Name: automatic identification of dendritic branches and their orientation
Uploaded: 2021-09-17T13:45:00
Description: Watch this Scientific Journal Video about Automatic Identification of Dendritic Branches and their Orientation at JoVE.com

作者要感谢Orly Weiss博士准备的文化图像。

注意：以色列卫生部批准根据协议IL-218-01-21使用小鼠用于实验动物的道德使用。SOA仅与Windows 10和Python 3.9兼容。它以开源代码的形式提供：https://github.com/inbar2748/DendriteProject。在此链接中，还有一个自述文件。DM 文件，其中包含软件下载说明、软件网站的链接以及包含所有软件包所需版本信息的要求文件。那里还提供了使用该软件执行的分析的其他示例。
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迫切需要从2D图像中提取形态信息的有效策略，以跟上生物成像数据。虽然成像数据可以在几小时内生成，但对图像的深入分析需要很长时间。因此，图像处理显然已成为许多领域的主要障碍。这部分是由于数据的高度复杂性，特别是在处理生物样本时。此外，由于许多用户缺乏专门的编程和图像处理技能，因此需要自动化工具，以便以简单和用户友好的方式完成图像处理。这就是为什么SOA有望?...

树突形态发生是神经科学的核心主题，因为树突树的结构会影响神经元突触整合的计算特性1，2，3。此外，树突状支的形态异常和修饰与退行性和神经发育障碍有关4，5，6。在树突状分支更容易可视化的神经元培养物中，非姐妹树突状分支之间的相互作用调节沿分支突触聚集的部位和程度，这种行为可能会影响突触共性和可塑性7，8，9。因此，使用二维（2D）神经元培养物表征树突状树的形态参数有利于了解树突形态发生和单个神经元和神经网络的功能。然而，这是一项具有挑战性的任务，因为即使在"简化"的2D神经元培养物中，树突状分支也会形成复杂的网格。
已经开发了几种工具来自动跟踪和分析树突结构10，11，12，13。但是，这些工具中的大多数都是为3D神经元网络设计的，并且自然太复杂而无法与2D网络一起使用。相比之下，不太先进的形态分析工具通常涉及计算机辅助体力劳动的重要组成部分，这非常耗时且容易受到操作员偏差的影响14。现有的半自动工具，如'ImageJ'15 （NIH开源图像处理软件包，其中包含大量社区开发的生物图像分析工具），大大减少了用户的体力劳动。然而，在图像处理过程中仍然需要一些人工干预，并且分割的质量可能低于预期。
本文介绍了SOA，这是一个简单的自动化工具，允许对2D神经元网络中的树突分支进行直接分割和定向分析。SOA可以检测2D图像中的各种线状物体，并表征它们的形态特性。在这里，我们使用SOA对培养物中树突网络的2D荧光图像中的树突分支进行分段。该软件识别树突分支，并成功执行形态参数的测量，如平行度和空间分布。SOA可以很容易地用于分析其他细胞类型的细胞过程和研究非生物网络。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Matplotlib</td>
			<td>&#160;2002 - 2012 John Hunter, Darren Dale, Eric Firing, Michael Droettboom and the Matplotlib development team; 2012 - 2021 The Matplotlib development team.</td>
			<td>3.4.2</td>
			<td>a Python 2D plotting library</td>
		</tr>
		<tr>
			<td>matplotlib-scalebar</td>
			<td>Philippe Pinard</td>
			<td>0.7.2</td>
			<td>artist for matplotlib to display a scale bar</td>
		</tr>
		<tr>
			<td>NumPy</td>
			<td>The NumPy community.</td>
			<td>1.20.3</td>
			<td>fundamental package for scientific computing library</td>
		</tr>
		<tr>
			<td>OpenCV</td>
			<td>OpenCV team</td>
			<td>4.5.2.54</td>
			<td>Open Source Computer Vision Library</td>
		</tr>
		<tr>
			<td>PyCharm</td>
			<td>JetBrains</td>
			<td>2020.3.1 (Community Edition) version</td>
			<td>Build #PC-203.6682.86, built on December 18, 2020. Runtime version: 11.0.9.1+11-b1145.37 amd64. VM: OpenJDK 64-Bit Server VM by JetBrains s.r.o. Windows 10 10.0. Memory: 978M, Cores: 4</td>
		</tr>
		<tr>
			<td>PyQt5</td>
			<td>Riverbank Computing</td>
			<td>5.15.4</td>
			<td>manage the GUI</td>
		</tr>
		<tr>
			<td>python</td>
			<td>Python Software Foundation License</td>
			<td>3.9 version</td>
			<td></td>
		</tr>
		<tr>
			<td>Qt Designer</td>
			<td>The QT Company Ltd.</td>
			<td>5.11.1 version</td>
			<td></td>
		</tr>
		<tr>
			<td>scipy</td>
			<td>Community library project</td>
			<td>1.6.3</td>
			<td>Python-based ecosystem of open-source software for mathematics, science, and engineering</td>
		</tr>
		<tr>
			<td>Seaborn</td>
			<td>Michael Waskom.</td>
			<td>0.11.1</td>
			<td>Python&#39;s Statistical Data Visualization Library.</td>
		</tr>
		<tr>
			<td>Windows 10</td>
			<td>Microsoft</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Xlsxwriter</td>
			<td>John McNamara</td>
			<td>1.4.3</td>
			<td>Python module for creating Excel XLSX files</td>
		</tr>
	</tbody>
</table>

automatic identification of dendritic branches and their orientation

神经元树突树的结构在神经元中突触输入的整合中起着关键作用。因此，树突形态的表征对于更好地了解神经元功能至关重要。然而，树突树的复杂性，无论是孤立的还是位于神经元网络内时，都尚未完全理解。我们开发了一种新的计算工具 SOA （分割和定向分析），它允许从2D神经元培养物的荧光图像中自动测量树突分支的方向。用Python编写的SOA使用分割来区分树突分支和图像背景，并在每个分支的空间方向上积累一个数据库。然后，该数据库用于计算形态参数，例如网络中树突分支的方向分布和平行树突分支生长的流行率。获得的数据可用于检测树突的结构变化，以响应神经元活动以及生物和药理学刺激。

对培养物中树突状网络的图像进行了代表性分析。细胞被提取如Baranes 等人所描述的那样。16，17.简而言之，从产后大鼠的大脑中提取海马细胞，并在2D玻璃盖玻片上培养1-2周。然后将培养物固定并通过间接免疫荧光染色，使用针对树突状蛋白标志物微管相关蛋白2（MAP2）的抗体进行染色。使用荧光显微镜收集树突状网络的图像，并使用SOA处理10张?...

Watch this Scientific Journal Video about Automatic Identification of Dendritic Branches and their Orientation at JoVE.com

Automatic Identification of Dendritic Branches and their Orientation

展示的是一种计算工具，允许从2D荧光图像中简单直接地自动测量神经元树突分支的方向。

树枝状分支及其方向的自动识别

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology ...

SOA是一种自动化工具，具有用户友好的界面，用于从复杂的2D线网络的图像中识别，分割和提取重要的形态信息。工作流程简单直观，数据立即轻松获取。此外，由于SOA具有适应性和灵活性，因此可以针对其他应用程序扩展其分析能力。SOA可用于分析其他类型的2D网络，例如非神经细胞网络，细胞骨架生成的复杂结构以及非生物网络，如纳米管。打开网页，找到 SOA。zip 压缩文件夹，然后双击下载 zip 文件。通过右键单击并选择解压缩文件来解压缩文件夹。观察在目标地址文本框中打开的选项窗口中的提取路径，该文本框显示提取文件的路径。接下来，打开提取的 SOA 文件，然后双击 SOA.exe。等待黑色窗口打开，之后将出现应用程序。在 SOA 查看器上传菜单栏中，选择选择文件，然后从计算机文件中选择一个图像并单击它。点按"打开"，观察文件的路径，然后点按"下一步"。对于边缘的分割优化，请通过选择阈值并输入数字来调整显示的阈值。在合并线中，通过选择要合并的最小距离并输入数字来调整要合并的显示的最小距离，以及通过选择要合并的最小角度并输入数字来调整要合并的显示的最小角度。然后，单击创建预览分割图像并更改参数以实现最大程度的分割识别。如果需要更改属性，请单击关闭窗口按钮，然后在要合并的最小距离和角度内重新调整阈值。要创建输出文件，请按"确定"以可视化分析图中的分割图像。在出现的窗口中，选择保存 xlsx 文件的位置。插入文件名，然后选择"保存"并等待创建并保存包含数据的 xlsx 文件。若要在以前定义的视图之间来回导航，请使用前进和后退按钮。按下缩放按钮激活平移和缩放，然后将鼠标移动到所需位置。然后，通过按住鼠标左键并将其拖动到新位置来平移图形。松开鼠标按钮，图像中的选定点将显示在新位置。按住 X 或 Y 键以限制轴的运动。要缩放，请按住鼠标右键并将其拖动到新位置。向右移动以放大 x 轴，向左移动以在 x 轴上缩小。在上下运动中对 y 轴执行相同的操作。缩放时，请注意鼠标下方的点保持静止，以便可以放大或缩小该点。使用修饰键 X、Y 或 Control 将缩放比例分别限制为保留的 X、Y 或纵横比。要激活缩放至矩形模式，请单击缩放至矩形按钮。将光标放在图像上，然后按鼠标左键。按住按钮将鼠标拖动到新位置以定义矩形区域。使用子情节配置工具配置子情节的外观。要打开文件保存对话框，请单击保存按钮并以适当的格式保存文件。典型的SOA工作流程应用于用荧光抗MAP2抗体标记的树突状网络的代表性2D图像。SOA 的 GUI 允许将原始映像与分段映像进行比较，并提供对分割设置的任何更改的影响的实时监视。树突分支分为生长平行和非平行。分析完成后，将提取测试范围内的并行分支数量并将其绘制在频率图中。为了了解树突分支之间平行生长的程度是随机的还是定向的，将该图的结果与从模拟中与培养物中树突分支的线的随机生长数量相同的线中提取的结果进行比较。然后，SOA 测量平行分支之间的距离，以及平行和非平行树突分支的长度及其平均长度。为了确定是否存在优先生长方向，SOA显示树突状支流的生长角度的分布直方图，从而可以快速识别每组中的首选生长方向和特定的树突支。SOA 输出可用作执行更深入、更复杂分析的工具的数据库。

Research

JoVE Journal

Neuroscience

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons

在培养的中枢神经系统的神经元树突棘形态分析

Dendritic spines are the sites of the majority of excitatory connections within the brain, and form the post-synaptic 
compartment of synapses. These ...

科研

神经科学

Inducing Dendritic Growth in Cultured Sympathetic Neurons

诱导培养的交感神经元的树突状增长

The shape of the dendritic arbor determines the total synaptic input a neuron can receive 1-3, and influences the types and distribution of these inputs ...

Fast Micro-iontophoresis of Glutamate and GABA: A Useful Tool to Investigate Synaptic Integration

谷氨酸和GABA快速微离子导入：一个有用​​的工具，调查突触整合

One of the fundamental interests in neuroscience is to understand the integration of excitatory and inhibitory inputs along the very complex structure of ...

Analyzing Dendritic Morphology in Columns and Layers

分析树突状形态的列和图层

In many regions of the central nervous systems, such as the fly optic lobes and the vertebrate cortex, synaptic circuits are organized in layers and ...

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

嗜德维人神经元树突状树突状复杂度的定量分析

Dendrites are the branched projections of a neuron, and dendritic morphology reflects synaptic organization during the development of the nervous system. ...

Where You Cut Matters: A Dissection and Analysis Guide for the Spatial Orientation of the Mouse Retina from Ocular Landmarks

你在哪里切的问题: 眼地标性小鼠视网膜空间定位的解剖与分析指南

Accurately and reliably identifying spatial orientation of the isolated mouse retina is important for many studies in visual neuroscience, including the ...

Purification of the Dendritic Filopodia-rich Fraction

富德瑞迪蒂菲洛波迪亚成分的纯化

Dendritic filopodia are thin and long protrusions based on the actin filament, and they extend and retract as if searching for a target axon. When the ...

Morphological and Functional Evaluation of Axons and their Synapses during Axon Death in Drosophila melanogaster

Morphological and Functional Evaluation of Axons and their Synapses during Axon Death in Drosophila melanogaster

在果蝇黑色无gaster的Axon死亡期间，对Axon及其突触的形态和功能评估

Axon degeneration is a shared feature in neurodegenerative disease and when nervous systems are challenged by mechanical or chemical forces. However, our ...

3D Modeling of Dendritic Spines with Synaptic Plasticity

具有突触可塑性树突脊柱的三维建模

Computational modeling of diffusion and reaction of chemical species in a three-dimensional (3D) geometry is a fundamental method to understand the ...

Imaging Dendritic Spines in Caenorhabditis elegans

Imaging Dendritic Spines in Caenorhabditis elegans

秀丽隐杆线虫树突棘成像

Dendritic spines are specialized sites of synaptic innervation modulated by activity and serve as substrates for learning and memory. Recently, dendritic ...