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.

项目总结