Imaging Cytometer
Imaging Cytometer - Related Videos
10:58
Assessment of GFP Expression and Viability Using the Tali Image-Based Cytometer
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Cited by 14 •
2011
This protocol describes how to perform cell viability and fluorescence expression assays using the Tali Image-Based Cytometer.
10:59
Mass Cytometry: Protocol for Daily Tuning and Running Cell Samples on a CyTOF Mass Cytometer
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Cited by 48 •
2012
The steps necessary for daily tuning and optimization of the performance of a CyTOF mass cytometer are described. Comments on optimal sample preparation and flow rate are...
11:00
Live Cell Cycle Analysis of Drosophila Tissues using the Attune Acoustic Focusing Cytometer and Vybrant DyeCycle Violet DNA Stain
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Cited by 13 •
2013
A protocol for cell cycle analysis of live Drosophila tissues using the Attune Acoustic Focusing Cytometer is described. This protocol simultaneously provides information about relative cell size, cell number, DNA content and cell type via lineage tracing or tissue specific expression of fluorescent proteins in vivo.
13:59
Reduced-gravity Environment Hardware Demonstrations of a Prototype Miniaturized Flow Cytometer and Companion Microfluidic Mixing Technology
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Cited by 9 •
2014
Spaceflight blood diagnostics need innovation. Few demonstrations have been published illustrating in-flight, reduced-gravity health diagnostic technology. Here we present a method for construction and operation of a parabolic flight test rig for a prototype point-of-care flow-cytometry design, with components and preparation strategies adaptable to other setups.
06:22
Standardization of Transfer across Labs between Flow Cytometers for Detection of Lymphocytes in Japanese Encephalitis Vaccinated Children
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Cited by 4 •
2023
In this study, a method was developed to facilitate the transfer of experimental settings and analysis templates between two flow cytometers in two laboratories for the detection of lymphocytes in Japanese encephalitis-vaccinated children. The standardization method for the flow cytometer experiments will allow research projects to be effectively conducted in multiple centers.
FAQs
The instrument captures bright-field or fluorescence images as cells pass through the system or as a field is scanned. Software then analyzes each individual cell using measurable features, including size, shape, signal intensity, and marker localization. Combining these measurements with image-based visual information allows populations to be classified while retaining structural detail that supports interpretation.
Marker localization shows where a signal appears within or around an individual cell, rather than reporting only its overall intensity. This spatial information can be considered alongside cell shape and size to distinguish cellular states or developmental changes. In developmental biology, that combination helps relate molecular signals to structural features during differentiation or tissue development.
The measured outcome depends on the cellular feature being analyzed and the imaging signal used. Size, shape, intensity, marker localization, viability, and spatial organization each provide a different view of a developing population. Comparing the same features across developmental stages or experimental conditions supports objective assessment of how cells change over time or in response to treatment.
Cells are either introduced so they flow through the instrument or placed within a field that the system scans. The optics acquire bright-field or fluorescence images, and automated software extracts features from individual cells. Researchers can then classify or compare the resulting measurements, linking visual characteristics with signals such as intensity and marker localization.
Researchers can apply imaging cytometry when they need quantitative comparisons of cell populations across developmental stages or experimental conditions. The method supports measurements of morphology, proliferation, differentiation, viability, and spatial organization. Because image data accompany the measurements, investigators can examine changes in cellular structure together with molecular signals rather than relying on a single numerical feature.
It can provide objective, cell-by-cell measurements that reveal how morphology, proliferation, differentiation, viability, or spatial organization changes between samples. The data also connect marker-related signals with cellular structure, helping investigators compare developmental stages and responses to experimental conditions. This combined readout supports classification of populations while preserving visual information for interpretation.