Automated Capsule Analysis
Automated Capsule Analysis - Related Videos
08:08
Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body
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Cited by 18 •
2018
This technique describes an automated batch image processor designed to measure polysaccharide capsule and body radii. While initially designed for Cryptococcus neoformans capsule measurements the automated image processor can also be applied to other contrast based detection of circular objects.
05:24
Teratoma Generation in the Testis Capsule
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Cited by 25 •
2011
Human pluripotent stem cells (hPSCs) have the potential to treat a myriad of different diseases. The utility of these cells lies in the fact that they can differentiate into any cell type in the body. Here we describe the teratoma assay, which is used to demonstrate the pluripotence of hPSCs.
02:17
Separation of Bacteria by Capsule Amount Using a Discontinuous Density Gradient
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2025
Source: Feltwell, T., et.al. Separating Bacteria by Capsule Amount Using a Discontinuous Density Gradient. J. Vis. Exp. (2019)This video demonstrates the use of a discontinuous density gradient to separate bacterial strains based on capsule amount. By centrifuging a transposon mutant library through the gradient, bacteria with different capsule densities localize to distinct layers. The separated fractions can then be collected for downstream analysis.
10:39
Automated Analysis of Intracellular Phenotypes of Salmonella Using ImageJ
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Cited by 3 •
2022
Salmonella invades and replicates inside intestinal epithelial cells both in Salmonella-specific vacuoles and free in the cytosol (hyper-replication). A high-throughput fluorescence microscopy-based protocol is described here to quantify the intracellular phenotypes of Salmonella by two complementary image analyses through ImageJ, reaching single-cell resolution and scoring.
06:27
Automated Analysis of C. elegans Fluorescence Images using SegElegans
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2025
Here we provide instructions on effectively utilizing SegElegans, a deep learning system we developed for the automated segmentation of individual worms in widefield microscopy images, for subsequent use in image analysis software such as ImageJ. We provide ways to use the system both online and offline.
FAQs
The analysis centers on locating two related boundaries: the microbial cell itself and the surrounding capsule. Once these boundaries are identified, the software can calculate capsule dimensions or quantify capsule-associated signal intensity using the same criteria across images. This boundary-based approach converts visual differences into measurements that support consistent comparisons among cells, samples, or strains.
Capsule dimensions and signal intensity provide complementary information. Dimensions describe the apparent extent of the protective layer around a cell, whereas signal intensity indicates the strength of the detected capsule-associated signal. Examining one or both measurements can reveal changes in capsule structure or detected signal when cells experience different growth conditions or when strains are compared.
Capsule formation may change during growth or in response to environmental conditions, so the same strain can produce different measurement profiles under different circumstances. Automated analysis allows those conditions to be compared with consistent criteria across many samples. The resulting differences can help investigators examine how capsule variation may relate to immune recognition, host-defense resistance, or microbial pathogenicity.
A typical workflow begins with microscopy to capture microbial cells and their surrounding structures. Image-analysis algorithms then identify cell and capsule boundaries, measure capsule dimensions or signal intensity, and process multiple samples according to consistent criteria. The final measurements can be organized for comparisons across growth conditions, strains, or experimental groups, rather than relying only on visual inspection.
Automation improves throughput by processing many samples and applies measurement criteria consistently across images. It can also reduce observer bias, which may arise when people judge capsule boundaries or apparent size differently. These advantages support more reproducible studies, particularly when researchers need to compare numerous cells, strains, or conditions while examining microbial traits relevant to infection.
In this field, capsule measurements help investigators examine protective structures that can influence virulence and immune recognition. Comparing capsule dimensions or signal intensity across strains and conditions can support studies of microbial pathogenicity and possible resistance to host defenses. The approach therefore connects quantitative imaging results with questions about how microbial surface organization affects infection-related behavior.