Systematic analysis of goal-related movement sequences during maternal behaviour in a female mouse model for Rett syndrome

Multidimensional Analysis of a Social Behavior Identifies Regression and Phenotypic Heterogeneity in a Female Mouse Model for Rett Syndrome

Regression is a key feature of neurodevelopmental disorders such as autism spectrum disorder, Fragile X syndrome, and Rett syndrome (RTT). RTT is caused by mutations in the X-linked gene methyl-CpG-binding protein 2 (MECP2). It is characterized by an early period of typical development with subsequent regression of previously acquired motor and speech skills in girls. The syndromic phenotypes are individualistic and dynamic over time. Thus far, it has been difficult to capture these dynamics and syndromic heterogeneity in the preclinical Mecp2-heterozygous female mouse model (Het). The emergence of computational neuroethology tools allows for robust analysis of complex and dynamic behaviors to model endophenotypes in preclinical models. Toward this first step, we utilized DeepLabCut, a marker-less pose estimation software to quantify trajectory kinematics and multidimensional analysis to characterize behavioral heterogeneity in Het in the previously benchmarked, ethologically relevant social cognition task of pup retrieval. We report the identification of two distinct phenotypes of adult Het: Het that display a delay in efficiency in early days and then improve over days like wild-type mice and Het that regress and perform worse in later days. Furthermore, regression is dependent on age and behavioral context and can be detected in the initial days of retrieval. Together, the novel identification of two populations of Het suggests differential effects on neural circuitry, opens new avenues to investigate the underlying molecular and cellular mechanisms of heterogeneity, and designs better studies for stratifying therapeutics.

Automated procedure to assess pup retrieval in laboratory mice

All mammalian mothers form some sort of caring bond with their infants that is crucial to the development of their offspring. The Pup Retrieval Test (PRT) is the leading procedure to assess pup-directed maternal care in laboratory rodents, used in a wide range of basic and preclinical research applications. Most PRT protocols require manual scoring, which is prone to bias and spatial and temporal inaccuracies. This study proposes a novel procedure using machine learning algorithms to enable reliable assessment of PRT performance. Automated tracking of a dam and one pup was established in DeepLabCut and was combined with automated behavioral classification of "maternal approach", "carrying" and "digging" in Simple Behavioral Analysis (SimBA). Our automated procedure estimated retrieval success with an accuracy of 86.7%, whereas accuracies of "approach", "carry" and "digging" were estimated at respectively 99.3%, 98.6% and 85.0%. We provide an open-source, step-by-step protocol for automated PRT assessment, which aims to increase reproducibility and reliability, and can be easily shared and distributed.