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

用于病毒靶向和回路操纵的脑细胞类型增强剂的大规模发现和验证

• 批准号: 10327151
• 负责人: Yarui Diao
• 金额: $ 393.13万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-23 至 2023-09-22
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10327151
• 关键词: Large; scale; discovery; validation; brain

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.

摘要 – 与原始应用程序没有变化 大脑功能源自神经回路计算的高度细致的时空动力学 由不同且精确互连的神经元类型介导。 这些细胞类型是破译大脑回路组织和功能的先决条件，但这一直是 神经科学领域的瓶颈虽然强大，但目前针对哺乳动物的遗传方法大多是针对哺乳动物的。 仅限于小鼠种系工程，并且在时间、成本、规模、多功能性方面具有根本限制 迫切需要的是以某种方式识别和操纵细胞类型的能力。 即：1）特定（针对由解剖学和生理学特性定义的真正类型），2） 全面（针对多种细胞类型），3) 快速（几天而不是几个月甚至几年），4) 便宜，5) 广泛 我们建议开发一个范式转变平台，以实现快速、准确的研究。 利用基础表观基因组学全面了解哺乳动物物种的脑细胞类型 我们将建立细胞分辨率和细胞类型的基因调控基础——转录增强子。 用于识别小鼠大脑中细胞类型增强剂的可扩展管道，结合了 1) 染色质 遗传驱动定义的神经亚群中的景观分析 (ATAC-seq)，2) 创新的 AAV- 和 在这些亚群中基于测序的大规模并行检测报告基因，3) 高通量验证 使用集成空间转录组学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。 大脑皮层，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进行了研究。 生成了针对主要皮质神经元亚群的组合遗传驱动线，并发现 任秉是增强子生物学领域的领导者，也是研究皮层神经元类型转录基础的先驱。 细胞类型和单细胞染色质分析的技术进步，包括计算方法。 Tony Zador 发明了 MAPseq、BARseq 和其他基于测序的方法，可实现高通量、 帕维尔·奥斯滕（Pavel Osten）率先开发了高分辨率的神经连接图。 和高通量全脑成像管道以及相关的计算分析。 知识和专业知识构成了一个协同团队，专注于为该领域提供优秀的实验系统 系统筛选、发现和验证细胞类型增强剂并生成细胞普查数据集 我们的方法基于基本的遗传学原理和 机制，并有潜力改变神经科学和神经科学领域的发现规模和速度 生物医学领域。