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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

16.6K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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What is Gene Expression?01:42

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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What is Gene Expression?01:36

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Chromatin Position Affects Gene Expression02:35

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
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Related Experiment Video

Updated: Feb 9, 2026

Using Fluorescence Activated Cell Sorting to Examine Cell-Type-Specific Gene Expression in Rat Brain Tissue
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Brain Cell Type Specific Gene Expression and Co-expression Network Architectures.

Andrew T McKenzie1,2,3, Minghui Wang1,2, Mads E Hauberg1,2,4,5,6

  • 1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.

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Understanding brain cell gene expression is key to brain function. This study defined consensus cell signatures and networks across multiple datasets, creating a tool (BRETIGEA) to analyze cell proportions in brain tissue.

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Area of Science:

  • Neuroscience
  • Genomics
  • Bioinformatics

Background:

  • Understanding cell-type-specific gene expression is crucial for deciphering brain function and dysfunction.
  • Cell-cell communication plays a significant role in neurological processes.

Purpose of the Study:

  • To compare and contrast five human and murine cell type-specific RNA expression datasets.
  • To define consensus brain cell signatures and gene modules conserved across datasets.
  • To develop an R package (BRETIGEA) for cell type proportion estimation in bulk brain gene expression data.

Main Methods:

  • Analysis of five human and murine cell type-specific transcriptome-wide RNA expression datasets.
  • Definition of specificity, enrichment, and absolute expression measures for relative gene expression.
  • Multiscale coexpression network analysis of single-cell RNA sequencing data.
  • Validation using bulk RNA sequencing data and ATAC-seq datasets.

Main Results:

  • Identification of consensus brain cell signatures well-conserved across datasets.
  • Validation of relative expression markers with cell type proportions in human brain samples.
  • Discovery of novel marker genes using orthogonal ATAC-seq data.
  • Identification of robust cell-specific gene modules through network analysis.

Conclusions:

  • Integration of multiple datasets identified novel brain cell consensus signatures and robust networks.
  • The developed R package, BRETIGEA, facilitates cell type proportion estimation and deconvolution from bulk brain gene expression data.
  • The findings overcome limitations associated with individual study technical variations.