High-throughput mapping of synaptic connectivity between transcriptomically defined cell types

项目总结 确定组成大脑每个区域的细胞类型和突触连接的模式 它们通过什么联系在一起，是理解神经回路如何产生所有感知、认知、 和行为。光学、分子和计算技术的快速发展使大规模- 旨在全面绘制组成哺乳动物大脑的细胞类型的规模项目。 然而，定义大脑中数千种细胞类型的微连接仍然具有挑战性 由于缺乏可伸缩的方法。该提案描述了一种解决这一问题的技术的发展 方法上的差距。采用高灵敏度荧光电压成像和单次扫描的新组合。 神经元光基因光刺激，我们将映射大脑区域内和大脑区域之间的突触连接。 使用这种方法，突触连接可以映射到吞吐量的两到三个数量级 比现有技术更高。重要的是，利用全光方法来映射连接将 允许我们将突触连接性测量与新兴技术相结合，以实现高度多元化 荧光原位杂交。通过这种方式，我们可以识别大型神经元的分子身份。 人口及其连通性。我们将在此提案中演示开发的技术 运动皮质，一个对基因表达模式的了解远远超过了我们识别能力的区域 连通性主题。揭示了细胞的局部连接性基序和精确的分子同一性 从丘脑接受远程输入--皮质活动的重要驱动因素--将提供新的 对支持自愿运动的巡回机制的洞察。

PROJECT SUMMARY Identifying the cell types that make up each region of the brain and the patterns of synaptic connections through which they are linked is key to understanding how neural circuits give rise to all perception, cognition, and behavior. Rapid improvements in optical, molecular, and computational technologies are enabling large- scale projects aiming to comprehensively map the cell types that comprise the mammalian brain. Nevertheless, defining the microconnectivity of the thousands of cell types in the brain remains challenging due to a lack of scalable methods. This proposal describes the development of a technology for addressing this methodological gap. Using a novel combination of high-sensitivity fluorescence voltage imaging and single- neuron optogenetic photostimulation, we will map synaptic connectivity within – and between – brain regions. Using this approach, synaptic connectivity can be mapped with throughput two to three orders of magnitude higher than existing techniques. Importantly, leveraging an all-optical approach to mapping connectivity will allow us integrate synaptic connectivity measurements with emerging techniques for highly multiplexed fluorescence in situ hybridization. In this way, we can identify the molecular identities of large neuronal populations and their connectivity. We will demonstrate the technology developed in this proposal in the motor cortex, a region where knowledge of gene expression patterns has far outpaced our ability to identify connectivity motifs. Revealing both local connectivity motifs and the precise molecular identity of cells that receive long-range input from the thalamus – an important driver of cortical activity – will provide new insights into the circuit mechanisms supporting voluntary movements.