Scalable integration of cell types and connectivity in the motor cortex of rodents and non-human primates

项目摘要 BICCN最近完成了对运动皮质细胞成分的广泛调查，包括 转录图谱、斑块序列、多重FISH、区域间连通性和单神经元形态。 这幅图缺少单个神经元和神经元类型大体上是如何相互连接的详细图片 部分是因为获取单个神经元连接的全面图像最好使用 大规模电子显微镜重建的困难方法。此外，没有可伸缩的方法来 将转录切割细胞类别分配给EM重建，特别是在考虑到无数非- 本地输入。为了完成BICCN的任务，我们需要可扩展的方法来衡量连通性 可以与跨模式数据集成，例如来自Patch-seq的数据，或与针对特定像元类的工具集成 在转基因和非转基因物种中，例如非人灵长类动物。在这里，我们建议利用这两个 方法-将EM数据与Patch-Seq数据集或细胞类别的预期目标进行追溯链接 EM重建中的标记-使用我们的大型电子显微镜流水线，同时继续改进 并降低其总体成本。我们建议使用这些工具来检查小鼠的运动皮质 和非人类灵长类动物，以演示一种可扩展的方法来交付集成本地蜂窝网络的数据 形态、超微结构细节和与转录信息的特定局部连通性，来自Patch-Seq， 同时利用病毒遗传工具识别单个传入细胞的来源和细胞类型。

Project Summary The BICCN has recently completed a broad survey of the cellular components of motor cortex, including transcriptomic profiling, patch-seq, multiplexed FISH, inter-areal connectivity, and single neuron morphology. Missing from this view is a detailed picture of how individual neurons and neuronal types interconnect, in large part because acquiring a comprehensive picture of individual neuronal connections is best achieved with the difficult methods of large-scale electron microscopic reconstructions. Further, there are no scalable methods to assign transcriptomic cell classes to EM reconstructions, particularly when considering the myriad set of non- local inputs. In order to fulfill the BICCN mandate, we need scalable methods for measuring connectivity that can be integrated with cross-modal data, such as from Patch-seq, or with tools that target specific cell classes in transgenic and non-transgenic species, such as non-human primate. Here, we propose to exploit both approaches—retrospectively linking EM data with Patch-seq datasets or the prospective targeting of cell classes labeling in EM reconstructions—with our large-scale electron microscopy pipeline, while continuing to improve throughput and lower its overall cost. We propose to deploy these tools to examine the motor cortex of mouse and non-human primate, to demonstrate a scalable approach to delivering data that integrate local cellular morphology, ultrastructural detail, and specific local connectivity with transcriptomic information, from Patch-seq, while identifying the source and cell type of individual afferents, with viral genetic tools.