A Scalable Method for Mapping Microconnectivity in Transcriptomically Distinct Neuron Types

一种可扩展的方法来绘制转录组上不同神经元类型的微连接

• 批准号: 10685266
• 负责人: Maria Victoria Moya
• 金额: $ 7.38万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10685266
• 关键词: Adopted; Anatomy; Architecture; Area; Axon; Basal Ganglia; Behavior; Biological Assay; Brain; Brain region; Catalogs; Cell Differentiation process; Cells; Cellular Structures; Cerebellum; Classification; Cognition; Complex; Coupled; Data; Development; Distant; Fellowship; Fluorescent in Situ Hybridization; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genetic Identity; Image; In Situ; In Situ Hybridization; In Vitro; Learning; Light; Link; Maps; Measures; Methodology; Methods; Molecular; Motor; Motor Cortex; Movement; Neurons; Neurosciences; Optical Methods; Optics; Output; Pathway interactions; Pattern; Perception; Physiology; Population; Reporting; Role; Specificity; Structure; Synapses; Synaptic Potentials; System; Techniques; Technology; Testing; Thalamic structure; Theoretical model; Visualization; Visualization software; Work; anatomical tracing; brain cell; career; cell type; computational pipelines; experience; experimental study; image registration; motor behavior; neural; neural circuit; neurophysiology; neurotransmission; next generation sequencing; optogenetics; presynaptic; prototype; single cell sequencing; skills; tool; transcriptomics; voltage

Project Summary Identifying the cell types that compose each brain region and the patterns of connectivity that link them is key to understanding how neural circuits give rise to all perception, cognition, and behavior. Large-scale projects enabled by next-generation sequencing technologies are revealing that the brain contains thousands of cell types, each with unique molecular features, axonal targets, and roles in brain function. However, the synaptic connections between these cell types is currently determined using low throughput methods in which connectivity between pairs of cells is tested one-by-one. Data describing connectivity at the cellular level have become a essential for theoretical models of brain function, and necessitate the development of larger scale and higher throughput methods. In remarkable proof of concept experiments, genetically encoded voltage indicators (GEVIs) have been employed to visualize activity and infer the connectivity of cells within the brain. I propose to leverage this advance to develop SYNMAP, an efficient all-optical method for measuring connectivity between the thousands of genetically defined cell types that make up the mammalian brain. In SYNMAP, neural activity will be both controlled and observed with light. Gene expression will be visualized across the same cells with highly multiplexed fluorescence in situ hybridization in situ. Using SYNMAP, synaptic connectivity can be assayed across molecularly defined cell types with 100X higher throughout than currently possible, allowing us to test important hypotheses about neural circuit architecture across systems neuroscience. I will apply SYNMAP to determine whether parallel thalamocortical pathways relay information from the basal ganglia and cerebellum to discrete subcircuits in the motor cortex, taking us one step further towards understanding how motor actions are planned and executed by motor systems spanning multiple brain regions. Optical physiology is being quickly adopted by neurophysiology labs, promising the widespread application of SYNMAP across neuroscience. Successful development of SYNMAP will be transformative, allowing us to study the structure and dynamics of any neural circuit and its component cell types.

项目摘要 识别组成每个大脑区域的细胞类型以及连接的模式是 了解神经回路如何产生所有感知，认知和行为的关键。大型项目 通过下一代测序技术启用，表明大脑包含数千个细胞 类型，每种具有独特的分子特征，轴突靶标和在大脑功能中的作用。但是，突触 这些单元类型之间的连接目前是使用低吞吐量方法确定的 两对之间的细胞之间进行一对一的测试。描述蜂窝级别连接性的数据已成为 对于大脑功能的理论模型必不可少的，需要大规模和更高的发展 吞吐量方法。在概念实验的显着证明中，遗传编码的电压指标 （GEVI）已被用来可视化活性并推断大脑内细胞的连通性。我建议 利用此进步发展Synmap，这是一种有效的全光学方法，用于测量 构成哺乳动物大脑的数千种遗传定义的细胞类型。在Synmap中，神经活动 将通过光线控制和观察。基因表达将在同一细胞上可视化与 高度多重的荧光原位杂交原位。使用synmap，突触连接可以是 在整个分子定义的细胞类型上进行测定，整个细胞类型比当前可能高100倍，使我们 测试有关跨系统神经科学的神经回路结构的重要假设。我将应用synmap 确定平行的丘脑皮层途径是否从基底神经节和小脑中继信息 要在电动机皮层中离散的子电路，使我们进一步迈向了解运动动作的方式 由跨越多个大脑区域的电机系统计划和执行。光学生理很快 神经生理学实验室通过，有望在神经科学中广泛应用Synmap。 成功发展synmap将具有变革性，使我们能够研究 任何神经回路及其组成细胞类型。