Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo, Japan.
BMC Biol. 2020 Mar 19;18(1):30. doi: 10.1186/s12915-020-0745-2.
Annotation of cell identity is an essential process in neuroscience that allows comparison of cells, including that of neural activities across different animals. In Caenorhabditis elegans, although unique identities have been assigned to all neurons, the number of annotatable neurons in an intact animal has been limited due to the lack of quantitative information on the location and identity of neurons.
Here, we present a dataset that facilitates the annotation of neuronal identities, and demonstrate its application in a comprehensive analysis of whole-brain imaging. We systematically identified neurons in the head region of 311 adult worms using 35 cell-specific promoters and created a dataset of the expression patterns and the positions of the neurons. We found large positional variations that illustrated the difficulty of the annotation task. We investigated multiple combinations of cell-specific promoters driving distinct fluorescence and generated optimal strains for the annotation of most head neurons in an animal. We also developed an automatic annotation method with human interaction functionality that facilitates annotations needed for whole-brain imaging.
Our neuron ID dataset and optimal fluorescent strains enable the annotation of most neurons in the head region of adult C. elegans, both in full-automated fashion and a semi-automated version that includes human interaction functionalities. Our method can potentially be applied to model species used in research other than C. elegans, where the number of available cell-type-specific promoters and their variety will be an important consideration.
细胞身份注释是神经科学中的一个基本过程,它允许对细胞进行比较,包括跨不同动物的神经活动的比较。在秀丽隐杆线虫中,尽管已经为所有神经元赋予了独特的身份,但由于缺乏关于神经元位置和身份的定量信息,完整动物中可注释的神经元数量有限。
在这里,我们提供了一个有助于注释神经元身份的数据集,并展示了其在全脑成像综合分析中的应用。我们使用 35 种细胞特异性启动子系统地鉴定了 311 条成年线虫头部区域的神经元,并创建了一个神经元表达模式和位置数据集。我们发现了大量的位置变化,这说明了注释任务的难度。我们研究了多个细胞特异性启动子驱动不同荧光的组合,并为在动物中注释大多数头部神经元生成了最佳菌株。我们还开发了一种具有人机交互功能的自动注释方法,该方法有助于全脑成像所需的注释。
我们的神经元 ID 数据集和最佳荧光菌株能够以全自动和包括人机交互功能的半自动版本注释成年秀丽隐杆线虫头部区域的大多数神经元。我们的方法可能适用于除秀丽隐杆线虫以外的研究中使用的模式物种,其中可用的细胞类型特异性启动子的数量及其多样性将是一个重要的考虑因素。