Scalable electron tomography for connectomics
用于连接组学的可扩展电子断层扫描
基本信息
- 批准号:10410742
- 负责人:
- 金额:$ 291.62万
- 依托单位:
- 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:2022-07-15 至 2025-07-14
- 项目状态:未结题
- 来源:
- 关键词:AddressAffectAnatomyBrainCellsCerebellar CortexCerebellar NucleiCerebellumChemical SynapseChemicalsCollectionComputer ModelsComputer softwareCouplingData SetDevelopmentElectrical SynapseElectron BeamElectron MicroscopyElectronsElementsEngineeringFoundationsGap JunctionsGoalsImageLabelMachine LearningMechanicsMethodsMicroscopicMicrotomyModelingModernizationMolecular GeneticsMorphologyMusNervous system structureNeurodegenerative DisordersNeuronsNeurosciencesOpticsPhysiologyPurkinje CellsReporterResearchResolutionResourcesReverse engineeringRoleScanningScanning Electron MicroscopySeriesSocial BehaviorSpecificitySpeedStructureSynapsesSystemTechniquesTestingThickThinnessTransmission Electron MicroscopyWorkadvanced systembasecell typecognitive functioncomparativeconnectomeconnexin 36effective therapyelectron opticselectron tomographymethod developmentmotor controlnanoscalenetwork modelsneural circuitneural networkneuronal circuitrynext generationnovelreconstructionsample collectiontomographytooltreatment strategyvoltage
项目摘要
Project Summary / Abstract
A fundamental goal in neuroscience is understanding how neural network function arises from circuit structure.
However, the immense complexity of most brain networks has been a significant barrier to progress. We do not
have a comprehensive wiring diagram for any mammalian local circuit, much less a whole brain. We do not yet
have a comprehensive list of cell types and how they are defined for even the simplest mammalian neuronal
circuit. Moreover, synapse resolution connectomics has focused almost entirely on chemical synapses and
ignored electrical synapses formed by gap junctions, which are thought to be crucial for network synchrony and
oscillations. Recent advances in automated sample collection and imaging for transmission electron microscopy
(TEM) and molecular genetic tools have allowed us to begin detailed mapping of neural network anatomy.
Development of intermediate voltage TEMs has demonstrated the ability to volumetrically image through thick
(~1 μm) sections with high resolution tomography. Here, we propose combining automated sample collection
and imaging using GridTape, with electron microscopic tomography (EMT) and conical beam procession
“VortexBeam” imaging. This new combination of approaches will increase both the resolution and throughput of
connectomics, while decreasing the number of sections that need to be collected and acquired. The cerebellum
is an ideal system to validate our novel platform as part of a systematic effort to reverse engineer a functional
neural circuit that is involved in motor control and social behavior. Its basic structure is well ordered, relatively
simple and sufficiently described to have inspired computational models that capture aspects of cerebellar
function. However, even the most advanced models are limited by an incomplete characterization of the cell
types and connectivity within the cerebellum. Here, we propose to validate our next-generation EMT platforms
and characterize long-range, local, and gap junctional connectivity in the cerebellum. We will combine tools
recently developed in our labs to a circuit that offers the advantages of relative simplicity and a strong starting
foundation. These studies will allow us to understand principles of cerebellar circuit organization and may help
us determine the role of specific circuit elements in neurodegenerative disorders.
项目摘要/摘要
神经科学的一个基本目标是了解神经网络功能是如何从电路结构中产生的。
然而,大多数大脑网络的巨大复杂性一直是进步的重大障碍。我们不会
任何哺乳动物的局部电路都有一个全面的接线图,更不用说整个大脑了。我们现在还不知道
我有一个完整的细胞类型列表,以及它们是如何定义的,即使是最简单的哺乳动物神经元
巡回赛。此外,突触分辨连接学几乎完全专注于化学突触和
被忽视的由缝隙连接形成的电突触,它被认为是网络同步和
震荡。透射电子显微镜自动样品采集和成像技术的最新进展
(TM)和分子遗传学工具使我们能够开始详细绘制神经网络解剖图。
中压TEMS的发展已经证明了通过厚的体积成像的能力
(~1μm)高分辨率断层扫描。在这里,我们建议结合自动样本采集
并使用GridTape、电子显微镜断层扫描(EMT)和圆锥束处理进行成像
“垂直光束”成像。这种新的方法组合将提高分辨率和吞吐量
连接,同时减少需要收集和获取的节的数量。小脑
是验证我们的新平台的理想系统,将其作为系统逆向工程功能的一部分
参与运动控制和社会行为的神经回路。它的基本结构是有序的,相对来说
简单和充分的描述激发了捕捉小脑各个方面的计算模型的灵感
功能。然而,即使是最先进的模型也受到细胞特征不完整的限制
小脑的类型和连通性。在这里,我们建议验证我们的下一代EMT平台
并描述小脑中远程、局部和缝隙连接的特征。我们将结合各种工具
最近在我们的实验室中开发的一种电路,具有相对简单和启动能力强的优点
基金会。这些研究将使我们能够理解小脑回路组织的原理,并可能有所帮助。
美国确定特定的电路元件在神经退行性疾病中的作用。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Mark H Ellisman其他文献
Mark H Ellisman的其他文献
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{{ truncateString('Mark H Ellisman', 18)}}的其他基金
200keV, Energy Filtered, Intermediate-High Voltage Transmission Electron Microscope(IVEM)"
200keV、能量过滤、中高压透射电子显微镜(IVEM)"
- 批准号:
10642585 - 财政年份:2023
- 资助金额:
$ 291.62万 - 项目类别:
Reversing Microglial Inflammarafts and Mitochondrial Dysfunction in Alzheimer's Disease
逆转阿尔茨海默病中的小胶质细胞炎症和线粒体功能障碍
- 批准号:
10607455 - 财政年份:2022
- 资助金额:
$ 291.62万 - 项目类别:
National Center for Microscopy and Imaging Research: A BRAIN Technology Integration and Dissemination Resource
国家显微镜和成像研究中心:大脑技术集成和传播资源
- 批准号:
10334513 - 财政年份:2021
- 资助金额:
$ 291.62万 - 项目类别:
National Center for Microscopy and Imaging Research: A BRAIN Technology Integration and Dissemination Resource
国家显微镜和成像研究中心:大脑技术集成和传播资源
- 批准号:
10544010 - 财政年份:2021
- 资助金额:
$ 291.62万 - 项目类别:
National Center for Microscopy and Imaging Research: A BRAIN Technology Integration and Dissemination Resource
国家显微镜和成像研究中心:大脑技术集成和传播资源
- 批准号:
10116087 - 财政年份:2021
- 资助金额:
$ 291.62万 - 项目类别:
The National Center for Microscopy and Imaging Research, a Community-wide Scientific Resource
国家显微镜和成像研究中心,社区范围的科学资源
- 批准号:
10399337 - 财政年份:2020
- 资助金额:
$ 291.62万 - 项目类别:
Advancing Multi-Color EM via Direct Detector-enabled 4D-STEM
通过支持直接检测器的 4D-STEM 推进多色 EM
- 批准号:
10031737 - 财政年份:2020
- 资助金额:
$ 291.62万 - 项目类别:
Advancing Multi-Color EM via Direct Detector-enabled 4D-STEM
通过支持直接检测器的 4D-STEM 推进多色 EM
- 批准号:
10795540 - 财政年份:2020
- 资助金额:
$ 291.62万 - 项目类别:
The National Center for Microscopy and Imaging Research, a Community-wide Scientific Resource
国家显微镜和成像研究中心,社区范围的科学资源
- 批准号:
10212509 - 财政年份:2020
- 资助金额:
$ 291.62万 - 项目类别:
The National Center for Microscopy and Imaging Research, a Community-wide Scientific Resource
国家显微镜和成像研究中心,社区范围的科学资源
- 批准号:
10400847 - 财政年份:2020
- 资助金额:
$ 291.62万 - 项目类别:
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