Automatic 3D Quantification of Synapse Distribution in Complex Dendritic Arbor

复杂树突乔木中突触分布的自动 3D 量化

• 批准号: 8574710
• 负责人: JIE ZHOU
• 金额: $ 46.12万
• 依托单位: NORTHERN ILLINOIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8574710
• 关键词: Academic Research Enhancement Awards; Algorithms; Area; Biology; Cells; Collaborations; Communities; Complex; Data; Dendrites; Detection; Development; Disease; Drosophila genus; Excitatory Synapse; Fluorescence; Generations; Genetic; Genetic Screening; Genetic Techniques; Goals; Hippocampus (Brain); Image; Image Analysis; Inhibitory Synapse; Investigation; Knowledge; Lead; Location; Machine Learning; Mammals; Manuals; Measures; Methodology; Methods; Microscopic; Microscopy; Modeling; Molecular; Morphologic artifacts; Morphology; Nervous system structure; Neurons; Neurosciences; Pattern; Pattern Recognition; Play; Public Health; Purkinje Cells; Pyramidal Cells; Research; Resolution; Role; Speed; Staging; Staining method; Stains; Surface; Synapses; System; Techniques; Testing; Three-Dimensional Image; Three-Dimensional Imaging; Trees; Variant; Work; base; cholinergic synapse; density; falls; genetic manipulation; genome-wide; graduate student; graphical user interface; high throughput technology; improved; in vivo; innovation; interdisciplinary approach; mutant; nervous system disorder; novel; open source; public health relevance; tool; undergraduate student; user-friendly

DESCRIPTION (provided by applicant): The subcellular distribution of synapses is critical for the assembly, function, and plasticity of the nervous system and plays a role in its disorders. Underlying molecular mechanisms, however, remain largely unknown. While advanced multidimensional images, in conjunction with single-cell genetic techniques, have afforded an unprecedented opportunity to understand synapse development at a new level, there is a knowledge gap in our capacity to effectively quantify subcellular synapses from large quantities of three-dimensional images. This is a significant problem and has hampered large-scale studies of the molecular mechanisms of synapse development, especially in neurons with complex arbor-such as Purkinje cells in mammals and lobula plate tangential cells (LPTC) in Drosophila-where existing approaches do not yield complete or robust synapse quantification for the entire dendritic tree and do not scale to efficient genetic screening. The objective of thi project is to bridge this gap by providing tools for quantitative investigation of subcellular synapse distribution and its molecular mechanisms using three-dimensional microscopy images. Specifically, our highly cross- disciplinary team will pursue two aims: (1) Develop automatic algorithms to analyze and quantify synapse distribution in the entire dendritic tree of neurons with complex arbor. Holistic and objective description of synapse density will enable automatic detection of mutant patterns. (2) Develop automatic algorithms to analyze and quantify synapse distribution in different parts of the entire dendritic tree of neurons with complex arbor. Efficient quantification at distinct subcellular locations will assist discovery of novel regulators for different subcellular parts. As a test case, we will use synapse distribution n Drosophila LPTC neurons, which are amenable to both genome-wide genetic screens and genetic manipulations with single-neuron resolution. We will develop reliable methods to characterize the density of inhibitory GABAergic and excitatory cholinergic synapses from three-dimensional fluorescence confocal images. Our algorithms will lead to the next level of mechanistic understanding that controls the subcellular distribution of inhibitory and excitatory synapses, and enable a wide range of quantitative analyses for other types of neurons with similar complexity. Powerful multichannel co-analysis and machine learning approaches will be used to improve synapse detection and subcellular compartment extraction for overcoming challenges in 3D confocal image, including staining artifacts and anisotropic resolution. Algorithms will be developed using a model-guided methodology that emphasizes efficiency for large volume 3D images during genetic screening. Pattern-recognition methods will be used to speed up proofreading of the synapse quantification results. A novel ordering strategy will be adapted for neurons of complex dendritic arbor to quantify subcellular synapses in a functionally meaningful way. The project will produce a set of open-source, extensible tools for automatic synapse quantification and proofreading, with friendly graphical-user interfaces, to serve the neuroscience community.

描述（由申请人提供）：突触的亚细胞分布对于神经系统的组装、功能和可塑性至关重要，并在其疾病中发挥作用。然而，潜在的分子机制在很大程度上仍然未知。虽然先进的多维图像，结合单细胞遗传技术，提供了一个前所未有的机会，了解突触的发展在一个新的水平，有一个知识差距，我们的能力，有效地量化亚细胞突触从大量的三维图像。这是一个重大的问题，阻碍了大规模的研究突触发育的分子机制，特别是在神经元复杂的树枝，如哺乳动物和小叶板切向细胞（LPTC）在果蝇的浦肯野细胞，现有的方法不产生完整的或强大的突触量化整个树突树，并不扩展到有效的遗传筛选。本项目的目标是通过三维显微镜图像为亚细胞突触分布及其分子机制的定量研究提供工具，从而弥补这一空白。具体而言，我们高度跨学科的团队将追求两个目标：（1）开发自动算法来分析和量化具有复杂乔木的神经元的整个树突树中的突触分布。对突触密度的整体和客观描述将使突变模式的自动检测成为可能。(2)开发自动算法来分析和量化具有复杂乔木的神经元的整个树突树的不同部分中的突触分布。在不同亚细胞位置的有效量化将有助于发现不同亚细胞部分的新型调节因子。作为一个测试案例，我们将使用突触分布n果蝇LPTC神经元，这是服从全基因组遗传筛选和遗传操作与单神经元分辨率。我们将开发可靠的方法来表征密度的抑制性GABA能和兴奋性胆碱能突触的三维荧光共聚焦图像。我们的算法将导致对控制抑制性和兴奋性突触的亚细胞分布的机械理解的下一个层次，并使具有类似复杂性的其他类型神经元的广泛定量分析成为可能。强大的多通道共分析和机器学习方法将用于改进突触检测和亚细胞区室提取，以克服3D共聚焦图像中的挑战，包括染色伪影和各向异性分辨率。算法将使用模型引导的方法来开发，该方法强调遗传筛选期间大体积3D图像的效率。模式识别方法将用于加快突触量化结果的校对。一种新的排序策略将适用于复杂的树突状乔木的神经元，以功能上有意义的方式量化亚细胞突触。该项目将产生一套开源的、可扩展的工具，用于自动突触量化和校对，并具有友好的图形用户界面，以服务于神经科学界。

项目成果

Bioimage Informatics for Big Data.

大数据生物图像信息学。

• DOI: 10.1007/978-3-319-28549-8_10
• 发表时间: 2016
• 期刊: Advances in anatomy, embryology, and cell biology
• 作者: Peng,Hanchuan;Zhou,Jie;Zhou,Zhi;Bria,Alessandro;Li,Yujie;Kleissas,DeanMark;Drenkow,NathanG;Long,Brian;Liu,Xiaoxiao;Chen,Hanbo
• 通讯作者: Chen,Hanbo