Scalable technologies for brain-wide connectomics of transcriptomic cell types: focus on brainstem

转录组细胞类型的全脑连接组学的可扩展技术：关注脑干

• 批准号: 10369309
• 负责人: Jayaram Chandrashekar
• 金额: $ 471.46万
• 依托单位: ALLEN INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10369309
• 关键词: Address; Area; Atlases; Axon; Behavior; Brain; Brain Stem; Brain imaging; Brain region; Breathing; Cell Nucleus; Cells; Censuses; Classification; Collection; Complex; Coupled; Custom; Data; Data Set; Deglutition; Dendrites; Environment; Feedback; Fluorescent in Situ Hybridization; Foundations; Functional Imaging; Gene Expression; Gene Expression Profiling; Genes; Genetic; Goals; Histologic; Image; Individual; Label; Laboratories; Learning; Lighting; Machine Learning; Manuals; Mastication; Measures; Medulla Oblongata; Mental disorders; Methods; Microscopy; Modality; Modernization; Molecular; Morphology; Motor; Mus; Neurons; Neurophysiology - biologic function; Neurotransmitter Receptor; Optics; Primates; Process; Psychological reinforcement; Reagent; Resolution; Role; Route; Tactile; Taste Perception; Technology; Time; Tissue Expansion; Transgenic Organisms; Tropism; Vasomotor; Viral; autoencoder; automated segmentation; base; biomarker panel; brain cell; brain research; cell type; cloud based; information processing; insight; machine learning algorithm; multimodal data; neural circuit; neuronal circuitry; orofacial; recombinase; reconstruction; relating to nervous system; sensory input; tool; transcriptomics

Project summary, Scalable technologies for brain-wide connectomics of transcriptomic cell types: focus on brainstem This proposal is to develop a scalable pipeline to combine high-resolution morphology and molecular classification of individual neurons to define morpho-molecular cell types in the brain. Complete morphology of individual neurons provides insights of connectivity and information processing in the brain and reveals how neuronal activity is routed across brain areas. Layering transcriptomic information on to morphologically distinct types provides the basis to access these defined neuron types for functional analysis. Such a combined classification of the brain’s cell types is foundational for understanding the role of defined neuron types within neural circuits and how information processing within multi-regional neural circuits orchestrate complex behaviors. Sequencing-based approaches have been used to categorize the brain’s cells into transcriptomic types (t- types) with high throughput. Parallel strategies for a complete description of the morphological types brain-wide are too slow and methods for a combined analysis of these two modalities are lacking. To address this, we will create a brain-wide imaging and neuronal reconstruction platform that provides faster imaging using selective plane illumination microscopy and accelerated reconstructions with modern machine learning tools based on U-Nets and reinforcement learning. We will combine this with post hoc transcriptomic characterization of reconstructed cells with multiplexed fluorescent in situ hybridization to define morpho-molecular types. We will create a data set of 2,000 such dual categorized neuron types from a critical brain area, the medulla, in the mouse. The medulla is comprised of diverse neuronal types organized in numerous inter-related nuclei essential for autonomic functions such as breathing, vasomotor control, integration of ascending inputs from sensory and interoceptive channels and coordination of motor actions such as chewing, licking and swallowing. Our census of morpho-molecular medullary neuron types will lay the foundation for a systematic cell type specific functional interrogation of these neurons within brainstem circuits and in the larger context of multi- regional brain circuits. Furthermore, this will serve as the blueprint for carrying out such studies throughout the mouse brain, and other brains, including that of primates.

项目摘要，可扩展的大脑连接组学技术 转录组细胞类型：关注脑干 该建议是开发可扩展的管道，以结合高分辨率形态和分子 单个神经元的分类以定义大脑中的形态分子细胞类型。完整的形态 单个神经元提供了大脑中连通性和信息处理的见解，并揭示了如何 神经元活动在大脑区域遍布。分层转录信息到形态上不同的 类型提供了访问这些定义的神经类型进行功能分析的基础。这样的组合 大脑细胞类型的分类是基本的，用于了解定义的神经元类型的作用 神经回路以及多区域神经电路内的信息处理如何编排复合物 行为。 基于测序的方法已用于将大脑细胞分类为转录组类型（t- 类型）高吞吐量。对形态类型的完整描述的平行策略，脑部范围 太慢，缺乏对这两种方式的合并分析的方法。为了解决这个问题，我们将 创建一个脑部范围的成像和神经元重建平台，该平台可使用选择性提供更快的成像 基于现代机器学习工具的平面照明显微镜和加速重建 U-NET和增强学习。我们将将其与事后转录组的特征结合在一起 具有多重荧光原位杂交的重建细胞，以定义形态分子类型。 我们将从关键的大脑区域，延髓，在 鼠标。髓质由在许多相关核中组织的潜水神经元类型组成 对于呼吸，血管舒适控制等自主功能至关重要 咀嚼，舔和吞咽等运动动作的感官和感受性通道以及运动动作的协调。 我们对形态分子髓质神经元类型的人口普查将为系统细胞类型奠定基础 这些神经元在脑干电路中的特定功能询问以及在更大的多种情况下 区域大脑电路。此外，这将是整个过程中进行此类研究的蓝图 小鼠大脑和其他大脑，包括原发性的大脑。