Name: Automatic Identification of Dendritic Branches and their Orientation (Video) | JoVE Journal
Uploaded: 2021-09-17T13:45:00.000+00:00
Duration: 6 min 8 s
Description: Discover automated dendritic branch analysis using the SOA computational tool for 2D fluorescence images. This method enables rapid extraction of morphological data including growth angles and branch lengths for neuroscience research.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

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. At this link, there is also a README.DM file that has directions for downloading the software, a link to the software'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. 
<img alt="Dendritic branch analysis flowchart; segmentation, spatial info extraction, classification." class="xfigimg" src="/files/ftp_upload/62679/62679fig01.jpg" data-altupdatedat="1788282455000" data-alt="Figure 1"> 
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's right panel to make it the destination folder. Click OK 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="SOA segmentation method diagram, image processing steps: upload, line detection, line merging." class="xfigimg" src="/files/ftp_upload/62679/62679fig02.jpg" data-altupdatedat="1788282455000" data-alt="Figure 2"> 
Figure 2: Example of a workflow using the SOA's GUI. Left column: GUI sections of the workflow. Middle column: image of a dendritic network, processed during the workflow (Scale bar: 20 µm). Right column: magnification of the area marked by a red rectangle in the images of the middle column (Scale bar: 4 µm). 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 | choose an image from the computer files | click on it (.png .jpg .tif .bmp files only) | Open | observe the 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 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°. 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. 
	​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 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/pubmed?term=((Functional+impact+of+dendritic+branch-point+morphology%5BTitle%5D)+AND+%22Journal+of+Neuroscience%22%5BJournal%5D)">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/pubmed?term=((Pyramidal+neurons%3A+dendritic+structure+and+synaptic+integration%5BTitle%5D)+AND+%22Nature+Reviews+Neuroscience%22%5BJournal%5D)">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/pubmed?term=((Synaptic+Connectivity+and+Neuronal+Morphology%3A+Two+Sides+of+the+Same+Coin%5BTitle%5D)+AND+%22Neuron%22%5BJournal%5D)">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/pubmed?term=((Modulation+of+dendritic+spine+development+and+plasticity+by+BDNF+and+vesicular+trafficking%3A+fundamental+roles+in+neurodevelopmental+disorders+associated+with+mental+retardation+and+autism%5BTitle%5D)+AND+%22Journal+of+Neurodevelopmental+Disorders%22%5BJournal%5D)">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/pubmed?term=((Dendritic+Spine+Structural+Anomalies+in+Fragile-X+Mental+Retardation+Syndrome%5BTitle%5D)+AND+%22Cerebral+Cortex%22%5BJournal%5D)">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/pubmed?term=((Dendritic+anomalies+in+disorders+associated+with+mental+retardation%5BTitle%5D)+AND+%22Cerebral+Cortex%22%5BJournal%5D)">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/pubmed?term=((Dendritic+branch+intersections+are+structurally+regulated+targets+for+efficient+axonal+wiring+and+synaptic+clustering%5BTitle%5D)+AND+%22PLoS+ONE%22%5BJournal%5D)">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/pubmed?term=((Contacts+among+non-sister+dendritic+branches+at+bifurcations+shape+neighboring+dendritic+branches+and+pattern+their+synaptic+inputs%5BTitle%5D)+AND+%22Brain+Research%22%5BJournal%5D)">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/pubmed?term=((Convergence+among+non-sister+dendritic+branches%3A+An+activity-controlled+mean+to+strengthen+network+connectivity%5BTitle%5D)+AND+%22PLoS+ONE%22%5BJournal%5D)">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/pubmed?term=((Neuron+imaging+with+neurolucida+-+PC-based+system+for+image+combining+microscopy%5BTitle%5D)+AND+%22Computerized+Medical+Imaging+and+Graphics%22%5BJournal%5D)">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/pubmed?term=((L-Measure%3A+a+web-accessible+tool+for+the+analysis%2C+comparison+and+search+of+digital+reconstructions+of+neuronal+morphologies%5BTitle%5D)+AND+%22Nature+Protocols%22%5BJournal%5D)">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/pubmed?term=((An+efficient+and+extendable+python+library+to+analyze+neuronal+morphologies%5BTitle%5D)+AND+%22Neuroinformatics%22%5BJournal%5D)">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/pubmed?term=((Neuronal+morphology+goes+digital%3A+A+research+hub+for+cellular+and+system+neuroscience%5BTitle%5D)+AND+%22Neuron%22%5BJournal%5D)">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/pubmed?term=((NeuronIQ%3A+A+novel+computational+approach+for+automatic+dendrite+SPINES+detection+and+analysis%5BTitle%5D)+AND+%222007+IEEE%2FNIH+Life+Science+Systems+and+Applications+Workshop%22%5BJournal%5D)">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/pubmed?term=((Superior+survival+and+durability+of+neurons+and+astrocytes+on+3-dimensional+aragonite+biomatrices%5BTitle%5D)+AND+%22Tissue+Engineering%22%5BJournal%5D)">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/pubmed?term=((Gliosis+of+astrocytes+cultivated+on+coral+skeleton+is+regulated+by+the+matrix+surface+topography%5BTitle%5D)+AND+%22Biomedical+Materials%22%5BJournal%5D)">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 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><tbody><tr><td>Matplotlib</td><td>&amp;nbsp;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&#039;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

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...

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

Research

JoVE Journal

Neuroscience

Source: Pekarek, B. T., et. al., Imaging and Quantification of Intact Neuronal Dendrites via CLARITY Tissue Clearing. J. Vis. Exp. (2021)In this video, we demonstrate the preparation, imaging, and analysis of a cleared mouse brain to examine dendritic arbors, spine morphology, and neuronal distribution using confocal microscopy and 3D reconstruction techniques.

Confocal Microscopy Imaging of Intact Dendritic Arbors and Spine Morphology in a Cleared Mouse Brain

Source: Ting, C., et al., Analyzing Dendritic Morphology in Columns and Layers. J. Vis. Exp. (2017)This video demonstrates the procedure of preparing a Drosophila brain, capturing horizontal and frontal image stacks of GFP-labeled medulla dendrites under a confocal microscope, and creating a 3D reconstruction to analyze their morphology across layers and columns.

Analyzing Dendritic Morphology Across Drosophila Medulla Layers and Columns

Source: Gouder, et. al. Three-dimensional Quantification of Dendritic Spines from Pyramidal Neurons Derived from Human Induced Pluripotent Stem Cells. J. Vis. Exp. (2015).This video demonstrates the imaging and analysis of dendritic spines in transduced, immunolabeled pyramidal neurons using confocal microscopy. Background noise is reduced, and dendrites are traced by estimating their diameter and length. Automated spine segmentation involves setting parameters for dendrite and spine dimensions...

Quantification of Dendritic Spines Using Confocal Microscopy Imaging

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.

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

Dendritic spines of pyramidal neurons are the sites of most excitatory synapses in mammalian brain cortex. This method describes a 3D quantitative analysis of spine morphologies in human cortical pyramidal glutamatergic neurons derived from induced pluripotent stem cells.

Three-dimensional Quantification of Dendritic Spines from Pyramidal Neurons Derived from Human Induced Pluripotent Stem Cells

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.

Analyzing Dendritic Morphology in Columns and Layers

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

Quantitative Analysis of Neuronal Dendritic Arborization Complexity in Drosophila

Here, we describe the method we employed to image highly motile dendritic filopodia in a live preparation of the Drosophila larval brain, and the protocol we developed to quantify time-lapse 3D imaging datasets for quantitative assessments of dendrite dynamics in developing neurons.

Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons

We present a protocol to label and analyze pyramidal neurons, which is critical for evaluating potential morphological alterations in neurons and dendritic spines that may underlie neurochemical and behavioral abnormalities.

Ballistic Labeling of Pyramidal Neurons in Brain Slices and in Primary Cell Culture

Neuronal dendritic morphology often underlies function. Indeed, many disease processes that affect the development of neurons manifest with a morphological phenotype. This protocol describes a simple and powerful method for analyzing intact dendritic arbors and their associated spines.

Imaging and Quantification of Intact Neuronal Dendrites via CLARITY Tissue Clearing

Dendritic spines are post-synaptic compartments of most excitatory synapses. Alterations to dendritic spine morphology occur during neurodevelopment, aging, learning, and many neurological and psychiatric disorders, underscoring the importance of reliable dendritic spine analysis. This protocol describes quantifying dendritic spine morphology accurately and reproducibly using automatic three-dimensional neuron reconstruction software.

Dendritic Spine Quantification Using an Automatic Three-Dimensional Neuron Reconstruction Software

Discover automated dendritic branch analysis using the SOA computational tool for 2D fluorescence images. This method enables rapid extraction of morphological data including growth angles and branch lengths for neuroscience research.

Automatic Identification of Dendritic Branches and their Orientation

Explore More Videos