Name: dendritic spine quantification using an automatic three-dimensional neuron reconstruction software
Uploaded: 2024-09-27T12:50:00.000+00:00
Duration: 7 min 45 s
Description: Synaptic connections allow for the exchange and processing of information between neurons. The post-synaptic site of excitatory synapses is often formed ...

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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J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+4D+analysis+of+dendritic+spine+morphology:+Applications+to+stimulus-induced+spine+remodeling+and+pharmacological+rescue+in+disease+model.">Automated 4D analysis of dendritic spine morphology: Applications to stimulus-induced spine remodeling and pharmacological rescue in disease model.</a> Mol Brain. 4, 38 (2011).</li><li>Basu, 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=Quantitative+3-D+morphometric+analysis+of+individual+dendritic+spines.">Quantitative 3-D morphometric analysis of individual dendritic spines.</a> Sci Rep. 8 (1), 3545 (2018).</li><li>Ekaterina, P., Peter, V., Smirnova, D., Vyacheslav, C., Ilya, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spinetool+is+an+open-source+software+for+analysis+of+morphology+of+dendritic+spines.">Spinetool is an open-source software for analysis of morphology of dendritic spines.</a> Sci Rep. 13 (1), 10561 (2023).</li><li>Li, B. 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><tbody><tr><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></tr><tr><td>Hemostat Forceps</td><td>FST</td><td>13020-12</td><td></td></tr><tr><td>Large Surgical Scissors</td><td>FST</td><td>14002-16</td><td></td></tr><tr><td>LSM 880 Confocal Microscope</td><td>Zeiss</td><td>LSM 880</td><td></td></tr><tr><td>Microscope Cover Glass</td><td>Fisherbrand</td><td>12-541-035</td><td></td></tr><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&amp;nbsp;</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 ...

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2.0K Views.  National Institutes of Health. 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. 

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

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

Dendritic spines are small protrusions that correspond to the post-synaptic compartments of excitatory synapses in the central nervous system. They are distributed along the dendrites. Their morphology is largely dependent on neuronal activity, and they are dynamic. Dendritic spines express glutamatergic receptors (AMPA and NMDA receptors) on their surface and at the levels of postsynaptic densities. Each spine allows the neuron to control its state and local activity independently. Spine morphologies have been extensively studied in glutamatergic pyramidal cells of the brain cortex, using both in vivo approaches and neuronal cultures obtained from rodent tissues. Neuropathological conditions can be associated to altered spine induction and maturation, as shown in rodent cultured neurons and one-dimensional quantitative analysis 1. The present study describes a protocol for the 3D quantitative analysis of spine morphologies using human cortical neurons derived from neural stem cells (late cortical progenitors). These cells were initially obtained from induced&#160;pluripotent stem cells. This protocol allows the analysis of spine morphologies at different culture periods, and with possible comparison between induced&#160;pluripotent stem cells obtained from control individuals with those obtained from patients with psychiatric diseases.

Dendritic spines of pyramidal neurons are the sites of most excitatory synapses in mammalian brain cortex. This method describes a 3D quantitative analysis of spine morphologies in human cortical pyramidal glutamatergic neurons derived from induced&#160;pluripotent stem cells.

Dendritic spines are small protrusions that correspond to the post-synaptic compartments of excitatory synapses in the central nervous system. They are ...

Developmental Biology

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 ...

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. ...

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

Dendritic arborization has been shown to be a reliable marker for examination of structural and functional integrity of neurons. Indeed, the complexity and extent of dendritic arborization correlates well with the synaptic plasticity in these cells. A reliable method for assessment of dendritic arborization is needed to characterize the deleterious effects of neurological disorders on these structures and to determine the effects of therapeutic interventions. However, quantification of these structures has proven to be a formidable task given their complex and dynamic nature. Fortunately, sophisticated imaging techniques can be paired with conventional staining methods to assess the state of dendritic arborization, providing a more reliable and expeditious means of assessment. Below is an example of how these imaging techniques were paired with staining methods to characterize the dendritic arborization in wild type mice. These complementary imaging methods can be used to qualitatively and quantitatively assess dendritic arborization that span a rather wide area within the hippocampal region.

We describe two methods for visualization and quantification of dendritic arborization in the hippocampus of mouse models: real-time and extended depth of field imaging. While the former method allows sophisticated topographical tracing and quantification of the extent of branching, the latter allows speedy visualization of the dendritic tree.

Dendritic arborization has been shown to be a reliable marker for examination of structural and functional integrity of neurons. Indeed, the complexity ...

3D Modeling of Dendritic Spines with Synaptic Plasticity

Computational modeling of diffusion and reaction of chemical species in a three-dimensional (3D) geometry is a fundamental method to understand the mechanisms of synaptic plasticity in dendritic spines. In this protocol, the detailed 3D structure of the dendrites and dendritic spines is modeled with meshes on the software Blender with CellBlender. The synaptic and extrasynaptic regions are defined on the mesh. Next, the synaptic receptor and synaptic anchor molecules are defined with their diffusion constants. Finally, the chemical reactions between synaptic receptors and synaptic anchors are included and the computational model is solved numerically with the software MCell. This method describes the spatiotemporal path of every single molecule in a 3D geometrical structure. Thus, it is very useful to study the trafficking of synaptic receptors in and out of the dendritic spines during the occurrence of synaptic plasticity. A limitation of this method is that the high number of molecules slows the speed of the simulations. Modeling of dendritic spines with this method allows the study of homosynaptic potentiation and depression within single spines and heterosynaptic plasticity between neighbor dendritic spines.

The protocol develops a three-dimensional (3D) model of a dendritic segment with dendritic spines for modeling synaptic plasticity. The constructed mesh can be used for computational modeling of AMPA receptor trafficking in the long-term synaptic plasticity using the software program Blender with CellBlender and MCell.

Computational modeling of diffusion and reaction of chemical species in a three-dimensional (3D) geometry is a fundamental method to understand the ...

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

It has been reported that the size and shape of dendritic spines is related to their structural plasticity. To identify the morphological structure of pyramidal neurons and dendritic spines, a ballistic labeling technique can be utilized. In the present protocol, pyramidal neurons are labeled with DilC18(3) dye and analyzed using neuronal reconstruction software to assess neuronal morphology and dendritic spines. To investigate neuronal structure, dendritic branching analysis and Sholl analysis are performed, allowing researchers to draw inferences about dendritic branching complexity and neuronal arbor complexity, respectively. The evaluation of dendritic spines is conducted using an automatic assisted classification algorithm integral to the reconstruction software, which classifies spines into four categories (i.e., thin, mushroom, stubby, filopodia). Furthermore, an additional three parameters (i.e., length, head diameter, and volume) are also chosen to assess alterations in dendritic spine morphology. To validate the potential of wide application of the ballistic labeling technique, pyramidal neurons from in vitro cell culture were successfully labeled. Overall, the ballistic labeling method is unique and useful for visualizing neurons in different brain regions in rats, which in combination with sophisticated reconstruction software, allows researchers to elucidate the possible mechanisms underlying neurocognitive dysfunction.

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

It has been reported that the size and shape of dendritic spines is related to their structural plasticity. To identify the morphological structure of ...

Imaging and Quantification of Intact Neuronal Dendrites via CLARITY Tissue Clearing

Brain activity, the electrochemical signals passed between neurons, is determined by the connectivity patterns of neuronal networks, and from the morphology of processes and substructures within these neurons. As such, much of what is known about brain function has arisen alongside developments in imaging technologies that allow further insight into how neurons are organized and connected in the brain. Improvements in tissue clearing have allowed for high-resolution imaging of thick brain slices, facilitating morphological reconstruction and analyses of neuronal substructures, such as dendritic arbors and spines. In tandem, advances in image processing software provide methods of quickly analyzing large imaging datasets. This work presents a relatively rapid method of processing, visualizing, and analyzing thick slices of labeled neural tissue at high-resolution using CLARITY tissue clearing, confocal microscopy, and image analysis. This protocol will facilitate efforts toward understanding the connectivity patterns and neuronal morphologies that characterize healthy brains, and the changes in these characteristics that arise in diseased brain states.

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

Brain activity, the electrochemical signals passed between neurons, is determined by the connectivity patterns of neuronal networks, and from the ...

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 ...

3D Reconstruction and Analysis of Thin Subcellular Neuronal Structures using Focused-Ion Beam Scanning Electron Microscopy Data

Recent advances in scanning electron microscope technologies now permit the rapid three-dimensional (3D) analysis of ultrathin subcellular processes. Here, a methodological pipeline is presented to identify, visualize, and analyze thin neuronal processes, such as those that project into the presynaptic boutons of other neurons (termed 'spinules'). Using freely available software packages, this protocol demonstrates how to use a decision tree to identify common neuronal subcellular structures using morphological criteria within focused ion beam scanning electron microscopy (FIB-SEM) image volumes, with particular attention on identifying a diversity of spinules projecting into presynaptic boutons. In particular, this protocol describes how to trace spinules within neuronal synapses to produce 3D reconstructions of these thin subcellular projections, their parent neurites, and postsynaptic partners. Additionally, the protocol includes a list of freely available open-source software programs for analyzing FIB-SEM data and offers tips (e.g., smoothing, lighting) toward improving 3D reconstructions for visualization and publication. This adaptable protocol offers an entry point into the rapid nanoscale analysis of subcellular structures within FIB-SEM image volumes.

A flexible methodological pipeline to identify, visualize, and quantify thin subcellular neuronal processes within focused ion beam scanning electron microscopy image volumes using user-friendly open-source software packages.

Recent advances in scanning electron microscope technologies now permit the rapid three-dimensional (3D) analysis of ultrathin subcellular processes. ...

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.