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

摘要 由具有不同性质的相互连接的神经元组成的神经回路奠定了物理基础 任何大脑功能。识别单个神经元之间的连接对于理解 信息在大脑中处理和传播。虽然新兴的高通量光学显微镜 技术在允许单细胞水平的全脑尺度成像、光学成像和光学成像方面是非常有前途的。 分辨率限制阻止了它们用于区分相同脑区中密集标记的神经元过程， 个脑袋此外，用于自动提取形态学信息的计算工具， 仍然缺乏具有高精度的混合神经元。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 电路重构我们将应用这些工具，以获得密集重建的抑制微电路， 老鼠的大脑皮层这些工具将随时适用于研究其他长期存在的问题， 神经科学和该项目产生的资源将有助于未来的计算工具 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.