Neuronal Cell Separation
Neuronal Cell Separation - Related Videos
04:38
Protein Phase Separation Assay: An Optogenetic Method for Mutant RNA-Binding Protein Phase Separation in Spinal Motor Neurons of Zebrafish Larvae
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In this video, we describe a phase separation assay, wherein intracellular proteins comprising intrinsically disordered regions fused to a photosensitive oligomerization domain are induced via blue-light exposure to self-associate into membrane-less liquid-like condensates.
09:48
Separation of Spermatogenic Cell Types Using STA-PUT Velocity Sedimentation
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The STA-PUT method allows for the separation of different populations of spermatogenic cells based on size and density.
01:20
Overview Of Cell Separation And Isolation
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2023
Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample. Commonly used cell separation techniques include filtration, density gradient...
16:04
Isolation and Culture of Post-Natal Mouse Cerebellar Granule Neuron Progenitor Cells and Neurons
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Here we present a method to isolate and culture cerebellar granule neuron progenitor cells and cerebellar granule neurons from postnatal mouse.
03:57
T-Cell Enrichment: A Technique to Isolate T-Cells from Mixed Cell Population by Magnetic Separation
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2023
This video describes T-cells enrichment from the spleen of a mouse by negative selection using a magnetic separator. The technique involves targeting the other cells in the sample for depletion using antibodies or ligands directed against specific cell surface antigens.
FAQs
Density-gradient centrifugation separates dissociated neural material according to differences in density, allowing neuronal populations to be collected from other components that distribute differently within the gradient. Its value is selective enrichment rather than absolute purification: the recovered fraction can contain fewer unwanted cell types, which helps researchers perform more focused analyses of neuronal morphology, gene expression, electrophysiology, or drug responses.
Molecular approaches identify cells through markers rather than relying on size or density. Immunomagnetic selection can enrich cells bearing a chosen marker, whereas fluorescence-activated cell sorting, or FACS, uses fluorescence-associated marker information to separate cells. These approaches are useful when neuronal populations need to be distinguished by molecular identity for downstream neuroscience analyses.
Physical strategies sort cells according to measurable properties such as size or density, while molecular strategies use markers associated with particular cell populations. Consequently, the two approaches provide different routes to enrichment: one emphasizes physical separation, and the other emphasizes marker-defined identity. This distinction helps align the separation method with the population researchers want to study.
A typical workflow starts with mixed neural tissue or a cell culture, followed by tissue dissociation to produce material that can be processed. Researchers then apply a physical or marker-based enrichment method, such as density-gradient centrifugation, immunomagnetic selection, or FACS, and collect the resulting neuronal population for focused analysis.
Enriched neuronal populations support measurements that can be difficult to interpret in a mixed sample. The approach enables analysis of neuronal morphology, gene expression, electrophysiology, and responses to injury or drugs. Reducing glial or other cellular contamination makes these measurements more focused on neurons, strengthening their relevance to questions about neuronal properties and responses.
In neuroscience, enriched neuronal populations support studies of development, neural circuit function, disease mechanisms, and potential therapeutic strategies. By reducing competing cell types, the method helps connect observed neuronal characteristics or responses with the biological process under investigation. This makes separated populations useful for examining both normal neuronal biology and changes associated with injury or disease.