In Cell Analyzer
In Cell Analyzer - Related Videos
05:38
Isolating and Analyzing Cells of the Pancreas Mesenchyme by Flow Cytometry
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Cited by 13 •
2017
Here, we describe a method for the isolation of cells in the pancreas microenvironment from embryonic, neonatal and adult mouse tissue, focusing on the isolation of mesenchymal cells. This method allows profiling of cell gene expression and protein secretion in order to elucidate the extrinsic signals that regulate pancreas development, function, and tumorigenesis.
09:46
Analyzing Murine Schwann Cell Development Along Growing Axons
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Cited by 1 •
2012
Here we describe a Schwann cell (SC) migration assay in which SCs are able to develop along extending axons.
09:38
Analyzing the Function of Small GTPases by Microinjection of Plasmids into Polarized Epithelial Cells
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Cited by 5 •
2011
This article details the procedures involved in overexpression and analysis of small GTPases in polarized epithelial cells using microinjection technique.
11:30
Analyzing the Effects of Stromal Cells on the Recruitment of Leukocytes from Flow
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Cited by 28 •
2015
The ability of inflamed endothelium to recruit leukocytes from flow is regulated by mesenchymal stromal cells. We describe two in vitro models incorporating primary human cells that can be used to assess neutrophil recruitment from flow and examine the role that mesenchymal stromal cells play in regulating this process.
09:07
Analyzing the Functions of Mast Cells In Vivo Using 'Mast Cell Knock-in' Mice
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Cited by 21 •
2015
We describe a method for the generation of in vitro derived mast cells, their engraftment into mast cell-deficient mice, and the analysis of the phenotype, numbers and distribution of engrafted mast cells at different anatomical sites. This protocol can be used to assess the functions of mast cells in vivo.
FAQs
The system captures optical signals from stained or genetically labeled cells and applies image-analysis software to those images. It can convert visible differences in protein localization, morphology, viability, or signal intensity into numerical measurements across many samples. This connection helps researchers examine how changes in cellular structure relate to functional responses rather than relying only on visual inspection.
These labeling approaches provide the optical signals needed to distinguish cellular features in intact cells. Fluorescence can indicate the presence or location of a target, while staining or genetic labels can mark structures or biological responses for imaging. The selected signal determines which feature the software can quantify, such as localization, intensity, morphology, or viability.
Multiparametric measurements evaluate several cellular features within the same experiment, rather than reducing a response to one value. An In Cell Analyzer can measure combinations such as morphology, viability, protein localization, and signal intensity across samples. This broader profile can reveal complex cellular responses and treatment effects that manual observation or a single measurement might miss.
Researchers arrange samples in microplates, use stains or genetic labels to produce measurable optical signals, and acquire images from the intact cells. Analysis software then evaluates selected features across the samples, generating quantitative results for comparison. This workflow supports consistent examination of many conditions and reduces dependence on manually inspecting cells one field at a time.
The approach is useful when a study needs cellular measurements from many samples and more than one response feature. In drug evaluation, it can help compare treatment-associated changes in viability, morphology, localization, or signal intensity. In disease modeling, the same measurements support cellular phenotyping, allowing researchers to characterize altered cell states and responses across experimental conditions.
It can provide quantitative evidence about protein localization, cell morphology, viability, and signal intensity in intact cells. These outcomes can be examined across many samples to identify treatment effects or distinguish cellular phenotypes. Because the measurements combine structural and functional information, they can support high-content screening and comparisons among complex biological responses.