首页> 外文期刊>Methods: A Companion to Methods in Enzymology >Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images.
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Automated reconstruction of three-dimensional neuronal morphology from laser scanning microscopy images.

机译:从激光扫描显微镜图像自动重建三维神经元形态。

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Experimental and theoretical studies demonstrate that both global dendritic branching topology and fine spine geometry are crucial determinants of neuronal function, its plasticity and pathology. Importantly, simulation studies indicate that the interaction between local and global morphologic properties is pivotal in determining dendritic information processing and the induction of synapse-specific plasticity. The ability to reconstruct and quantify dendritic processes at high resolution is therefore an essential prerequisite to understanding the structural determinants of neuronal function. Existing methods of digitizing 3D neuronal structure use interactive manual computer tracing from 2D microscopy images. This method is time-consuming, subjective and lacks precision. In particular, fine details of dendritic varicosities, continuous dendritic taper, and spine morphology cannot be captured by these systems. We describe a technique for automated reconstruction of 3D neuronal morphology from multiple stacks of tiled confocal and multiphoton laser scanning microscopy (CLSM and MPLSM) images. The system is capable of representing both global and local structural variations, including gross dendritic branching topology, dendritic varicosities, and fine spine morphology with sufficient resolution for accurate 3D morphometric analyses and realistic biophysical compartment modeling. Our system provides a much needed tool for automated digitization and reconstruction of 3D neuronal morphology that reliably captures detail on spatial scales spanning several orders of magnitude, that avoids the subjective errors that arise during manual tracing with existing digitization systems, and that runs on a standard desktop workstation.
机译:实验和理论研究表明,全局树突分支拓扑和精细的脊柱几何形状都是神经元功能,其可塑性和病理学的关键决定因素。重要的是,模拟研究表明,局部和全局形态特性之间的相互作用对于确定树突信息处理和诱导突触特异性可塑性至关重要。因此,以高分辨率重建和量化树突状过程的能力是理解神经元功能的结构决定因素的必要前提。现有的将3D神经元结构数字化的方法使用来自2D显微镜图像的交互式手动计算机跟踪。该方法耗时,主观且缺乏准确性。特别是,这些系统无法捕获树突静脉曲张,连续的树突锥度和脊柱形态的细节。我们描述了一种从多个平铺的共聚焦和多光子激光扫描显微镜(CLSM和MPLSM)图像堆栈中自动重建3D神经元形态的技术。该系统能够代表全局和局部结构变化,包括总体树状分支拓扑,树状静脉曲张和精细的脊柱形态,具有足够的分辨率,可进行精确的3D形态分析和现实的生物物理区室建模。我们的系统为自动数字化和重建3D神经元形态提供了急需的工具,该工具可以可靠地捕获跨越几个数量级的空间尺度上的细节,避免了在使用现有数字化系统进行手动跟踪时出现的主观误差,并且该误差可以在标准条件下运行桌面工作站。

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