Differential Cell Counter
Differential Cell Counter - Related Videos
09:33
Determining Cell Number During Cell Culture using the Scepter Cell Counter
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Cited by 57 •
2010
The Scepter Cell Counter is a handheld automated device that can be used to count cells, monitor cell diameter and volume, and be used to check the health and quality of cellular populations from one culture to the next.
13:16
Determination of Mammalian Cell Counts, Cell Size and Cell Health Using the Moxi Z Mini Automated Cell Counter
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Cited by 18 •
2012
The Moxi Z miniature automated cell counter is a novel instrument that combines the Coulter Principle with patented thin-film sensor technology and a proprietary software algorithm to perform sizing and counting of a broad size range of particles as well as to determine the overall health of monodisperse mammalian cell cultures. This protocol describes the use of this instrument for counting and assessing the health of cell cultures.
07:31
Agarose-Based Model Ecosystem for Cultivating Methanotrophs in a Methane-Oxygen Counter Gradient
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2024
A protocol is described for preparing a simple model ecosystem that recreates the methane-oxygen counter gradient found in the natural habitat of aerobic methane-oxidizing bacteria, enabling the study of their physiology in a spatially resolved context. Modifications to common biochemical assays for use with the agarose-based model ecosystem are also described.
12:17
Initiating Differentiation in Immortalized Multipotent Otic Progenitor Cells
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Cited by 11 •
2016
The current protocols to maintain immortalized multipotent otic progenitor (iMOP) cells and otic differentiation are described. Culture conditions and molecular markers that indicate differentiation into sensory epithelia and spiral ganglion neurons (SGN) are highlighted.
11:38
A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation
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2024
Available pluripotent stem cell (PSC)-to-functional cell differentiation systems are currently impeded by problems of severe line-to-line and batch-to-batch variability. Here, using cardiac differentiation as the main example, we present a protocol to intelligently monitor and modulate the process of PSC differentiation based on image-based machine learning.
FAQs
Classification depends on measurable signal differences among cells. Size, internal complexity, light scattering, fluorescence, or electrical impedance can each contribute information about a cell as it passes through the sensing region. Combining these characteristics helps distinguish populations within a mixed sample, allowing investigators to examine both total counts and the relative representation of different cellular groups.
Individual passage links each detected signal to a separate cell rather than to the mixed sample as a whole. This supports population-level measurements by allowing cells to be grouped according to their signal characteristics. The resulting distribution can reveal differences in cellular composition that would be obscured if only an overall sample measurement were collected.
A total-cell count reports the amount of cellular material without resolving the composition of the sample. Differential Cell Counter measurements add information about distinct cell types and their relative proportions. That distinction is important when two samples contain similar overall cell numbers but differ in cellular heterogeneity, phenotype, or the abundance of particular populations.
The sample is evaluated as cells pass individually through a sensing region, where the instrument or method records signals associated with each event. Those signals are then used to classify cells according to measurable properties such as size, complexity, scattering, fluorescence, or impedance. The resulting classifications provide counts and proportions for the detected populations.
In genetics, this analysis helps compare cellular phenotypes associated with gene variants, engineered populations, and controls. It can also track population changes during cell culture or experimental treatment. By measuring differences in cell-type abundance, investigators gain cellular evidence that can support studies of gene function, disease mechanisms, and changes linked to experimental conditions.
Population measurements provide a way to examine cellular heterogeneity alongside molecular or cellular assay results. Researchers can determine whether engineered and control samples differ in their cellular composition, or whether treatment changes the representation of particular populations. These measurements strengthen interpretation by documenting sample-level variation and supporting more reproducible comparisons across experiments.