatment impact without treating total biofilm presence as equivalent to survival.
Persistent biofilms can complicate the study of infections that are difficult to eradicate. Measuring living and nonviable microorganisms provides a way to relate microbial survival to host-pathogen interactions and infection persistence. The resulting viability data can support comparisons among experimental treatments while adding cellular and spatial context to infection studies.