Tools and approaches for functional connectomics of dense neuropils

项目摘要 神经系统中的许多计算都发生在大范围内单个神经突的水平上。 广泛分枝的乔木（即，亚细胞加工）。大多数神经突在致密的 神经柱，其中各种细胞类型的突起紧密堆积， 相互关联为了了解亚细胞加工，我们需要测量神经突对 生理刺激，并将它们与突触输入的局部模式相关联。划定 神经柱的功能结构并揭示其连接的逻辑，我们需要 在高密度下表征神经突反应和突触模式。神经突反应可以是 通过双光子成像观察，突触输入可以在连续切片中重建 电子显微镜（ssEM）。一些技术障碍阻碍了 这些技术（即，功能性连接组学）来研究密集的 神经髓鞘在这里，我们开发新的工具和方法来克服这些障碍。目标1： 开发用于多光谱双光子钙成像的遗传、病毒和计算工具 和信号分离以实现神经柱的密集功能表征。在目标2中， 设计一种新的策略来组合双光子成像和ssEM（即，多模式成像）， 并建立一种高通量的ssEM方法来分析局部突触连接模式 在大规模电路布线的上下文中（即，多分辨率成像）。我们利用我们的进步 研究无长突细胞（AC），一种不同类型的视网膜中间神经元。神经突的数量超过 50 AC类型在内部视网膜的致密神经元中提取显著的视觉信息。我们将 获得AC的完整功能性连接组数据集。这个数据集， 公开提供，将构成未来R01应用程序的基础，以研究 AC中的亚细胞加工、AC神经元的功能结构及其逻辑 连通性。

PROJECT SUMMARY Many computations in the nervous system occur at the level of individual neurites within large extensively branched arbors (i.e., subcellular processing). Most neurites operate in dense neuropils, in which processes of diverse cell types are tightly packed and abundantly interconnected. To understand subcellular processing, we need to measure neurite responses to physiological stimuli and relate them to local patterns of synaptic inputs. To delineate the functional architecture of neuropils and reveal the logic of their connectivity, we need to characterize neurite responses and synapse patterns at high density. Neurite responses can be observed by two-photon imaging, and synaptic inputs can be reconstructed in serial-section electron microscopy (ssEM). A number of technical obstacles have precluded the combination of these techniques (i.e., functional connectomics) to study subcellular processing in dense neuropils. Here, we develop new tools and approaches to overcome these obstacles. In Aim 1, we develop genetic, viral, and computational tools for multispectral two-photon calcium imaging and signal demixing to enable dense functional characterization of neuropils. In Aim 2, we devise a novel strategy for combining two-photon imaging and ssEM (i.e., multimodal imaging), and establish a high-throughput ssEM method for analyzing local synaptic connectivity patterns in the context of larger-scale circuit wiring (i.e., multiresolution imaging). We use our advances to study amacrine cells (ACs), a diverse class of retinal interneurons. The neurites of more than 50 AC types extract salient visual information in a dense neuropil the inner retina. We will acquire a complete functional connectomic dataset of ACs. This dataset, which will be made publicly available, will form the basis of a future R01 application to study the mechanisms of subcellular processing in ACs, the functional architecture of the AC neuropil, and the logic of its connectivity.