Macromolecular Architecture Of The Synapse
突触的大分子结构
基本信息
- 批准号:10915958
- 负责人:
- 金额:$ 175.43万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:
- 资助国家:美国
- 起止时间:至
- 项目状态:未结题
- 来源:
- 关键词:AccelerationAffectAlgorithm DesignArchitectureAutomationBindingBrainClassificationCollaborationsComplexComputer softwareCryo-electron tomographyCryoelectron MicroscopyDLG4 geneDarknessDataData SetDendritic SpinesDevelopmentElectronsElementsFailureFilamentFreeze SubstitutionFreezingFutureGeneticGlutamate ReceptorGoalsGoldHandHippocampusHorseradish PeroxidaseHourHydration statusHydrogen PeroxideImageImaging TechniquesIndividualInvestigationIonsLabelLinkMachine LearningManualsMapsMembraneMemoryMethodsMicroscopeMolecularMolecular ProbesMolecular StructureMorphologyNatureNeuronsNoiseOrganellesPhosphotransferasesPositioning AttributePostsynaptic MembranePreparationPresynaptic TerminalsProceduresProcessProteinsPublicationsPublishingRattusResolutionShapesSonicationSpecificityStainsStructureSubcellular structureSynapsesSynaptic CleftSynaptic MembranesSynaptic VesiclesSynaptosomesTechniquesTechnologyThickTimeTomogramUnited States National Institutes of HealthUse of New TechniquesVertebral columnVesicleVisualizationWorkalgorithmic methodologiesartificial intelligence algorithmautomated segmentationcalmodulin-dependent protein kinase IIclassification algorithmdata pipelinedensitydesignelectron opticselectron tomographyimage processingimprovedinnovationmannanonanobodiesnanoscaleoxidationparticlepostsynapticpressurepresynapticreconstructionresponsesynaptic functiontomographytooltrafficking
项目摘要
Progress Summary:
A key priority for this project is to develop methods of determining molecular identity within tomograms of the PSD. First, we developed a new technique using nanobody labeling for tomograms. A nanobody binds directly to the target protein with high specificity which allows us to use gold particle labels on synaptic proteins and identify them directly in tomograms. We have data on PSD-95, CaMKII, and Homer1b, and we are in the process of finishing this work and preparing an initial publication for this type of work. Second, we succeeded in fine-tuning a genetic labeling procedure for EM tomography using APEX2, a cloneable horseradish peroxidase, which catalyzes the oxidation of DAB into electron-dense material in the presence of hydrogen peroxide. CaMKII is a kinase required for LTP and is the most abundant protein in the brain. Using this APEX2 method in rat hippocampal neurons imaged by dark-field STEM tomography, individual APEX2 labeled CaMKII are readily identified in tomographic reconstructions of dendritic spines. As a result, we are beginning to understand the distribution of CaMKII in spines, on the membrane, and in the PSD, their self-association, and their response to synaptic activity. We are preparing this work for publication.
We have made great strides in characterizing synaptic structures using high-pressure freezing and freeze substitution (HPF/FS). However, HPF/FS is time intensive with many points of failure and relies on stain to visualize the structure. With cryo-EM tomography (cryo-ET), we can trade a slight degradation in resolution for ease in preparation and pure visualization of structure. We are experimenting with three cryo-focused methods in collaboration with NIH cryo-EM facilities (NICE and MICEF). First, we acquired over 150 tomograms on the NCI 300 kV Krios cryo-ET microscope, specifically on frozen-hydrated isolated PSDs from rat brains or sonicated PSD fragments. Second, we are also using the cryo-focused ion beam (FIB) milling. With FIB milling we can shave neuronal processes and synapses to obtain 200-300 nm thick lamellar. Third, we are freezing synaptosomes isolated from the brain. Our goal with each cryo method is to develop the method to a point where we achieve results comparable to our HPF/FS techniques.
Our transsynaptic assembly project investigates intracellular structures linked by cleft-spanning structures. In this project, transcleft structures and all connected transmembrane and intracellular structures are segmented and analyzed in tomograms of synapses from high-pressure frozen, freeze-substituted neuronal cultures. In renderings, cleft-spanning structures typically make continuous connections from one intracellular compartment to the other, forming what we call transsynaptic assemblies.
This project has yielded several clear findings. First, nearly all transcleft objects have some intracellular component. Second, transsynaptic assemblies with large intracellular volumes and more than one intracellular component are very likely to be associated with synaptic vesicles. Third, transsynaptic assemblies share intracellular components and produce large domains of associated assemblies or just association domains. We believe association domains explain the underpinnings of the nanodomain phenomena and reveal a more complex picture of their composition and function, as our results show that less than half of assemblies associate with synaptic vesicles.
Further, we classified and enumerated over three thousand structures. We designed an algorithm to display a structure at random, prompt the user for a description, and then parse descriptions for common morphological elements. We were able to use this information to find common structures associated within assemblies. This work was published this year.
We hypothesize that there are functionally critical transsynaptic combinations of presynaptic, postsynaptic, and cleft molecules. Unfortunately, we do not have the technology to analyze the number of synapses necessary to confidently identify these with satisfying specificity. To get the number of structures necessary, we are pairing this project with the automated segmentation project.
Automated segmentation is crucial for the future of previously discussed projects and electron tomography in all forms. With our automated segmentation project, our goal is to accelerate the segmentation and visualization of synaptic structures with automation. We developed an automatic segmentation optimization method (ASOM). With ASOM, we are processing many large tomograms. For one project, ASOM segmented detailed structures of fragments isolated from sonicated and control PSDs imaged by cryo-EM. However, many structures within PSDs segmented by ASOM are still interconnected in complicated ways. To examine those structures more in detail, we improved ASOM further by adding watershed segmentation, widely used to separate connected structures automatically. This enabled the automatic segmentation of hundreds of tightly packed granular structures in intact PSDs into individual modules. These results were recently published.
Recently, we improved ASOM to automatically segment filaments connected to only the postsynaptic membrane in one step, revealing that PSD-95-like filaments can be segmented by automation. The improved ASOM automatically segments other distinct classes such as those connected to the presynaptic membrane, postsynaptic membranes, and vesicle membranes. Further, the automatic segmentation of transsynaptic components are consistent with those assemblies obtained by painstaking manual segmentation, demonstrating that this approach will contribute to expediting the segmentation of the assemblies.
We need a platform for users to apply ASOM algorithms. Most segmentation tools do not have the most basic functions of ASOM. Therefore, we have been developing our own software package that streamlines the entire tomography data pipeline, from image alignment to visualization. In addition to ASOM, we will integrate object classification algorithms based on machine learning and AI. Our goal is to decrease the time needed to fully analyze a tomogram from several months to a few days.
Recently, we successfully implemented an advanced reconstruction method called the Simultaneous Iterative Reconstruction Technique (SIRT), widely used for generating tomograms. Our method has improved accuracy and reduced noise for both conventional ET and cryo-ET. Our SIRT method produces EM tomograms more efficiently than IMOD while qualities of the tomograms were found to be equal to or better than those generated by IMOD. Also, we combined ASOM with skeletonization and found that ASOM automatically segmented distinct transsynaptic structures similar to those segmented by hand. More work is required to provide the same level of detail as hand segmentation.
Currently, we are combining the advanced ASOM algorithm with new AI algorithms that are potentially applicable to various subcellular structures. This project will make our software package a more efficient and robust segmentation platform for more varied structures, thus expanding the scope of our investigations to include large amounts of datasets and various experimental conditions.
进展总结:
该项目的一个关键优先事项是开发确定 PSD 断层图像中分子身份的方法。首先,我们开发了一种使用纳米抗体标记进行断层扫描的新技术。纳米抗体以高特异性直接与目标蛋白结合,这使我们能够在突触蛋白上使用金颗粒标记并直接在断层扫描中识别它们。我们有 PSD-95、CaMKII 和 Homer1b 的数据,我们正在完成这项工作并准备此类工作的初始出版物。其次,我们成功地使用 APEX2(一种可克隆的辣根过氧化物酶)微调了 EM 断层扫描的基因标记程序,该酶在过氧化氢存在的情况下催化 DAB 氧化成电子致密材料。 CaMKII 是 LTP 所需的激酶,也是大脑中最丰富的蛋白质。在通过暗场 STEM 断层扫描成像的大鼠海马神经元中使用这种 APEX2 方法,可以在树突棘的断层扫描重建中轻松识别单个 APEX2 标记的 CaMKII。因此,我们开始了解 CaMKII 在棘、膜上和 PSD 中的分布、它们的自我关联以及它们对突触活动的反应。我们正在准备出版这项工作。
我们在使用高压冷冻和冷冻替代(HPF/FS)表征突触结构方面取得了巨大进步。然而,HPF/FS 耗时且存在许多故障点,并且依赖染色来可视化结构。通过冷冻电镜断层扫描 (cryo-ET),我们可以用分辨率的轻微降低来换取易于准备和结构的纯粹可视化。我们正在与 NIH 冷冻电镜设施(NICE 和 MICEF)合作试验三种以冷冻为中心的方法。首先,我们在 NCI 300 kV Krios 冷冻电子显微镜下获取了超过 150 张断层图,特别是来自大鼠大脑的冷冻水合分离 PSD 或超声处理 PSD 碎片。其次,我们还使用低温聚焦离子束 (FIB) 铣削。通过 FIB 铣削,我们可以削去神经元突起和突触以获得 200-300 nm 厚的层状结构。第三,我们冷冻从大脑中分离出来的突触体。我们对每种冷冻方法的目标是开发该方法,使其达到与 HPF/FS 技术相当的结果。
我们的跨突触组装项目研究由跨裂结构连接的细胞内结构。在该项目中,在来自高压冷冻、冷冻替代神经元培养物的突触断层扫描图中,对跨裂结构以及所有连接的跨膜和细胞内结构进行分段和分析。在渲染图中,跨裂结构通常从一个细胞内区室到另一个细胞内区室进行连续连接,形成我们所说的跨突触组件。
该项目取得了一些明确的发现。首先,几乎所有经裂物体都具有一些细胞内成分。其次,具有大细胞内体积和超过一种细胞内成分的跨突触组件很可能与突触小泡有关。第三,跨突触组件共享细胞内组件并产生关联组件的大域或仅关联域。我们相信关联域解释了纳米域现象的基础,并揭示了其组成和功能的更复杂的图景,因为我们的结果表明,不到一半的组件与突触小泡相关。
进一步,我们对三千多个结构进行了分类和列举。我们设计了一种算法来随机显示结构,提示用户进行描述,然后解析常见形态元素的描述。我们能够使用此信息来查找程序集中关联的常见结构。这部作品于今年出版。
我们假设存在功能关键的突触前、突触后和裂隙分子的跨突触组合。不幸的是,我们没有技术来分析所需的突触数量,以自信地以令人满意的特异性识别这些突触。为了获得所需的结构数量,我们将此项目与自动分割项目配对。
自动分割对于之前讨论的项目和各种形式的电子断层扫描的未来至关重要。通过我们的自动化分割项目,我们的目标是通过自动化加速突触结构的分割和可视化。我们开发了一种自动分割优化方法(ASOM)。通过 ASOM,我们正在处理许多大型断层图。在一个项目中,ASOM 对从超声处理和冷冻电镜成像的控制 PSD 中分离出的碎片的详细结构进行了分段。然而,由 ASOM 分割的 PSD 内的许多结构仍然以复杂的方式互连。为了更详细地检查这些结构,我们通过添加分水岭分割进一步改进了 ASOM,分水岭分割广泛用于自动分离连接的结构。这使得能够将完整 PSD 中的数百个紧密堆积的颗粒结构自动分割成单独的模块。这些结果最近发表。
最近,我们改进了 ASOM,以一步自动分割仅连接到突触后膜的细丝,揭示了类似 PSD-95 的细丝可以通过自动化进行分割。改进的 ASOM 自动分割其他不同的类别,例如与突触前膜、突触后膜和囊泡膜相关的类别。此外,突触组件的自动分割与通过艰苦的手动分割获得的组件一致,表明这种方法将有助于加快组件的分割。
我们需要一个平台让用户应用ASOM算法。大多数分割工具都不具备ASOM最基本的功能。因此,我们一直在开发自己的软件包,以简化从图像对齐到可视化的整个断层扫描数据管道。除了ASOM之外,我们还将集成基于机器学习和AI的对象分类算法。我们的目标是将全面分析断层图像所需的时间从几个月缩短到几天。
最近,我们成功实现了一种先进的重建方法,称为同时迭代重建技术(SIRT),广泛用于生成断层图像。我们的方法提高了传统 ET 和冷冻 ET 的准确性并降低了噪音。我们的 SIRT 方法比 IMOD 更有效地生成 EM 断层图,同时断层图的质量等于或优于 IMOD 生成的质量。此外,我们将 ASOM 与骨架化相结合,发现 ASOM 自动分割不同的突触结构,类似于手工分割的结构。需要做更多的工作才能提供与手部分割相同级别的细节。
目前,我们正在将先进的 ASOM 算法与新的 AI 算法相结合,这些算法可能适用于各种亚细胞结构。该项目将使我们的软件包成为更高效、更强大的分割平台,适用于更多样化的结构,从而扩大我们的研究范围,包括大量数据集和各种实验条件。
项目成果
期刊论文数量(25)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Identifying individual scaffolding molecules in the postsynaptic density.
识别突触后密度中的单个支架分子。
- DOI:10.1017/s1431927608085449
- 发表时间:2008
- 期刊:
- 影响因子:0
- 作者:Chen,X;Winters,C;Azzam,R;Crocker,V;Li,X;Galbraith,J;Leapman,Rd;Reese,Ts
- 通讯作者:Reese,Ts
Neuropeptidergic integration of behavior in Trichoplax adhaerens, an animal without synapses.
- DOI:10.1242/jeb.162396
- 发表时间:2017-09-15
- 期刊:
- 影响因子:0
- 作者:Senatore A;Reese TS;Smith CL
- 通讯作者:Smith CL
Electron tomography on γ-aminobutyric acid-ergic synapses reveals a discontinuous postsynaptic network of filaments.
电子断层扫描在γ-氨基丁基酸性突触上揭示了细丝的突触后网络。
- DOI:10.1002/cne.23453
- 发表时间:2014-03
- 期刊:
- 影响因子:2.5
- 作者:Linsalata, Alexander E.;Chen, Xiaobing;Winters, Christine A.;Reese, Thomas S.
- 通讯作者:Reese, Thomas S.
Coordinated Feeding Behavior in Trichoplax, an Animal without Synapses.
- DOI:10.1371/journal.pone.0136098
- 发表时间:2015
- 期刊:
- 影响因子:3.7
- 作者:Smith CL;Pivovarova N;Reese TS
- 通讯作者:Reese TS
PSD-95 is required to sustain the molecular organization of the postsynaptic density.
- DOI:10.1523/jneurosci.5968-10.2011
- 发表时间:2011-04-27
- 期刊:
- 影响因子:0
- 作者:Chen X;Nelson CD;Li X;Winters CA;Azzam R;Sousa AA;Leapman RD;Gainer H;Sheng M;Reese TS
- 通讯作者:Reese TS
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Thomas S Reese其他文献
Thomas S Reese的其他文献
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