Integrative labeling, imaging, and reconstruction tools for high-throughput inhibitory microconnectivity analysis in the mouse brain

摘要 由具有不同特性的相互连接的神经元组成的神经电路奠定了物理基础 大脑的任何功能。识别单个神经元之间的联系是理解 信息在大脑中进行处理和传播。虽然新兴的高通量光学显微镜 技术非常有前途，可以在单细胞水平上进行全脑成像，光学 分辨率的限制阻碍了它们在区分同一 大脑。此外，自动提取形态信息的计算工具 目前仍缺乏高准确度的混合神经元。我们的团队将同时开发新的基因 用于神经元标记、超分辨率成像和高通量自动神经元追踪的工具 电路重建。我们将应用这些工具来获得密集重构的抑制微电路 老鼠的大脑皮层。这些工具将很容易应用于研究其他长期存在的问题 神经科学和这个项目产生的资源将对未来的计算工具有用 发展。

Abstract Neural circuits composed of interconnected neurons with distinct properties lay the physical foundation of any brain function. Identifying connections between individual neurons is central to understand how information is processed and propagated in the brain. While emerging high throughput light microscopy technologies are highly promising in allowing whole brain scale imaging at the single cell level, optical resolution limitation prevents their use in differentiating densely labeled neuronal processes in the same brain. In addition, computational tools for automatically extracting morphological information from intermingled neurons with high accuracy are still lacking. Our team will concurrently develop novel genetic tools for neuronal labeling, super-resolution imaging, and automated neuronal tracing for high-throughput circuit reconstruction. We will apply these tools to obtain densely reconstructed inhibitory microcircuits in the mouse cortex. These tools will be readily applicable for studying other long-standing questions in neuroscience and the resources generated by this project will be useful for future computational tool development.