Multidimensional Analysis of a Social Behavior Identifies Regression and Phenotypic Heterogeneity in a Female Mouse...

Area of Science:

• Neuroscience
• Genetics
• Computational Biology

Background:

• Regression is a hallmark of neurodevelopmental disorders like Rett syndrome (RTT), a condition caused by mutations in the methyl-CpG-binding protein 2 (MECP2) gene.
• RTT is characterized by initial normal development followed by loss of motor and speech skills, with significant individual variability over time.
• Preclinical models, particularly Mecp2-heterozygous female mice (Het), struggle to capture the dynamic and heterogeneous nature of RTT phenotypes.

Purpose of the Study:

• To apply computational neuroethology tools to analyze complex behaviors in a preclinical RTT model.
• To characterize behavioral heterogeneity in Mecp2-heterozygous female mice using advanced pose estimation and multidimensional analysis.
• To identify distinct behavioral phenotypes and understand the dynamics of regression in the context of RTT.

Main Methods:

• Utilized DeepLabCut, a marker-less pose estimation software, to quantify trajectory kinematics.
• Employed multidimensional analysis to characterize behavioral heterogeneity in Het mice during a pup retrieval task.
• Focused on an ethologically relevant social cognition task to benchmark behavioral analysis.

Main Results:

• Identified two distinct phenotypes in adult Het mice: those that improve over time and those that exhibit progressive regression.
• Demonstrated that behavioral regression is dependent on age and the specific behavioral context.
• Showed that regression can be detected even in the initial days of the behavioral task.

Conclusions:

• The identification of two Het populations suggests differential impacts on neural circuitry.
• Opens new avenues for investigating the molecular and cellular mechanisms underlying RTT heterogeneity.
• Provides a foundation for designing improved studies for stratifying therapeutics in RTT research.