EXPLORING BRAIN TRANSCRIPTOMIC PATTERNS: A TOPOLOGICAL ANALYSIS USING SPATIAL EXPRESSION NETWORKS.

Area of Science:

• Neuroscience
• Bioinformatics
• Systems Biology

Background:

• Understanding human brain function and disease requires characterizing its complex transcriptome architecture.
• Previous studies utilized Allen Human Brain Atlas (AHBA) data for broad anatomical gene expression patterns.

Purpose of the Study:

• To develop a novel method for representing gene transcription activity based on spatial co-expression patterns.
• To identify and functionally characterize clusters of genes with similar spatial expression profiles in the human brain.

Main Methods:

• Defined Spatial Expression Networks (SENs) for each gene to quantify co-expression across anatomical regions.
• Applied network similarity measures to SENs to identify topological clusters.
• Utilized gene ontology analysis to assess the biological functions associated with identified SEN clusters.

Main Results:

• Detected three distinct clusters of genes based on SEN topological properties.
• One cluster predominantly comprised genes involved in nervous system function and brain disorders.
• The other two clusters were associated with immunity, transcription, and translation processes.

Conclusions:

• Spatial Expression Networks provide a robust framework for analyzing brain gene expression patterns.
• SEN clustering reveals a functional organization of the human brain transcriptome, aligning with known biological processes.
• This approach offers insights into the genetic underpinnings of brain function and disease.