Cell Type Characterization
Cell Type Characterization - Related Videos
08:53
Isolation and Transcriptome Analysis of Plant Cell Types
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Cited by 2 •
2023
The feasibility and effectiveness of high-throughput scRNA-seq methods herald a single-cell era in plant research. Presented here is a robust and complete procedure for isolating specific Arabidopsis thaliana root cell types and subsequent transcriptome library construction and analysis.
09:25
Isolation and Characterization of Single Cells from Zebrafish Embryos
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Cited by 19 •
2016
This protocol describes a method for isolating single cells from zebrafish embryos, enriching for cells of interest, capturing zebrafish cells in microfluidic based single cell multiplex systems, and assessing gene expression from single cells.
09:48
Separation of Spermatogenic Cell Types Using STA-PUT Velocity Sedimentation
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Cited by 99 •
2013
The STA-PUT method allows for the separation of different populations of spermatogenic cells based on size and density.
01:24
T Cell Types and Functions
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2024
When T cells with CD4 markers are activated, they give rise to two types of effector cells: helper T cells and regulatory T cells. Meanwhile, T cells with CD8 markers differentiate into effector cytotoxic T cells. The differentiation of CD4 T cells into helper T cell subsets, such as Th1, Th2, and Th17 cells, is dependent on the antigen type, antigen-presenting cell, and regulatory cytokines. Th1 cells stimulate dendritic cells to express necessary co-stimulatory molecules on their surfaces for...
10:07
Isolation of Type I and Type II Pericytes from Mouse Skeletal Muscles
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Cited by 11 •
2017
This work describes a FACS-based protocol that allows for easy and simultaneous isolation of type I and type II pericytes from skeletal muscles.
FAQs
No single feature captures the full identity or role of a neural cell. Gene expression and protein markers provide molecular information, microscopy reveals structure, and electrophysiological recordings show activity. Adding synaptic connectivity and functional responses connects these properties to circuit participation, helping researchers distinguish populations that may look similar when evaluated with only one type of measurement.
Gene expression profiles reveal which genes are active in individual cells, while protein markers identify detectable molecular features within neural tissue. Single-cell transcriptomics and immunolabeling therefore provide complementary evidence: one measures cell-associated transcripts and the other visualizes selected proteins. Together, these measurements help separate cell populations and relate molecular signatures to neural organization.
Electrophysiological recording shows how a cell behaves electrically rather than only what molecules or structures it contains. Patch-clamp recording can therefore complement transcriptomic and microscopic measurements by linking cellular identity with physiological activity. This connection is important when researchers need to understand how distinct neurons or glial cells communicate, respond to stimuli, or contribute to neural circuit function.
A workflow may integrate immunolabeling, single-cell transcriptomics, microscopy, and patch-clamp recording, with synaptic connectivity considered as an additional functional feature. Molecular assays identify gene or protein patterns, microscopy examines cellular structure, and recording assesses physiological activity. Combining these approaches produces a multidimensional profile instead of relying on one isolated measurement.
Heterogeneous tissue contains multiple neural cell populations whose properties may overlap. Researchers can examine molecular markers, cellular morphology, physiological activity, and connectivity together to distinguish those populations and assign their likely circuit roles. This approach improves interpretation of experiments in which measurements from different neurons or glial cells would otherwise be combined into a single, misleading result.
Detailed characterization helps researchers investigate brain organization, neural development, communication, and responses to stimuli. It also supports studies of disease mechanisms by revealing which cell populations or properties are associated with altered neural function. Because the resulting profiles connect cell features with circuit roles, they can help identify potential therapeutic targets and improve interpretation of neuroscience experiments.