This work was supported in part by a Busch Biomedical Grant, NSF grant IBN-0548543, NSF grant IBN-0919747, March of Dimes Foundation Grant 1-FY04-107, March of Dimes Foundation Grant 1-FY08-464 (to B.L.F). M.K.K. and C.G.L. were supported by NIH Biotechnology Training Grant T32 GM008339-20, and C.G.L. was also supported by a NJ Commission on Spinal Cord Research Predoctoral Fellowship 08-2941-SCR-E-0.

1. Before You Begin:
1) E18 rat dissection:
Standard dissection methods of E18 hippocampal neurons have previously been described13. In order to use the Bonfire program to analyze the morphological characteristics of the neurites, 8 bit .tif images of individual neurons must be obtained. This can be accomplished in a number of ways depending on the experimental protocol you are following. Neurons can be plated at a low enough density so that single neurons appear in the microscope field. Alternatively, to image individual neurons that are grown in a dense culture, neurons can be transfected using a variety of transfection methods with a plasmid encoding a fluorescent protein.
2) Software Requirements and Installation:
<ol class="ualpha">
<li>The NeuronJ plugin for the ImageJ software from NIH and NeuronStudio must both be installed in order to run the Bonfire program. The software packs can be found at the following websites: 

	ImageJ - <a href="http://rsbweb.nih.gov/ij/">http://rsbweb.nih.gov/ij/</a> 
	NeuronJ - <a href="http://www.imagescience.org/meijering/software/neuronj/">http://www.imagescience.org/meijering/software/neuronj/</a> 
	NeuronStudio - <a href="http://research.mssm.edu/cnic/tools-ns.html">http://research.mssm.edu/cnic/tools-ns.html</a>
</li>
<li>The Bonfire program can be found at http://lifesci.rutgers.edu/~firestein. The Bonfire user manual is also available at this website. Additionally, the initial description of Bonfire can be found in Langhammer et al, Cytometry 2010. All of our data have been analyzed using a Windows operating system.</li>
</ol>
3) Image Resolution Adjustment: 
You will need to adjust the Bonfire program based on the image resolution of the images that you wish to analyze. In the bonfire_parameters portion of the Bonfire program, replace the current value for the variable pix_conv with the value of the image resolution (&mu;m/pixel) of your images.

2. File Structure:

In order for Bonfire to analyze your data, the files must be organized in this specific structure (Figure 2). You will have:
<ul>
<li>A master folder</li>
<li>Sub-folders (containing each of your different conditions)</li>
<li>Cell image files (filename.tif files) contained in the different condition folders</li>
<li>A Bonfire folder containing the Bonfire Matlab mfiles (also contained in the master folder)</li>
</ul>
3. Tracing Neurons in NeuronJ:
<ol>
<li>Prepare the image for tracing
<ol class="lalpha">
<li>Open the image by selecting the 'Open' button on the NeuronJ toolbar and selecting the image you would like to trace.</li>
<li>Resize the image by selecting the 'maximize' button.</li>
<li>Adjust the brightness and contrast of the image so that you can visualize all of the neurites by selecting 'Image' on the NeuronJ toolbar then selecting 'Adjust brightness/contrast'.</li></ol></li>
<li>Trace the cell body in NeuronJ and identify the trace as being 'Type 06'
<ol class="lalpha">
<li>Select the 'Add Traces' button on the NeuronJ toolbar.</li>
<li>Trace around the perimeter of the cell body.</li>
<li>Select the 'Label Tracings' button on the NeuronJ toolbar.</li>
<li>Select 'N1' from the 'Tracing ID' dropdown menu.</li>
<li>Select 'Type 06' in the NeuronJ: Attributes window and select 'OK'.</li>
</ol></li>
<li>Trace the neurites in NeuronJ
<ol class="lalpha">
<li>Select the 'Add Traces' button on the NeuronJ toolbar.</li>
<li>Add a trace along each neurite branch. You can select only the dendrites or the axon, depending on your experiment.</li>
<li>We suggest that the segments you draw stop at each branch point, with each daughter branch point starting at that point as a new trace.</li>
<li>Select the 'Save Tracings' button on the NeuronJ toolbar.</li>
<li>Save the tracing you have just created in the same folder as the original image file.</li>
</ol></li>
<li>Export the trace files and the trace identifier file from NeuronJ 
Two types of files must be exported from NeuronJ and saved into the appropriate condition folders along with the .tif and .ndf files.
<ol class="lalpha">
<li>Select the 'Export Trace' button on the NeuronJ toolbar.</li>
<li>Select the "Tab-deliminated text-files: separate file for each tracing' option on the 'NeuronJ: Export' dialogue box and select 'OK'.</li>
<li>Allow NeuronJ to choose the names of the files and the save location.</li>
<li>Select the 'Measure Tracings' button on the NeuronJ toolbar.</li>
<li>Select the 'Display tracing measurements' option on the 'NeuronJ: Measurements' window and select 'Run'.</li>
<li>Select 'File' in the 'NeuronJ: Tracings' window.</li>
<li>Select 'Save As' and save the file as filename_info. 'filename' must exactly match the name of the original image file (including capitalization) followed by _info and should not contain a 3-letter file-extension. For example, for original image name Cell20.tif, this file would be named 'Cell20_info'. Check that your computer does not automatically add a .xls file extension. If it does, the file extension must be manually deleted.</li>
</ol></li>
</ol>
4. Use Bonfire to Build Preliminary .swc Files from NeuronJ Data:
<ol>
<li>Reorganize folders using 'bonfire_load'
<ol class="lalpha">
<li>Open Matlab by double clicking on the icon.</li>
<li>Click on the ' ' button in the upper right of the command window.</li>
<li>Select the 'Bonfire' folder in your master folder in the 'Browse For Folder' window.</li>
<li>Type 'bonfire_load' into the Matlab command window and press enter.</li>
<li>Select the condition folder that you wish to analyze in the 'Browse For Folder' window and select 'OK'.</li>
<li>This will reorganize the folder structure by creating cell sub-folders containing all of the data for each individual cell.</li>
</ol></li>
<li>Create filename_prelim.swc files using 'bonfire_ndf2swc'
<ol class="lalpha">
<li>Type 'bonfire_ndf2swc' into the command window and press enter.</li>
<li>Select the same condition folder you just reorganized with 'bonfire_load' in the 'Browse For Folder' window and select 'OK'.</li>
<li>This will create a .swc file in each cell folder for the chosen condition. Each cell folder will now contain 5 files; the original .tif image, the .ndf file, the _info trace identifier file, a .txt file and a .swc file.</li> 
</ol></li></ol>
5. Use NeuronStudio to Finalize .swc Files:
<ol>
<li>Open and calibrate neuron images in NeuronStudio
<ol class="lalpha">
<li>Open the NeuronStudio program.</li>
<li>Select File&#8594;Open on the NeuronStudio toolbar.</li>
<li>Find the .tif image of the neuron you want to edit and open it.</li>
<li>Select Run&#8594;Settings and enter '1' into each of the 3 (X,Y,Z) boxes in the 'Voxel Size' window.</li>
<li>Select File&#8594;Import SWC. Select the appropriate .swc file. The image file will now be overlaid with a trace image. The cell soma should be overlaid with a red circle.</li>
</ol></li>
<li>Use NeuronStudio to link neurites
<ol class="lalpha">
<li>Use the tools in NeuronStudio to correctly identify branch points and end points (we recommend that you become acquainted with NeuronStudio's features and shortcuts before analyzing your data).</li>
<li>Specifically, use the 'neurite tool' to join nodes so that:
<ul>
<li>Each branch point (yellow nodes) can only create two branches</li>
<li>All traces are continuous with the soma (only one red node)</li>
</ul></li>
</ol></li>
<li>Export data from NeuronStudio
<ol class="lalpha">
<li>Select File&#8594;Save Neurites (save as the default name).</li>
</ol></li>
<li>Check for errors in .swc files using 'bonfire_trace_check'
<ol class="lalpha">
<li>Type 'bonfire_trace_check' into the Mathlab command window and press enter.</li>
<li>Select the condition folder containing the data you have just processed in the 'Browse For Folder' window and select 'OK'.</li>
<li>If there are any errors in any of the images in the folder the program will output images displaying where the error is located.</li>
<li>Fixing errors in NeuronStudio
<ol class="lroman">
<li>Open the image file you saved in the exporting step.</li>
<li>Locate and fix the problem(s).</li>
<li>Click File&#8594;Save Neurites.</li>
<li>Repeat for other images that need to be fixed.</li>
<li>Rerun 'bonfire_trace_check' (5.4).</li>
</ol></li></ol></li></ol>
6. Use 'bonfire' to Extract Morphological Data from .swc Files:
<ol class="lalpha">
<li>Type 'bonfire' into the command window and press enter.</li>
<li>Select the condition folder that contains the data you want to analyze in the 'Browse For Folder' window and select 'OK'.</li>
<li>The Bonfire analysis will generate a window for each of the neurons being analyzed, in which it graphs the neuronal morphology along with the Sholl rings used in the analysis. Additionally, the 'bonfire' command will generate the .mat file that contains all of the morphological information that has been taken from the analysis.</li>
</ol>
7. Use 'bonfire_results' to View the Data:
<ol class="lalpha">
<li>Type 'bonfire_results' into the Matlab command window.</li>
<li>Select the condition folder for the condition you would like to view in the 'Browse For Folder' window and select 'OK'.</li> 
<li>The 'Browse for Folder' window will close temporarily and re-open, allowing you to select additional conditions you would like to view.</li>
<li>When you are finished selecting condition folders, select 'Cancel' to exit the selection process.</li>
<li>'Bonfire_results' will return summary charts including the data from the condition folders you selected.</li>
</ol>
8. Use 'bonfire_export' to Export Data to Excel:
<ol class="lalpha">
<li>Type 'bonfire_export' into the command window.</li>
<li>Select the condition folder containing the data you would like to export in the 'Browse For Folder' window and select 'OK'. Bonfire_export will create Excel files of the morphological data and will place them in the condition folder that was selected.</li>
</ol>
9. Representative Results:
An example of the data generated by the Bonfire program on a data set containing two conditions is shown in Figure 3. In this example, Condition 1 neurons contain more neurites distal to the cell body. This phenomenon can be observed in the example images (Figure 3B) as well as in the Sholl curve of the total dendritic arbor (Figure 3A) and in the graph of the number of terminal points (Figure 3C). Additionally, because the Bonfire program also performs Sholl analysis on subregions of the images, we are able to identify more specifically the identity of the neurites that have increased. Both the total number of intersections of 3rd order or greater neurites (Figure 3F) and the total number of both intermediary and terminal neurites (Figure 3G) are increased distal to the cell body. These trends can also be observed in Figures 3D and 3E.
<img src="/files/ftp_upload/2354/2354fig1.jpg" alt="Figure 1" /> 
Figure 1: Flow chart of the Bonfire program. Neurons are traced using ImageJ. The data are then exported and converted by the Bonfire program into preliminary .swc files. NeuronStudio is used to define the connectivity of the neurites. Bonfire checks for errors and then calculates Sholl curves, the number of primary, secondary, and higher order neurites, and the number of branch points and neurite tips. Finally, the data are exported to Excel for statistical analysis.
<img src="/files/ftp_upload/2354/2354fig2.jpg" alt="Figure 2" /> 
Figure 2: File structure required for Bonfire analysis. The file structure must match this or the program will not run correctly. The names of the folders and files and the quantity of the folders and files can be changed.
<img src="/files/ftp_upload/2354/2354fig3.jpg" alt="Figure 3" /> 
Figure 3: Example output data from Bonfire program. A) Total Sholl curves. B) Example inverted images of both conditions. C) Average number of branch points and terminal points/cell. D) Average number of processes/cell for primary, secondary, and tertiary or greater neurites. E) Average number of processes/cell for root, intermediate, and terminal neurites. F) Segment identity-specific Sholl analysis curves. Segments are grouped as primary, secondary, or tertiary or greater. G) Segment identity-specific Sholl analysis curves. Segments are grouped as root segments, intermediate segments, or terminal segments.

<ol>
	<li>Elston, G. 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+cells+of+the+frontal+lobe:+all+the+more+spinous+to+think+with.">Pyramidal cells of the frontal lobe: all the more spinous to think with.</a> J Neurosci. 20, (2000).</li><li>Koch, C., Segev, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+single+neurons+in+information+processing.">The role of single neurons in information processing.</a> Nat Neurosci. , 1171-1177 (2000).</li><li>Poirazi, P., Mel, B. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+active+dendrites+and+structural+plasticity+on+the+memory+capacity+of+neural+tissue.">Impact of active dendrites and structural plasticity on the memory capacity of neural tissue.</a> Neuron. 29, 779-796 (2001).</li><li>Schaefer, A. T., Larkum, M. E., Sakmann, B., Roth, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coincidence+detection+in+pyramidal+neurons+is+tuned+by+their+dendritic+branching+pattern.">Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern.</a> J Neurophysiol. 89, 3143-3154 (2003).</li><li>Vetter, P., Roth, A., Hausser, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Propagation+of+action+potentials+in+dendrites+depends+on+dendritic+morphology.">Propagation of action potentials in dendrites depends on dendritic morphology.</a> J Neurophysiol. 85, 926-937 (2001).</li><li>Hausser, M., Spruston, N., Stuart, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diversity+and+dynamics+of+dendritic+signaling.">Diversity and dynamics of dendritic signaling.</a> Science. 290, 739-744 (2000).</li><li>Brette, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simulation+of+networks+of+spiking+neurons:+a+review+of+tools+and+strategies.">Simulation of networks of spiking neurons: a review of tools and strategies.</a> J Comput Neurosci. 23, 349-398 (2007).</li><li>Arendt, T., Zvegintseva, H. G., Leontovich, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dendritic+changes+in+the+basal+nucleus+of+Meynert+and+in+the+diagonal+band+nucleus+in+Alzheimer's+disease--a+quantitative+Golgi+investigation.">Dendritic changes in the basal nucleus of Meynert and in the diagonal band nucleus in Alzheimer's disease--a quantitative Golgi investigation.</a> Neuroscience. 19, 1265-1278 (1986).</li><li>Harrison, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+neuropathology+of+schizophrenia.+A+critical+review+of+the+data+and+their+interpretation.">The neuropathology of schizophrenia. A critical review of the data and their interpretation.</a> Brain. 122, 593-624 (1999).</li><li>Lewis, D. A., Glantz, L. A., Pierri, J. N., Sweet, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Altered+cortical+glutamate+neurotransmission+in+schizophrenia:+evidence+from+morphological+studies+of+pyramidal+neurons.">Altered cortical glutamate neurotransmission in schizophrenia: evidence from morphological studies of pyramidal neurons.</a> Ann N Y Acad Sci. 1003, 102-112 (2003).</li><li>Kaufmann, W. E., Moser, H. 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> Cereb Cortex. 10, 981-991 (2000).</li><li>Georges, P. C., Hadzimichalis, N. M., Sweet, E. S., Firestein, B. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+yin-yang+of+dendrite+morphology:+unity+of+actin+and+microtubules.">The yin-yang of dendrite morphology: unity of actin and microtubules.</a> Mol Neurobiol. 38, 270-284 (2008).</li><li>Firestein, B. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cypin:+a+cytosolic+regulator+of+PSD-95+postsynaptic+targeting.">Cypin: a cytosolic regulator of PSD-95 postsynaptic targeting.</a> Neuron. 24, 659-672 (1999).</li></ol>

The authors declare no competing interests. The funding agencies had no scientific role in the development of Bonfire.

The Bonfire program is a semi-automated program for the analysis of dendrite and axon morphology. It greatly increases the efficiency and accuracy of Sholl analysis over performing the analysis manually. In addition, the Bonfire program saves the data at every step of the process, making it possible to audit the data and to verify the accuracy of the analysis. Therefore, the task of data analysis can be distributed to numerous individuals without compromising accuracy. Lastly, by performing the analysis on subregions...

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		<div>
		<table border="1">
		<thead>
		<tr>
		<th>Material Name</th>
		<th>Type</th>
		<th>Company</th>
		<th>Catalogue Number</th>
		<th>Comment</th>
		</tr>
		</thead>
		<tbody>
		
<tr>
<td>NeuronJ plugin</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td><a href="http://www.imagescience.org/meijering/software/neuronj/">http://www.imagescience.org/meijering/software/neuronj/</a></td>
</tr>
<tr>
<td>ImageJ software</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td><a href="http://rsbweb.nih.gov/ij/">http://rsbweb.nih.gov/ij/</a></td>
</tr>
<tr>
<td>Bonfire program</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td><a href="http://lifesci.rutgers.edu/~firestein">http://lifesci.rutgers.edu/~firestein</a></td>
</tr>
<tr>
<td>NeuronStudio</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td><a href="http://research.mssm.edu/cnic/tools-ns.html">http://research.mssm.edu/cnic/tools-ns.html</a></td>
</tr>
<tr>
<td>MatLab Program</td>
<td>&nbsp;</td>
<td>MathWorks</td>
<td>&nbsp;</td>
<td>&nbsp;</td>		</tbody>
		</table>
		</div>

automated sholl analysis of digitized neuronal morphology at multiple scales

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3. Specifically, it affects the ability of neurons to receive inputs from other cells2 and contributes to the propagation of action potentials4,5. The morphology of the neurites also affects how information is processed. The diversity of dendrite morphologies facilitate local and long range signaling and allow individual neurons or groups of neurons to carry out specialized functions within the neuronal network6,7. Alterations in dendrite morphology, including fragmentation of dendrites and changes in branching patterns, have been observed in a number of disease states, including Alzheimer's disease8, schizophrenia9,10, and mental retardation11. The ability to both understand the factors that shape dendrite morphologies and to identify changes in dendrite morphologies is essential in the understanding of nervous system function and dysfunction.
Neurite morphology is often analyzed by Sholl analysis and by counting the number of neurites and the number of branch tips. This analysis is generally applied to dendrites, but it can also be applied to axons. Performing this analysis by hand is both time consuming and inevitably introduces variability due to experimenter bias and inconsistency. The Bonfire program is a semi-automated approach to the analysis of dendrite and axon morphology that builds upon available open-source morphological analysis tools. Our program enables the detection of local changes in dendrite and axon branching behaviors by performing Sholl analysis on subregions of the neuritic arbor. For example, Sholl analysis is performed on both the neuron as a whole as well as on each subset of processes (primary, secondary, terminal, root, etc.) Dendrite and axon patterning is influenced by a number of intracellular and extracellular factors, many acting locally. Thus, the resulting arbor morphology is a result of specific processes acting on specific neurites, making it necessary to perform morphological analysis on a smaller scale in order to observe these local variations12.
The Bonfire program requires the use of two open-source analysis tools, the NeuronJ plugin to ImageJ and NeuronStudio. Neurons are traced in ImageJ, and NeuronStudio is used to define the connectivity between neurites. Bonfire contains a number of custom scripts written in MATLAB (MathWorks) that are used to convert the data into the appropriate format for further analysis, check for user errors, and ultimately perform Sholl analysis. Finally, data are exported into Excel for statistical analysis. A flow chart of the Bonfire program is shown in Figure 1.

Watch this Scientific Journal Video about Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales at JoVE.com

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

We have developed a computer program to analyze neuronal morphology. In combination with two existing open source analysis tools, our program performs Sholl analysis and determines the number of neurites, branch points, and neurite tips. The analyses are performed so that local changes in neurite morphology can be observed.

Neuronal morphology plays a significant role in determining how neurons function and communicate1-3.  Specifically, it affects the ability of neurons to ...

automated-sholl-analysis-digitized-neuronal-morphology-at-multiple

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33.2K Views.  Rutgers University. We have developed a computer program to analyze neuronal morphology. In combination with two existing open source analysis tools, our program performs Sholl analysis and determines the number of neurites, branch points, and neurite tips. The analyses are performed so that local changes in neurite morphology can be observed.

Video: Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

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 ...

The Analysis of Purkinje Cell Dendritic Morphology in Organotypic Slice Cultures

Purkinje cells are an attractive model system for studying dendritic development, because they have an impressive dendritic tree which is strictly oriented in the sagittal plane and develops mostly in the postnatal period in small rodents 3. Furthermore, several antibodies are available which selectively and intensively label Purkinje cells including all processes, with anti-Calbindin D28K being the most widely used. For viewing of dendrites in living cells, mice expressing EGFP selectively in Purkinje cells 11 are available through Jackson labs. Organotypic cerebellar slice cultures cells allow easy experimental manipulation of Purkinje cell dendritic development because most of the dendritic expansion of the Purkinje cell dendritic tree is actually taking place during the culture period 4. We present here a short, reliable and easy protocol for viewing and analyzing the dendritic morphology of Purkinje cells grown in organotypic cerebellar slice cultures. For many purposes, a quantitative evaluation of the Purkinje cell dendritic tree is desirable. We focus here on two parameters, dendritic tree size and branch point numbers, which can be rapidly and easily determined from anti-calbindin stained cerebellar slice cultures. These two parameters yield a reliable and sensitive measure of changes of the Purkinje cell dendritic tree. Using the example of treatments with the protein kinase C (PKC) activator PMA and the metabotropic glutamate receptor 1 (mGluR1) we demonstrate how differences in the dendritic development are visualized and quantitatively assessed. The combination of the presence of an extensive dendritic tree, selective and intense immunostaining methods, organotypic slice cultures which cover the period of dendritic growth and a mouse model with Purkinje cell specific EGFP expression make Purkinje cells a powerful model system for revealing the mechanisms of dendritic development.

We present a protocol that permits to view and to quantitatively asses the morphology of the dendritic tree of individual Purkinje cells grown in organotypic cerebellar slice cultures. This protocol is intended to promote studies on the mechanisms of Purkinje cell dendritic development.

Purkinje cells are an attractive model system for studying dendritic development, because they have an impressive dendritic tree which is strictly ...

Genetic Manipulation of Cerebellar Granule Neurons In Vitro and In Vivo to Study Neuronal Morphology and Migration

Developmental events in the brain including neuronal morphogenesis and migration are highly orchestrated processes. In vitro and in vivo analyses allow for an in-depth characterization to identify pathways involved in these events. Cerebellar granule neurons (CGNs) that are derived from the developing cerebellum are an ideal model system that allows for morphological analyses. Here, we describe a method of how to genetically manipulate CGNs and how to study axono- and dendritogenesis of individual neurons. With this method the effects of RNA interference, overexpression or small molecules can be compared to control neurons. In addition, the rodent cerebellar cortex is an easily accessible in vivo system owing to its predominant postnatal development. We also present an in vivo electroporation technique to genetically manipulate the developing cerebella and describe subsequent cerebellar analyses to assess neuronal morphology and migration.

Genetic Manipulation of Cerebellar Granule Neurons In Vitro and In Vivo to Study Neuronal Morphology and Migration

Neuronal morphogenesis and migration are crucial events underlying proper brain development. Here, we describe methods to genetically manipulate cultured cerebellar granule neurons and the developing cerebellum for the assessment of morphology and migratory characteristics of neurons.

Developmental events in the brain including neuronal morphogenesis and migration are highly orchestrated processes. In vitro and in vivo analyses allow ...

Simultaneous Long-term Recordings at Two Neuronal Processing Stages in Behaving Honeybees

In both mammals and insects neuronal information is processed in different higher and lower order brain centers. These centers are coupled via convergent and divergent anatomical connections including feed forward and feedback wiring. Furthermore, information of the same origin is partially sent via parallel pathways to different and sometimes into the same brain areas. To understand the evolutionary benefits as well as the computational advantages of these wiring strategies and especially their temporal dependencies on each other, it is necessary to have simultaneous access to single neurons of different tracts or neuropiles in the same preparation at high temporal resolution. Here we concentrate on honeybees by demonstrating a unique extracellular long term access to record multi unit activity at two subsequent neuropiles1, the antennal lobe (AL), the first olfactory processing stage and the mushroom body (MB), a higher order integration center involved in learning and memory formation, or two parallel neuronal tracts2 connecting the AL with the MB.&#160;The latter was chosen as an example and will be described in full. In the supporting video the construction and permanent insertion of flexible multi&#160;channel wire electrodes is demonstrated. Pairwise differential amplification of the micro&#160;wire electrode channels drastically reduces the noise and verifies that the source of the signal is closely related to the position of the electrode tip. The mechanical flexibility of the used wire electrodes allows stable invasive long&#160;term recordings over many hours up to days, which is a clear advantage compared to conventional extra&#160;and intracellular in vivo recording techniques.

Simultaneous extracellular long term recordings from two different brain neuropiles or two different anatomical tracts were established in honeybees. These recordings allow the investigation of temporal aspects of neuronal processing across different brain areas at the single neuron as well as at the ensemble level in a behaving animal.

In both mammals and insects neuronal information is processed in different higher and lower order brain centers. These centers are coupled via convergent ...

Utilizing Combined Methodologies to Define the Role of Plasma Membrane Delivery During Axon Branching and Neuronal Morphogenesis

During neural development, growing axons extend to multiple synaptic partners by elaborating axonal branches. Axon branching is promoted by extracellular guidance cues like netrin-1 and results in dramatic increases to the surface area of the axonal plasma membrane. Netrin-1-dependent axon branching likely involves temporal and spatial control of plasma membrane expansion, the components of which are supplied through exocytic vesicle fusion. These fusion events are preceded by formation of SNARE complexes, comprising a v-SNARE, such as VAMP2 (vesicle-associated membrane protein 2), and plasma membrane t-SNAREs, syntaxin-1 and SNAP25 (synaptosomal-associated protein 25). Detailed herein isa multi-pronged approach used to examine the role of SNARE mediated exocytosis in axon branching. The strength of the combined approach is data acquisition at a range of spatial and temporal resolutions, spanning from the dynamics of single vesicle fusion events in individual neurons to SNARE complex formation and axon branching in populations of cultured neurons. This protocol takes advantage of established biochemical approaches to assay levels of endogenous SNARE complexes and Total Internal Reflection Fluorescence (TIRF) microscopy of cortical neurons expressing VAMP2 tagged with a pH-sensitive GFP (VAMP2-pHlourin) to identify netrin-1 dependent changes in exocytic activity in individual neurons. To elucidate the timing of netrin-1-dependent branching, time-lapse differential interference contrast (DIC) microscopy of single neurons over the order of hours is utilized. Fixed cell immunofluorescence paired with botulinum neurotoxins that cleave SNARE machinery and block exocytosis demonstrates that netrin-1 dependent axon branching requires SNARE-mediated exocytic activity.

Light microscopy techniques coupled with biochemical assays elucidate the involvement of SNARE-mediated exocytosis in netrin-dependent axon branching. This combination of techniques permits identification of molecular mechanisms controlling axon branching and cell shape change.

During neural development, growing axons extend to multiple synaptic partners by elaborating axonal branches. Axon branching is promoted by extracellular ...

Peering at Brain Polysomes with Atomic Force Microscopy

The translational machinery, i.e., the polysome or polyribosome, is one of the biggest and most complex cytoplasmic machineries in cells. Polysomes, formed by ribosomes, mRNAs, several proteins and non-coding RNAs, represent integrated platforms where translational controls take place. However, while the ribosome has been widely studied, the organization of polysomes is still lacking comprehensive understanding. Thus much effort is required in order to elucidate polysome organization and any novel mechanism of translational control that may be embedded. Atomic force microscopy (AFM) is a type of scanning probe microscopy that allows the acquisition of 3D images at nanoscale resolution. Compared to electron microscopy (EM) techniques, one of the main advantages of AFM is that it can acquire thousands of images both in air and in solution, enabling the sample to be maintained under near physiological conditions without any need for staining and fixing procedures. Here, a detailed protocol for the accurate purification of polysomes from mouse brain and their deposition on mica substrates is described. This protocol enables polysome imaging in air and liquid with AFM and their reconstruction as three-dimensional objects. Complementary to cryo-electron microscopy (cryo-EM), the proposed method can be conveniently used for systematically analyzing polysomes and studying their organization.

While ribosome structure has been extensively characterized, the organization of polysomes is still understudied. To overcome this lack of knowledge, we present here a detailed preparation protocol for accurate imaging of mammalian polysomes by atomic force microscopy (AFM) in air and liquid.

The translational machinery, i.e., the polysome or polyribosome, is one of the biggest and most complex cytoplasmic machineries in cells. Polysomes, ...

Measuring Progressive Neurological Disability in a Mouse Model of Multiple Sclerosis

After intracerebral infection with the Theiler's Murine Encephalomyelitis Virus (TMEV), susceptible SJL mice develop a chronic-progressive demyelinating disease, with clinical features similar to the progressive forms of multiple sclerosis (MS). The mice show progressive disability with loss of motor and sensory functions, which can be assessed with multiple apparatuses and protocols. Among them, the Rotarod performance test is a very common behavioral test, its advantage being that it provides objective measurements, but it is often used assuming that it is straightforward and simple. In contrast to visual scoring systems used in some models of MS, which are highly subjective, the Rotarod test generates an objective, measurable, continuous variable (i.e., length of time), allowing almost perfect inter-rater concordances. However, inter-laboratory reliability is only achieved if the various testing parameters are replicated. In this manuscript, recommendations of specific testing parameters, such as size, speed, and acceleration of the rod; amount of training given to the animals; and data processing, are presented for the Rotarod test.

An optimized testing protocol is presented in this paper for the Rotarod performance test, used for measuring progressive neurological disability in TMEV-infected mice.

After intracerebral infection with the Theiler's Murine Encephalomyelitis Virus (TMEV), susceptible SJL mice develop a chronic-progressive demyelinating ...

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 ...

Automated Gait Analysis in Mice with Chronic Constriction Injury

The von Frey test is a classical method that has been widely used to examine the sensory function of neuropathic pain animals. However, it has some disadvantages such as subjective data and the requirement of a skilled, experienced experimenter. To date, a variety of modifications have improved the von Frey method, but it still has a few limitations. Recent reports have suggested that gait analysis produces more accurate and objective data from the neuropathic animals. This protocol demonstrates how to perform the automated gait analysis to determine the degree of neuropathic pain in mice. After several days of acclimation, the mice were allowed to walk freely on the glass floor to illuminate footprints. Then, quantification of the footprints and gait were performed through video clips with automatic analysis of various walking parameters, such as area of paw print, swing time, angle of paw, etc.
The main purpose of this study is to describe the methodology of automated gait analysis and briefly compare it with data from the classical sensory test using von Frey filament.

The precise assessment of pain response in a neuropathic animal model is critical to investigate the pathophysiology of pain diseases and develop new analgesics. We present a sensitive and objective method to determine the sensory function of the rodent hind paw by an automated gait analysis system.

The von Frey test is a classical method that has been widely used to examine the sensory function of neuropathic pain animals. However, it has some ...

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. ...