Dendritome mapping of genetically-defined and sparsely-labeled cortical and striatal projection neurons

项目摘要 整合分子、形态学和连接组学特性对于无偏 哺乳动物大脑中神经元细胞类型的分类。在这里，我们提出了一种新的方法， 分类神经元细胞类型通过全脑范围内的树突状形态的综合分析， 基因定义的神经元。我们开发了一种创新的老鼠基因工具， 称为具有重复移码的镶嵌（或MORF），它可以实现稀疏和随机标记 基因定义的神经元。MORF报告小鼠可以精确地标记单个神经元， 树突和刺到轴突和轴突终末，标记频率为给定神经元的1-5%。 人口我们建议将我们的新MORF系与Cre小鼠系进行交叉，以获得纹状体中型棘状神经元 （MSNs）的直接和间接途径，并为不同的皮质层的皮质锥体神经元（即， L2/3/4、L5和L 6）。每只MORF/Cre小鼠将允许我们对详细的树突形态进行成像， 数以千计的遗传定义的纹状体和皮质神经元（即树突）。我们还开发了 流线型成像和计算工具，用于获取和记录全脑单个神经元 将形态学数据绘制到标准参考小鼠脑图谱上。我们将数字化重建数百个 数千个MORF标记的神经元使用我们的新程序称为G-Cut。重建的神经元将 随后用于基于形态学的聚类以定义新的形态学亚型， 可以分析新的分子标记神经元细胞类型（例如来自单细胞 RNA测序）。最后，我们将把数据传播到脑细胞数据中心（BCDC）进行数据处理 与来自其他BRAIN倡议细胞普查网络（BICCN）的数据集成， 更广泛的神经科学研究社区。 除了树突组数据生成和分析，我们将进一步推进我们的MORF 方法通过产生新的MORF报告小鼠系，其Cre- 依赖的标记频率，这将允许成像的完整的，全脑的形态， 遗传定义的单个神经元，包括树突和轴突分支。该工具应 极大地促进了基于神经元形态的细胞类型分类。最后，我们将开发 用于自动成像的特定领域计算的集成计算机硬件和软件 图像处理和神经元重建是分析大型生物图像数据集的主要瓶颈。 总之，我们将提供丰富的树突状细胞信息，使公正的，基于形态学的神经元 细胞类型分类，新型小鼠遗传工具和计算机软件和硬件， 大规模神经元形态学成像和分析领域的综合研究， 哺乳动物的大脑

PROJECT SUMMARY Integrating molecular, morphological, and connectomic properties is critical for unbiased classification of neuronal cell types in the mammalian brain. Here we propose a novel approach to classify neuronal cell types by brainwide comprehensive profiling of the dendritic morphology of genetically-defined neurons in the mouse brain. We have developed an innovative mouse genetic tool, called Mosaicism with Repeat Frameshift (or MORF), which enables sparsely and stochastically labeling of genetically-defined neurons in mice. MORF reporter mice can label in exquisite detail single neurons from dendrite and spines to axons and axonal terminals at a labeling frequency of 1-5% of a given neuronal population. We propose to cross our new MORF lines with Cre mouse lines for striatal medium spiny neurons (MSNs) of direct- and indirect pathways, and for cortical pyramidal neurons of distinct cortical layers (i.e. L2/3/4, L5 and L6). Each MORF/Cre mouse will allow us to image the detailed dendritic morphology for thousands of genetically-defined striatal and cortical neurons (i.e. dendritome). We have also developed and streamlined imaging and computational tools to acquire and register brainwide single neuron morphological data onto a standard reference mouse brain atlas. We will digitally reconstruct hundreds of thousands of MORF-labeled neurons using our novel program called G-Cut. Reconstructed neurons will subsequently used for morphology based clustering to define new morphological subtypes, which in turn can be analyzed for the expression of novel molecular markers neuronal cell types (e.g. from single cell RNA-sequencing). Finally, we will disseminate the data to the Brain Cell Data Center (BCDC) for data integration with those from other BRAIN Initiative Cell Census Network (BICCN) and for data access by the broader neuroscience research community. In addition to dendritome data generation and analyses, we will further advance our MORF method by generating new MORF reporter mouse lines with logarithmic fold decrease in the Cre- dependent labeling frequencies, which will permit imaging of the complete, brainwide morphology of genetically-defined single neurons that include both dendritic and axonal arborization. Such tool should greatly facilitate the neuronal morphology based cell type classification. Finally, we will develop integrated computer hardware and software for domain-specific computing for automated image processing and neuronal reconstruction, a major bottleneck in analyzing large bioimage datasets. Altogether we will provide rich dendritome information to enable unbiased, morphology-based neuronal cell type classification, and novel mouse genetic tools and computer software and hardware to advance the field of large-scale neuronal morphological imaging and analyses for the comprehensive study of the mammalian brain.