Name: 3d modeling of dendritic spines with synaptic plasticity
Uploaded: 2020-05-18T13:30:00
Description: Watch this Scientific Journal Video about 3D Modeling of Dendritic Spines with Synaptic Plasticity at JoVE.com

This work was supported in part by the Sao Paulo State Science Foundation (FAPESP) grant #2015/50122-0 and IRTG-GRTK 1740/2, by the IBM/FAPESP grant #2016/18825-4, and by the FAPESP grant #2018/06504-4.

NOTE: Please see the Supplementary file 1 for the glossary of terms used in this protocol.
1. Install Blender, CellBlender, and MCell
NOTE: This protocol requires installation of MCell, Blender, and Cell Blender.
<ol>
	<li>Download and install the software on the MCell homepage (https://mcell.org/tutorials_iframe.html). Go to downloads on the top of the page and then follow the step-by-step instructions to download and install the software in the ...

<ol>
	<li>Sweatt, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Neural+plasticity+and+behavior+-+sixty+years+of+conceptual+advances.">Neural plasticity and behavior - sixty years of conceptual advances.</a> Journal of Neurochemistry. 139, 179-199 (2016).</li><li>Heine, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Surface+mobility+of+postsynaptic+AMPARs+tunes+synaptic+transmission.">Surface mobility of postsynaptic AMPARs tunes synaptic transmission.</a> Science. 320 (5873), 201-205 (2008).</li><li>Buonarati, O. R., Hammes, E. A., Watson, J. F., Greger, I. H., Hell, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanisms+of+postsynaptic+localization+of+AMPA-type+glutamate+receptors+and+their+regulation+during+long-term+potentiation.">Mechanisms of postsynaptic localization of AMPA-type glutamate receptors and their regulation during long-term potentiation.</a> Science Signaling. 12 (562), 6889 (2019).</li><li>Nair, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Super-Resolution+Imaging+Reveals+That+AMPA+Receptors+Inside+Synapses+Are+Dynamically+Organized+in+Nanodomains+Regulated+by+PSD95.">Super-Resolution Imaging Reveals That AMPA Receptors Inside Synapses Are Dynamically Organized in Nanodomains Regulated by PSD95.</a> Journal of Neuroscience. 33 (32), 13204-13224 (2013).</li><li>Cz&#246;nd&#246;r, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unified+quantitative+model+of+AMPA+receptor+trafficking+at+synapses.">Unified quantitative model of AMPA receptor trafficking at synapses.</a> Proceeding of the National Academy of Sciences of the United States of America. 109 (9), 3522-3527 (2012).</li><li>Triesch, J., Vo, A. D., Hafner, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Competition+for+synaptic+building+blocks+shapes+synaptic+plasticity.">Competition for synaptic building blocks shapes synaptic plasticity.</a> eLife. 7, 37836 (2018).</li><li>Earnshaw, B. A., Bressloff, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biophysical+model+of+AMPA+receptor+trafficking+and+its+regulation+during+long-term+potentiation/long-term+depression.">Biophysical model of AMPA receptor trafficking and its regulation during long-term potentiation/long-term depression.</a> Journal of Neuroscience. 26 (47), 12362-12373 (2006).</li><li>Earnshaw, B. A., Bressloff, P. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+the+role+of+lateral+membrane+diffusion+in+AMPA+receptor+trafficking+along+a+spiny+dendrite.">Modeling the role of lateral membrane diffusion in AMPA receptor trafficking along a spiny dendrite.</a> Journal of Computational Neuroscience. 25 (2), 366-389 (2008).</li><li>Antunes, G., Roque, A. C., Simoes-de-Souza, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stochastic+Induction+of+Long-Term+Potentiation+and+Long-Term+Depression.">Stochastic Induction of Long-Term Potentiation and Long-Term Depression.</a> Scientific Reports. 6, 30899 (2016).</li><li>Kotaleski, J. H., Blackwell, K. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+the+molecular+mechanisms+of+synaptic+plasticity+using+systems+biology+approaches.">Modelling the molecular mechanisms of synaptic plasticity using systems biology approaches.</a> Nature Reviews Neuroscience. 11 (4), 239-251 (2010).</li><li>Bhalla, U. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+computation+in+neurons:+a+modeling+perspective.">Molecular computation in neurons: a modeling perspective.</a> Current Opinion in Neurobiology. 25, 31-37 (2014).</li><li>Cz&#246;nd&#246;r, K., Thoumine, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biophysical+mechanisms+regulating+AMPA+receptor+accumulation+at+synapses.">Biophysical mechanisms regulating AMPA receptor accumulation at synapses.</a> Brain Research Bulletin. 93, 57-68 (2013).</li><li>Bromer, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Long-term+potentiation+expands+information+content+of+hippocampal+dentate+gyrus+synapses.">Long-term potentiation expands information content of hippocampal dentate gyrus synapses.</a> Proceedings of the National Academy of Sciences of the United States of America. 115 (10), 2410-2418 (2018).</li><li>Antunes, G., Simoes-de-Souza, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AMPA+receptor+trafficking+and+its+role+in+heterosynaptic+plasticity.">AMPA receptor trafficking and its role in heterosynaptic plasticity.</a> Scientific Reports. 8 (1), 10349 (2018).</li><li>Kerr, R. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fast+monte+carlo+simulation+methods+for+biological+reaction-diffusion+systems+in+solution+and+on+surfaces.">Fast monte carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces.</a> SIAM Journal on Scientific Computing. 30 (6), 3126 (2008).</li><li>Czech, J., Dittrich, M., Stiles, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+Creation,+Monte+Carlo+Simulation,+and+Visualization+of+Realistic+3D+Cell+Models.">Rapid Creation, Monte Carlo Simulation, and Visualization of Realistic 3D Cell Models.</a> Systems Biology. 500, 237-287 (2009).</li><li>Stiles, J., Bartol, T., De Schutter, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Monte+Carlo+Methods+for+Simulating+Realistic+Synaptic+Microphysiology+Using+MCell.">Monte Carlo Methods for Simulating Realistic Synaptic Microphysiology Using MCell.</a> Computational Neuroscience. , (2000).</li><li>Jorstad, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NeuroMorph:+A+Toolset+for+the+Morphometric+Analysis+and+Visualization+of+3D+Models+Derived+from+Electron+Microscopy+Image+Stacks.">NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks.</a> Neuroinformatics. 13 (1), 83-92 (2015).</li><li>Antunes, G., Roque, A. C., Simoes de Souza, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modelling+intracellular+competition+for+calcium:+kinetic+and+thermodynamic+control+of+different+molecular+modes+of+signal+decoding.">Modelling intracellular competition for calcium: kinetic and thermodynamic control of different molecular modes of signal decoding.</a> Scientific Reports. 6, 23730 (2016).</li><li>Antunes, G., Roque, A. C., Simoes-de-Souza, F. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+mechanisms+of+detection+and+discrimination+of+dynamic+signals.">Molecular mechanisms of detection and discrimination of dynamic signals.</a> Scientific Reports. 8 (1), 2480 (2018).</li><li>Hoops, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=COPASI--a+COmplex+PAthway+SImulator.">COPASI--a COmplex PAthway SImulator.</a> Bioinformatics. 22 (24), 3067-3074 (2006).</li><li>Faeder, J. R., Blinov, M. L., Hlavacek, W. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rule-based+modeling+of+biochemical+systems+with+BioNetGen.">Rule-based modeling of biochemical systems with BioNetGen.</a> Methods in Molecular Biology. 500, 113-167 (2009).</li><li>Gillespie, D. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exact+stochastic+simulation+of+coupled+chemical+reactions.">Exact stochastic simulation of coupled chemical reactions.</a> Journal of Physical Chemistry. 81 (25), 21 (1977).</li><li>Anggono, V., Huganir, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+AMPA+receptor+trafficking+and+synaptic+plasticity.">Regulation of AMPA receptor trafficking and synaptic plasticity.</a> Current Opinion in Neurobiology. 22 (3), 461-469 (2012).</li><li>Matsuda, S., Launey, T., Mikawa, S., Hirai, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Disruption+of+AMPA+receptor+GluR2+clusters+following+long-term+depression+induction+in+cerebellar+Purkinje+neurons.">Disruption of AMPA receptor GluR2 clusters following long-term depression induction in cerebellar Purkinje neurons.</a> EMBO Journal. 19 (12), 2765-2774 (2000).</li><li>Ahmad, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Postsynaptic+Complexin+Controls+AMPA+Receptor+Exocytosis+during+LTP.">Postsynaptic Complexin Controls AMPA Receptor Exocytosis during LTP.</a> Neuron. 73 (2), 260-267 (2012).</li><li>Sheng, M., Hoogenraad, C. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+postsynaptic+architecture+of+excitatory+synapses:+a+more+quantitative+view.">The postsynaptic architecture of excitatory synapses: a more quantitative view.</a> Annual Review of Biochemistry. 76, 823-847 (2007).</li></ol>

This article presents a method for the construction of 3D meshes for modeling reaction-diffusion synaptic plasticity processes in a dendritic segment with dendritic spines. The developed model contains a dendritic segment with few dendritic spines. The lateral diffusion and reaction of AMPARs with synaptic anchors allow the simulation of the basal dynamics. The critical steps in the protocol are cutting the sphere for the creation of the top of the spine head (Figure 1, ...

Synaptic plasticity has been associated with learning and memory1. Synaptic plasticity, such as long-term potentiation (LTP) and long-term depression (LTD), is associated respectively with the insertion and removal of AMPA receptors (AMPARs) in and out of the synaptic membrane2. The AMPAR synapses are located on top of the small volume structures called dendritic spines3. Each spine contains a protein dense region in the postsynaptic membrane called the postsynaptic density (PSD). Anchor proteins at the PSD trap AMPARs in the synaptic region. There are few copies of AMPARs within a single synapse and the trafficking and reaction of AMPARs with other species in dendritic spines is a stochastic process2,4. There are several compartmental models of synaptic receptor trafficking at dendritic spines5,6,7,8. However, there is a lack of stochastic computational models of the trafficking of AMPARs associated with synaptic plasticity at the 3D structures of the dendrites and their dendritic spines.
Computational modeling is a useful tool to investigate the mechanisms underlying the dynamics of complex systems such as the reaction-diffusion of AMPARs in dendritic spines during the occurrence of synaptic plasticity9,10,11,12. The model can be used to visualize complex scenarios, varying sensitive parameters and making important predictions in scientific conditions involving many variables that are difficult or impossible to control experimental12,13. Defining the level of detail of a computational model is a fundamental step in obtaining accurate information about the modeled phenomenon. An ideal computational model is a delicate balance between complexity and simplicity to capture the essential characteristics of the natural phenomena without being computationally prohibitive. Computational models that are too detailed can be expensive to compute. On the other hand, systems that are poorly detailed can lack the fundamental components that are essential to capture the dynamics of the phenomenon. Although 3D modeling of dendritic spines is computationally more expensive than 2D and 1D, there are conditions, such as in complex systems with many nonlinear variables reacting and diffusing in time and 3D space, for which modeling at a 3D level is essential to obtain insights about the functioning of the system. Further, the complexity can be reduced carefully to preserve the essential characteristics of a lower-dimensional model.
In a stochastic system with few copies of a given species within a small volume, the average dynamics of the system deviates from the average dynamics of a large population. In this case, the stochastic computational modeling of reaction-diffusing particles is required. This work introduces a method for stochastic modeling reaction-diffusion of a few copies of AMPARs in 3D dendritic spines. The purpose of this method is to develop a 3D computational model of a dendritic segment with dendritic spines and their synapses for modeling synaptic plasticity.
The method uses the software&#160;MCell to solve the model numerically, Blender for constructing 3D meshes, and CellBlender to create and visualize the MCell simulations, including the spatiotemporal reaction-diffusion of molecules in 3D meshes14,15,16. Blender is a suite for the creation of meshes and CellBlender is an add-on for the base software Blender. MCell is a Monte Carlo simulator for the reaction-diffusion of single molecules17.
The rationale behind the use of this method consists of modeling synaptic plasticity to achieve a better understanding of this phenomenon in the microphysiological environment of the dendritic spines14. Particularly, this method allows the simulation of homosynaptic potentiation, homosynaptic depression, and heterosynaptic plasticity between dendritic spines14.
The features of this method include modeling the 3D geometric structure of the dendrite and its synapses, the diffusion by random walk, and the chemical reactions of the molecules involved with synaptic plasticity. This method provides the advantage of creating rich environments to test hypotheses and make predictions about the functioning of a complex nonlinear system with a large number of variables. In addition, this method can be applied not only for studying synaptic plasticity but also for studying stochastic reaction-diffusion of molecules in 3D mesh structures in general.
Alternatively, 3D meshes of dendritic structures can be constructed directly in Blender from electron microscope serial reconstructions18. Although meshes based on serial reconstructions provide 3D structures, access to the experimental data is not always available. Thus, the construction of meshes adapted from basic geometric structures, as described in the present protocol, provides flexibility to develop customized dendritic segments with dendritic spines.
Another alternative computational method is the bulk simulation of well-mixed reactions in a regular volume9,10,11,19,20,21,22. The bulk simulations are very efficient in solving the reactions of many species within a single well-mixed volume23, but the bulk approach is extremely slow to solve the reaction-diffusion of molecules within many well-mixed voxels in a high-resolution 3D mesh. On the other hand, the present method using MCell simulations of reaction-diffusion of individual particles works efficiently in high-resolution 3D meshes15.
Before using this method, one should ask whether the phenomenon studied requires a stochastic reaction-diffusion approach in a 3D mesh. If the phenomenon has few copies (less than 1,000) of at least one of the reacting species diffusing in a complex geometric structure with small volume compartments such as dendritic spines, then stochastic modeling of reaction-diffusion in 3D meshes is appropriate for the application.
There are several steps required to construct a 3D computational model of a dendritic segment containing dendritic spines with synaptic plasticity. The main steps are the installation of the proper software for the construction of the model, the construction of a single dendritic spine to be used as a template to create multiple spines, and the creation of a dendritic segment that is connected with multiple dendritic spines. The step for modeling synaptic plasticity consists of inserting anchors at&#160;the PSD region and AMPARs in the dendritic segment and dendritic spines. Then, kinetic reactions between the anchors located at&#160;the PSD and AMPARs are defined to produce complexed anchor-AMPAR species that trap the AMPARs at&#160;the synaptic region. Respectively, the increase and decrease of the affinity between the anchors and the synaptic AMPARs create the process of LTP and LTD.

<table>
	<tbody>
		<tr>
			<td>Blender</td>
			<td>Blender Foundation</td>
			<td></td>
			<td>https://www.blender.org/</td>
		</tr>
		<tr>
			<td>CellBlender</td>
			<td>University of Pittsburgh</td>
			<td></td>
			<td>https://mcell.org/</td>
		</tr>
		<tr>
			<td>Mcell</td>
			<td>University of Pittsburgh</td>
			<td></td>
			<td>https://mcell.org/</td>
		</tr>
	</tbody>
</table>

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.

These results provide the steps for the construction of a 3D mesh that simulates a dendritic spine with a spine head and spine neck (Figure 1 to Figure 4). In addition, multiple dendritic spines can be inserted in a single dendritic segment (Figure 5) to study heterosynaptic plasticity of AMPARs14. The PSD on the top of the spine head (Figure 6) is the place where synaptic anchor...

Watch this Scientific Journal Video about 3D Modeling of Dendritic Spines with Synaptic Plasticity at JoVE.com

3D Modeling of Dendritic Spines with Synaptic Plasticity

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 ...

Computational 3D Geometric Modeling of chemical species reaction through fusion. is a useful method for studying the mechanisms of receptor trafficking in and out of dendritic spine is doing synaptic plasticity. This technique has the advantage of creating a rich environment for making hypotheses and projections about nonlinear systems with a large number of variables.To create a mesh of a single dendritic spine with a spine head and a spine neck using a modified sphere. First open Blender. With Cell Blender already installed and press 5 on the keypad, to change from the perspective to the orthogonal view.Press 1 to change to front view and press Shift C to center the cursor. To create the spine head press Shift A to open the Mesh palette, and select the Mesh. Select UV Sphere and in the add UV Sphere, set the size to 0.25 and the rings to 32.To make the top of the head flat press Tab to switch Blender from the Object mode to the Edit mode. Press B to select the top three quarters of the sphere and press Delete, select Vertices and Enter to remove the vertices. Press B and select the top of the sphere.Press E, S, 0 and Enter to seal the top of the vertices still selected. And move the blue arrow down to align to the top of the spine head. To increase the mesh resolution at the top of the spine, select Tool and knife and use the knife to cut a circle around the center of the top.Then select Tool and Loop cut and slide four times to create four concentric circles around the center of the top. To create the spine neck press B and select the bottom of the mesh. Press Delete Vertices and B and select the bottom of the mesh.Press E and Z and select minus 0.45 to create an extrusion to the Z axis position, at minus 0.45 micro meters. To make the mesh compatible with M-cell press Ctrl T to triangulate the mesh and select Tool and Remove doubles. To create a multiple spine Dendrite press Shift A to open the Mesh palette and select Mesh and Cylinder.In the Add Cylinder menu, set the radius 0.3 micrometers and the depth to two micrometers and press Enter. Press R and enter 90 to rotate the cylinder 90 degrees and use the blue arrow to drag the cylinder to the bottom of the spine. Press 3 to obtain a front view of the cylinder and press Z to make the mesh transparent.Use the blue normal arrow to move the base of the spine to the interior of the cylinder and right click to select the Dendrite. Select Modifier and Add Modifier and select Boolean, Operation Union and select Object Spine. Click Apply to create a joint mesh of the Dendrite and the spine.Then use the mouse to select the mesh of the isolated spine, changing the position and angle to insert each new spine in a physiological position. To create AMPARs, select Molecules and to insert a new molecule. Change the name to AMPAR and change the molecule type to Surface molecule.Then change the diffusion constant 0.05 times 10 to the eighth square centimeters per second. To plot the anchors bound to the AMPARs, at the PSD1 during the basal condition, open Plot Output Settings and press to define the molecules. Then set the Molecule to Anchor_AMPAR, the Object to Dendrite and the Region to PSD1.To run a Basal Condition simulation select Run simulation. And set the iterations to 30, 000 and the time step to one times 10 to the minus three seconds. Click Export and Run and wait for the simulation to end.At the end of the simulation, select Reload Visualization Data, Play animation, Plot output settings and plot to visualize the spatial temporal results. To run the homosynaptic potentiation condition select Molecule Placement and rel_anchorLTP_psd1. Select rel_anchorLTP_psd1 and change Quantity to Release to 200.Then change Quantity to Release to zero. Select rel_anchor_psd1. Change Quantity to release to zero and run the simulation as just demonstrated.Synaptic plasticity can be roughly verified through changes in the number of species of AMPARs bound to anchors at each spine. For the exact calculation of the occurrence of synaptic plasticity. It is recommended to calculate the variation of the total numbers of anchored and free AMPARs at the synapse.AMPAR homosynaptic potentiation and depression can be verified through increases and decreases in the number of anchored AMPARs respectively, caused by changes in the affinity of AMPARs by anchors in comparison to the basal condition. For example homosynaptic long-term potentiation induction at a single spine, creates a heterosynaptic long-term depression effect at the neighboring spines. Following this procedure, the model can be expanded to investigate the process of LTP and LTD induction at Dendritic spines.This methods allows the testing of hypothesis about the functioning of complex non-linear systems with a large number of variables.

3d-modeling-of-dendritic-spines-with-synaptic-plasticity

Research

JoVE Journal

Neuroscience

Preparation of Oligomeric &beta;-amyloid1-42 and Induction of Synaptic Plasticity Impairment on Hippocampal Slices

Impairment of synaptic connections is likely to underlie the subtle amnesic changes occurring at the early stages of Alzheimer s Disease (AD). &beta;-amyloid (A&beta;), a peptide produced in high amounts in AD, is known to reduce Long-Term Potentiation (LTP), a cellular correlate of learning and memory. Indeed, LTP impairment caused by A&beta; is a useful experimental paradigm for studying synaptic dysfunctions in AD models and for screening drugs capable of mitigating or reverting such synaptic impairments. Studies have shown that A&beta; produces the LTP disruption preferentially via its oligomeric form. Here we provide a detailed protocol for impairing LTP by perfusion of oligomerized synthetic A&beta;1-42 peptide onto acute hippocampal slices. In this video, we outline a step-by-step procedure for the preparation of oligomeric A&beta;1-42. Then, we follow an individual experiment in which LTP is reduced in hippocampal slices exposed to oligomerized A&beta;1-42 compared to slices in a control experiment where no A&beta;1-42 exposure had occurred.

Preparation of Oligomeric &#946;-amyloid1-42 and Induction of Synaptic Plasticity Impairment on Hippocampal Slices

One feature of Alzheimer's Disease is the elevation of A&beta;1-42 peptide. Here we provide a protocol for preparing synthetic A&beta;1-42 oligomers, which impairs hippocampal Long-Term Potentiation, a cellular correlate of memory. This procedure is useful for investigating mechanisms of A&beta;-induced pathology and drug screening.

Impairment of synaptic connections is likely to underlie the subtle amnesic changes occurring at the early stages of Alzheimer s Disease (AD). ...

Fluorescence Recovery After Photobleaching (FRAP) of Fluorescence Tagged Proteins in Dendritic Spines of Cultured Hippocampal Neurons

FRAP has been used to quantify the mobility of GFP-tagged proteins. Using a strong excitation laser, the fluorescence of a GFP-tagged protein is bleached in the region of interest. The fluorescence of the region recovers when the unbleached GFP-tagged protein from outside of the region diffuses into the region of interest. The mobility of the protein is then analyzed by measuring the fluorescence recovery rate. This technique could be used to characterize protein mobility and turnover rate.
In this study, we express the (enhanced green fluorescent protein) EGFP vector in cultured hippocampal neurons. Using the Zeiss 710 confocal microscope, we photobleach the fluorescence signal of the GFP protein in a single spine, and then take time lapse images to record the fluorescence recovery after photobleaching. Finally, we estimate the percentage of mobile and immobile fractions of the GFP in spines, by analyzing the imaging data using ImageJ and Graphpad softwares.
This FRAP protocol shows how to perform a basic FRAP experiment as well as how to analyze the data.

Fluorescence Recovery After Photobleaching FRAP of Fluorescence Tagged Proteins in Dendritic Spines of Cultured Hippocampal Neurons

FRAP has been used to quantify the mobility of Green Fluorescence Protein (GFP)-tagged proteins in cultured cells. We examined the mobile/immobile fractions of the GFP by analyzing the fluorescence recovery percentage after photobleaching. In this study, FRAP was performed at spines of hippocampal neurons.

FRAP has been used to quantify the mobility of GFP-tagged proteins. Using a strong excitation laser, the fluorescence of a GFP-tagged protein is bleached ...

Lateral Diffusion and Exocytosis of Membrane Proteins in Cultured Neurons Assessed using Fluorescence Recovery and Fluorescence-loss Photobleaching

Membrane proteins such as receptors and ion channels undergo active trafficking in neurons, which are highly polarised and morphologically complex. This directed trafficking is of fundamental importance to deliver, maintain or remove synaptic proteins.
Super-ecliptic pHluorin (SEP) is a pH-sensitive derivative of eGFP that has been extensively used for live cell imaging of plasma membrane proteins1-2. At low pH, protonation of SEP decreases photon absorption and eliminates fluorescence emission. As most intracellular trafficking events occur in compartments with low pH, where SEP fluorescence is eclipsed, the fluorescence signal from SEP-tagged proteins is predominantly from the plasma membrane where the SEP is exposed to a neutral pH extracellular environment. When illuminated at high intensity SEP, like every fluorescent dye, is irreversibly photodamaged (photobleached)3-5. Importantly, because low pH quenches photon absorption, only surface expressed SEP can be photobleached whereas intracellular SEP is unaffected by the high intensity illumination6-10. 
FRAP (fluorescence recovery after photobleaching) of SEP-tagged proteins is a convenient and powerful technique for assessing protein dynamics at the plasma membrane. When fluorescently tagged proteins are photobleached in a region of interest (ROI) the recovery in fluorescence occurs due to the movement of unbleached SEP-tagged proteins into the bleached region. This can occur via lateral diffusion and/or from exocytosis of non-photobleached receptors supplied either by de novo synthesis or recycling (see Fig. 1). The fraction of immobile and mobile protein can be determined and the mobility and kinetics of the diffusible fraction can be interrogated under basal and stimulated conditions such as agonist application or neuronal activation stimuli such as NMDA or KCl application8,10. 
We describe photobleaching techniques designed to selectively visualize the recovery of fluorescence attributable to exocytosis. Briefly, an ROI is photobleached once as with standard FRAP protocols, followed, after a brief recovery, by repetitive bleaching of the flanking regions. This 'FRAP-FLIP' protocol, developed in our lab, has been used to characterize AMPA receptor trafficking at dendritic spines10, and is applicable to a wide range of trafficking studies to evaluate the intracellular trafficking and exocytosis.

This report describes the use of live cell imaging and photobleach techniques to determine the surface expression, transport pathways and trafficking kinetics of exogenously expressed, pH-sensitive GFP-tagged proteins at the plasma membrane of neurons.

Membrane proteins such as receptors and ion channels undergo active trafficking in neurons, which are highly polarised and morphologically complex. This ...

Voltage-sensitive Dye Recording from Axons, Dendrites and Dendritic Spines of Individual Neurons in Brain Slices

Understanding the biophysical properties and functional organization of single neurons and how they process information is fundamental for understanding how the brain works. The primary function of any nerve cell is to process electrical signals, usually from multiple sources. Electrical properties of neuronal processes are extraordinarily complex, dynamic, and, in the general case, impossible to predict in the absence of detailed measurements. To obtain such a measurement one would, ideally, like to be able to monitor, at multiple sites, subthreshold events as they travel from the sites of origin on neuronal processes and summate at particular locations to influence action potential initiation. This goal has not been achieved in any neuron due to technical limitations of measurements that employ electrodes. To overcome this drawback, it is highly desirable to complement the patch-electrode approach with imaging techniques that permit extensive parallel recordings from all parts of a neuron. Here, we describe such a technique - optical recording of membrane potential transients with organic voltage-sensitive dyes (Vm-imaging) - characterized by sub-millisecond and sub-micrometer resolution. Our method is based on pioneering work on voltage-sensitive molecular probes 2. Many aspects of the initial technology have been continuously improved over several decades 3, 5, 11. Additionally, previous work documented two essential characteristics of Vm-imaging. Firstly, fluorescence signals are linearly proportional to membrane potential over the entire physiological range (-100 mV to +100 mV; 10, 14, 16). Secondly, loading neurons with the voltage-sensitive dye used here (JPW 3028) does not have detectable pharmacological effects. The recorded broadening of the spike during dye loading is completely reversible 4, 7. Additionally, experimental evidence shows that it is possible to obtain a significant number (up to hundreds) of recordings prior to any detectable phototoxic effects 4, 6, 12, 13. At present, we take advantage of the superb brightness and stability of a laser light source at near-optimal wavelength to maximize the sensitivity of the Vm-imaging technique. The current sensitivity permits multiple site optical recordings of Vm transients from all parts of a neuron, including axons and axon collaterals, terminal dendritic branches, and individual dendritic spines. The acquired information on signal interactions can be analyzed quantitatively as well as directly visualized in the form of a movie.

An imaging technique for monitoring of membrane potential changes with sub-micrometer spatial and sub-millisecond temporal resolution is described. The technique, based on laser excitation of voltage-sensitive dyes, allows measurements of signals in axons and axon collaterals, terminal dendritic branches, and individual dendritic spines.

Understanding the biophysical properties and functional organization of single neurons and how they process information is fundamental for understanding ...

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 ...

Deriving the Time Course of Glutamate Clearance with a Deconvolution Analysis of Astrocytic Transporter Currents

The highest density of glutamate transporters in the brain is found in astrocytes. Glutamate transporters couple the movement of glutamate across the membrane with the co-transport of 3 Na+ and 1 H+ and the counter-transport of 1 K+. The stoichiometric current generated by the transport process can be monitored with whole-cell patch-clamp recordings from astrocytes. The time course of the recorded current is shaped by the time course of the glutamate concentration profile to which astrocytes are exposed, the kinetics of glutamate transporters, and the passive electrotonic properties of astrocytic membranes. Here we describe the experimental and analytical methods that can be used to record glutamate transporter currents in astrocytes and isolate the time course of glutamate clearance from all other factors that shape the waveform of astrocytic transporter currents. The methods described here can be used to estimate the lifetime of flash-uncaged and synaptically-released glutamate at astrocytic membranes in any region of the central nervous system during health and disease.

We describe an analytical method to estimate the lifetime of glutamate at astrocytic membranes from electrophysiological recordings of glutamate transporter currents in astrocytes.

The highest density of glutamate transporters in the brain is found in astrocytes. Glutamate transporters couple the movement of glutamate across the ...

Imaging Dendritic Spines of Rat Primary Hippocampal Neurons using Structured Illumination Microscopy

Dendritic spines are protrusions emerging from the dendrite of a neuron and represent the primary postsynaptic targets of excitatory inputs in the brain. Technological advances have identified these structures as key elements in neuron connectivity and synaptic plasticity. The quantitative analysis of spine morphology using light microscopy remains an essential problem due to technical limitations associated with light's intrinsic refraction limit. Dendritic spines can be readily identified by confocal laser-scanning fluorescence microscopy. However, measuring subtle changes in the shape and size of spines is difficult because spine dimensions other than length are usually smaller than conventional optical resolution fixed by light microscopy's theoretical resolution limit of 200 nm.
Several recently developed super resolution techniques have been used to image cellular structures smaller than the 200 nm, including dendritic spines. These techniques are based on classical far-field operations and therefore allow the use of existing sample preparation methods and to image beyond the surface of a specimen. Described here is a working protocol to apply super resolution structured illumination microscopy (SIM) to the imaging of dendritic spines in primary hippocampal neuron cultures. Possible applications of SIM overlap with those of confocal microscopy. However, the two techniques present different applicability. SIM offers higher effective lateral resolution, while confocal microscopy, due to the usage of a physical pinhole, achieves resolution improvement at the expense of removal of out of focus light. In this protocol, primary neurons are cultured on glass coverslips using a standard protocol, transfected with DNA plasmids encoding fluorescent proteins and imaged using SIM. The whole protocol described herein takes approximately 2 weeks, because dendritic spines are imaged after 16-17 days in vitro, when dendritic development is optimal. After completion of the protocol, dendritic spines can be reconstructed in 3D from series of SIM image stacks using specialized software.

This article describes a working protocol to image dendritic spines from hippocampal neurons in vitro using Structured Illumination Microscopy (SIM). Super-resolution microscopy using SIM provides image resolution significantly beyond the light diffraction limit in all three spatial dimensions, allowing the imaging of individual dendritic spines with improved detail.

Dendritic spines are protrusions emerging from the dendrite of a neuron and represent the primary postsynaptic targets of excitatory inputs in the brain. ...

Pentylenetetrazole-Induced Kindling Mouse Model

Pentylenetetrazole (PTZ) is a GABA-A receptor antagonist. An intraperitoneal injection of PTZ into an animal induces an acute, severe seizure at a high dose, whereas sequential injections of a subconvulsive dose have been used for the development of chemical kindling, an epilepsy model. A single low-dose injection of PTZ induces a mild seizure without convulsion. However, repetitive low-dose injections of PTZ decrease the threshold to evoke a convulsive seizure. Finally, continuous low-dose administration of PTZ induces a severe tonic-clonic seizure. This method is simple and widely applicable to investigate the pathophysiology of epilepsy, which is defined as a chronic disease that involves repetitive seizures. This chemical kindling protocol causes repetitive seizures in animals. With this method, vulnerability to PTZ-mediated seizures or the degree of aggravation of epileptic seizures was estimated. These advantages have led to the use of this method for screening anti-epileptic drugs and epilepsy-related genes. In addition, this method has been used to investigate neuronal damage after epileptic seizures because the histological changes observed in the brains of epileptic patients also appear in the brains of chemical-kindled animals. Thus, this protocol is useful for conveniently producing animal models of epilepsy.

This protocol describes a method of chemical kindling with pentylenetetrazole and provides a mouse model of epilepsy. This protocol can also be used to investigate vulnerability to seizure induction and pathogenesis after epileptic seizures in mice.

Pentylenetetrazole (PTZ) is a GABA-A receptor antagonist. An intraperitoneal injection of PTZ into an animal induces an acute, severe seizure at a high ...

Combining Optogenetics with Artificial microRNAs to Characterize the Effects of Gene Knockdown on Presynaptic Function within Intact Neuronal Circuits

The purpose of this protocol is to characterize the effect of gene knockdown on presynaptic function within intact neuronal circuits. We describe a workflow on how to combine artificial microRNA (miR)-mediated RNA interference with optogenetics to achieve selective stimulation of manipulated presynaptic boutons in acute brain slices. The experimental approach involves the use of a single viral construct and a single neuron-specific promoter to drive the expression of both an optogenetic probe and artificial miR(s) against presynaptic gene(s). When stereotactically injected in the brain region of interest, the expressed construct makes it possible to stimulate with light exclusively the neurons with reduced expression of the gene(s) under investigation. This strategy does not require the development and maintenance of genetically modified mouse lines and can in principle be applied to other organisms and to any neuronal gene of choice. We have recently applied it to investigate how the knockdown of alternative splice isoforms of presynaptic P/Q-type voltage-gated calcium channels (VGCCs) regulates short-term synaptic plasticity at CA3 to CA1 excitatory synapses in acute hippocampal slices. A similar approach could also be used to manipulate and probe the neuronal circuitry in vivo.

This protocol provides a workflow on how to combine artificial microRNA-mediated RNA interference with optogenetics to stimulate specifically presynaptic boutons with reduced expression of selective gene(s) within intact neuronal circuits.

The purpose of this protocol is to characterize the effect of gene knockdown on presynaptic function within intact neuronal circuits. We describe a ...

In Vivo Optical Calcium Imaging of Learning-Induced Synaptic Plasticity in Drosophila melanogaster

Decades of research in many model organisms have led to the current concept of synaptic plasticity underlying learning and memory formation. Learning-induced changes in synaptic transmission are often distributed across many neurons and levels of processing in the brain. Therefore, methods to visualize learning-dependent synaptic plasticity across neurons are needed. The fruit fly Drosophila melanogaster represents a particularly favorable model organism to study neuronal circuits underlying learning. The protocol presented here demonstrates a way in which the processes underlying the formation of associative olfactory memories, i.e., synaptic activity and their changes, can be monitored in vivo. Using the broad array of genetic tools available in Drosophila, it is possible to specifically express genetically encoded calcium indicators in determined cell populations and even single cells. By fixing a fly in place, and opening the head capsule, it is possible to visualize calcium dynamics in these cells whilst delivering olfactory stimuli. Additionally, we demonstrate a set-up in which the fly can be subjected, simultaneously, to electric shocks to the body. This provides a system in which flies can undergo classical olfactory conditioning - whereby a previously na&#239;ve odor is learned to be associated with electric shock punishment - at the same time as the representation of this odor (and other untrained odors) is observed in the brain via two-photon microscopy. Our lab has previously reported the generation of synaptically localized calcium sensors, which enables one to confine the fluorescent calcium signals to pre- or postsynaptic compartments. Two-photon microscopy provides a way to spatially resolve fine structures. We exemplify this by focusing on neurons integrating information from the mushroom body, a higher-order center of the insect brain. Overall, this protocol provides a method to examine the synaptic connections between neurons whose activity is modulated as a result of olfactory learning.

In Vivo Optical Calcium Imaging of Learning-Induced Synaptic Plasticity in Drosophila melanogaster

Here we present a protocol with which pre- and/or postsynaptic calcium can be visualized in the context of Drosophila learning and memory. In vivo calcium imaging using synaptically localized calcium sensors is combined with a classical olfactory conditioning paradigm such that the synaptic plasticity underlying this type of associative learning may be determined.