We would like to acknowledge Carolyn Smith, Sarah Williams Avram, Ted Usdin, and the NIMH SNIR for technical assistance. We would additionally like to acknowledge the Colgate University Bethesda Biomedical Research Study Group. This work is supported by the NIMH Intramural Program (1ZIAMH002881 to Z.L.).

All animal procedures followed the US National Institutes of Health Guidelines Using Animals in Intramural Research and were approved by the National Institute of Mental Health Animal Care and Use Committee.
1. Preparation of fixed hippocampal slices
<ol>
	<li>Anesthetize mice with an intraperitoneal injection of Ketamine/Xylazine (Ketamine: 100 mg/kg; Xylazine: 8 mg/kg). Validate anesthesia via tail pinch and affix mouse to perfusion plate.</li>
	<li>Using large surgical scissors, remove the skin and fur from the chest, allowing for easier visualization of the underlying ribcage.</li>
	<li>Make a horizontal cut below the width of the ribcage, avoiding the liver and diaphragm. Using fine forceps, pull the xiphoid process up and cut each lateral side of the rib cage. Flip up the rib cage to the neck region and clamp in place using hemostat forceps.</li>
	<li>Insert a 21 G butterfly needle into the left ventricle of the heart and begin to perfuse with room temperature 1x PBS at approximately 5 mL/min. Make a small cut in the right atrium with small surgical scissors. Perfuse with PBS until the solution leaving the atrium runs clear.</li>
	<li>Turn off the perfusion pump to ensure no bubbles enter the tubing. Place the tubing from PBS into ice-cold 4% paraformaldehyde (PFA) in PBS. Perfuse with PFA at a rate of 5 mL/min until the animal is fully stiffened, approximately 25 mL. 
	NOTE: Ensure the PFA is fresh (no more than 1 week old if stock is stored at 4&#176; C) for optimal fixation.</li>
	<li>Remove the skin from the surface of the skull with small surgical scissors. Make a midsagittal cut with small surgical scissors along the central fissure of the skull. Make lateral cuts rostral to the olfactory bulb and over the cerebellum.</li>
	<li>Open the skull with fine forceps to expose the brain. Using a spatula, scoop out the brain gently from the olfactory bulbs and place in 4% PFA in PBS overnight. 
	NOTE: For a more comprehensive protocol of rodent perfusion, please refer to Gage et al.33.</li>
	<li>Cryoprotect the fixed brain by replacing the 4% PFA with 15% sucrose in PBS for 1 day. Following this, replace the 15% sucrose with 30% sucrose in PBS solution for 1 day until the brain sinks in the solution.</li>
	<li>Remove the brain from the sucrose solution and place it in a Petri dish with PBS. Cut off the cerebellum and olfactory bulb using a scalpel blade.</li>
	<li>Place a small amount, 1-2 cm in diameter, of optimal cutting temperature compound (OCT) on the specimen holder surface. Mount the brain coronally to the specimen holder with the caudal cut surface in the OCT. Quick freeze the brain by placing the specimen holder in pulverized dry ice until visibly frozen, approximately 5-7 min.</li>
	<li>Ensure that the blade angle of the cryostat is set between 0&#176; and 5&#176; to produce uniform sections. Adjust the roll plate angle for optimal slice flattening. Please refer to the equipment manual for specific instructions.</li>
	<li>Place the specimen holder in the cryostat with the ventral surface of the brain closest to the blade. Cut the brain into 30 &#181;m dorsal hippocampal sections, discarding all slices rostral to the hippocampus. 
	NOTE: This part of the protocol can be adapted to any desired brain region of choice. Steps 1.9-1.12 would change depending on the region of interest.</li>
	<li>Transfer dorsal hippocampal slices to PBS. Using a paintbrush, gently mount the hippocampal sections onto a microscope slide. Remove any excess solution with cotton swabs or delicate task wipes.</li>
	<li>Apply 100 &#181;L of hard-set mounting medium to the microscope slide covering all brain slices. To prevent bubbles, lower the coverslip slowly using forceps onto the mounting medium. If bubbles form, gently tap the coverslip with forceps to allow them to escape. Let the slides set overnight before imaging.</li>
</ol>
2. High-resolution confocal imaging
<ol>
	<li>Use low magnification eyepieces to identify fluorescent cells. Switch to a 63x (NA = 1.4) or higher objective, applying proper immersion medium to the objective. 
	NOTE: For the best results, utilize a laser scanning confocal microscope with a 63x or higher objective.</li>
	<li>Identify well-labeled dendritic segments with limited overlap for image acquisition. Set the laser power and gain to ensure the fluorescent dendrites are not saturated. Additionally, reducing the scanning speed can provide better image resolution.</li>
	<li>Acquire z-stacks encompassing the full dendritic segments for future analysis. Z-stacks larger than 10 &#181;m are undesirable due to the added potential for dendritic overlap in the z-plane. 
	NOTE: Utilize the smallest z-step size available (0.2-0.7 &#181;m)and 1 airy unit pinhole size. The smaller step size results in more images in the z-stack, compensating for the limited Z resolution of many microscopes.</li>
	<li>Optional: If available, utilize the microscope&#39;s respective deconvolution software functionality to deconvolve images. This will allow for higher-resolution images.</li>
</ol>
3. Dendritic spine quantification
<ol>
	<li>Open Neurolucida 360 (v2022.1.1 or later). Open the image file in the spine analysis software (File &#62; Open &#62; Image). Ensure the image file is visible in the main window and the 3D Environment. If the 3D environment window does not appear, left-click on 3D Environment in the top toolbar of the main window in the Trace tab (Supplementary Figure 1) 
	NOTE: This section of the protocol can be adapted for any dendritic images, not exclusively dendrites from mouse tissue.</li>
	<li>While in the Change Image Display tab of the 3D Environment window, ensure the image is displayed as 3D Volume in the Display Image As box. In the Image Stack Settings box of the Image tab, select Max Projection on the Show Surface As drop-down menu. (Supplementary Figure 2)</li>
	<li>Identify a suitable dendritic segment for tracing.
	<ol>
		<li>Left-click the Move Pivot Point tool in the top toolbar of the 3D Environment window. Left-click on the desired dendrite to set a new pivot point. This will change the orientation to enable effective zooming in.</li>
		<li>Re-establish the original orientation by left-clicking the Reset Orientation icon. After setting the pivot point, left-click the Move Pivot Point tool to begin tracing the dendrite. (Supplementary Figure 2). 
		NOTE: The ideal dendrite is one with limited overlap with other dendrites in any of the coordinate planes and not intersecting with another dendrite or superficial to another underneath. Dendrites in low proximity to others in the XY plane are also preferable to prevent inappropriate assignment of neighboring spines to the traced dendrite. It must also be noted that dendrites of differing thicknesses, orders, and distances from soma have different dendritic spine densities34,35. This needs to be accounted for in the experimental design. Secondary order dendrites &#60;1.5 &#181;m in thickness are ideal candidates for tracing (Figure 1).</li>
	</ol>
	</li>
	<li>Left-click the Tree tab of the 3D Environment window. Left-click User-Guided for the tracing mode and Directional Kernels as the method in User-Guided Tracing Options.</li>
	<li>Left click on the dendrite when a circular kernel appears to begin the tracing. Move the cursor along the dendritic segment. This will populate the kernels automatically. If the kernels are not populating automatically, see step 3.51.
	<ol>
		<li>Gently move the cursor back and forth on the dendrite until kernels populate. Left click to preserve the existing detected kernels. If kernels stop populating, left-click when a kernel populates further down the dendrite to place one manually. Right-click to end the tracing. Ensure the traced dendrite is a minimum of 7 &#181;m in length (Supplementary Figure 3).</li>
	</ol>
	</li>
	<li>Verify the accuracy of the dendritic tracing using all three directions of the 3D Environment, pitch, yaw, and roll, by left-clicking and dragging the 3D Environment window. Identify points where the dendritic tracing is outside of the appropriate location on the dendrite. There can be instances where it looks accurately traced from the top down, but in the z-dimension, the points are not on the dendrite (Figure 2).</li>
	<li>To correct improperly traced dendritic segments, left-click the Edit tab within the Tree menu. Left-click the dendrite of interest, then left-click Points.</li>
	<li>Move improperly placed points back onto the dendrite segment via click and drag. Delete extraneous points by left-clicking the point and clicking the Delete button. Alter the size of the points if the dendrite is not adequately filled. To alter the size of the point, left-click a point and adjust the thickness slider to change the size (Supplementary Figure 4). 
	NOTE: Inadequately filled dendrites can result in identifying false spines that are components of the dendritic segment. Conversely, overfilling dendrites can obscure true spines.</li>
	<li>Repeat steps 3.3-3.8 for multiple dendrites in the image before proceeding to spine identification in step 3.10.</li>
	<li>Left-click the Spine tab in the 3D Environment window. Set Detection Settings for Outer Range, Minimum Height, and Minimum Voxel Count. Depending on the preparation, the preset values may need to be altered in the case of clear and specific justification for changes. The preset conditions are Outer Range: 2.5 &#181;m, Minimum Height: 0.3 &#181;m, Minimum Voxel Count: 10 Voxels. 
	NOTE: Different preparations, such as cell cultures vs. acute tissues, as well as different developmental time points will require different criteria that must be derived from existing literature. It is also vital to note that altering the detection settings can significantly alter results. For example, a higher minimum height can exclude short spines. Detection settings must remain consistent throughout the entire course of the experiment.</li>
	<li>Set the Detector Sensitivity to 70% and left-click Detect All. This will populate the spines identified by this detector sensitivity on all dendrites. If selecting spines in a dendrite-specific manner is desired, left-click the box Click Image to Detect All on Nearest Branch, and left-click on each dendrite manually with different detector sensitivities. 
	NOTE: At this stage it is normal that not all of the dendritic spines will populate. Similarly, non-spines may improperly populate. The initial 70% sensitivity is also flexible; this may change depending on the preparation.</li>
	<li>Examine the spines selected by this detector sensitivity by clicking and dragging the dendrite in all three directions. If the majority of detected spines are not fully filled, proceed to step 3.12.1. If the spines that have been detected are overfilled, proceed to step 3.12.2. If the detected spines appear to be adequately filled, proceed to step 3.13.
	<ol>
		<li>Increase the Detector Sensitivity by 5%-10% and left-click Detect All again. This will replace all previously detected spines with new ones at a higher sensitivity. Repeat as needed until the detected spines are adequately filled.</li>
		<li>Decrease the Detector Sensitivity by 5%-10% and left-click Detect All again. This will replace all previously detected spines with new ones at lower sensitivity . Repeat as needed until the detected spines are adequately filled.</li>
	</ol>
	</li>
	<li>Left-click Keep Existing Spines in the Spine tab of the 3D Environment. If Click Image to Detect All on Nearest Branch has been selected, deselect it. 
	NOTE: By checking Keep Existing Spines ensures that newly identifying dendritic spines manually will not overwrite previously identified spines. Ensure this box is selected before proceeding so as not to overwrite the previous work.</li>
	<li>Left click Move Pivot Point and left click on the dendrite requiring further spine detection to set the pivot point.
	<ol>
		<li>Deselect Move Pivot Point. Identify an unfilled dendritic spine. Increase Detector Sensitivity 10%-20% beyond the previous detection and left-click on the spine. If the detected spine is under or overfilled, proceed to step 3.14.3 or 3.14.4. If the spine does not populate, the message Unable to detect a spine at the selected location will appear. In this case, proceed to step 3.14.2.</li>
		<li>Increase the Detector Sensitivity incrementally, possibly above 100%, until the spine has been detected and adequately filled. If the spine is detected but inadequately filled, proceed to step 3.14.3. If the spine is overfilled, proceed to step 3.14.4 (Figure 3).</li>
		<li>Left-click the Edit tab and left-click on the underfilled spine. Left click Remove. Deselect the Edit tab. Increase the sensitivity by 5%-10% and left-click on the spine. Repeat this step if the spine is still underfilled.</li>
		<li>Left-click the Edit tab and left-click on the overfilled spine. Left click Remove. Deselect the Edit tab. Decrease the sensitivity by 5%-10% and click on the spine. Repeat this step if the spine is still overfilled.</li>
	</ol>
	</li>
	<li>Repeat steps 3.14-3.14.4 until all spines identified by visual identification have been detected. Double check the dendrite for any spines belonging to neighboring dendrites, false spines corresponding to no true signal, or potential segments of dendrite mislabeled as a spine. Delete these false spines with the Remove function.</li>
	<li>Examine the identified spines on the dendrite. In some instances, multiple spines may appear as one conglomerate spine. If a spine appears to encompass two, left-click the Edit tab. Left-click the spine and left-click Hide Selection. After confirming a conglomerate spine, in the Edit tab, left-click Show Selection and select Split. If more than two spines are in one conglomerate, this step may need to be repeated (Figure 4). 
	NOTE: If the conglomerate spine does not split after step 3.16, remove the spine . Then select the more intense spine of the conglomerate at a lower sensitivity. Once the more intense spine is filled, increase the sensitivity to select the other unfilled spine. Alternatively, deleting the conglomerate spine and then increasing the sensitivity may allow for proper splitting.</li>
	<li>With all visually identifiable spines detected and filled appropriately, in the Spine tab, left-click Classify All to classify the spines into four subtypes: thin, mushroom, stubby, and filopodia (Figure 5). 
	NOTE: Spine classification parameters may be changed in the Settings window of the Classification box in the Spine tab. As with detector settings, a clear rationale for altering the existing parameters is strongly encouraged. The preset values are head-to-neck ratio: 1.1, length-to-head ratio: 2.5, mushroom head size: 0.35 &#181;m, Filopodium Length- 3 &#181;m.</li>
	<li>In the top toolbar of the 3D Environment window, select Save and View in Neurolucida Explorer. Neurolucida Explorer is where the data is gathered from the tracings. The work will be saved as a .dat file containing all tracings and spines.</li>
	<li>Within the Explorer window in the View tab, left-click Select All to highlight all dendrites and spines.</li>
	<li>Left-click the Analyze tab in the upper toolbar. Left-click the Structure drop-down menu. Left click Branched Structure Analysis.</li>
	<li>Depending on the variables of interest, any of the analyses can be selected. The two most useful for questions centered around spine density and average spine volume are Each Tree &#62; Each Dendrite and Spines &#62; Spine Details. Select OK, and the data will appear in two separate windows.</li>
	<li>Copy the data to a spreadsheet for further compilation and analysis. 
	NOTE: The individual tree will be separated by dendrite, but the spine volume will not be. Using the sort function in the spreadsheet, the spine details can be filtered by features.</li>
</ol>

Protocol for Dendritic Spine Analysis Using Neurolucida 360

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Z., Sumera, A., Booker, S. A., Mccullagh, E. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Current+best+practices+for+analysis+of+dendritic+spine+morphology+and+number+in+neurodevelopmental+disorder+research.">Current best practices for analysis of dendritic spine morphology and number in neurodevelopmental disorder research.</a> ACS Chem Neurosci. 14 (9), 1561-1572 (2023).</li><li>Dickstein, D. L., 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=Automatic+dendritic+spine+quantification+from+confocal+data+with+Neurolucida+360.">Automatic dendritic spine quantification from confocal data with Neurolucida 360.</a> Curr Protoc Neurosci. 77, 1-21 (2016).</li><li>Gage, G. J., Kipke, D. R., Shain, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole+animal+perfusion+fixation+for+rodents.">Whole animal perfusion fixation for rodents.</a> J Vis Exp. (65), e3564 (2012).</li><li>Parajuli, L. K., Koike, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+structure+of+dendritic+spines+revealed+by+volume+electron+microscopy+techniques.">Three-dimensional structure of dendritic spines revealed by volume electron microscopy techniques.</a> Front Neuroanat. 15, 627368 (2021).</li><li>Ferreira, J. 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=Distance-dependent+regulation+of+NMDAR+nanoscale+organization+along+hippocampal+neuron+dendrites.">Distance-dependent regulation of NMDAR nanoscale organization along hippocampal neuron dendrites.</a> Proc Natl Acad Sci U S A. 117 (39), 24526-24533 (2020).</li><li>Megias, M., Emri, Z. s., Freund, T. F., Gulyas, A. I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Total+number+and+distribution+of+inhibitory+and+excitatory+synapses+on+hippocampal+ca1+pyramidal+cells.">Total number and distribution of inhibitory and excitatory synapses on hippocampal ca1 pyramidal cells.</a> Neuroscience. 102 (3), 527-540 (2001).</li><li>Katz, Y., 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=Synapse+distribution+suggests+a+two-stage+model+of+dendritic+integration+in+ca1+pyramidal+neurons.">Synapse distribution suggests a two-stage model of dendritic integration in ca1 pyramidal neurons.</a> Neuron. 63 (2), 171-177 (2009).</li><li>Bourne, J., Harris, K. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Do+thin+spines+learn+to+be+mushroom+spines+that+remember.">Do thin spines learn to be mushroom spines that remember.</a> Curr Opin Neurobiol. 17 (3), 381-386 (2007).</li><li>Runge, K., Cardoso, C., De Chevigny, 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+spine+plasticity:+Function+and+mechanisms.">Dendritic spine plasticity: Function and mechanisms.</a> Front Synaptic Neurosci. 12, 36 (2020).</li><li>Tonnesen, J., Katona, G., Rozsa, B., Nagerl, U. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spine+neck+plasticity+regulates+compartmentalization+of+synapses.">Spine neck plasticity regulates compartmentalization of synapses.</a> Nat Neurosci. 17 (5), 678-685 (2014).</li><li>Mattila, P. K., Lappalainen, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Filopodia:+Molecular+architecture+and+cellular+functions.">Filopodia: Molecular architecture and cellular functions.</a> Nat Rev Mol Cell Biol. 9 (6), 446-454 (2008).</li><li>Grutzendler, J., Kasthuri, N., Gan, W. <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+dendritic+spine+stability+in+the+adult+cortex.">Long-term dendritic spine stability in the adult cortex.</a> Nature. 420 (6917), 812-816 (2002).</li></ol>

The authors have no conflicts of interest to disclose.

This protocol details the specific steps of sample preparation, imaging, and the process of dendritic spine quantification and classification using three-dimensional reconstruction software. This software is a powerful tool capable of producing robust structural data that contributes to a diverse array of investigations. Throughout the process, there are some critical steps that make this protocol less of a methodological burden and enhance the overall output of the data. The method for labeling dendritic spines is one o...

Dendritic spines are protrusions of dendrites often comprising the post-synaptic site of glutamatergic synapses1,2. Dendritic spines are of particular interest in the field of synaptic plasticity. Spines are often altered when synaptic strength changes, becoming larger and stronger in long-term synaptic potentiation or smaller and weaker in long-term synaptic depression3,4,5,6,7. Beyond synaptic plasticity, the profile of dendritic spines changes throughout the lifespan. In early development, there is a period of dendritic spine formation and growth, followed by dendritic spine pruning until reaching a steady state8,9,10. In the aging brain, spine loss accompanies brain shrinkage and cognitive decline11. Additionally, many neurological, neurodegenerative, and psychiatric disorders are characterized by aberrant dendritic spines. Multiple brain regions in individuals affected with schizophrenia have fewer dendritic spines, likely resulting from altered synaptic pruning12. Autism spectrum disorders are also characterized by dendritic spine pathologies13. Dendritic spine loss is a hallmark of both Alzheimer&#39;s and Parkinson&#39;s disease14,15. Given the wide array of research topics encompassing investigations into dendritic spine properties, techniques for accurate spine quantification are of paramount importance.
Staining, i.e., the Golgi method, or labeling neurons via dye filling or expressing fluorescent proteins are common methods for dendritic spine visualization16,17,18. Once visualized, spines can be analyzed with a variety of free and commercially available software clients. The desired output of the analysis is an important factor in determining which software will be of the most use. Fiji is a viable software option for questions centered around dendritic spine density. However, this technique largely relies on time consuming manual counting that can introduce the potential for bias. New plugins such as SpineJ allow for automatic quantification, additionally allowing for more accurate spine neck analysis19. A drawback of these approaches is the loss of a three-dimensional analysis for determining spine volume, as SpineJ is limited to two-dimensional image stacks. Additionally, obtaining spine subtype information becomes challenging via these processes. The four predominant spine subtypes, thin, mushroom, stubby, and filopodia, all connote individual functions and are largely classified via morphology20. Thin spines are characterized by an elongated neck and defined head21. Mushroom spines have a much larger and pronounced spine head22. Stubby spines are short and have little variance between head and neck23. Filopodia are immature spines with a long, thin neck and no obviously observable head24. While classification provides valuable information, spines exist on a continuum of dimensions. Classification into categories is based on ranges of morphological measurements25,26. Manually measuring spines for classification compounds the logistical burden for researchers in this approach.
Other software options focusing specifically on three-dimensional dendritic spine analysis are better suited for investigations into spine volume and subtype properties27,28,29,30,31. Despite the difficulty presented by three-dimensional analysis, such as poor z-plane resolution and smear, these software options allow for reliable three-dimensional reconstruction of dendrites and dendritic spines in a user-guided semi-automated fashion. Automatic classification of identified spines into their subtypes is also a feature present in some of these spine analysis software packages. This can ameliorate concerns of potential workload and experimental bias. Neurolucida 360 is one commercially available software allowing for reliable and reproducible three-dimensional dendritic spine identification and classification32. Here, we present a comprehensive protocol to effectively prepare fixed tissue, acquire images, and ultimately quantify and classify dendritic spines using this software.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
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			<td>518F Immersion Oil</td>
			<td>Zeiss</td>
			<td>444960-0000-000</td>
			<td></td>
		</tr>
		<tr>
			<td>Cryostat</td>
			<td>Leica</td>
			<td>CM3050S</td>
			<td>For slice preparation</td>
		</tr>
		<tr>
			<td>Fine Forceps</td>
			<td>FST</td>
			<td>11150-10</td>
			<td></td>
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		<tr>
			<td>Hemostat Forceps</td>
			<td>FST</td>
			<td>13020-12</td>
			<td></td>
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			<td>Large Surgical Scissors</td>
			<td>FST</td>
			<td>14002-16</td>
			<td></td>
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			<td>LSM 880 Confocal Microscope</td>
			<td>Zeiss</td>
			<td>LSM 880</td>
			<td></td>
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			<td>Microscope Cover Glass</td>
			<td>Fisherbrand</td>
			<td>12-541-035</td>
			<td></td>
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		<tr>
			<td>Mini-Peristaltic Pump II</td>
			<td>Harvard Apparatus</td>
			<td>70-2027</td>
			<td>For perfusions</td>
		</tr>
		<tr>
			<td>Neurolucida 360</td>
			<td>MBF Bioscience</td>
			<td>v2022.1.1</td>
			<td>Spine Analysis Software</td>
		</tr>
		<tr>
			<td>Neurolucida Explorer</td>
			<td>MBF Bioscience</td>
			<td>v2022.1.1</td>
			<td>Spine Analysis Software</td>
		</tr>
		<tr>
			<td>OCT Compound</td>
			<td>Sakura Finetek</td>
			<td>4583</td>
			<td>For cryostat sectioning</td>
		</tr>
		<tr>
			<td>Paraformaldehyde (37%)</td>
			<td>Fisherbrand</td>
			<td>F79-1</td>
			<td></td>
		</tr>
		<tr>
			<td>Plan-Apochromat 63x/1.40 Oil DIC</td>
			<td>Zeiss</td>
			<td>440762-9904-000</td>
			<td></td>
		</tr>
		<tr>
			<td>Scalpel Blade</td>
			<td>FST</td>
			<td>10022-00</td>
			<td></td>
		</tr>
		<tr>
			<td>Small Surgical Scissors</td>
			<td>FST</td>
			<td>14060-09</td>
			<td></td>
		</tr>
		<tr>
			<td>Spatula&#160;</td>
			<td>FST</td>
			<td>10091-12</td>
			<td></td>
		</tr>
		<tr>
			<td>Sucrose</td>
			<td>FIsherbrand</td>
			<td>S5-500</td>
			<td></td>
		</tr>
		<tr>
			<td>Superfrost Plus Microslides</td>
			<td>Diagger</td>
			<td>ES4951+</td>
			<td></td>
		</tr>
		<tr>
			<td>Vectashield HardSet Mounting Medium</td>
			<td>Vector Laboratories</td>
			<td>H-1400-10</td>
			<td></td>
		</tr>
	</tbody>
</table>

dendritic spine quantification using an automatic three-dimensional neuron reconstruction software

Synaptic connections allow for the exchange and processing of information between neurons. The post-synaptic site of excitatory synapses is often formed on dendritic spines. Dendritic spines are structures of great interest in research centered around synaptic plasticity, neurodevelopment, and neurological and psychiatric disorders. Dendritic spines undergo structural modifications during their lifespan, with properties such as total spine number, dendritic spine size, and morphologically defined subtype altering in response to different processes. Delineating the molecular mechanisms regulating these structural alterations of dendritic spines relies on morphological measurement. This mandates accurate and reproducible dendritic spine analysis to provide experimental evidence. The present study outlines a detailed protocol for dendritic spine quantification and classification using Neurolucida 360 (automatic three-dimensional neuron reconstruction software). This protocol allows for the determination of key dendritic spine properties such as total spine density, spine head volume, and classification into spine subtypes thus enabling effective analysis of dendritic spine structural phenotypes.

Effectively utilizing this analysis method begins with the selection of dendritic segments for tracing. As described in Figure 1, the ideal dendrites for tracing are not in close proximity to other dendrites. Dendrites running in parallel can result in improperly identifying spines from a neighboring dendrite. Dendrites directly intersecting or running perpendicular in a different z-plane add significant difficulty to accurate dendritic tracing as well. It is also important to note the diffe...

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

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

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

Synaptic connections allow for the exchange and processing of information between neurons. The post-synaptic site of excitatory synapses is often formed ...

The scope of our research is to examine the structural profile of dendritic spines in the hippocampus, specifically the CA-one apical dendrite. Through this, we're trying to answer questions centered around how different inputs to different parts of the same dendrite can be encoded at the cellular level. One challenge is obtaining high resolution images of dendritic spines for spine analysis.Once you have these images, the method to most accurately and reliably analyze spines in a fashion that isn't overly burdensome for the researcher presents an additional challenge. A balance must be struck between the two components. This protocol offers a happy medium between fully manual and fully automated dendritic spine analysis.Our protocol reduces the burden of manually counting individual spines. Additionally, the 3D reconstruction makes it possible to obtain spine volume. Classify the spines at the subtypes automatically.

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1.8K Views.  National Institutes of Health. The scope of our research is to examine the structural profile of dendritic spines in the hippocampus, specifically the CA-one apical dendrite. Through this, we're trying to answer questions centered around how different inputs to different parts of the same dendrite can be encoded at the cellular level. One challenge is obtaining high resolution images of dendritic spines for spine analysis.Once you have these images, the method to most accurately and reliably analyze spines in a fash...

Video: Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

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 ...

Three Dimensional Vestibular Ocular Reflex Testing Using a Six Degrees of Freedom Motion Platform

The vestibular organ is a sensor that measures angular and linear accelerations with six degrees of freedom (6DF). Complete or partial defects in the vestibular organ results in mild to severe equilibrium problems, such as vertigo, dizziness, oscillopsia, gait unsteadiness nausea and/or vomiting. A good and frequently used measure to quantify gaze stabilization is the gain, which is defined as the magnitude of compensatory eye movements with respect to imposed head movements. To test vestibular function more fully one has to realize that 3D VOR ideally generates compensatory ocular rotations not only with a magnitude (gain) equal and opposite to the head rotation but also about an axis that is co-linear with the head rotation axis (alignment). Abnormal vestibular function thus results in changes in gain and changes in alignment of the 3D VOR response.
Here we describe a method to measure 3D VOR using whole body rotation on a 6DF motion platform. Although the method also allows testing translation VOR responses 1, we limit ourselves to a discussion of the method to measure 3D angular VOR. In addition, we restrict ourselves here to description of data collected in healthy subjects in response to angular sinusoidal and impulse stimulation.
Subjects are sitting upright and receive whole-body small amplitude sinusoidal and constant acceleration impulses. Sinusoidal stimuli (f = 1 Hz, A = 4&deg;) were delivered about the vertical axis and about axes in the horizontal plane varying between roll and pitch at increments of 22.5&deg; in azimuth. Impulses were delivered in yaw, roll and pitch and in the vertical canal planes. Eye movements were measured using the scleral search coil technique 2. Search coil signals were sampled at a frequency of 1 kHz.
The input-output ratio (gain) and misalignment (co-linearity) of the 3D VOR were calculated from the eye coil signals 3.
Gain and co-linearity of 3D VOR depended on the orientation of the stimulus axis. Systematic deviations were found in particular during horizontal axis stimulation. In the light the eye rotation axis was properly aligned with the stimulus axis at orientations 0&deg; and 90&deg; azimuth, but gradually deviated more and more towards 45&deg; azimuth.
The systematic deviations in misalignment for intermediate axes can be explained by a low gain for torsion (X-axis or roll-axis rotation) and a high gain for vertical eye movements (Y-axis or pitch-axis rotation (see Figure 2). Because intermediate axis stimulation leads a compensatory response based on vector summation of the individual eye rotation components, the net response axis will deviate because the gain for X- and Y-axis are different.
In darkness the gain of all eye rotation components had lower values. The result was that the misalignment in darkness and for impulses had different peaks and troughs than in the light: its minimum value was reached for pitch axis stimulation and its maximum for roll axis stimulation.
Case Presentation
Nine subjects participated in the experiment. All subjects gave their informed consent. The experimental procedure was approved by the Medical Ethics Committee of Erasmus University Medical Center and adhered to the Declaration of Helsinki for research involving human subjects.
Six subjects served as controls. Three subjects had a unilateral vestibular impairment due to a vestibular schwannoma. The age of control subjects (six males and three females) ranged from 22 to 55 years. None of the controls had visual or vestibular complaints due to neurological, cardio vascular and ophthalmic disorders.
The age of the patients with schwannoma varied between 44 and 64 years (two males and one female). All schwannoma subjects were under medical surveillance and/or had received treatment by a multidisciplinary team consisting of an othorhinolaryngologist and a neurosurgeon of the Erasmus University Medical Center. Tested patients all had a right side vestibular schwannoma and underwent a wait and watch policy (Table 1; subjects N1-N3) after being diagnosed with vestibular schwannoma. Their tumors had been stabile for over 8-10 years on magnetic resonance imaging.

A method is described to measure three-dimensional vestibulo ocular reflexes (3D VOR) in humans using a six degrees of freedom (6DF) motion simulator. The gain and misalignment of the 3D angular VOR provide a direct measure of the quality of vestibular function. Representative data on healthy subjects are provided

The vestibular organ is a sensor that measures angular and linear accelerations with six degrees of freedom (6DF). Complete or partial defects in the ...

Protocol for Three-dimensional Confocal Morphometric Analysis of Astrocytes

As glial cells in the brain, astrocytes have diverse functional roles in the central nervous system. In the presence of harmful stimuli, astrocytes modify their functional and structural properties, a condition called reactive astrogliosis. Here, a protocol for assessment of the morphological properties of astrocytes is presented. This protocol includes quantification of 12 different parameters i.e. the surface area and volume of the tissue covered by an astrocyte (astrocyte territory), the entire astrocyte including branches, cell body, and nucleus, as well as total length and number of branches, the intensity of fluorescence immunoreactivity of antibodies used for astrocyte detection, and astrocyte density (number/1,000 &#181;m2). For this purpose three-dimensional (3D) confocal microscopic images were created, and 3D image analysis software such as Volocity 6.3 was used for measurements. Rat brain tissue exposed to amyloid beta1-40 (A&#946;1-40) with or without a therapeutic&#160;intervention was used to present the method. This protocol can also be used for 3D morphometric analysis of other cells from either in vivo or in vitro conditions.

Astrocytes in the CNS change their functional and structural properties in response to harmful stimuli. This report presents a protocol for assessment of three-dimensional astrocyte morphology in diseased conditions or after therapeutic interventions.

As glial cells in the brain, astrocytes have diverse functional roles in the central nervous system. In the presence of harmful stimuli, astrocytes modify ...

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling

Functional recovery rarely occurs&#160;following injury or disease-induced degeneration within the central nervous system (CNS)&#160;due to the inhibitory environment and the limited capacity for neurogenesis. We are developing&#160;a strategy to simultaneously address&#160;neuronal and axonal pathway loss within the damaged CNS. This manuscript presents the fabrication protocol for micro-tissue engineered neural networks (micro-TENNs), implantable constructs consisting of neurons and aligned axonal tracts spanning the extracellular matrix (ECM) lumen of a preformed hydrogel cylinder hundreds of microns in diameter that may extend centimeters in length. Neuronal aggregates are delimited to the extremes of the three-dimensional encasement and are spanned by axonal projections. Micro-TENNs are uniquely poised as a strategy for CNS reconstruction, emulating aspects of brain connectome cytoarchitecture and potentially providing means for network replacement. The neuronal aggregates may synapse with host tissue to form new functional relays to restore and/or modulate missing or damaged circuitry. These constructs may also act as pro-regenerative&#160;&#34;living scaffolds&#34; capable of exploiting developmental mechanisms for cell migration and axonal pathfinding, providing synergistic structural and soluble cues based on the state of regeneration. Micro-TENNs are fabricated by pouring liquid hydrogel into a cylindrical mold containing a longitudinally centered needle. Once the hydrogel has gelled, the needle is removed, leaving a hollow micro-column. An ECM solution is added to the lumen to provide an environment suitable for neuronal adhesion and axonal outgrowth. Dissociated neurons are mechanically aggregated for precise seeding within one or both ends of the micro-column. This methodology reliably produces self-contained miniature constructs with long-projecting axonal tracts that may recapitulate features of brain neuroanatomy. Synaptic immunolabeling and genetically encoded calcium indicators suggest that micro-TENNs possess extensive synaptic distribution and intrinsic electrical activity. Consequently, micro-TENNs represent a promising strategy for targeted neurosurgical reconstruction of brain pathways and may also be applied as biofidelic models to study neurobiological phenomena in vitro.

This manuscript details the fabrication of micro-tissue engineered neural networks: three-dimensional micron-sized constructs comprised of long aligned axonal tracts spanning aggregated neuronal population(s) encased in a tubular hydrogel. These living scaffolds can serve as functional relays to reconstruct or modulate neural circuitry or as biofidelic test-beds mimicking gray-white matter neuroanatomy.

Functional recovery rarely occurs&#160;following injury or disease-induced degeneration within the central nervous system (CNS)&#160;due to the inhibitory ...

High-resolution Confocal Imaging of the Blood-brain Barrier: Imaging, 3D Reconstruction, and Quantification of Transcytosis

The blood-brain barrier (BBB) is a dynamic multicellular interface that regulates the transport of molecules between the circulation and the brain. Transcytosis across the BBB regulates the delivery of hormones, metabolites, and therapeutic antibodies to the brain parenchyma. Here, we present a protocol that combines immunofluorescence of free-floating sections with laser scanning confocal microscopy and image analysis to visualize subcellular organelles within endothelial cells at the BBB. Combining this data-set with 3D image analysis software allows for the semi-automated segmentation and quantification of capillary volume and surface area, as well as the number and intensity of intracellular organelles at the BBB. The detection of mouse endogenous immunoglobulin (IgG) within intracellular vesicles and their quantification at the BBB is used to illustrate the method. This protocol can potentially be applied to the investigation of the mechanisms controlling BBB transcytosis of different molecules in vivo.

Here we present a microscopy-based protocol for high-resolution imaging and a three-dimensional reconstruction of the mouse neurovascular unit and blood-brain barrier using brain free-floating sections. This method allows for the visualization, analysis, and quantification of intracellular organelles at the BBB.

The blood-brain barrier (BBB) is a dynamic multicellular interface that regulates the transport of molecules between the circulation and the brain. ...

Visualization of Motor Axon Navigation and Quantification of Axon Arborization In Mouse Embryos Using Light Sheet Fluorescence Microscopy

Spinal motor neurons (MNs) extend their axons to communicate with their innervating targets, thereby controlling movement and complex tasks in vertebrates. Thus, it is critical to uncover the molecular mechanisms of how motor axons navigate to, arborize, and innervate their peripheral muscle targets during development and degeneration. Although transgenic Hb9::GFP mouse lines have long served to visualize motor axon trajectories during embryonic development, detailed descriptions of the full spectrum of axon terminal arborization remain incomplete due to the pattern complexity and limitations of current optical microscopy. Here, we describe an improved protocol that combines light sheet fluorescence microscopy (LSFM) and robust image analysis to qualitatively and quantitatively visualize developing motor axons. This system can be easily adopted to cross genetic mutants or MN disease models with Hb9::GFP lines, revealing novel molecular mechanisms that lead to defects in motor axon navigation and arborization.

Here, we describe a protocol for visualizing motor neuron projection and axon arborization in transgenic Hb9::GFP mouse embryos. After immunostaining for motor neurons, we used light sheet fluorescence microscopy to image embryos for subsequent quantitative analysis. This protocol is applicable to other neuron navigation processes in the central nervous system.

Spinal motor neurons (MNs) extend their axons to communicate with their innervating targets, thereby controlling movement and complex tasks in ...

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Statistical shape analysis of brain structures has been used to investigate the association between their structural changes and pathological processes. We have developed a software package for accurate and robust shape modeling and group-wise analysis. Here, we introduce an pipeline for the shape analysis, from individual 3D shape modeling to quantitative group shape analysis. We also describe the pre-processing and segmentation steps using open software packages. This practical guide would help researchers save time and effort in 3D shape analysis on brain structures.

We introduce a semi-automatic protocol for shape analysis on brain structures, including image segmentation using open software, and further group-wise shape analysis using an automated modeling package. Here, we demonstrate each step of the 3D shape analysis protocol with hippocampal segmentation from brain MR images.

Statistical shape analysis of brain structures has been used to investigate the association between their structural changes and pathological processes. ...

Three-Dimensional Motor Nerve Organoid Generation

A fascicle of axons is one of the major structural motifs observed in the nervous system. Disruption of axon fascicles could cause developmental and neurodegenerative diseases. Although numerous studies of axons have been conducted, our understanding of formation and dysfunction of axon fascicles is still limited due to the lack of robust three-dimensional in vitro models. Here, we describe a step-by-step protocol for the rapid generation of a motor nerve organoid (MNO) from human induced pluripotent stem (iPS) cells in a microfluidic-based tissue culture chip. First, fabrication of chips used for the method is described. From human iPS cells, a motor neuron spheroid (MNS) is formed. Next, the differentiated MNS is transferred into the chip. Thereafter, axons spontaneously grow out of the spheroid and assemble into a fascicle within a microchannel equipped in the chip, which generates an MNO tissue carrying a bundle of axons extended from the spheroid. For the downstream analysis, MNOs can be taken out of the chip to be fixed for morphological analyses or dissected for biochemical analyses, as well as calcium imaging and multi-electrode array recordings. MNOs generated with this protocol can facilitate drug testing and screening and can contribute to understanding of mechanisms underlying development and diseases of axon fascicles.

This protocol provides a comprehensive procedure to fabricate human iPS cell-derived motor nerve organoid through spontaneous assembly of a robust bundle of axons extended from a spheroid in a tissue culture chip.

A fascicle of axons is one of the major structural motifs observed in the nervous system. Disruption of axon fascicles could cause developmental and ...

Automatic Identification of Dendritic Branches and their Orientation

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology of dendrites is essential for a better understanding of neuronal function. However, the complexity of dendritic trees, both when isolated and especially when located within neuronal networks, has not been completely understood. We developed a new computational tool, SOA (Segmentation and Orientation Analysis), which allows automatic measurement of the orientation of dendritic branches from fluorescence images of 2D neuronal cultures. SOA, written in Python, uses segmentation to distinguish dendritic branches from the image background and accumulates a database on the spatial direction of each branch. The database is then used to calculate morphological parameters such as the directional distribution of dendritic branches in a network and the prevalence of parallel dendritic branch growth. The data obtained can be used to detect structural changes in dendrites in response to neuronal activity and to biological and pharmacological stimuli.

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

The structure of neuronal dendritic trees plays a key role in the integration of synaptic inputs in neurons. Therefore, characterization of the morphology ...

Rapid Golgi Stain for Dendritic Spine Visualization in Hippocampus and Prefrontal Cortex

Golgi impregnation, using the Golgi staining kit with minor adaptations, is used to impregnate dendritic spines in the rat hippocampus and medial prefrontal cortex. This technique is a marked improvement over previous methods of Golgi impregnation because the premixed chemicals are safer to use, neurons are consistently well impregnated, there is far less background debris, and for a given region, there are extremely small deviations in spine density between experiments. Moreover, brains can be accumulated after a certain point and kept frozen until further processing. Using this method any brain region of interest can be studied. Once stained and cover slipped, dendritic spine density is determined by counting the number of spines for a length of dendrite and expressed as spine density per 10 &#181;m dendrite.

The protocol describes a modification of the rapid Golgi method, which can be adapted to any part of the nervous system, for staining neurons in the hippocampus and medial prefrontal cortex of the rat.