Peles David, Netser Shai, Ray Natalie, Suliman Taghreed, Wagner Shlomo
Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.
Elife. 2025 Aug 28;13:RP100739. doi: 10.7554/eLife.100739.
In many mammals, including rodents, social interactions are often accompanied by active urination (micturition), which is considered a mechanism for spatial scent marking. Urine and fecal deposits contain a variety of chemosensory signals that convey information about the individual's identity, genetic strain, social rank, and physiological or hormonal state. Furthermore, scent marking has been shown to be influenced by the social context and by the individual's internal state and experience. Therefore, analyzing scent-marking behavior during social interactions can provide valuable insight into the structure of mammalian social interactions in health and disease. However, conducting such analyses has been hindered by several technical challenges. For example, the widely used void spot assay lacks temporal resolution and is prone to artifacts, such as urine smearing. To solve these issues, recent studies employed thermal imaging for the spatio-temporal analysis of urination activity. However, this method involved manual analysis, which is time-consuming and susceptible to observer bias. Moreover, defecation activity was hardly analyzed by previous studies. In the present study, we integrate thermal imaging with an open-source algorithm based on a transformer-based video classifier for automatic detection and classification of urine and fecal deposits made by male and female mice during various social behavior assays. Our results reveal distinct dynamics of urination and defecation in a test-, strain-, and sex-dependent manner, indicating two separate processes of scent marking in mice. We validate this algorithm, termed by us DeePosit, and show that its accuracy is comparable to that of a human annotator and that it is efficient in various setups and conditions. Thus, the method and tools introduced here enable efficient and unbiased automatic spatio-temporal analysis of scent-marking behavior in the context of behavioral experiments in small rodents.
在包括啮齿动物在内的许多哺乳动物中,社交互动通常伴随着主动排尿(排尿),这被认为是一种空间气味标记的机制。尿液和粪便沉积物包含多种化学感应信号,这些信号传达了有关个体身份、遗传品系、社会等级以及生理或激素状态的信息。此外,气味标记已被证明会受到社会环境以及个体内部状态和经历的影响。因此,分析社交互动过程中的气味标记行为可以为健康和疾病状态下哺乳动物社交互动的结构提供有价值的见解。然而,进行此类分析受到了几个技术挑战的阻碍。例如,广泛使用的空白点测定法缺乏时间分辨率,并且容易出现诸如尿液涂抹等假象。为了解决这些问题,最近的研究采用热成像技术对排尿活动进行时空分析。然而,这种方法涉及人工分析,既耗时又容易受到观察者偏差的影响。此外,先前的研究几乎没有对排便活动进行分析。在本研究中,我们将热成像与基于基于Transformer的视频分类器的开源算法相结合,用于自动检测和分类雄性和雌性小鼠在各种社交行为测定过程中产生的尿液和粪便沉积物。我们的结果揭示了排尿和排便在测试、品系和性别依赖方式下的不同动态,表明小鼠中存在两种独立的气味标记过程。我们验证了我们命名为DeePosit的这种算法,并表明其准确性与人类注释者相当,并且在各种设置和条件下都很有效。因此,本文介绍的方法和工具能够在小型啮齿动物行为实验的背景下,对气味标记行为进行高效且无偏差的自动时空分析。