Gene expression profiling in rats with depressive-like behavior.

Area of Science:

• Neuroscience
• Animal Behavior
• Genomics

Background:

• Individual differences in animal models can significantly impact behavioral study results, particularly in high-throughput analyses.
• High-throughput methods like cDNA microarray require more samples to mitigate individual variability compared to single-factor analyses such as real-time PCR.

Purpose of the Study:

• To establish a method for minimizing individual differences in rat models for behavioral studies.
• To provide detailed methods and quality control parameters for cDNA microarray data derived from a carefully selected rat cohort.
• To generate a reliable dataset reflecting increased depressive-like behavior.

Main Methods:

• Over 100 normal rats were assessed for depressive-like behavior using the forced swimming test.
• Rats were selectively assigned to control and depression groups based on their forced swimming test performance to reduce inter-individual variability.
• cDNA microarray analysis was performed, with detailed methods and quality control parameters provided.

Main Results:

• The selection method successfully minimized individual differences between control and depression groups.
• The generated cDNA microarray dataset accurately reflects an increase in depressive-like behavior.
• The dataset is publicly available in the Gene Expression Omnibus (GEO) under series GSE63377.

Conclusions:

• Careful selection of animal subjects based on behavioral data is crucial for enhancing the reliability of high-throughput studies.
• This methodology provides a robust dataset for investigating the molecular underpinnings of depression.
• The study offers a valuable resource for researchers in behavioral neuroscience and depression research.