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Cell Specific Gene Expression

Cell Specific Gene Expression

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...

What is Gene Expression?

What is Gene Expression?

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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Related Experiment Video

Updated: Jul 28, 2025

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Visualizing gene expression changes in time, space, and single cells with expressyouRcell.

Martina Paganin¹, Toma Tebaldi^2,3, Fabio Lauria¹

¹Institute of Biophysics, CNR Unit Trento, Trento, Italy.

|May 30, 2023

Summary

This summary is machine-generated.

New R package expressyouRcell simplifies visualizing complex gene expression and protein data. It transforms multi-dimensional variations into dynamic cell pictographs for better understanding and communication.

Keywords:

Computational bioinformatics

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

High-throughput techniques generate complex gene expression datasets.
Large data volumes and intricate experimental designs hinder result interpretation and communication.

Purpose of the Study:

To introduce expressyouRcell, an R package for visualizing multi-dimensional gene and protein expression variations.
To simplify the understanding and communication of complex biological data through dynamic pictographs.

Main Methods:

Development of the expressyouRcell R package.
Visualization of gene and protein expression using dynamic cell pictographs.
Application to single-cell, bulk RNA sequencing (RNA-seq), and proteomics datasets.

Main Results:

expressyouRcell effectively maps multi-dimensional variations in transcript and protein levels.
The package generates dynamic, pictographic representations of cell-type thematic maps.
Demonstrated flexibility and usability across diverse high-throughput datasets.

Conclusions:

expressyouRcell enhances the interpretation and communication of complex gene expression data.
The tool visually reduces complexity, aiding researchers in understanding dynamic cellular changes.
Improves standard quantitative interpretation for biological big data.