Large scale discovery and validation of brain cell type enhancers for viral targeting and circuit manipulation

摘要-与原始应用程序不变 大脑功能来自神经回路计算的高度微妙的时空动力学 由不同的和精确相互连接的神经元类型介导。具体系统的实验准入 这些细胞类型是破译大脑回路组织和功能的先决条件，但这是一个 神经科学中令人望而却步的瓶颈。尽管功能强大，但目前哺乳动物的遗传方法大多是 仅限于小鼠的生殖系工程，在时间、成本、规模、通用性方面都有根本限制 和临床应用。迫切需要的是以一种方式识别和操作细胞类型的能力 即：1)特定的(根据解剖和生理特性定义的真实类型)，2) 全面(适用于多种蜂窝类型)、3)快速(数天而不是数月到数年)、4)经济实惠以及5)跨 哺乳动物物种。我们建议开发一个范式转换平台，以实现快速和 通过利用基本表观基因组学全面获取跨哺乳动物物种的脑细胞类型 以及细胞类型的基因调控基础--转录增强子。我们将建立一种细胞分辨率 和可扩展的管道，用于识别小鼠大脑中结合染色质的细胞类型增强子 遗传驱动因素定义的神经元亚群中的景观分析(ATAC-SEQ)，2)创新的AAV-和 在这些亚群中基于测序的大规模平行报告分析，3)高通量验证 使用一种集成空间转录和基于测序的投影映射的新方法，以及 4)高分辨率全脑形态成像。我们的目标是全面覆盖神经元类型 大脑皮质，包括谷氨酸能锥体神经元和GABA能中间神经元 我们的策略和工具将适用于其他大脑区域和物种。黄实验室已经系统地 产生了针对主要皮质神经元亚群的组合遗传驱动系，并发现 皮质神经元类型的转录基础。任冰冰是增强子生物学领域的领导者，他开创了 细胞类型和单细胞染色质分析的技术进展，包括计算方法。 托尼·扎多发明了MAPseq、BARseq和其他基于测序的方法，使高吞吐量成为可能， 神经元连接的细胞分辨率标测。帕维尔·奥斯汀率先开发了高分辨率 以及高通量全脑成像管道和相关的计算分析。在一起，我们的 知识和专业知识组成了一个协同团队，专注于为 对细胞类型增强子和用于生成细胞普查数据集的系统筛选、发现和验证 这有助于实现BICCN的目标。我们的方法是基于基本的遗传原理和 机制，并有可能改变发现的规模和速度在神经科学和 生物医学领域。

Abstract – No change from original application Brain functions emerge from highly nuanced spatiotemporal dynamics of neural circuit computation mediated by diverse and precisely interconnected neuron types. Specific and systematic experimental access to these cell types are prerequisites to deciphering brain circuit organization and function, but this has been a prohibitive bottleneck in neuroscience. Although powerful, current genetic approaches in mammals are mostly restricted to germline engineering in the mouse and have fundamental limitations in time, cost, scale, versatility and clinical application. What is urgently needed is the ability to identify and manipulate cell types in a way that is: 1) specific (to bona fide types defined by anatomical and physiological properties), 2) comprehensive (to many cell types), 3) fast (days instead of months to years), 4) inexpensive, and 5) across mammalian species. We propose to develop a paradigm-shifting platform that will enable rapid and comprehensive access for brain cell types across mammalian species by leveraging fundamental epigenomic and gene regulatory basis of cell types - the transcriptional enhancers. We will establish a cellular resolution and scalable pipeline for identifying cell type enhancers in the mouse brain that combines 1) chromatin landscape analysis (ATAC-seq) in genetic driver-defined neuronal subpopulations, 2) innovative AAV- and sequencing-based massively parallel reporter assays in these subpopulations, 3) high-throughput validation using a novel method of integrated spatial transcriptomics and sequencing-based projection mapping, and 4) high-resolution whole brain morphological imaging. We aim for comprehensive coverage of neuron types of the cerebral cortex, including both glutamatergic pyramidal neurons and GABAergic interneurons, though our strategy and tools will be general to other brain regions and species. The Huang lab has systematically generated combinatorial genetic driver lines targeting major cortical neuron subpopulations and has discovered the transcriptional basis of cortical neuron types. Bing Ren is a leader in enhancer biology and has pioneered the technical advances in cell type and single cell chromatin analysis, including computational approaches. Tony Zador invented MAPseq, BARseq and other sequencing-based methods that enable high throughput, cellular resolution mapping of neuronal connectivity. Pavel Osten has pioneered developing high-resolution and high-throughput whole brain imaging pipelines with associated computational analysis. Together, our knowledge and expertise constitute a synergistic team focusing on an excellent experimental system for the systematic screening, discovery and validation of cell type enhancers and for generating cell census datasets that contribute to the BICCN goals. Our approach is grounded in fundamental genetic principles and mechanisms and has the potential to transform the scale and rate of discovery across neuroscience and biomedical fields.