Takeuchi Yuki, Yamashiro Kotaro, Noguchi Asako, Liu Jiayan, Mitsui Shinichi, Ikegaya Yuji, Matsumoto Nobuyoshi
Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan.
Department of Rehabilitation Sciences, Graduate School of Health Sciences, Gunma University, Maebashi, Gunma, Japan.
Front Neuroanat. 2023 Jun 28;17:1172512. doi: 10.3389/fnana.2023.1172512. eCollection 2023.
The hippocampus is a center of learning, memory, and spatial navigation. This region is divided into the CA1, CA2, and CA3 areas, which are anatomically different from each other. Among these divisions, the CA2 area is unique in terms of functional relevance to sociality. The CA2 area is often manually detected based on the size, shape, and density of neurons in the hippocampal pyramidal cell layer, but this manual segmentation relying on cytoarchitecture is impractical to apply to a large number of samples and dependent on experimenters' proficiency. Moreover, the CA2 area has been defined based on expression pattern of molecular marker proteins, but it generally takes days to complete immunostaining for such proteins. Thus, we asked whether the CA2 area can be systematically segmented based on cytoarchitecture alone. Since the expression pattern of regulator of G-protein signaling 14 (RGS14) signifies the CA2 area, we visualized the CA2 area in the mouse hippocampus by RGS14-immunostaining and Nissl-counterstaining and manually delineated the CA2 area. We then established "CAseg," a machine learning-based automated algorithm to segment the CA2 area with the F1-score of approximately 0.8 solely from Nissl-counterstained images that visualized cytoarchitecture. CAseg was extended to the segmentation of the prairie vole CA2 area, which raises the possibility that the use of this algorithm can be expanded to other species. Thus, CAseg will be beneficial for investigating unique properties of the hippocampal CA2 area.
海马体是学习、记忆和空间导航的中心。该区域分为CA1、CA2和CA3区，它们在解剖学上彼此不同。在这些分区中，CA2区在与社交性的功能相关性方面具有独特性。CA2区通常根据海马锥体细胞层中神经元的大小、形状和密度进行手动检测，但这种依赖细胞结构的手动分割方法不适用于大量样本，且依赖实验者的熟练程度。此外，CA2区是根据分子标记蛋白的表达模式定义的，但对这类蛋白进行免疫染色通常需要数天时间。因此，我们询问CA2区是否可以仅基于细胞结构进行系统分割。由于G蛋白信号调节因子14（RGS14）的表达模式标志着CA2区，我们通过RGS14免疫染色和尼氏复染对小鼠海马体中的CA2区进行可视化，并手动勾勒出CA2区。然后，我们建立了“CAseg”，这是一种基于机器学习的自动算法，仅从可视化细胞结构的尼氏复染图像中分割CA2区，F1分数约为0.8。CAseg被扩展到对草原田鼠CA2区的分割，这增加了该算法可扩展应用于其他物种的可能性。因此，CAseg将有助于研究海马体CA2区的独特特性。
