Single Cell Population
Single Cell Population - Related Videos
10:54
Studying Proteolysis of Cyclin B at the Single Cell Level in Whole Cell Populations
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Cited by 2 •
2012
Metaphase to anaphase transition is triggered through anaphase-promoting complex (APC/C)-dependent ubiquitination and subsequent destruction of cyclin B. Here, we established a system which, following pulse-chase labeling, allows monitoring cyclin B proteolysis in entire cell populations and facilitates the detection of interference by the mitotic checkpoint.
10:55
Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations
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Cited by 12 •
2017
A robust protocol to monitor neural populations by time-lapse video-microscopy followed by software-based post-processing is described. This method represents a powerful tool to identify biological events in a selected population during live imaging experiments.
01:20
Sign Test for Median of Single Population
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2025
In general, the sign test serves as a nonparametric method to test hypotheses about the median of a single population when the data does not follow a known distribution. This simplicity makes it particularly useful for small sample sizes or when the assumptions of parametric tests cannot be met. The process begins with identifying a null hypothesis, typically stating that the population median equals a specific value. The alternative hypothesis could be that the median is either not equal to,...
04:12
Isolation of a Single Giant Virus from a Mixed Viral Population Using Amoebae
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2026
Source: Sahmi-Bounsiar, D. et al. Single Cell Micro-aspiration as an Alternative Strategy to Fluorescence-activated Cell Sorting for Giant Virus Mixture Separation. J. Vis. Exp. (2019)This video demonstrates single-cell micro-aspiration to isolate and expand a single giant virus population from a mixed viral population using amoebae as host cells.
12:29
Population and Single-Cell Analysis of Antibiotic Persistence in Escherichia coli
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Cited by 3 •
2023
Antibiotic persistence describes the ability of small subpopulations within a sensitive isogenic population to transiently tolerate high doses of bactericidal antibiotics. The present protocol combines approaches to characterize the antibiotic persistence phenotype at the molecular and cellular levels after exposing Escherichia coli to lethal doses of ofloxacin.
FAQs
Its analytical value comes from preserving cell-to-cell variation instead of combining signals across an entire sample. Distinct cellular states may produce different gene-expression profiles, surface-protein patterns, morphologies, or functional behaviors that become averaged together in bulk measurements. Examining these differences allows researchers to detect heterogeneous responses and distinguish biologically meaningful subpopulations within the same tissue or sample.
Researchers can examine gene expression, surface proteins, morphology, and cellular function at the level of individual cells. These measurements provide complementary views of cellular state: gene expression reflects molecular activity, surface proteins help distinguish cell types or states, morphology captures structural differences, and functional measurements indicate how cells behave. Combining such features can strengthen subpopulation identification.
Computational clustering organizes cells according to shared measured characteristics, grouping cells that show similar molecular, structural, or functional profiles. The resulting clusters can reveal subpopulations that were not obvious from the original sample description. In biological studies, these groupings help researchers interpret cellular heterogeneity and compare how distinct cell states are distributed within tissues or conditions.
A typical workflow begins by isolating or partitioning individual cells so they can be examined separately. Researchers then apply single-cell assays to measure selected features, such as gene expression, surface proteins, morphology, or function. Computational analysis can subsequently organize cells with shared characteristics into subpopulations, producing a structured view of cellular diversity for interpretation.
Single-cell analysis is particularly useful when a sample may contain rare cell types or multiple developmental or disease-related states. It can also reveal how cellular composition or state changes over time within tissues. These applications help researchers identify populations that bulk measurements might obscure and clarify how biological processes differ among individual groups of cells.
By resolving distinct cellular subpopulations and their measured characteristics, these data can contribute to cell atlas construction, which maps the cellular organization of biological tissues. The same information may support biomarker discovery by associating particular cell states or features with normal or pathological processes. More detailed population profiles also enable more precise models of tissue biology and disease.