Image Cytometric Assay
Image Cytometric Assay - Related Videos
15:32
Identification and Analysis of Mouse Erythroid Progenitors using the CD71/TER119 Flow-cytometric Assay
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Cited by 147 •
2011
A flow-cytometric method for identification and molecular analysis of differentiation-stage-specific murine erythroid progenitors and precursors, directly in freshly –harvested mouse bone marrow, spleen or fetal liver. The assay relies on cell-surface markers CD71, Ter119, and cell size.
10:23
Standardization of a Cytometric Bead Assay Based on Egg-Yolk Antibodies
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Cited by 2 •
2023
This protocol describes a methodology for the preparation of latex beads for assays using IgY antibodies for antigen detection.
02:40
Natural Killer Cell-Mediated Cytotoxicity Assay Sample Preparation for Flow Cytometric Analysis
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2025
This video demonstrates the lytic activity of natural killer cells against fluorescently-stained target cells. The method enables NK cell-induced cytotoxicity measurement by staining dead cells and using a flow cytometer to quantify live and dead target cells.
08:14
Isolation and Flow Cytometric Characterization of Murine Small Intestinal Lymphocytes
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Cited by 55 •
2016
There is growing interest in the quantitative characterization of intestinal lymphocytes owing to increasing recognition that these cells play a critical role in a variety of intestinal and systemic diseases. In this protocol, we describe how to isolate single cell populations from different small-intestinal compartments for subsequent flow cytometric characterization.
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.
FAQs
Computational segmentation separates captured images into individual cells or defined subcellular regions before measurements are calculated. This step determines which objects contribute to the analysis and allows features such as size, shape, signal intensity, and marker localization to be assigned to specific cellular units. Reliable segmentation therefore supports quantitative comparisons among individual cells and across whole populations.
Brightfield and fluorescence imaging provide complementary ways to capture cellular information. Brightfield images support visualization of cell structure, while fluorescence images enable measurement of signals associated with markers or other labeled features. The selected imaging mode influences which cellular characteristics can be quantified, including morphology, signal intensity, and the location of markers within cells or subcellular regions.
Spatial information links a measured signal to its cellular or subcellular location rather than treating the signal as an isolated population value. This makes it possible to examine marker localization alongside cell shape, size, or other features. In biological studies, that added context supports more detailed phenotyping and helps characterize how cells respond to drugs or environmental conditions.
The important distinction is the preservation of image-based spatial context. Image cytometry combines quantitative cytometric measurement with microscopy, so individual-cell features can be evaluated together with their visible organization and marker locations. This approach supports high-throughput analysis while retaining information useful for studying morphology, subcellular distribution, and heterogeneous responses within a cell population.
A typical workflow begins by capturing brightfield or fluorescence images of cells. Computational segmentation then identifies individual cells or subcellular regions within those images. The assay extracts quantitative measurements, including size, shape, signal intensity, and marker localization, and these data are used to evaluate cell populations or compare biological conditions such as treatments and environmental exposures.
Researchers can apply these assays to cell phenotyping, viability assessment, cell-cycle analysis, and protein localization studies. The method also supports investigations of cellular responses to drugs or environmental conditions. Because it measures individual cells while retaining spatial information and supporting high-throughput analysis, it connects detailed cellular observations with population-level comparisons in basic and applied biological research.