Over the years, it has become increasingly clear that males and females respond differently towards environmental stressors, highlighting the importance of including both sexes when studying the effects of stress. This study aims to provide further insight into the detailed consequences of exposing female mice to 21 days of chronic social defeat stress (CSDS). We used a protocol that relies on the ability of odorants and pheromones in male urine to trigger male mouse aggressive behavior. Collected male C57Bl/6n urine was applied to female C57Bl/6n mice who were then attacked by a novel male CD1 mouse each day according to the CDSD protocol. Control females were pair-housed and handled daily. Physiological, neuroendocrine and behavioral changes were evaluated during the experiment. CSDS exposure resulted in number of physiological changes, such as body weight gain, enlarged adrenals and reduced thymus weight, exaggerated HPA-axis negative feedback and increased anxiety-like behavior. However, no generalized social avoidance behavior was observed. This study provides important insights in the physiological, neuroendocrine and behavioral responses of female mice to CSDS, which are partially dependent on estrous cycle stage. This protocol will allow direct comparison of male and female responses to CSDS and enable sex-specific study of mechanisms underlying individual stress resilience.

for their excellent technical assistant and support. We thank Stefanie Unkmeir, Sabrina Bauer and the scientific core unit Genetically Engineered Mouse Models for genotyping support. We also thank Jessica Keverne for language editing the manuscript. The graphical abstract was created with Biorender.com

Severe stress exposure is a global problem with long-lasting negative behavioral and physiological consequences, which increases the risk of stress-related disorders such as major depressive disorder (MDD). An essential characteristic of MDD is the impairment of social functioning and lack of social motivation. Chronic social defeat stress is an established animal model for MDD research, which induces a cascade of physiological and social behavioral changes. The current developments of markerless pose estimation tools allow for more complex and socially relevant behavioral tests, but the application of these tools to social behavior remains to be explored. Here, we introduce the open-source tool DeepOF to investigate the individual and social behavioral profile in mice by providing supervised and unsupervised pipelines using DeepLabCut-annotated pose estimation data. The supervised pipeline relies on pre-trained classifiers to detect defined traits for both single and dyadic animal behaviors. Subsequently, the unsupervised pipeline explores the behavioral repertoire of the animals without label priming, which has the potential of pointing towards previously unrecognized motion motifs that are systematically different across conditions. We here provide evidence that the DeepOF supervised and unsupervised pipelines detect a distinct stress-induced social behavioral pattern, which was particularly observed at the beginning of a novel social encounter. The stress-induced social behavior shows a state of arousal that fades with time due to habituation. In addition, while the classical social avoidance task does identify the stress-induced social behavioral differences, both DeepOF behavioral pipelines provide a clearer and more detailed profile. DeepOF aims to facilitate reproducibility and unification of behavioral classification of social behavior by providing an open-source tool, which can significantly advance the study of rodent individual and social behavior, thereby enabling novel biological insights and subsequent drug development for psychiatric disorders.

Depressive disorders are the most burdensome psychiatric disorders worldwide. Although huge efforts have been made to advance treatment, outcomes remain unsatisfactory. Many factors contribute to this gridlock including suboptimal animal models. Especially limited study comparability and replicability due to imprecise terminology concerning depressive-like states are major problems. To overcome these issues, new approaches are needed. Here, we introduce a taxonomical concept for modelling depression in laboratory mice, which we call depression-like syndrome (DLS). It hinges on growing evidence suggesting that mice possess advanced socioemotional abilities and can display non-random symptom patterns indicative of an evolutionary conserved disorder-like phenotype. The DLS approach uses a combined heuristic method based on clinical depression criteria and the Research Domain Criteria to provide a biobehavioural reference syndrome for preclinical rodent models of depression. The DLS criteria are based on available, species-specific evidence and are as follows: (I) minimum duration of phenotype, (II) significant sociofunctional impairment, (III) core biological features, (IV) necessary depressive-like symptoms. To assess DLS presence and severity, we have designed an algorithm to ensure statistical and biological relevance of findings. The algorithm uses a minimum combined threshold for statistical significance and effect size (p value ≤ 0.05 plus moderate effect size) for each DLS criterion. Taken together, the DLS is a novel, biologically founded, and species-specific minimum threshold approach. Its long-term objective is to gradually develop into an inter-model validation standard and microframework to improve phenotyping methodology in translational research.

Severe stress exposure increases the risk of stress-related disorders such as major depressive disorder (MDD). An essential characteristic of MDD is the impairment of social functioning and lack of social motivation. Chronic social defeat stress is an established animal model for MDD research, which induces a cascade of physiological and behavioral changes. Current markerless pose estimation tools allow for more complex and naturalistic behavioral tests. Here, we introduce the open-source tool DeepOF to investigate the individual and social behavioral profile in mice by providing supervised and unsupervised pipelines using DeepLabCut-annotated pose estimation data. Applying this tool to chronic social defeat in male mice, the DeepOF supervised and unsupervised pipelines detect a distinct stress-induced social behavioral pattern, which was particularly observed at the beginning of a novel social encounter and fades with time due to habituation. In addition, while the classical social avoidance task does identify the stress-induced social behavioral differences, both DeepOF behavioral pipelines provide a clearer and more detailed profile. Moreover, DeepOF aims to facilitate reproducibility and unification of behavioral classification by providing an open-source tool, which can advance the study of rodent individual and social behavior, thereby enabling biological insights and, for example, subsequent drug development for psychiatric disorders.

No abstract

No abstract

No abstract