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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: Jun 5, 2025

Single-cell Profiling of Developing and Mature Retinal Neurons

Single-cell Profiling of Developing and Mature Retinal Neurons

Published on: April 19, 2012

Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and

Shuailin Xue¹, Fangfang Zhu², Jinyu Chen³

¹School of Information Science and Engineering, Yunnan University, 650500 Yunnan, China.

Briefings in Bioinformatics

|December 10, 2024

Summary

Spatial transcriptomics (ST) technology reveals cellular details in tissues but suffers from low resolution. Our scstGCN method enhances ST data resolution, improving gene expression accuracy and tissue analysis for biological discovery.

Keywords:

Graph Convolutional Network enhancement histology image single-cell resolution spatial transcriptomics

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Last Updated: Jun 5, 2025

Single-cell Profiling of Developing and Mature Retinal Neurons

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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

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Published on: February 25, 2017

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics (ST) technology provides cellular transcriptome data with spatial context.
Current ST methods face limitations in resolution, impacting gene expression accuracy.
Understanding cellular heterogeneity and interactions requires higher resolution ST data.

Purpose of the Study:

To develop scstGCN, a multimodal fusion method for inferring super-resolution gene expression profiles from ST data.
To enhance the accuracy of gene expression levels and enable single-cell level analysis.
To improve spatial pattern identification, functional enrichment analysis, and tissue annotation.

Main Methods:

scstGCN integrates histological images, spot-based ST data, and spatial location information.
The method utilizes a multimodal information fusion approach based on Vision Transformer and Graph Convolutional Network.
Evaluated on diverse ST datasets from healthy and diseased tissues.

Main Results:

scstGCN accurately predicts super-resolution gene expression profiles.
The method aids in discovering biologically meaningful differentially expressed genes and pathways.
scstGCN enables finer granularity tissue segmentation and annotation with high consistency.

Conclusions:

scstGCN effectively overcomes the resolution limitations of current ST technologies.
The method provides accurate super-resolution gene expression profiles for advanced biological insights.
scstGCN offers a valuable tool for detailed tissue analysis and biological discovery.