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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...

Cell Specific Gene Expression

Cell Specific Gene Expression

Reporter Genes

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.

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Related Experiment Video

Updated: Dec 10, 2025

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

Published on: October 4, 2013

Identifying signaling genes in spatial single-cell expression data.

Dongshunyi Li¹, Jun Ding¹, Ziv Bar-Joseph^1,2

¹Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Bioinformatics (Oxford, England)

|September 5, 2020

Summary

This summary is machine-generated.

A new method called Mixture of Experts for Spatial Signaling genes Identification (MESSI) accurately identifies key signaling genes in spatial single-cell expression data, improving biological insights.

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Last Updated: Dec 10, 2025

Temporal Quantification of MAPK Induced Expression in Single Yeast Cells

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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

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Spatial single-cell expression profiling technologies are advancing rapidly.
Analyzing this data is crucial for understanding cell-cell interactions and their underlying signaling genes.
Current analytical methods often focus on descriptive modeling, limiting the identification and impact assessment of key signaling genes.

Purpose of the Study:

To develop a novel computational method for identifying active signaling genes in spatial single-cell expression data.
To enable quantitative assessment of signaling gene impact and cell subtype identification.
To improve upon existing methods for analyzing spatial transcriptomic data.

Main Methods:

Developed the Mixture of Experts for Spatial Signaling genes Identification (MESSI) method.
Utilized a mixture of experts strategy to facilitate cell subtyping.
Employed multi-task learning, incorporating information from neighboring cells to enhance response gene prediction within individual cells.

Main Results:

MESSI accurately predicts response gene levels in spatial single-cell expression data.
The method demonstrates improved performance compared to prior analytical approaches.
Biological insights into key signaling genes and excitatory neuron subtypes were gained.

Conclusions:

MESSI provides an effective approach for identifying active signaling genes and cell subtypes from spatial single-cell expression data.
The method enhances the understanding of cell-cell communication in complex biological tissues.
MESSI offers a valuable tool for researchers in computational biology and genomics.