Department of Neurology, Iowa Neuroscience Institute, University of Iowa, PBDB 1320, 169 Newton Rd, Iowa City, IA, 52246, USA.
Brain Struct Funct. 2022 Jul;227(6):1921-1932. doi: 10.1007/s00429-022-02504-y. Epub 2022 Jun 1.
Neurons emit axons, which form synapses, the fundamental unit of the nervous system. Neuroscientists use genetic anterograde tracing methods to label the synaptic output of specific neuronal subpopulations, but the resulting data sets are too large for manual analysis, and current automated methods have significant limitations in cost and quality. In this paper, we describe a pipeline optimized to identify anterogradely labeled presynaptic boutons in brain tissue sections. Our histologic pipeline labels boutons with high sensitivity and low background. To automatically detect labeled boutons in slide-scanned tissue sections, we developed BoutonNet. This detector uses a two-step approach: an intensity-based method proposes possible boutons, which are checked by a neural network-based confirmation step. BoutonNet was compared to expert annotation on a separate validation data set and achieved a result within human inter-rater variance. This open-source technique will allow quantitative analysis of the fundamental unit of the brain on a whole-brain scale.
神经元发出轴突，形成突触，这是神经系统的基本单位。神经科学家使用遗传顺行示踪方法来标记特定神经元亚群的突触输出，但由此产生的数据集太大，无法进行手动分析，而当前的自动化方法在成本和质量方面存在重大限制。在本文中，我们描述了一个经过优化的管道，用于识别脑组织切片中的顺行标记的突触前末梢。我们的组织学管道以高灵敏度和低背景标记末梢。为了在幻灯片扫描的组织切片中自动检测标记的末梢，我们开发了 BoutonNet。这个检测器使用两步法：基于强度的方法提出可能的末梢，然后由基于神经网络的确认步骤进行检查。BoutonNet 在单独的验证数据集上与专家注释进行了比较，结果在人类内部评分者差异范围内。这种开源技术将允许在全脑范围内对大脑的基本单位进行定量分析。
