Cell-specific Profiling
Cell-specific Profiling - Related Videos
07:28
TChIP-Seq: Cell-Type-Specific Epigenome Profiling
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Cited by 1 •
2019
We describe a step-by-step protocol for tandem chromatin immunoprecipitation sequencing (tChIP-Seq) that enables the analysis of cell-type-specific genome-wide histone modification.
10:10
Investigating Tissue- and Organ-specific Phytochrome Responses using FACS-assisted Cell-type Specific Expression Profiling in Arabidopsis thaliana
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Cited by 5 •
2010
The molecular basis of spatial-specific phytochrome responses is being investigated using transgenic plants that exhibit tissue- and organ-specific phytochrome deficiencies. The isolation of specific cells exhibiting induced phytochrome chromophore depletion by Fluorescence-Activated Cell Sorting followed by microarray analyses is being utilized to identify genes involved in spatial-specific phytochrome responses.
10:06
Cell Type-specific Gene Expression Profiling in the Mouse Liver
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Cited by 1 •
2019
Translating ribosome affinity purification (TRAP) enables rapid and efficient isolation of cell type-specific translating mRNA. Here, we demonstrate a method that combines hydrodynamic tail-vein injection in a mouse model of liver repopulation and TRAP to examine the expression profile of repopulating hepatocytes.
05:21
A Peptide Microarray Technology for Specificity Profiling of Antibodies
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2025
This video demonstrates a technique that applies peptide microarray technology to profile the specificity of antibodies recognizing post-translational modifications (PTMs) on histone peptides. A microarray slide containing immobilized fluorophore-conjugated histone peptides with known PTM combinations is treated with target PTM-specific antibodies. This process helps identify the antibody's specificity for the target and its potential cross-reactivity with non-target PTMs.
10:20
Single-cell Profiling of Developing and Mature Retinal Neurons
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Cited by 13 •
2012
A method for the isolation of single retinal cells and subsequent amplification of their cDNAs is described. Single-cell transcriptomics reveals the degree of cellular heterogeneity present in a tissue and uncovers new marker genes for rare cell populations. The accompanying protocol can be adjusted to suit many different cell types.
FAQs
Marker selection determines which cells can be distinguished and which subpopulations remain unresolved. Surface proteins, morphology, and gene-expression patterns provide different evidence for classification, so the most informative choice depends on the biological features being studied. Careful marker use helps separate related cell types and supports more accurate comparisons of composition, cellular state, or molecular activity.
Bulk measurements combine signals from many cells, allowing abundant populations to dominate the overall result. Cell-specific profiling preserves differences among subpopulations, making it possible to recognize rare groups, contrasting cellular states, or opposing responses within the same sample. This distinction is important when tissue composition changes or when only particular cells respond to developmental or environmental signals.
These approaches provide complementary views of cellular heterogeneity. Flow cytometry measures selected features across distinguishable cells, imaging adds spatial or morphological information, and single-cell sequencing examines gene-expression patterns at cellular resolution. The choice depends on whether the study prioritizes marker-based classification, visual context, or molecular profiles. Combining approaches can connect cell identity with structure or gene activity.
A cell type reflects a recognizable cellular population, whereas a cell state can change in response to developmental or environmental signals. Profiling compares markers, morphology, or gene-expression patterns to determine whether differences reflect population identity, altered activity, or both. This distinction helps interpret whether a sample contains different cell types, state transitions, or responses occurring within the same population.
A typical workflow begins with a heterogeneous biological sample, followed by identification of cell populations using surface markers, morphology, or gene-expression patterns. Researchers then measure selected molecular features with flow cytometry, imaging, or single-cell sequencing and compare the resulting profiles among populations or conditions. The analysis can describe tissue composition, cellular states, and differences between samples.
The approach is useful when tissue composition or cellular behavior may differ among conditions. Researchers apply it to map cell populations, follow developmental or environmental responses, compare healthy and diseased samples, and search for features associated with particular cellular groups. These analyses can support biomarker discovery and produce more precise models of cellular function than measurements that treat the sample as uniform.