Imaging-based Assay
Imaging-based Assay - Related Videos
07:35
Preparation, Imaging, and Quantification of Bacterial Surface Motility Assays
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Cited by 68 •
2015
Swarming motility is influenced by physical and environmental factors. We describe a two-phase protocol and guidelines to circumvent the challenges commonly associated with swarm assay preparation and data collection. A macroscopic imaging technique is employed to obtain detailed information on swarm behavior that is not provided by current analysis techniques.
02:33
A Rapid Image-Based Assay for Screening Surface-Induced Bacterial Virulence
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2026
Source: Perinbam, K., and Siryaporn, A. A Rapid Image-based Bacterial Virulence Assay Using Amoeba. J. Vis. Exp. (2018)This video demonstrates a rapid, image-based method to assess bacterial virulence by incubating amoebae with surface-attached or planktonic bacteria, applying a calcein-acetoxymethyl ester (calcein-AM) agarose pad, and imaging fluorescence to detect host cell damage.
12:11
A Rhodopsin Transport Assay by High-Content Imaging Analysis
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Cited by 1 •
2019
Here, we described a high-content imaging method to quantify the transport of rhodopsin mutants associated with retinitis pigmentosa. A multiple-wavelength scoring analysis was used to quantify rhodopsin protein on the cell surface or in the whole cell.
10:41
An Organotypic Slice Assay for High-Resolution Time-Lapse Imaging of Neuronal Migration in the Postnatal Brain
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Cited by 6 •
2010
This protocol describes an organotypic slice assay optimized for the postnatal brain and high-resolution time-lapse imaging of neuroblast migration in the rostral migratory stream.
03:17
Analyzing the Antigenic Relationship between Viruses using an Image-Based Microneutralization Assay
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2025
This video demonstrates the imaging-based micro-neutralization assay to analyze the antigenic relations between influenza A and B viruses. It provides a quantitative and visual way to assess the effectiveness of antibodies or other treatments in preventing viral infection.
FAQs
Quantification comes from translating selected visual features into measurements rather than relying on visual judgment alone. Depending on the assay, analysis may capture fluorescence intensity, morphology, spatial localization, or cell number. These measurements allow investigators to compare biological states under defined conditions and relate image-derived differences to cellular, tissue, or organism-level changes.
Labeling or contrast makes biological structures or changes more distinguishable to the imaging system when they are not adequately visible on their own. The choice depends on what feature the assay must measure, while defined sample conditions help keep observations comparable. This preparation supports reliable assessment of intensity, morphology, localization, or cell number.
Standardization reduces variation introduced during image capture and data interpretation. Consistent acquisition conditions and analytical procedures make measurements more reproducible across samples and experiments, which strengthens comparisons of cellular or tissue features. In biomedical research, this reliability is important when image-derived results are used to evaluate biomarkers, characterize disease, or assess treatment response.
Researchers first prepare samples under defined conditions, then apply labeling or contrast when needed. They acquire images with microscopy or another imaging system and analyze the resulting data for selected features, such as intensity, morphology, localization, or cell number. This workflow converts sample observations into quantitative results that can be compared across experimental conditions.
In medicine, the approach is useful when disease-related changes can be observed in cells, tissues, or organisms. It supports disease characterization, biomarker evaluation, drug screening, and studies of treatment response. By connecting visible biological changes with quantitative measurements, investigators can examine how experimental conditions or therapies are associated with measurable outcomes.
Image-derived measurements can show whether treatment is associated with changes in fluorescence intensity, morphology, localization, or cell number. Examining these features provides a quantitative basis for comparing treated and other defined conditions, rather than depending only on visual impressions. The resulting data can help connect cellular or tissue changes with treatment-response outcomes.