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>

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

automatic identification of dendritic branches and their orientation

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 ...

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

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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

Analysis of Dendritic Spine Morphology in Cultured CNS Neurons

Dendritic spines are the sites of the majority of excitatory connections within the brain, and form the post-synaptic 
compartment of synapses. These structures are rich in actin and have been shown to be highly dynamic. In response to classical Hebbian plasticity 
as well as neuromodulatory signals, dendritic spines can change shape and number, which is thought to be critical for the refinement of neural 
circuits and the processing and storage of information within the brain. Within dendritic spines, a complex network of proteins link extracellular 
signals with the actin cyctoskeleton allowing for control of dendritic spine morphology and number. Neuropathological studies have demonstrated that 
a number of disease states, ranging from schizophrenia to autism spectrum disorders, display abnormal dendritic spine morphology or numbers. 
Moreover, recent genetic studies have identified mutations in numerous genes that encode synaptic proteins, leading to suggestions that these 
proteins may contribute to aberrant spine plasticity that, in part, underlie the pathophysiology of these disorders. In order to study the potential 
role of these proteins in controlling dendritic spine morphologies/number, the use of cultured cortical neurons offers several advantages. Firstly, 
this system allows for high-resolution imaging of dendritic spines in fixed cells as well as time-lapse imaging of live cells. Secondly, this in 
vitro system allows for easy manipulation of protein function by expression of mutant proteins, knockdown by shRNA constructs, or pharmacological 
treatments. These techniques allow researchers to begin to dissect the role of disease-associated proteins and to predict how mutations of these 
proteins may function in vivo.

Numerous recent studies have identified mutations in synaptic proteins associated with brain pathologies. Primary cultured cortical neurons offer great flexibility in examining the effects of these disease-associated proteins on dendritic spine morphology and motility.

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

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 ...

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. ...

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

Accurately and reliably identifying spatial orientation of the isolated mouse retina is important for many studies in visual neuroscience, including the analysis of density and size gradients of retinal cell types, the direction tuning of direction-selective ganglion cells, and the examination of topographic degeneration patterns in some retinal diseases. However, there are many different ocular dissection methods reported in the literature that are used to identify and label retinal orientation in the mouse retina. While the method of orientation used in such studies is often overlooked, not reporting how retinal orientation is determined can cause discrepancies in the literature and confusion when attempting to compare data between studies. Superficial ocular landmarks such as corneal burns are commonly used but have recently been shown to be less reliable than deeper landmarks such as the rectus muscles, the choroid fissure, or the s-opsin gradient. Here, we provide a comprehensive guide for the use of deep ocular landmarks to accurately dissect and document the spatial orientation of an isolated mouse retina. We have also compared the effectiveness of two s-opsin antibodies and included a protocol for s-opsin immunohistochemistry. Because orientation of the retina according to the s-opsin gradient requires retinal reconstruction with Retistruct software and rotation with custom code, we have presented the important steps required to use both of these programs. Overall, the goal of this protocol is to deliver a reliable and repeatable set of methods for accurate retinal orientation that is adaptable to most experimental protocols. An overarching goal of this work is to standardize retinal orientation methods for future studies.

This protocol provides a comprehensive dissection and analysis guide for the use of deep ocular landmarks, s-opsin immunohistochemistry, Retistruct, and custom code to accurately and reliably orient the isolated mouse retina in anatomical space.

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

Purification of the Dendritic Filopodia-rich Fraction

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 ...

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

Axon degeneration is a shared feature in neurodegenerative disease and when nervous systems are challenged by mechanical or chemical forces. However, our understanding of the molecular mechanisms underlying axon degeneration remains limited. Injury-induced axon degeneration serves as a simple model to study how severed axons execute their own disassembly (axon death). Over recent years, an evolutionarily conserved axon death signaling cascade has been identified from flies to mammals, which is required for the separated axon to degenerate after injury. Conversely, attenuated axon death signaling results in morphological and functional preservation of severed axons and their synapses. Here, we present three simple and recently developed protocols that allow for the observation of axonal morphology, or axonal and synaptic function of severed axons that have been cut-off from the neuronal cell body, in the fruit fly Drosophila. Morphology can be observed in the wing, where a partial injury results in axon death side-by-side of uninjured control axons within the same nerve bundle. Alternatively, axonal morphology can also be observed in the brain, where the whole nerve bundle undergoes axon death triggered by antennal ablation. Functional preservation of severed axons and their synapses can be assessed by a simple optogenetic approach coupled with a post-synaptic grooming behavior. We present examples using a highwire loss-of-function mutation and by over-expressing dnmnat, both capable of delaying axon death for weeks to months. Importantly, these protocols can be used beyond injury; they facilitate the characterization of neuronal maintenance factors, axonal transport, and axonal mitochondria.

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

Here, we provide protocols to perform three simple injury-induced axon degeneration (axon death) assays in Drosophila melanogaster to evaluate the morphological and functional preservation of severed axons and their synapses.

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

3D Modeling of Dendritic Spines with Synaptic Plasticity

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 ...