The authors would like to thank Dr. Orly Weiss for the preparation of the culture images.

NOTE: The Israeli Ministry of Health approved the use of mice under protocol IL-218-01-21 for the ethical use of experimental animals. SOA is only compatible with Windows 10 and Python 3.9. It is available as an open-source code: https://github.com/inbar2748/DendriteProject.&#160;At this link, there is also a README.DM file that has directions for downloading the software, a link to the software&#39;s website, and a requirements file containing information on the required versions of all the packages. Additional examples...

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Effective strategies for extracting morphological information from 2D images are urgently required to keep up with biological imaging data. Although imaging data can be generated in hours, in-depth analysis of the images takes a long time. As a result, image processing has clearly become a major obstacle in many fields. This is due in part to the high complexity of the data, especially when dealing with biological samples. Furthermore, as many users lack specialized programming and image processing skills, automated tool...

Dendritic morphogenesis is a central subject in neuroscience, as the structure of the dendritic tree affects the computational properties of synaptic integration in neurons1,2,3. Moreover, morphological abnormalities and modifications in dendritic branches are implicated in degenerative and neuro-developmental disorders4,5,6. In neuronal cultures where dendritic ramification can be more readily visualized, the interactions between non-sister dendritic branches regulate the sites and extent of synaptic clustering along the branches, a behavior that may affect synaptic coactivity and plasticity7,8,9. Therefore, characterization of the morphological parameters of the dendritic tree using two-dimensional (2D) neuronal cultures is advantageous for understanding dendritic morphogenesis and functionality of single and networks of neurons. Yet, this is a challenging task because dendritic branches form a complex mesh even in &#34;simplified&#34; 2D neuronal cultures.
Several tools have been developed to automatically trace and analyze dendritic structures10,11,12,13. However, most of these tools are designed for 3D neuronal networks and are naturally too complex to use with 2D networks. In contrast, less advanced morphological analysis tools typically involve a significant component of computer-assisted manual labor, which is very time-consuming and susceptible to operator bias14. Existing semi-automatic tools, such as &#39;ImageJ&#39;15 (an NIH open-source image processing package with a vast collection of community-developed biological image analysis tools), largely reduce user manual labor. However, some manual interventions are still needed during image processing, and the quality of the segmentation can be less than desirable.
This paper presents the SOA, a simple automated tool that allows direct segmentation and orientation analysis of dendritic branches within 2D neuronal networks. The SOA can detect various line-like objects in 2D images and characterize their morphological properties. Here, we used the SOA for segmenting dendritic branches in 2D fluorescence images of dendritic networks in culture. The software identifies the dendritic branches and successfully performs measurements of morphological parameters such as parallelism and spatial distribution. The SOA can be easily adapted for the analysis of cellular processes of other cell types and for studying non-biological networks.

<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>
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		<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>
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			<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>
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		<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

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology of dendrites is essential for a better understanding of neuronal function. However, the complexity of dendritic trees, both when isolated and especially when located within neuronal networks, has not been completely understood. We developed a new computational tool, SOA (Segmentation and Orientation Analysis), which allows automatic measurement of the orientation of dendritic branches from fluorescence images of 2D neuronal cultures. SOA, written in Python, uses segmentation to distinguish dendritic branches from the image background and accumulates a database on the spatial direction of each branch. The database is then used to calculate morphological parameters such as the directional distribution of dendritic branches in a network and the prevalence of parallel dendritic branch growth. The data obtained can be used to detect structural changes in dendrites in response to neuronal activity and to biological and pharmacological stimuli.

A representative analysis was performed on images of dendritic networks in culture. Cells were extracted as described by Baranes et al.16,17. Briefly, hippocampal cells were extracted from the brains of postnatal rats and cultivated on 2D glass coverslips for 1-2 weeks. The cultures were then fixed and stained through indirect immunofluorescence using an antibody against the dendritic protein marker, microtubule-associated protein 2 (MAP2). Images of den...

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

Presented is a computational tool that allows simple and direct automatic measurement of orientations of neuronal dendritic branches from 2D fluorescence images.

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

The SOA is an automated tool that has a user-friendly interface, for identification, segmentation, and extraction of important morphological information from images of complex 2D line networks. The work process is simple and intuitive and the data is obtained immediately and easily. Moreover, because SOA is adaptable and flexible, its analytic capacity can be expanded for other applications.SOA can be used for the analysis of other types of 2D networks, such as networks of non-neural cells, complex structures generated by the cytoskeleton, and non-biological networks, like nanotubes. Open the webpage, find the SOA. zip zipped folder, and download the zip file by double clicking.Unzip the folder by right clicking and selecting extract files. Observe the extraction path in the options window that opens in the destination address text box that displays the path for the extracted files. Next, open the extracted SOA file and double click on SOA.exe.Wait for a black window to open, after which the application will appear. In the SOA viewer upload menu bar, select choose file, then choose an image from the computer files and click on it. Click open, observe the file's path, then click next.For segmentation optimization in edges, adjust the threshold for the display by selecting threshold and entering a number. In merge lines, adjust the minimum distance to merge for the display by selecting the min distance to merge and entering a number, and the minimum angle to merge for the display by selecting the min angle to merge and entering a number. Then, click on create preview segmentation image and change the parameters to achieve maximum identification of segments.If properties need to be changed, click on the close window button and readjust the threshold in the minimum distance and angle to merge. To create the output files, press okay to visualize the segmentation images in the analyzing graphs. In the window that appears, select a location where the xlsx file will be saved.Insert a file name, then choose save and wait for the xlsx file with data to be created and saved. To navigate back and forth between previously defined views, use the forward and back buttons. Activate panning and zooming by pressing the zoom button, then move the mouse to the desired location.Then, pan the figure by pressing and holding the left mouse button while dragging it to a new position. Release the mouse button and the selected point in the image will appear in the new position. Hold down the X or Y keys to restrict the motion of the axes.To zoom, hold down the right mouse button and drag it to a new location. Move right to zoom in on the x-axis and move left to zoom out on the x-axis. Do the same for the y-axis in up and down motions.When zooming, note that the point under the mouse remains stationary, making it possible to zoom in or out around that point. Use the modifier keys, X, Y, or Control, to limit the zoom to the X, Y, or aspect ratio preserve respectively. To activate the zoom to rectangle mode, click the zoom to rectangle button.Place the cursor over the image and press the left mouse button. Drag the mouse to a new location while holding the button to define a rectangular region. Use the subplot configuration tool to configure the appearance of the subplot.To open a file save dialogue, click the save button and save the file in the appropriate formats. A typical SOA workflow is applied to a representative 2D image of a dendritic network labeled with a fluorescent anti-MAP2 antibody. The SOA's GUI enables comparison of the original image to the segmented image and provides realtime monitoring of the effect of any changes to the segmentation settings.The dendritic branches are classified as growing parallel and non-parallel. Once the analysis is complete the number of parallel branches within the tested range is extracted and plotted in a frequency graph. To understand whether the extent of parallel growth among the dendritic branches is random or directed, the results of this graph are compared to those extracted from simulation of random growth of lines of the same number as those of the dendritic branches in the cultures.The SOA then measures the distances among the parallel branches, as well as the lengths of the parallel and non-parallel dendritic branches and their average lengths. To determine whether preferential growth directions exist, the SOA displays a distribution histogram of the growth angles of the dendritic branches, allowing rapid identification of preferred growth directions and specific dendritic branches in each group. The SOA output can be used as a database for tool that perform more in-depth and complex analyses.

automatic-identification-of-dendritic-branches-and-their-orientation

Research

JoVE Journal

Neuroscience

1.8K ビュー.  Ariel University. SOAは、複雑な2Dラインネットワークの画像から重要な形態情報の識別、セグメンテーション、抽出を行うための、ユーザーフレンドリーなインターフェースを備えた自動化ツールです。作業プロセスはシンプルで直感的で、データはすぐに簡単に取得されます。また、SOAは適応性と柔軟性があるため、他のアプリケーションに対応して分析能力を拡張することができます...