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 of analysis performed using the software have been provided there as well.&#160;
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62679/62679fig01.jpg" /> 
Figure 1: SOA workflow for segmentation and growth direction analysis. Shown are the processing steps of fluorescent images of dendritic networks and data analysis. The 2D image is uploaded, segmented (in two steps: dendritic branches are detected as lines, and then the relevant lines are merged), and the spatial information of each dendritic branch is obtained. The data are collected for all the dendritic branches in the image. Finally, the data are analyzed to give the desired morphological parameters. Abbreviation: SOA = segmentation and orientation analysis. <a href="https://www.jove.com/files/ftp_upload/62679/62679fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
1. Open the SOA application.
<ol>
	<li>Open the URL address: https://mega.nz/folder/bKZhmY4I#4WAaec4biiGt4_1lJlL4WA, find the SOA.zip zipped folder, and download the ZIP file by double-clicking.</li>
	<li>Unzip the folder by right-clicking on SOA.zip and choose Extract Files. Observe the Extraction Path and Options window that opens and the Destination Address text box that displays the path for the extracted files. To extract to a different location, click on one of the folders in the window&#39;s right panel to make it the destination folder. Click OK&#160;to extract the files to that folder.</li>
	<li>Open the extracted SOA file and double-click on SOA.exe. Wait for a black window to open, after which the application will appear.</li>
</ol>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62679/62679fig02.jpg" /> 
Figure 2: Example of a workflow using the SOA&#39;s GUI. Left column: GUI sections of the workflow. Middle column: image of a dendritic network, processed during the workflow (Scale bar: 20 &#181;m). Right column: magnification of the area marked by a red rectangle in the images of the middle column (Scale bar: 4 &#181;m).&#160;Step 1: Selection and uploading of an image. Step 2: The first stage of segmentation is the detection of lines that represent the identified dendritic branches. Step 3: The second stage of segmentation is the proximity-based merger of segment lining in individual dendritic branches. The settings of all steps can be modified. Abbreviations: SOA = segmentation and orientation analysis; GUI = graphical user interface. <a href="https://www.jove.com/files/ftp_upload/62679/62679fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Open an image to analyze.
<ol>
	<li>In the SOA Viewer Upload menu bar | select Choose File&#160;| choose an image from the computer files | click on it (.png .jpg .tif .bmp files only) | Open | observe the&#160;path of the file | Next.</li>
</ol>
3. Segmentation optimization
NOTE: In the SOA Viewer Properties menu bar, change the values of the selected parameters to adjust the segmentation process settings. A detailed description of the parameters, such as the Threshold, is given in the Supplemental Material.
<ol>
	<li>In Edges, adjust the threshold for the display by selecting the Threshold&#160;and entering a number. 
	NOTE: The lower the number for the threshold, the more lines are detected. Threshold is a number that ranges from 0 to 255. The default value has been set to 0.</li>
	<li>In Merge Lines:
	<ol>
		<li>Adjust the minimum distance to merge for the display by selecting the Min distance to merge and entering a number. 
		NOTE: The Min distance to merge ranges from 0 to 30 pixels. The default value is set to 20.</li>
		<li>Adjust the minimum angle to merge for the display by selecting the Min angle to merge and entering a number. 
		NOTE: The Min angle to merge ranges from 0 to 30&#176;. The default value is set to 10.</li>
	</ol>
	</li>
	<li>Click on Create Preview Segmentation Image. 
	NOTE: A preview image of the segmentation results will be displayed according to the updated values. In addition, the number of lines before merging and the number of lines after merging will be displayed.</li>
	<li>Change the parameters to achieve maximum identification of segments. If there is a need to change the Properties, click on the Close window button and follow steps 3.1-3.4.</li>
</ol>
4. Create the output files.
<ol>
	<li>Press OK to visualize the segmentation images and the analyzing graphs. Observe the window that appears for selecting a location where the .xlsx file will be saved.</li>
	<li>insert a file name | Choose Save | wait for the .xlsx file with data to be created and saved. 
	&#8203;NOTE: In addition to the .xlsx file, the following files will be automatically displayed: a file that presents the original image, the line recognition image, the final image of the segmentation, and three analysis graphs.</li>
</ol>
5. Navigation toolbar
NOTE: A navigation toolbar is included in all figure windows and can be used to navigate through the data set. Each of the buttons at the bottom of the toolbar is described below.
<ol>
	<li>To navigate back and forth between previously defined views, use the Forward and Back buttons. 
	NOTE: The Home, Forward, and Back buttons are similar to the Home, Forward, and Back controls on a web browser. Home returns to the default screen, the original image.</li>
	<li>Use the Zoom&#160;button to pan and zoom. To activate panning and zooming, press the Zoom button, then move the mouse to a desired location in the image.
	<ol>
		<li>To pan the figure, press and hold 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. While panning, hold down the x or y keys to restrict the motion to the x or y axes, respectively.</li>
		<li>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 and up/down motions. When zooming, note that the point under the mouse remains stationary, allowing 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.</li>
	</ol>
	</li>
	<li>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. 
	NOTE: The axes view limits will be zoomed to the defined region when the left mouse button is pressed. The axes view limits will be zoomed out when the right mouse button is pressed, placing the original axes in the defined region.</li>
	<li>Use the Subplot-configuration tool to configure the appearance of the subplot. 
	NOTE: The left, right, top, and bottom sides of the subplot, as well as the space between rows and columns, can be stretched or compressed.</li>
	<li>To open a file save dialog, click the Save button and save the file in the following formats: .png, .ps, .eps, .svg, or .pdf.</li>
</ol>

<ol>
	<li>Ferrante, M., Migliore, M., Ascoli, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+impact+of+dendritic+branch-point+morphology.">Functional impact of dendritic branch-point morphology.</a> Journal of Neuroscience. 33 (5), 2156-2165 (2013).</li><li>Spruston, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pyramidal+neurons:+dendritic+structure+and+synaptic+integration.">Pyramidal neurons: dendritic structure and synaptic integration.</a> Nature Reviews Neuroscience. 9 (3), 206-221 (2008).</li><li>Chklovskii, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synaptic+Connectivity+and+Neuronal+Morphology:+Two+Sides+of+the+Same+Coin.">Synaptic Connectivity and Neuronal Morphology: Two Sides of the Same Coin.</a> Neuron. 43 (5), 609-617 (2004).</li><li>Chapleau, C., Larimore, J., Theibert, A., Pozzo-Miller, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modulation+of+dendritic+spine+development+and+plasticity+by+BDNF+and+vesicular+trafficking:+fundamental+roles+in+neurodevelopmental+disorders+associated+with+mental+retardation+and+autism.">Modulation of dendritic spine development and plasticity by BDNF and vesicular trafficking: fundamental roles in neurodevelopmental disorders associated with mental retardation and autism.</a> Journal of Neurodevelopmental Disorders. 1 (3), 185-196 (2009).</li><li>Irwin, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dendritic+Spine+Structural+Anomalies+in+Fragile-X+Mental+Retardation+Syndrome.">Dendritic Spine Structural Anomalies in Fragile-X Mental Retardation Syndrome.</a> Cerebral Cortex. 10 (10), 1038-1044 (2000).</li><li>Kaufmann, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dendritic+anomalies+in+disorders+associated+with+mental+retardation.">Dendritic anomalies in disorders associated with mental retardation.</a> Cerebral Cortex. 10 (10), 981-991 (2000).</li><li>Pinchas, M., Baranes, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dendritic+branch+intersections+are+structurally+regulated+targets+for+efficient+axonal+wiring+and+synaptic+clustering.">Dendritic branch intersections are structurally regulated targets for efficient axonal wiring and synaptic clustering.</a> PLoS ONE. 8 (12), 82083 (2013).</li><li>Cove, J., Blinder, P., Baranes, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Contacts+among+non-sister+dendritic+branches+at+bifurcations+shape+neighboring+dendritic+branches+and+pattern+their+synaptic+inputs.">Contacts among non-sister dendritic branches at bifurcations shape neighboring dendritic branches and pattern their synaptic inputs.</a> Brain Research. 1251, 30-41 (2009).</li><li>Blinder, P., Cove, J., Foox, M., Baranes, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Convergence+among+non-sister+dendritic+branches:+An+activity-controlled+mean+to+strengthen+network+connectivity.">Convergence among non-sister dendritic branches: An activity-controlled mean to strengthen network connectivity.</a> PLoS ONE. 3 (11), 3782 (2008).</li><li>Glaser, J., Glaser, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuron+imaging+with+neurolucida+-+PC-based+system+for+image+combining+microscopy.">Neuron imaging with neurolucida - PC-based system for image combining microscopy.</a> Computerized Medical Imaging and Graphics. 14 (5), 307-317 (1990).</li><li>Scorcioni, R., Polavaram, S., Ascoli, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=L-Measure:+a+web-accessible+tool+for+the+analysis,+comparison+and+search+of+digital+reconstructions+of+neuronal+morphologies.">L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies.</a> Nature Protocols. 3 (5), 866-876 (2008).</li><li>Torben-Nielsen, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+efficient+and+extendable+python+library+to+analyze+neuronal+morphologies.">An efficient and extendable python library to analyze neuronal morphologies.</a> Neuroinformatics. 12 (4), 619-622 (2014).</li><li>Parekh, R., Ascoli, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neuronal+morphology+goes+digital:+A+research+hub+for+cellular+and+system+neuroscience.">Neuronal morphology goes digital: A research hub for cellular and system neuroscience.</a> Neuron. 78 (1), 206 (2013).</li><li>heng, J., Zhou, X., Sabatini, B. L., Wong, S. T. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NeuronIQ:+A+novel+computational+approach+for+automatic+dendrite+SPINES+detection+and+analysis.">NeuronIQ: A novel computational approach for automatic dendrite SPINES detection and analysis.</a> 2007 IEEE/NIH Life Science Systems and Applications Workshop. , 168-171 (2007).</li><li>Image processing and analysis in Java. NIH. ImageJ Available from: <a target="_blank" href="https://imagej.nih.gov/ij">https://imagej.nih.gov/ij</a> (2021)</li><li>Peretz, H., Talpalar, A. E., Vago, R., Baranes, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Superior+survival+and+durability+of+neurons+and+astrocytes+on+3-dimensional+aragonite+biomatrices.">Superior survival and durability of neurons and astrocytes on 3-dimensional aragonite biomatrices.</a> Tissue Engineering. 13, 461-472 (2007).</li><li>Morad, T. I., Hendler, R. M., Weiss, O. E., Canji, E. A., Merfeld, I., Dubinsky, Z., Minnes, R., Francis, Y. I., Baranes, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gliosis+of+astrocytes+cultivated+on+coral+skeleton+is+regulated+by+the+matrix+surface+topography.">Gliosis of astrocytes cultivated on coral skeleton is regulated by the matrix surface topography.</a> Biomedical Materials. 14 (4), 045005 (2019).</li></ol>

The authors declare that they have no competing financial interests.

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 tools that allow image processing to be done in an easy and user-friendly manner are needed. That is why SOA is expected to be useful. The representative results demonstrate the automated segmentation that enables identification of the dendritic branches in the image in a few simple steps, despite the complexity of the dendritic network. The work process is simple and intuitive, and diverse spatial information is obtained immediately and without effort. Several algorithms were employed to detect individual dendritic branches in the fluorescence images and conduct multiple analyses on the results. A detailed description of the algorithms can be found in the Supplemental Material and information on the system requirements. This paper will briefly present the algorithms and their role in the software.
Segmentation 
Segmentation was the most challenging part of this project. In segmentation, a digital image is converted into several segments (image objects). The aim of segmentation is to identify objects in the image and thus make them more understandable and available. Here, segmentation was utilized to identify dendritic branches and isolate them from the background. The image segmentation process was split into two stages: the first stage involved the detection of dendritic branches as lines, and the second stage involved merging related lines based on distance and direction criteria set by the user.
Dendritic information and analysis 
Information on the location, angle, and length of each line detected was collected. The software performs the following analyses on the data obtained from all the lines identified in the image: 1. Dendritic Branch Classification (Parallel/Non-Parallel) 2. The average length of parallel vs. non-parallel dendritic branches was measured. 3. Angular distribution measurement and display 4. Distance measurement between parallel dendritic branches
The SOA&#39;s user interface allows users to upload an image from the computer&#39;s files. It also allows for adjustments of segmentation settings. Because each dendritic network image is unique, we suggest &#34;tinkering&#34; with the settings to achieve the best possible segmentation. The user interface allows for comparison of the original and the segmented images and immediate monitoring of the effect of any change in the settings on the segmentation.
After modifying all settings, SOA creates the final figure of the segmentation that shows each of the identified dendritic branches. The SOA generates graphs of the analyses performed and a .xlsx file with all the data.
The output of the SOA can be utilized as a starting point for the input of tools for further analysis. For example, we are now developing software that calculates the average parallelism value for dendrites that have received various treatments using SOA output on a large number of images.
Summary 
SOA is an automated tool for identification, segmentation, and extraction of important morphological information from images of complex 2D line networks and has a user-friendly and intuitive interface. In this work, the use of SOA was introduced through an example of analysis of dendritic networks. SOA can be used for the analysis of other types of 2D cellular networks, such as networks of different neural and non-neural cells, intracellular complex structures such as those generated by the cytoskeleton and non-biological networks, e.g., nanotubes and more. SOA was developed for a very specific purpose, and it is important to know its advantages and limitations. SOA&#39;s limitations include the fact that it is only suitable for 2D image analysis and not for 3D image analysis. SOA can only be used to analyze images with objects that are line-like. Moreover, the information obtained from the software is limited to the spatial information of the identified dendrites and to the specific analyses described here. Additional analyses are not performed by the SOA. The software&#39;s key advantages are its simplicity and user-friendliness. The software allows complex images to be analyzed quickly and in a few easy steps. Moreover, SOA is flexible and readily adjustable; hence, its analytic capacity can be expanded beyond morphometry and be beneficial for other applications.

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>
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		<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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		<tr>
			<td>PyQt5</td>
			<td>Riverbank Computing</td>
			<td>5.15.4</td>
			<td>manage the GUI</td>
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		<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>
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		<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 dendritic networks were collected using a fluorescence microscope, and 10 images were processed using SOA.
Figure 1 shows a typical SOA workflow for the analysis of a dendritic network. The input is a 2D fluorescent microscope image. Image segmentation is performed in two stages: the first stage is the identification of dendritic branches as lines, and the second stage is merging of relevant lines according to distance and direction criteria determined by the user. After segmentation, spatial information is collected for each identified dendritic branch. Then, the following information is extracted from the data of all dendritic branches in the image: Parallel/Non-Parallel Classification, average length of parallel vs. non-parallel dendritic branches, distance between parallel dendritic branches, and angular distribution.
Figure 2 illustrates the workflow laid out in Figure 1 applied to a representative 2D image of a dendritic network labeled with a fluorescent anti-MAP2 antibody. A Graphical User Interface (GUI) was developed in which the user can select and upload an image out of a set of files (Step 1). Then, the user can modify the Edges settings (Step 2) and Merge Lines settings (Step 3). Following that, lines representing the identified dendritic branches are detected (Step 2) and then merged, based on the proximity of the segmented lines and their directional differences (Step 3). The GUI enables the comparison of the original image to the segmented image (Step 3) and provides real-time monitoring of the effect of any changes to the segmentation settings. The spatial information collected is then analyzed, and the results are presented as graphs or tables (Figure 3, Figure 4, Figure 5, and Figure 6)
Figure 3 demonstrates how dendritic branches are classified as growing parallel (Figure 3A) and non-parallel (Figure 3B). All segments classified as &#34;non-parallel&#34; are not parallel to any other segment. Because dendritic branches do not actually extend in straight lines, there was a need to provide some degree of freedom for the definition of parallel growth. To achieve that, the orientation of a particular branch was measured, and then a range of angles around the measured orientation was permitted for parallelism. This range is fixed for each image and depends on the number of lines detected; however, it cannot exceed 10&#176; (a detailed description of the parallelism sorting process is given in Supplemental Material/Analysis, section 1). Then, the fitness of the orientation of all other branches to this angle range was examined. Once the analysis was completed, the number of parallel branches within the tested range was extracted and plotted in a frequency graph (Figure 3C).
To understand whether the extent of parallel growth among the dendritic branches is random or directed, the results of the graph in Figure 3C were compared to those extracted from simulation of random growth of lines of the same number as those of the dendritic branches in the cultures (Figure 3D). SOA then measures the distances among the parallel branches (Figure 4) as well as the lengths of parallel and non-parallel dendritic branches. Figure 5 displays bar graphs of the lengths of the non-parallel dendritic branches (Figure 5A) and parallel dendritic branches (Figure 5B) and their average lengths. To determine whether preferential growth directions exist, SOA displays a distribution histogram of the growth angles of the dendritic branches (Figure 6). Such a presentation allows for rapid identification of preferred growth directions and identification of specific dendritic branches in each group (by ID number) (Figure 6A).
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62679/62679fig03.jpg" /> 
Figure 3: Classification of dendritic branch parallel growth vs. random simulation. Dendritic branches of the image in Figure 2B identified by SOA were classified as (A) parallel and (B) non-parallel. (C) The number of parallel dendritic branches in each angle range checked was collected and divided into groups of pairs/triples/quartets as explained in Supplemental Material/Analysis, section 1. The occurrence frequency of each group is shown in the graph. (D) The results of grouping parallel lines extracted from a random line distribution simulation. Abbreviation: SOA = segmentation and orientation analysis. <a href="https://www.jove.com/files/ftp_upload/62679/62679fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62679/62679fig04.jpg" /> 
Figure 4: SOA measurement of the distance among parallel dendritic branches. Shown is an example of how SOA displays the distance among branches grown in parallel. Each dendrite detected receives a unique number (ID number). The SOA measures the minimum distance between each pair of parallel dendrites. A detailed description can be found in Supplemental Material/Analysis, section 1. Examples: 1. Dendritic branch ID=2 is at a distance of 60 &#181;m from another branch. 2. Dendrite ID=17 has two parallel branches at distances of 60 &#181;m and 13.7 &#181;m. Abbreviation: SOA = segmentation and orientation analysis. <a href="https://www.jove.com/files/ftp_upload/62679/62679fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62679/62679fig05.jpg" /> 
Figure 5: SOA&#39;s display of the length distribution of parallel vs. non-parallel dendritic branches. Shown is a frequency plot distribution of&#160;SOA&#39;s comparison of the lengths of (A) non-parallel and (B) parallel dendritic branches, as well as the average lengths. Abbreviation: SOA = segmentation and orientation analysis. <a href="https://www.jove.com/files/ftp_upload/62679/62679fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62679/62679fig06.jpg" /> 
Figure 6: SOA&#39;s display of the angular distribution of dendritic branches. (A) A graphical representation of each identified line according to its growth angle. (B) A display of A that allows quick recognition of preferred growth directions. Combining A and B, each dendritic branch can be allocated to a specific growth direction category. This form of representation of the distribution of dendritic growth directions allows rapid identification of preferred growth directions (B: the higher the column, this growth direction is more preferred) and directions in which dendrites do not grow (B: at an angle where there is no column, dendrites do not grow in the same direction). Abbreviation: SOA = segmentation and orientation analysis. <a href="https://www.jove.com/files/ftp_upload/62679/62679fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Material. <a href="https://www.jove.com/files/ftp_upload/62679/Supplemental Material.docx" target="_blank">Please click here to download this File.</a>

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 ...

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

Universidad San Sebastian

Research

JoVE Journal

Neuroscience

1.8K Views.  Ariel University. Presented is a computational tool that allows simple and direct automatic measurement of orientations of neuronal dendritic branches from 2D fluorescence images. 

Video: Automatic Identification of Dendritic Branches and their Orientation

Inducing Dendritic Growth in Cultured Sympathetic Neurons

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 4-6. Altered patterns of dendritic growth and plasticity are associated with impaired neurobehavioral function in experimental models 7, and are thought to contribute to clinical symptoms observed in both neurodevelopmental disorders 8-10 and neurodegenerative diseases 11-13. Such observations underscore the functional importance of precisely regulating dendritic morphology, and suggest that identifying mechanisms that control dendritic growth will not only advance understanding of how neuronal connectivity is regulated during normal development, but may also provide insight on novel therapeutic strategies for diverse neurological diseases.
Mechanistic studies of dendritic growth would be greatly facilitated by the availability of a model system that allows neurons to be experimentally switched from a state in which they do not extend dendrites to one in which they elaborate a dendritic arbor comparable to that of their in vivo counterparts. Primary cultures of sympathetic neurons dissociated from the superior cervical ganglia (SCG) of perinatal rodents provide such a model. When cultured in defined medium in the absence of serum and ganglionic glial cells, sympathetic neurons extend a single process which is axonal, and this unipolar state persists for weeks to months in culture 14,15. However, the addition of either bone morphogenetic protein-7 (BMP-7) 16,17 or Matrigel 18 to the culture medium triggers these neurons to extend multiple processes that meet the morphologic, biochemical and functional criteria for dendrites. Sympathetic neurons dissociated from the SCG of perinatal rodents and grown under defined conditions are a homogenous population of neurons 19 that respond uniformly to the dendrite-promoting activity of Matrigel, BMP-7 and other BMPs of the decapentaplegic (dpp) and 60A subfamilies 17,18,20,21. Importantly, Matrigel- and BMP-induced dendrite formation occurs in the absence of changes in cell survival or axonal growth 17,18.
Here, we describe how to set up dissociated cultures of sympathetic neurons derived from the SCG of perinatal rats so that they are responsive to the selective dendrite-promoting activity of Matrigel or BMPs.

We describe a protocol for using bone morphogenetic protein-7 (BMP-7) or Matrigel to selectively induce dendritic growth in primary sympathetic neurons dissociated from the superior cervical ganglia (SCG) of perinatal rats.

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

One of the fundamental interests in neuroscience is to understand the integration of excitatory and inhibitory inputs along the very complex structure of the dendritic tree, which eventually leads to neuronal output of action potentials at the axon. The influence of diverse spatial and temporal parameters of specific synaptic input on neuronal output is currently under investigation, e.g. the distance-dependent attenuation of dendritic inputs, the location-dependent interaction of spatially segregated inputs, the influence of GABAergig inhibition on excitatory integration, linear and non-linear integration modes, and many more.
With fast micro-iontophoresis of glutamate and GABA it is possible to precisely investigate the spatial and temporal integration of glutamatergic excitation and GABAergic inhibition. Critical technical requirements are either a triggered fluorescent lamp, light-emitting diode (LED), or a two-photon scanning microscope to visualize dendritic branches without introducing significant photo-damage of the tissue. Furthermore, it is very important to have a micro-iontophoresis amplifier that allows for fast capacitance compensation of high resistance pipettes. Another crucial point is that no transmitter is involuntarily released by the pipette during the experiment.
Once established, this technique will give reliable and reproducible signals with a high neurotransmitter and location specificity. Compared to glutamate and GABA uncaging, fast iontophoresis allows using both transmitters at the same time but at very distant locations without limitation to the field of view. There are also advantages compared to focal electrical stimulation of axons: with micro-iontophoresis the location of the input site is definitely known and it is sure that only the neurotransmitter of interest is released. However it has to be considered that with micro-iontophoresis only the postsynapse is activated and presynaptic aspects of neurotransmitter release are not resolved. In this article we demonstrate how to set up micro-iontophoresis in brain slice experiments.

In this article we introduce fast micro-iontophoresis of neurotransmitters as a technique to investigate integration of postsynaptic signals with high spatial and temporal precision.

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

Assessment of Dendritic Arborization in the Dentate Gyrus of the Hippocampal Region in Mice

Dendritic arborization has been shown to be a reliable marker for examination of structural and functional integrity of neurons. Indeed, the complexity and extent of dendritic arborization correlates well with the synaptic plasticity in these cells. A reliable method for assessment of dendritic arborization is needed to characterize the deleterious effects of neurological disorders on these structures and to determine the effects of therapeutic interventions. However, quantification of these structures has proven to be a formidable task given their complex and dynamic nature. Fortunately, sophisticated imaging techniques can be paired with conventional staining methods to assess the state of dendritic arborization, providing a more reliable and expeditious means of assessment. Below is an example of how these imaging techniques were paired with staining methods to characterize the dendritic arborization in wild type mice. These complementary imaging methods can be used to qualitatively and quantitatively assess dendritic arborization that span a rather wide area within the hippocampal region.

We describe two methods for visualization and quantification of dendritic arborization in the hippocampus of mouse models: real-time and extended depth of field imaging. While the former method allows sophisticated topographical tracing and quantification of the extent of branching, the latter allows speedy visualization of the dendritic tree.

Dendritic arborization has been shown to be a reliable marker for examination of structural and functional integrity of neurons. Indeed, the complexity ...

Analyzing Dendritic Morphology in Columns and Layers

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 columns to facilitate brain wiring during development and information processing in developed animals. Postsynaptic neurons elaborate dendrites in type-specific patterns in specific layers to synapse with appropriate presynaptic terminals. The fly medulla neuropil is composed of 10 layers and about 750 columns; each column is innervated by dendrites of over 38 types of medulla neurons, which match with the axonal terminals of some 7 types of afferents in a type-specific fashion. This report details the procedures to image and analyze dendrites of medulla neurons. The workflow includes three sections: (i) the dual-view imaging section combines two confocal image stacks collected at orthogonal orientations into a high-resolution 3D image of dendrites; (ii) the dendrite tracing and registration section traces dendritic arbors in 3D and registers dendritic traces to the reference column array; (iii) the dendritic analysis section analyzes dendritic patterns with respect to columns and layers, including layer-specific termination and planar projection direction of dendritic arbors, and derives estimates of dendritic branching and termination frequencies. The protocols utilize custom plugins built on the open-source MIPAV (Medical Imaging Processing, Analysis, and Visualization) platform and custom toolboxes in the matrix laboratory language. Together, these protocols provide a complete workflow to analyze the dendritic routing of Drosophila medulla neurons in layers and columns, to identify cell types, and to determine defects in mutants.

Here, we show how to analyze dendritic routing of Drosophila medulla neurons in columns and layers. The workflow includes a dual-view imaging technique to improve the image quality and computational tools for tracing, registering dendritic arbors to the reference column array and for analyzing the dendritic structures in 3D space.

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

Dendrites are the branched projections of a neuron, and dendritic morphology reflects synaptic organization during the development of the nervous system. Drosophila larval neuronal dendritic arborization (da) is an ideal model for studying morphogenesis of neural dendrites and gene function in the development of nervous system. There are four classes of da neurons. Class IV is the most complex with a branching pattern that covers almost the entire area of the larval body wall. We have previously characterized the effect of silencing the Drosophila ortholog of SOX5 on class IV neuronal dendritic arborization complexity (NDAC) using four parameters: the length of dendrites, the surface area of dendrite coverage, the total number of branches, and the branching structure. This protocol presents the workflow of NDAC quantitative analysis, consisting of larval dissection, confocal microscopy, and image analysis procedures using ImageJ software. Further insight into da neuronal development and its underlying mechanisms will improve the understanding of neuronal function and provide clues about the fundamental causes of neurological and neurodevelopmental disorders.

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

This protocol focuses on quantitative analysis of neuronal dendritic arborization complexity (NDAC) in Drosophila, which can be used for studies of dendritic morphogenesis.

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

Purification of the Dendritic Filopodia-rich Fraction

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 dendritic filopodia establish contact with a target axon, they begin maturing into spines, leading to the formation of a synapse. Telencephalin (TLCN) is abundantly localized in dendritic filopodia and is gradually excluded from spines. Overexpression of TLCN in cultured hippocampal neurons induces dendritic filopodia formation. We showed that telencephalin strongly binds to an extracellular matrix molecule, vitronectin. Vitronectin-coated microbeads induced phagocytic cup formation on neuronal dendrites. In the phagocytic cup, TLCN, TLCN-binding proteins such as phosphorylated Ezrin/Radixin/Moesin (phospho-ERM), and F-actin are accumulated, which suggests that components of the phagocytic cup are similar to those of dendritic filopodia. Thus, we developed a method for purifying the phagocytic cup instead of dendritic filopodia. Magnetic polystyrene beads were coated with vitronectin, which is abundantly present in the culture medium of hippocampal neurons and which induces phagocytic cup formation on neuronal dendrites. After 24 h of incubation, the phagocytic cups were mildly solubilized with detergent and collected using a magnet separator. After washing the beads, the binding proteins were eluted and analyzed by silver staining and Western blotting. In the binding fraction, TLCN and actin were abundantly present. In addition, many proteins identified from the fraction were localized to the dendritic filopodia; thus, we named the binding fraction as the dendritic filopodia-rich fraction. This article describes details regarding the purification method for the dendritic filopodia-rich fraction.

In this protocol, we introduce a method for purifying the dendritic filopodia-rich fraction from the phagocytic cup-like protrusion structure on cultured hippocampal neurons by taking advantage of the specific and strong affinity between a dendritic filopodial adhesion molecule, TLCN, and an extracellular matrix molecule, vitronectin.

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 ...

3D Modeling of Dendritic Spines with Synaptic Plasticity

Computational modeling of diffusion and reaction of chemical species in a three-dimensional (3D) geometry is a fundamental method to understand the mechanisms of synaptic plasticity in dendritic spines. In this protocol, the detailed 3D structure of the dendrites and dendritic spines is modeled with meshes on the software Blender with CellBlender. The synaptic and extrasynaptic regions are defined on the mesh. Next, the synaptic receptor and synaptic anchor molecules are defined with their diffusion constants. Finally, the chemical reactions between synaptic receptors and synaptic anchors are included and the computational model is solved numerically with the software MCell. This method describes the spatiotemporal path of every single molecule in a 3D geometrical structure. Thus, it is very useful to study the trafficking of synaptic receptors in and out of the dendritic spines during the occurrence of synaptic plasticity. A limitation of this method is that the high number of molecules slows the speed of the simulations. Modeling of dendritic spines with this method allows the study of homosynaptic potentiation and depression within single spines and heterosynaptic plasticity between neighbor dendritic spines.

The protocol develops a three-dimensional (3D) model of a dendritic segment with dendritic spines for modeling synaptic plasticity. The constructed mesh can be used for computational modeling of AMPA receptor trafficking in the long-term synaptic plasticity using the software program Blender with CellBlender and MCell.

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

Dendritic spines are specialized sites of synaptic innervation modulated by activity and serve as substrates for learning and memory. Recently, dendritic spines have been described for DD GABAergic neurons as the input sites from presynaptic cholinergic neurons in the motor circuit of Caenorhabditis elegans. This synaptic circuit can now serve as a powerful new in vivo model of spine morphogenesis and function that exploits the facile genetics and ready accessibility of C. elegans to live-cell imaging. 
This protocol describes experimental strategies for assessing DD spine structure and function. In this approach, a super-resolution imaging strategy is used to visualize the intricate shapes of actin-rich dendritic spines. To evaluate the DD spine function, the light-activated opsin, Chrimson, stimulates the presynaptic cholinergic neurons, and the calcium indicator, GCaMP, reports the evoked calcium transients in postsynaptic DD spines. Together, these methods comprise powerful approaches for identifying genetic determinants of dendritic spines in C. elegans that could also direct spine morphogenesis and function in the brain.

Imaging Dendritic Spines in Caenorhabditis elegans

Dendritic spines are important cellular features of the nervous system. Here live imaging methods are described for assessing the structure and function of dendritic spines in C. elegans.&#160;These approaches support the development of mutant screens for genes that define dendritic spine shape or function.

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

Serial Block-Face Scanning Electron Microscopy (SBEM) for the Study of Dendritic Spines

Three-dimensional electron microscopy (3D EM) gives a possibility to analyze morphological parameters of dendritic spines with nanoscale resolution. In addition, some features of dendritic spines, such as volume of the spine and post-synaptic density (PSD) (representing post-synaptic part of the synapse), presence of presynaptic terminal, and smooth endoplasmic reticulum or atypical form of PSD (e.g., multi-innervated spines), can be observed only with 3D EM. By employing serial block-face scanning electron microscopy (SBEM) it is possible to obtain 3D EM data easier and in a more reproducible manner than when performing traditional serial sectioning. Here we show how to prepare mouse hippocampal samples for SBEM analysis and how this protocol can be combined with immunofluorescence study of dendritic spines. Mild fixation perfusion allows us to perform immunofluorescence studies with light microscopy on one half of the brain, while the other half was prepared for SBEM. This approach reduces the number of animals to be used for the study.

Serial Block-Face Scanning Electron Microscopy SBEM for the Study of Dendritic Spines

Serial Block-Face Scanning Electron Microscopy (SBEM) is applied to image and analyze dendritic spines in the murine hippocampus.

Three-dimensional electron microscopy (3D EM) gives a possibility to analyze morphological parameters of dendritic spines with nanoscale resolution. In ...

In vivo Assessment of Microtubule Dynamics and Orientation in Caenorhabditis elegans Neurons

In neurons, microtubule orientation has been a key assessor to identify axons that have plus-end out microtubules and dendrites that generally have mixed orientation. Here we describe methods to label, image, and analyze the microtubule dynamics and growth during the development and regeneration of touch neurons in C. elegans. Using genetically encoded fluorescent reporters of microtubule tips, we imaged the axonal microtubules. The local changes in microtubule behavior that initiates axon regeneration following axotomy can be quantified using this protocol. This assay is adaptable to other neurons and genetic backgrounds to investigate the regulation of microtubule dynamics in various cellular processes.

In vivo Assessment of Microtubule Dynamics and Orientation in Caenorhabditis elegans Neurons

A protocol for imaging the dynamic microtubules in vivo using fluorescently labeled End binding protein has been presented. We described the methods to label, image, and analyze the dynamic microtubules in the Posterior lateral microtubule (PLM) neuron of C. elegans.

In neurons, microtubule orientation has been a key assessor to identify axons that have plus-end out microtubules and dendrites that generally have mixed ...