Insights from spatially mapped gene expression in the mouse brain

Area of Science:

• Neuroscience
• Genomics
• Bioinformatics

Background:

• Numerous publicly available murine gene expression databases exist from genomic-scale profiling projects.
• Diverse methodologies, including microarrays and transcript tag analysis, yield complementary data on brain gene expression.
• Colorimetric in situ hybridization is a scalable, high-throughput method for gene expression analysis.

Purpose of the Study:

• To review gene expression profiling projects in the mouse brain.
• To highlight the utility of these datasets in complementing other research studies.
• To explore applications of these datasets in advancing the understanding of neurological diseases.

Main Methods:

• Review of publicly available gene expression datasets from mouse brain studies.
• Focus on genomic-scale transcriptome and proteome profiling projects.
• Emphasis on colorimetric in situ hybridization techniques, exemplified by the Allen Brain Atlas.

Main Results:

• Gene expression atlases provide genomic-scale anatomical data with cellular-level spatial resolution.
• Accessible informatics tools facilitate the analysis of large gene expression datasets.
• These datasets offer both high-level and detailed insights into gene regulation.

Conclusions:

• Publicly available mouse brain gene expression data are valuable resources for neurological disease research.
• Integration of diverse datasets enhances understanding of gene function and regulation in the brain.
• These resources facilitate the identification of genes involved in brain diseases and disorders.