Mouse Cell Type-Specific Brain Mapping in Health and Disease

描述(由申请人提供)： 解剖回路是信息在大脑中传播的基础。因此，全面的接线图是理解电路如何控制复杂的行为和认知过程的基础。然而，对于哺乳动物模式物种，只有稀疏的连接性数据集，并且大多数是从轨迹跟踪文献中整理出来的。这是有问题的，因为不同的实验室对连接强度的方法、目标区域、命名和评估多种多样。相反，在艾伦研究所，我们基于现代顺行轴突追踪和高通量双光子成像方法创建了一个全新的、标准化的、公开的和免费可用的数据集(Mouse Connectivity Atlas；：//Connectivity.Brain-map.org)，以可视化整个年轻成年小鼠大脑中约300个区域的区域间和基因定义的细胞类型特定投影。老鼠连接图谱数据揭示了健康大脑的一般组织原则，但许多细节仍然缺失。例如，投射到单个或多个目标的区域内的神经元亚类并不总是被定义，也没有定义特定目标的皮质输入神经元的起始层。在细胞类型而不是区域水平上对结构连接模式进行更高分辨率的描述将使关于健康和疾病中的皮质信息处理和认知的更准确的建模和假设成为可能。在剥夺患者认知能力的神经退行性疾病中，如阿尔茨海默病(AD)，病理在选择性脆弱的区域以特定的模式传播，这些模式类似于但并不完全复制网络结构。因此，考虑到特定的皮质投射路径可以为理解、预测和治疗疾病提供更好的结构框架。我们建议通过集中解剖追踪由疾病相关的大规模皮质网络内外的投射目标定义的神经元的全脑轴突路径来增强小鼠连接图谱。这将通过开发基于病毒的逆行和顺行联合交叉荧光标记策略以及我们建立的、成功的高通量成像和信息学平台来实现。我们将密集调查和绘制小鼠默认模式样和海马区网络中靶区定义的细胞类型投影，这些区域参与基本的正常认知任务，并容易受到AD的影响。新数据将被整合到艾伦研究所的在线资源中，为外部用户提供对原始2-D图像、带注释的3-D投影模型和其他分析工具的访问。我们将专门测试这样的预测，即属于功能定义的默认模式网络的区域内的解剖类别的投射神经元优先连接到网络内的其他区域。我们还建议使用这个基础数据集作为指导，为AD小鼠模型生成和分析全脑细胞类型的结构连接矩阵，这可以促进AD社区关于疾病相关网络功能障碍和病理传播的研究。

DESCRIPTION (provided by applicant): Anatomical circuitry is the foundation by which information travels within the brain. Therefore, comprehensive wiring diagrams are fundamental for understanding how circuits control complex behavioral and cognitive processes. However, only sparse connectivity datasets exist for mammalian model species, and most are collated from tract tracing literature. This is problematic due to the large variety of methods, targeted areas, nomenclature, and assessment of connection strengths by different labs. Instead, at the Allen Institute, we created an entirely new, standardized, publicly and freely available, dataset (Mouse Connectivity Atlas; ://connectivity.brain-map.org) based on modern anterograde axon tracing and high-throughput 2-photon imaging methods to visualize inter-areal and genetically defined cell type-specific projections across the entire young adult mouse brain from ~300 regions. The Mouse Connectivity Atlas data reveals general organizational principles in healthy brains, but many details are still missing. For example, subclasses of neurons within a region projecting to either single or multiple targets are not always defined, nor are the layers of origin of cortical input neurons to specific targets defined. Higher resolution descriptions of structural connection patterns at the cell type, rather than areal, level will enable more accurate modeling and hypotheses on cortical information processing and cognition in health and disease. In neurodegenerative diseases that rob patients of cognitive abilities, such as Alzheimer's disease (AD), pathology spreads in selectively vulnerable regions in specific patterns that resemble, but do not exactly duplicate, network architecture. Taking into account specific cortical projection pathways could thus provide a better structural framework for understanding, predicting, and treating disease. We propose to enhance the Mouse Connectivity Atlas through focused anatomical tracing of whole-brain axon pathways from neurons defined by their projection targets within and outside of disease- relevant large-scale cortical networks. This will be accomplished through the development of a combined viral- based retrograde and anterograde intersectional fluorescent labeling strategy, and our established, successful high-throughput imaging and informatics platform. We will densely survey and map cell type projections defined by target regions in the mouse default mode-like and hippocampal networks, which participate in essential normal cognitive tasks and are vulnerable to AD. New data will be integrated into Allen Institute online resources, providing external users access to raw 2-D images, annotated 3-D projection models, and other analysis tools. We will specifically test the prediction that anatomical classes of projection neurons within an area belonging to the functionally-defined default mode network preferentially connect to other areas within the network. We also propose to use this foundational dataset as a guide for generating and analyzing a brain- wide cell type structural connectivity matrix for an AD mouse model, which could facilitate research in the AD community on disease-related network dysfunction and propagation of pathology.