Quantitative Culture
Quantitative Culture - Related Videos
11:20
Quantitative Assessment of Human Neutrophil Migration Across a Cultured Bladder Epithelium
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Cited by 8 •
2013
We developed an in vitro model that mimics an important component of the acute inflammatory response during infection of the bladder with uropathogenic Escherichia coli. The transuroepithelial neutrophil migration assay enables quantitative assessment of human neutrophil migration across bladder epithelia, cultured on permeable supports, in response to bacterial infection or chemoattractant substances.
09:06
Culture of Small Colony Variant of Pseudomonas aeruginosa and Quantitation of its Alginate
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Cited by 8 •
2020
Here, we describe a growth condition to culture the small colony variant of Pseudomonas aeruginosa. We also describe two separate methods for the detection and quantitation of the exopolysaccharide alginate produced by P. aeruginosa using a traditional uronic acid carbazole assay and an alginate-specific monoclonal antibody (mAb) based ELISA.
11:39
A Quantitative Fitness Analysis Workflow
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Cited by 25 •
2012
Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.
06:24
Individual Culturing of Tigriopus Copepods and Quantitative Analysis of Their Mate-guarding Behavior
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Cited by 6 •
2018
Mate-guarding behavior plays an important role in reproduction of intertidal copepods of the genus Tigriopus. However, methods for studying this behavior have not been well described. Here we describe methods for: 1) individual culture of virgin Tigriopus animals, and 2) quantitative analysis of their mate-guarding behavior.
09:04
Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
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2018
Lens-free video microscopy enables us to monitor cell cultures directly inside the incubator. Here we describe the full protocol used to acquire and analyze a 2.7 day long acquisition of cultured HeLa cells, leading to a dataset of 2.2 x 106 measurements of individual cell morphology and 10584 cell cycle tracks.
FAQs
CFU counts provide a numerical estimate of viable microorganisms in the portion of sample placed on growth media. When adjusted for dilution, they represent microbial burden in the original biological sample rather than merely confirming that microorganisms are present. This numerical format allows investigators to compare samples, infection severity, and treatment outcomes using the same measurement.
Serial dilution converts a sample with potentially high microbial burden into aliquots that can be assessed by colony counting. The dilution factor then links the observed CFU count back to the original sample concentration. Without accounting for that factor, colony numbers would describe only the inoculated dilution, not the biological specimen being compared.
Using the same defined incubation conditions helps ensure that differences in CFU estimates are interpreted as differences in measured microbial burden rather than changes in how samples were cultured. This consistency is important when comparing pathogen growth, antimicrobial activity, infection severity, or immune and treatment effects across experimental groups.
An experiment begins with a biological sample, followed by serial dilution and spreading or inoculation onto growth media. After incubation under defined conditions, investigators count the resulting colonies and apply the dilution factor to estimate the original concentration. Consistency across these stages supports reproducible measurements when comparing experimental groups.
Antimicrobial activity can be assessed by comparing microbial-load measurements between treated and comparison samples. A lower estimated concentration in the treated condition indicates less measured microbial growth under the culture conditions, while comparable or greater values suggest a smaller reduction in burden. This provides a quantitative treatment outcome rather than a presence-or-absence result.
In host-pathogen studies, microbial-load measurements connect immune responses or experimental treatments with changes in pathogen growth. Researchers can compare infection severity across samples and determine whether an intervention alters the amount of viable microorganism detected. Quantitative results therefore provide a common outcome for analyzing interactions between the infecting organism, host immune responses, and treatment conditions.