Department of Organismic and Evolutionary Biology, Harvard University, 52 Oxford St, 02138, Cambridge, MA, USA.
Princeton Neuroscience Institute, Princeton University, Washington Rd, 08544, Princeton, NJ, USA.
Mol Autism. 2022 Mar 12;13(1):12. doi: 10.1186/s13229-022-00492-8.
Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD.
We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait.
Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy.
Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability.
Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.
重复性动作、对环境变化的抵抗力和精细动作障碍是自闭症谱系障碍（ASD）和其他神经发育障碍的特征，且个体之间存在较大差异。在动物模型中，传统的行为表型分析不完全捕捉到这种细微的变化。在这里，我们观察了雄性和雌性 C57BL/6J 小鼠，以系统地记录多天的适应性运动，并针对这种动态基线检查了两种发育障碍的啮齿动物模型。然后，我们研究了小脑特异性 Tsc1 蛋白缺失和全脑 Cntnap2 蛋白缺失对小鼠的行为后果。这两种突变都存在于临床条件中，并与 ASD 有关。
我们使用计算机视觉和深度学习的进展，即高维统计分析的广义形式，开发了一种使用单一流行行为测定法（旷场测试）来描述小鼠多时间尺度运动的框架。该管道从姿势估计中获取虚拟标记，以找到行为群集并生成行为类别的小波特征。我们测量了几分钟和几天内对新环境的空间和时间习惯化、不同类型的自我梳理、运动和步态。
Cntnap2 敲除和 L7-Tsc1 突变体在步态时都表现出前肢滞后。L7-Tsc1 突变体和 Cntnap2 敲除体在多日适应方面表现出复杂的缺陷，缺乏野生型小鼠逐渐在竞技场角落花费更多时间的趋势。在 L7-Tsc1 突变体小鼠中，适应不良的形式是维持闲逛、转弯和运动，以及整体梳理减少。然而，这些特征的适应在野生型小鼠和 Cntnap2 敲除体之间是相似的。L7-Tsc1 突变体和 Cntnap2 敲除鼠模型显示出不同的行为状态占据模式。
自闭症的遗传风险因素很多，我们只测试了两个。我们的管道仅在自由行为条件下进行。在任务或社交条件下进行测试将揭示更多关于行为动态和可变性的信息。
我们的深度表型自动化管道成功捕捉到了适应和运动的特定模型偏差，以及行为动态的详细结构差异。报告的缺陷表明，深度表型构成了一组强大的 ASD 症状，可作为临床环境中定量诊断标准的考虑因素。
