High-throughput mapping of synaptic connectivity between transcriptomically defined cell types

转录组定义的细胞类型之间突触连接的高通量作图

基本信息

批准号：
10413540
负责人：
Michael Nicholas Economo
金额：
$ 142.38万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10413540
关键词：
Address Adopted Anterolateral Architecture Behavior Behavioral Benchmarking Biological Assay Brain Brain region Cells Cognition Data Development Disease Electron Microscopy Electrophysiology (science)Emerging Technologies Fluorescence Fluorescent in Situ Hybridization Gene Expression Profile Genes Genetic Goals Gold Image Individual Ion Channel Knowledge Learning Light Link Maps Measurement Measures Methodology Methods Microscopy Molecular Molecular Computations Motor Motor Cortex Movement Mus Neocortex Neurons Neurosciences Optics Pathway interactions Pattern Perception Physiology Population Probability Process Reporting Resources Short-Term Memory Structure Synapses System Techniques Technology Thalamic structure Theoretical model Time Tissues cell type experience experimental study imaging informatics insight inter-individual variation neural circuit neurophysiology new technology novel optogenetics relating to nervous system response sensor single cell analysis single-cell RNA sequencing standard measure tool transcriptomics voltage

项目摘要

PROJECT SUMMARY Identifying the cell types that make up each region of the brain and the patterns of synaptic connections through which they are linked is key to understanding how neural circuits give rise to all perception, cognition, and behavior. Rapid improvements in optical, molecular, and computational technologies are enabling large- scale projects aiming to comprehensively map the cell types that comprise the mammalian brain. Nevertheless, defining the microconnectivity of the thousands of cell types in the brain remains challenging due to a lack of scalable methods. This proposal describes the development of a technology for addressing this methodological gap. Using a novel combination of high-sensitivity fluorescence voltage imaging and single- neuron optogenetic photostimulation, we will map synaptic connectivity within – and between – brain regions. Using this approach, synaptic connectivity can be mapped with throughput two to three orders of magnitude higher than existing techniques. Importantly, leveraging an all-optical approach to mapping connectivity will allow us integrate synaptic connectivity measurements with emerging techniques for highly multiplexed fluorescence in situ hybridization. In this way, we can identify the molecular identities of large neuronal populations and their connectivity. We will demonstrate the technology developed in this proposal in the motor cortex, a region where knowledge of gene expression patterns has far outpaced our ability to identify connectivity motifs. Revealing both local connectivity motifs and the precise molecular identity of cells that receive long-range input from the thalamus – an important driver of cortical activity – will provide new insights into the circuit mechanisms supporting voluntary movements.

项目摘要识别构成大脑每个区域的细胞类型和突触连接的模式通过它们链接的关键是了解中性电路如何产生所有感知，认知，和行为。光学，分子和计算技术的快速改善正在实现大量比例项目旨在全面绘制完成哺乳动物大脑的细胞类型。然而，定义大脑中数千种细胞类型的微连接性仍然受到挑战由于缺乏可扩展的方法。该建议描述了解决此问题的技术的发展方法论差距。使用高敏感性荧光电压成像和单次的新型组合神经元光遗传光刺激，我们将在大脑区域内以及之间绘制突触连通性。使用这种方法，可以用两到三个数量级的吞吐量映射突触连通性高于现有技术。重要的是，利用全光学方法来映射连接将允许我们与高度多重的新兴技术集成了合成连通性测量荧光原位杂交。这样，我们可以识别大神经元的分子身份人群及其连通性。我们将证明该提案中开发的技术运动皮层，基因表达模式知识的区域远远超过了我们识别的能力连通性主题。揭示局部连接基序和细胞的精确分子身份从Thalamus接收长期输入（皮层活动的重要驱动力）将提供新的洞悉支持自愿运动的电路机制。