Single-cell Level Analysis
Single-cell Level Analysis - Related Videos
14:53
Analysis of Global RNA Synthesis at the Single Cell Level following Hypoxia
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Cited by 2 •
2014
We describe a technique for analysis of global RNA synthesis in hypoxia using imaging. Click-chemistry labeling of RNA has not previously been performed under hypoxia and allows visualization of global RNA changes at the single cell level. This approach complements the existing averaged RNA techniques, allowing direct visualization of cell-to-cell changes in global RNA synthesis.
11:52
Analysis of LINE-1 Retrotransposition at the Single Nucleus Level
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2016
Here we employ FISH methodology to track LINE-1 retrotransposition at the single nuclei level in chromosome spreads of HepG2 cell lines stably expressing synthetic LINE-1.
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.
07:27
Transcriptome Analysis of Single Cells
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Cited by 72 •
2011
In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.
07:33
Bacterial Cell Culture at the Single-cell Level Inside Giant Vesicles
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Cited by 1 •
2019
We demonstrate single-cell culture of bacteria inside giant vesicles (GVs). GVs containing bacterial cells were prepared by the droplet transfer method and were immobilized on a supported membrane on a glass substrate for direct observation of bacterial growth. This approach may also be adaptable to other cells.
FAQs
Computational comparison organizes measurements from individual cells into patterns that can be evaluated across the sampled population. Similar molecular, protein, morphological, or electrical features can indicate shared cellular characteristics, while differences may reveal distinct types or states. Comparing these patterns also helps researchers examine relationships among cells and connect cellular variation with development, communication, injury, or disease.
Gene expression, protein abundance, morphology, and electrical activity provide complementary views of a cell. Molecular measurements can describe cellular programs, morphology can capture structural features, and electrical activity can reflect functional behavior. Examining these data together allows researchers to relate a cell’s molecular profile to its neural function instead of interpreting any single feature in isolation.
Bulk measurements average signals across many cells, so uncommon or contrasting cellular features may disappear in the combined result. Examining cells separately preserves this variation and can expose distinct neurons or glial cells, including differences associated with cellular states. That resolution supports more precise interpretations of brain organization and of changes linked to neurological disorders.
A typical workflow begins by selecting the cellular feature or features relevant to the research question, such as gene expression, protein abundance, morphology, or electrical activity. Researchers then measure those features in individual neurons or glial cells and use computational comparisons to group or relate cells. The resulting patterns can be interpreted in the context of development, function, injury, or disease.
The approach is useful when researchers need to determine which cellular populations occupy a brain region and how those populations differ. Measurements from individual neurons and glial cells can reveal cellular diversity that averaged data may obscure. Computationally identified groups can then support maps of brain organization and help connect particular cellular profiles with neural function.
By preserving differences among individual cells, the analysis can identify cellular changes associated with injury or neurological disease rather than treating all cells as equally affected. Researchers can compare molecular, protein, morphological, or electrical features across cells to locate altered populations. These findings support more precise models of disease-related brain organization and may help identify cellularly focused therapeutic targets.