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 确定组成每个大脑区域的细胞类型以及连接它们的连接模式是 了解神经回路如何引起所有感知、认知和行为的关键。大型项目 由下一代测序技术实现的技术揭示了大脑中包含数千个细胞 每种类型都有独特的分子特征、轴突靶标和在脑功能中的作用。然而，突触 这些信元类型之间的连接目前是使用低吞吐量方法确定的，其中连接 细胞对之间的测试是逐一进行的。描述蜂窝级别连通性的数据已经成为 对大脑功能的理论模型至关重要，并需要更大规模和更高层次的发展 吞吐量方法。在引人注目的概念验证实验中，基因编码的电压指示器 (GEVI)已被用于可视化活动和推断大脑内细胞的连通性。我提议 利用这一进步来开发SYNMAP，这是一种高效的全光方法，用于测量 构成哺乳动物大脑的数千种由基因定义的细胞类型。在SYNMAP中，神经活动 将会被控制，并用光来观察。基因表达将在相同的细胞中可视化， 高度复合荧光原位杂交。使用SYNMAP，突触连接可以 跨分子定义的细胞类型进行分析，吞吐量比目前可能的高出100倍，使我们能够 以测试系统神经科学中有关神经回路结构的重要假说。我将应用SYNMAP 确定平行的丘脑皮质通路是否传递来自基底节和小脑的信息 来分离运动皮质中的子电路，让我们更进一步地了解运动如何动作 由跨越多个大脑区域的运动系统计划和执行。光学生理学正在迅速发展 被神经生理学实验室采用，有望在神经科学中广泛应用SYNMAP。 SYNMAP的成功开发将是革命性的，使我们能够研究 任何神经电路及其组成细胞类型。