Wahl Lucas, Karim Arun, Hassett Amy R, van der Doe Max, Dijkhuizen Stephanie, Badura Aleksandra
Department of Neuroscience, Erasmus MC, Rotterdam, the Netherlands.
Biol Psychiatry Glob Open Sci. 2024 Jul 20;4(6):100366. doi: 10.1016/j.bpsgos.2024.100366. eCollection 2024 Nov.
Current phenotyping approaches for murine autism models often focus on one selected behavioral feature, making the translation onto a spectrum of autistic characteristics in humans challenging. Furthermore, sex and environmental factors are rarely considered. Here, we aimed to capture the full spectrum of behavioral manifestations in 3 autism mouse models to develop a "behavioral fingerprint" that takes environmental and sex influences under consideration.
To this end, we employed a wide range of classical standardized behavioral tests and 2 multiparametric behavioral assays-the Live Mouse Tracker and Motion Sequencing-on male and female , , and Purkinje cell-specific mutant mice raised in standard or enriched environments. Our aim was to integrate our high dimensional data into one single platform to classify differences in all experimental groups along dimensions with maximum discriminative power.
Multiparametric behavioral assays enabled a more accurate classification of experimental groups than classical tests, and dimensionality reduction analysis demonstrated significant additional gains in classification accuracy, highlighting the presence of sex, environmental, and genotype differences in our experimental groups.
Together, our results provide a complete phenotypic description of all tested groups, suggesting that multiparametric assays can capture the entire spectrum of the heterogeneous phenotype in autism mouse models.
目前用于小鼠自闭症模型的表型分析方法通常聚焦于某一特定行为特征，这使得将其转化为人类自闭症特征谱系具有挑战性。此外，性别和环境因素很少被考虑在内。在此，我们旨在捕捉三种自闭症小鼠模型中的全部行为表现谱，以开发一种考虑环境和性别影响的“行为指纹”。
为此，我们对饲养于标准环境或丰富环境中的雄性和雌性BTBR T+tf/J、C57BL/6J和浦肯野细胞特异性Shank3突变小鼠，采用了一系列经典标准化行为测试以及两种多参数行为分析——活体小鼠追踪器和运动序列分析。我们的目标是将高维数据整合到一个单一平台上，以便在具有最大判别力的维度上对所有实验组的差异进行分类。
与经典测试相比，多参数行为分析能够更准确地对实验组进行分类，降维分析表明分类准确性有显著提高，突出了我们实验组中存在的性别、环境和基因型差异。
总之，我们的结果提供了所有测试组的完整表型描述，表明多参数分析能够捕捉自闭症小鼠模型中异质表型的全部范围。
