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

Summary

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.

Abstract

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.

Introduction

Dendritic spines are protrusions of dendrites often comprising the post-synaptic site of glutamatergic synapses^1,2. 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 depression^3,4,5,6,7. 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 state^8,9,10. 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's and Parkinson's disease^14,15. 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 visualization^16,17,18. 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 measurements^25,26. 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 properties^{27,28,29,30,31}. 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.

Protocol

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

1. 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.
2. Using large surgical scissors, remove the skin and fur from the chest, allowing for easier visualization of the underlying ribcage.
3. 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.
4. 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.
5. 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° C) for optimal fixation.
6. 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.
7. 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.
8. 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.
9. 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.
10. 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.
11. Ensure that the blade angle of the cryostat is set between 0° and 5° to produce uniform sections. Adjust the roll plate angle for optimal slice flattening. Please refer to the equipment manual for specific instructions.
12. Place the specimen holder in the cryostat with the ventral surface of the brain closest to the blade. Cut the brain into 30 µm 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.
13. 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.
14. Apply 100 µL 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.

2. High-resolution confocal imaging

1. 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.
2. 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.
3. Acquire z-stacks encompassing the full dendritic segments for future analysis. Z-stacks larger than 10 µm 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 µm)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.
4. Optional: If available, utilize the microscope's respective deconvolution software functionality to deconvolve images. This will allow for higher-resolution images.

3. Dendritic spine quantification

1. Open Neurolucida 360 (v2022.1.1 or later). Open the image file in the spine analysis software (File > Open > 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.
2. 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)
3. Identify a suitable dendritic segment for tracing.
   1. 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.
   2. 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 densities^34,35. This needs to be accounted for in the experimental design. Secondary order dendrites <1.5 µm in thickness are ideal candidates for tracing (Figure 1).
4. 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.
5. 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.
   1. 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 µm in length (Supplementary Figure 3).
6. 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).
7. 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.
8. 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.
9. Repeat steps 3.3-3.8 for multiple dendrites in the image before proceeding to spine identification in step 3.10.
10. 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 µm, Minimum Height: 0.3 µm, 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.
11. 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.
12. 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.
    1. 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.
    2. 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.
13. 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.
14. Left click Move Pivot Point and left click on the dendrite requiring further spine detection to set the pivot point.
    1. 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.
    2. 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).
    3. 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.
    4. 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.
15. 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.
16. 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.
17. 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 µm, Filopodium Length- 3 µm.
18. 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.
19. Within the Explorer window in the View tab, left-click Select All to highlight all dendrites and spines.
20. Left-click the Analyze tab in the upper toolbar. Left-click the Structure drop-down menu. Left click Branched Structure Analysis.
21. 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 > Each Dendrite and Spines > Spine Details. Select OK, and the data will appear in two separate windows.
22. 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.

Results

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...

Discussion

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...

Materials

Name	Company	Catalog Number	Comments
518F Immersion Oil	Zeiss	444960-0000-000
Cryostat	Leica	CM3050S	For slice preparation
Fine Forceps	FST	11150-10
Hemostat Forceps	FST	13020-12
Large Surgical Scissors	FST	14002-16
LSM 880 Confocal Microscope	Zeiss	LSM 880
Microscope Cover Glass	Fisherbrand	12-541-035
Mini-Peristaltic Pump II	Harvard Apparatus	70-2027	For perfusions
Neurolucida 360	MBF Bioscience	v2022.1.1	Spine Analysis Software
Neurolucida Explorer	MBF Bioscience	v2022.1.1	Spine Analysis Software
OCT Compound	Sakura Finetek	4583	For cryostat sectioning
Paraformaldehyde (37%)	Fisherbrand	F79-1
Plan-Apochromat 63x/1.40 Oil DIC	Zeiss	440762-9904-000
Scalpel Blade	FST	10022-00
Small Surgical Scissors	FST	14060-09
Spatula	FST	10091-12
Sucrose	FIsherbrand	S5-500
Superfrost Plus Microslides	Diagger	ES4951+
Vectashield HardSet Mounting Medium	Vector Laboratories	H-1400-10