Spot Spine, a freely available ImageJ plugin for 3D detection and morphological analysis of dendritic spines

Background Dendritic spines are tiny protrusions found along the dendrites of neurons, and their number is a measure of the density of synaptic connections. Altered density and morphology is observed in several pathologies, and spine formation as well as morphological changes correlate with learning and memory. The detection of spines in microscopy images and the analysis of their morphology is therefore a prerequisite for many studies. We have developed a new open-source, freely available, plugin for ImageJ/FIJI, called Spot Spine, that allows detection and morphological measurements of spines in three dimensional images. Method Local maxima are detected in spine heads, and the intensity distribution around the local maximum is computed to perform the segmentation of each spine head. Spine necks are then traced from the spine head to the dendrite. Several parameters can be set to optimize detection and segmentation, and manual correction gives further control over the result of the process. Results The plugin allows the analysis of images of dendrites obtained with various labeling and imaging methods. Quantitative measurements are retrieved including spine head volume and surface, and neck length. Conclusion The plugin and instructions for use are available at https://imagej.net/plugins/spot-spine.


Introduction
Dendritic spines are small protrusions distributed along the dendrites of neurons.Since each spine corresponds to a glutamatergic synapse, the density of spines located along the dendrite is a measure of the density of neuronal connectivity.Spine density measurement is thus pivotal for assessing connectivity changes during development, upon synaptic plasticity and learning, as well as in the context of psychiatric diseases (Holtmaat and Svoboda, 2009;Penzes et al., 2011;Ma and Zuo, 2022;Heck and Santos, 2023).The morphology of dendritic spines, which are composed of a neck and a head, is another relevant parameter.The size of the head is tightly correlated with the size of the postsynapse density and synaptic current amplitude (Noguchi et al., 2011;Holler et al., 2021).The length and width of the neck may have an influence on the integrative properties of the spine, since the neck represents a morphological constrains so the spine head is a functional compartment isolated for the dendritic shaft (Adrian et al., 2014;Tonnesen and Nägerl, 2016;Cornejo et al., 2022).Therefore, quantification of spine density, spine head size and spine neck length are desirable for each of these parameters retrieve valuable information on neural connectivity, synapse efficacy and plasticity.
Dendritic spines however have a size that make them lay at the edge of resolution possibilities of photonic microscopy, hence detection and morphological analysis represent a challenge.Different strategies have been adopted in order to obtain reliable detection and segmentation of dendritic spines, reviewed in Mancuso et al. (2013) and Okabe (2020).Several algorithms have been developed, among which some have been implemented into softwares performing 3D spine detection and analysis.The first developed tools were 3DMA-neuron (Koh et al., 2002) and NeuronIQ (Cheng et al., 2007;Zhang et al., 2007).The widely used NeuronStudio (Rodriguez et al., 2008) has been transferred to commercially available Neurolucida (Dickstein et al., 2016).Another commercial software is Imaris Filament Tracer module (Swanger et al., 2011;Benavides-Piccione et al., 2013).Other applications for spine detection and analysis from confocal microscopy images include Spiso3D (Mukai et al., 2011), SpineLab (Jungblut et al., 2012) and a tool included in the FARSIGHT project (Yuan et al., 2009).More recent applications include 3dSpAn (Das et al., 2022) and a spine detector plugin for the Vaa3D software (Iascone et al., 2020), as well as tools dedicated to two-photon microscopy (Singh et al., 2017;Rada et al., 2018;Argunşah et al., 2022;Vogel et al., 2023) and structured illumination microscopy (Kashiwagi et al., 2019).Machine learning based method has also been developed to identify dendritic spines (Blumer et al., 2015;Smirnov et al., 2018;Guerra et al., 2023), and deep learning approach was used to provide the automated spine segmentation softwares DeepSpineNet and DeepD3 (Vidaurre-Gallart et al., 2022;Fernholz et al., 2024).Herein, we present an ImageJ plugin that allows spine detection, spine heads segmentation and spine necks tracing.

Neuronal labeling
Fluorescent labeling of dendrites was obtained by diolistic method (Heck et al., 2012).DiI (3 mg, ThermoFischer D282) is precipitated on the surface of 1.3 microns tungsten beads (50 mg, BioRad M-20).The coated beads are projected by helium gas pressure (150psi) through a 3 micron pore-size filter (Isopore polycarbonate, Millipore) on brain sections from animals perfused with 1.5% paraformaldehyde.The hydrophobic DiI molecule inserts into plasma membrane and passively diffuse along the dendrite, enabling a fluorescent membrane labeling that outlines neuronal morphology.After labeling, the sections were kept in phosphate buffer saline for 2 hours then mounted in Prolong Gold media (Molecular Probes, P36930).

Image acquisition and deconvolution
Confocal Laser Scanning Microscope (SP5, Leica) equipped with a 1.4 NA objective (oil immersion, Leica) was used to acquire image stacks with pixel size of 60 nm and z-step of 200 nm, at excitation wavelength of 561 nm and emission range 570-650 nm.Laser intensity was set so that each image occupies the full dynamic range of the detector (low noise Hybrid detector, Leica).Deconvolution with experimental PSF from 175 nm PS-speck Microscope Point Source fluorescent beads using Maximum Likelihood Estimation algorithm was performed with Huygens software (Scientific Volume Imaging).150 iterations were applied in classical mode, background intensity was averaged from the voxels with lowest intensity, and signal to noise ratio values were set to a value of 20.

Implementation, features and usage
Our plugin Spot Spine uses our Spot Segmentation workflow (Ollion et al., 2013;Heck et al., 2015) and the tracing algorithm from the plugin SNT (Arshadi et al., 2021) to perform three-dimensional detection and analysis of dendritic spines in image stacks (Figure 1).The algorithms are adapted to both isotropic and non isotropic voxels, since, typically, microscopy image stacks have lower axial resolution.Spot Spine is implemented as a plugin for ImageJ/FIJI (ImageJ 1.53, Schindelin et al., 2012a, 2012b).The plugin and instructions for use are available at https://imagej.net/plugins/spot-spine.
Figure 1.Flowchart of the Spot Spine plugin working process.After an image stack is opened in FIJI/ImageJ and the plugin launched, the user is invited to set parameters for local maxima detection.The plugin imports the dendrite model encoded in a.swc file and compute the local maxima.The user can modify the parameters and manually edit each local maximum.Spine head segmentation is performed by the plugin, and the results can be updated by modifying segmentation parameters as well as manually editing each spine head.The spine necks are then traced, but for images in which the necks are not labeled, the plugin can draw straight lines.A result table is given, including spine head volumes, spine neck length, among other measurements.
Before proceeding, it first requires importing a reconstructed model of the dendrite in SWC format.The tracing of the dendrite, coded in a swc file, can be obtained with various freely available tools such as SNT (Arshadi et al., 2021) or others (Parekh and Ascoli, 2013;Liu et al., 2022).The dendrite model is automatically imported after launching the plugin.A dendrite coded as SWC is a sequence of connected nodes, thus our plugin applies a frustum between each sphere to improve the representation of the dendrite volume.
Spot Spine detects the dendritic spines by computing the local maxima in the neighbouring region of the dendrite.Since some local maxima can be false positives from the background, or spines from another dendrite located near the studied dendrite, the user is invited to define the three following parameters: intensity value underneath which local maxima are ignored, and minimum and maximum distance from the border of the dendrite model, delimiting around the dendrite a 3D region in which local maxima will be computed.The computed local maxima are listed and displayed in the image stack.A maximum projection of the stack is displayed as well, enabling to easily apprehend the content of the image stack (Figure 2A).The user can then control further intensity and distance criteria to remove false positives.The minimal distance imposed between each local maxima can be reduced or expanded, enabling to adapt to either sparse or dense spine density along the dendrite.Moreover, full manual editing is easily achieved by removing single local maxima or adding local maxima by simply clicking in the image.The manual addition of local maxima is independent of the criteria of intensity and distance.It is noteworthy that the image stack and the maximum projection are synchronized.The user can thus interact on the maximum image projection for obvious cases of false positive and false negative, or in the image stack for better precision.When clicking in the maximum intensity projection, the location of the mouse in the image is recorded, and pixels contained in the region centered around the mouse coordinates are examined in each slice through the depth of the image stack.For spine deletion, the closest maximum is selected based on Nearest neighbor algorithm and deleted from the list.For spine addition, the coordinates of the voxel of highest intensity within a 5x5 region centered around the mouse location is added to the list.
Once the local maxima are found to correspond to each spine head, those are segmented in 3D using the spot segmentation workflow described in Gilles et al. (2017).Segmentation is run with set parameters that lead to consistent results with images obtained by confocal and two-photon microscopy from dendrites with membrane and cytosolic labeling, nevertheless, the user can update the segmentation after setting new values for the parameters.The segmented spine heads appear both on the synchronized image stack and maximum intensity projection (Figure 2B).As described for the selection of local maxima, when clicking in the maximum projection, the object is selected by the analysis of the 3D content of the image stack within a region centered around the 2D coordinates of the mouse location.The user can select spine head that would need to be removed.It is possible to add new spine in case a spine head was not detected by a local maxima by clicking in the spine head.Two spine heads can also be selected to be merged.Indeed, each local maximum will give an object, hence if two local maxima are found in one large spine head, the best strategy is to keep both and thus generate two adjacent objects that can be merged.
In the third step, spine necks are traced using the SNT algorithm (Figure 2C,D).The minimal euclidian distance between the spine head and the dendrite is computed to identify one voxel at the border of the spine head and one voxel at the border of the dendrite, and the optimal path between these two points is computed using SNT.After tracing, the neck is the one-voxel wide path from the voxel positioned at the edge of the spine head to the voxel preceding the first voxel positioned at the border of the dendrite.Manual editing is provided to delete wrong path and update the neck trace by imposing a new starting point at the border of the spine head.In the case of images in which the necks are not visible, the user can rather choose to obtain the minimal distance between the spine head center and the dendrite which is an estimate of spine length.For each case for which the spine head is in contact with the dendrite, no neck is traced and the spine is categorized as belonging to the stubby type.
After completion of spine detection, spine head segmentation and spine neck tracing, a four-channel image is displayed overlaying the original image, the dendrite coded in the SWC file, the segmented spine heads and the traces of the spine necks.A table is retrieved in which the measurements of several morphological parameters are given, including spine head volume and surface as well as neck length.

Discussion
We have implemented a new tool for 3D spine detection and analysis as an ImageJ/Fiji plugin.Of note, spine heads detection and segmentation works on 2D images as well, but not the tracing of the necks.To our knowledge, two other ImageJ plugins dedicated to dendritic spines exist: the Dendritic_Spine_Counter for 2D images, and SpineJ which is dedicated to 2D analysis of STED microscopy images (Levet et al., 2020).Since full automatization is unattainable when considering the wide range of neuronal labeling and image acquisition protocols, manual editing in a user-friendly interface allows to correct spine detection and segmentation.However, most of the procedure remains automated, so spine head segmentation and spine neck tracing retrieve consistent results.One limitation of our plugin is that it does not give any estimate of the spine neck width.We have chosen to avoid the measurement of that parameter because it falls below the resolution of most confocal and two-photon image stacks.Studies specifically dedicated to the spine neck properties may better benefit from custom procedures for spine neck size estimates.An important concern is the signal to noise ratio of the image stacks.Local maxima detection is noise sensitive, hence noise filtering may help to avoid false positive.Deconvolution is generally recommended (Dumitriu et al., 2011;Heck et al., 2012) since it improves axial resolution, but noise filtering using tools such as the plugin PureDenoise (Luisier et al., 2010) can also yield good results.
Morphological analysis of dendritic spine is often based on classification into discreet categories, namely stubby, thin, mushroom and filopodia.Stubby spines are devoid of neck, which has functional implication since the neck isolates the spine head from the dendrite.Therefore, the plugin indicates in the result table if the spine is of stubby type.The categorization into thin and mushroom spines has however been shown to be arbitrary, since dendritic spines exhibit a continuum of morphologies (Wilson et al., 1983;Tonnesen et al., 2014;Ofer et al., 2021Ofer et al., , 2022)).Filopodia are elongated spine without head which correspond to an immature stage observed during development.Nevertheless, it can be difficult to distinguish filopodia from long thin spines.Therefore, the distribution of unbiased measurement allows an objective assessment of spine morphology (Pchitskaya and Bezprozvanny, 2020).Categorization of spines by cluster analysis of quantified features have been made (Luengo-Sanchez et al., 2018;Urban et al., 2019;Kashiwagi et al., 2019).Recent softwares, Dxplorer (Choi et al., 2023) and SpineTOOL (Ekaterina et al., 2023), perform a fine analysis of spine morphology by analyzing either 3D surface mesh or chord lens distribution histogram, respectively.The quantitative results obtained with Spot Spine allow the description of the statistical distribution of morphological features, but the user can build categories by grouping dendritic spines according to defined range of values.
The article and tool are targeting an important need.Current tools are incredibly expensive and proprietary.Many in the field still use completely manual counting, which precludes unbiased evaluation of volume or shape.The tool description and the article are thus important.
For the article, I do recommend the authors say spines are an "estimate" of synaptic number, rather than a "measure".
Two other changes are just likely typos, my suggestion in brackets: "since the neck represents a morphological constrains [constraint], the spine head is a functional compartment isolated [from] the dendritic shaft" "Therefore, quantification of spine density, spine head size and spine neck length are desirable for each of these parameters [in order to] retrieve valuable information on neural connectivity, synapse efficacy and plasticity." For the tool: The FAQ/about text for the tool has multiple typos and should be edited.I was not able to open any of my own 2-photon imaging data sets (despite having an associated SWC file that I made specifically to test this tool).Trying to open them immediately caused an error.The error was not specific so I am unclear what I was doing wrong.
I was able to open the sample images and tested the program on both a mac and PC running FIJI.The program did find spines in a reliable manner on this one sample image, and produced data that looked exciting and promising.However, as soon as any manual manipulation was attempted, there were difficulties: 1) Often (on both platforms) the manual edit stopped working.If the wrong button was touched in FIJI it led to two things occurring simultaneously.Restarting and not touching any buttons on FIJI (outside of spot spine) sometimes worked.
2) The question mark button did not do anything 3) Green pixels shown on the max local projection are sometimes not seen on the max local merge file.4) deleting a spine made it so you could not add a spine back at the same location.Often trying to add a spine somewhere else would lead to error "needs a point".5) it was hard to tell when spines are selected-often leading to deleting multiple spines by mistake.There is no undo so this means starting over.Most mistakes I made led to needing to start over.6) When getting into the spine neck path, clicking update caused the log to show errors.7) I could not tell which were which.I know from the FAQ they are coded by intensity but I could not find how to figure out which was which on the screen.This made it difficult to see how my manual edits were correlating with actual data output.8) Any amount of fussing to try to add or improve a spine manually led to bits of spine all over the dendrite that I couldn't figure out how to edit.
Initially-the tool looked quite powerful and did map spines on the one sample image in a way that looked like it might be very useful.Unfortunately, all the manual editing caused disruptions, and this was in a perfect demo image.If this is due to my own misreading of the instructions, then perhaps a video demonstrating manual editing of spines would help.As with all such tools, it is possible the particulars of my setup made using the tool more difficult, and others might find it more useful.Alternatively, the tool may just need a bit more refinement to be user-ready.

Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? No
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: I have >15 years experience counting dendritic spines.I use FIJI relatively frequently, including to count spines and I am familiar with installing and using plugins, but I am not myself familiar with building/coding plugins.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.
Author Response 30 Aug 2024

Nicolas Heck
The article itself is well written and described, but the software tool is currently difficult.I discuss the article first and the tool second.
The article and tool are targeting an important need.Current tools are incredibly expensive and proprietary.Many in the field still use completely manual counting, which precludes unbiased evaluation of volume or shape.The tool description and the article are thus important.
For the article, I do recommend the authors say spines are an "estimate" of synaptic number, rather than a "measure".
Response: We agree, modifications have been done in the manuscript.Two other changes are just likely typos, my suggestion in brackets: "since the neck represents a morphological constrains [constraint], the spine head is a functional compartment isolated [from] the dendritic shaft" "Therefore, quantification of spine density, spine head size and spine neck length are desirable for each of these parameters [in order to] retrieve valuable information on neural connectivity, synapse efficacy and plasticity." Response: Thank you, the manuscript has been corrected.

For the tool:
The FAQ/about text for the tool has multiple typos and should be edited.Response: Opening images files is completely independent of our plugin.Opening images is integral part of FIJI/ImageJ.For proprietary files such as .lifor .czicoming from commercial microscopes, the plugin LOCI Importer is needed, however the plugin is by default included in FIJI.In any case, our plugin starts after an image has been opened so the problem is unrelated to our plugin.

Response: Thank you for pointing that out, we will correct the webpage
Or you mean that after the image was open, you could not run spot spine.It could come from the swc file.Some swc contain the xyz coordinates and the diameter of spheres along the dendrite.Some swc contain only the xyz coordinates thus coding a line.When you tested our plugin, it was not able to work with swc without diameter estimation of the dendrite.We have updated the plugin so both type of swc file are compatible.
I was able to open the sample images and tested the program on both a mac and PC running FIJI.The program did find spines in a reliable manner on this one sample image, and produced data that looked exciting and promising.However, as soon as any manual manipulation was attempted, there were difficulties: 1) Often (on both platforms) the manual edit stopped working.If the wrong button was touched in FIJI it led to two things occurring simultaneously.Restarting and not touching any buttons on FIJI (outside of spot spine) sometimes worked.
Response: The other reviewer and some colleagues did not report similar problems.It may be related to the ImageJ or FIJI version used, or to the version of the plugins 3DImageSuite or SNT installed.A more detailed description of the problem is required to fully respond about the problem mentionned here.For our previously published plugin DiAna, as well as for Spot Spine, our email adress is on the webpage of the plugin and in the publication.We keep answering to all request and help users.In addition, we answer to questions posted on the widely used FIJI/ImageJ oriented « scientific community image forum » : https://forum.image.sc.
2) The question mark button did not do anything Response: Thank you for pointing that out.We updated the plugin so the question mark drives the user to the spot spine webpage.The question mark at the segmentation step drives the user to the webpage describing the segmentation method.
3) Green pixels shown on the max local projection are sometimes not seen on the max local merge file.

Response: Unfortunately, green pixels can be difficult to see depending on the local intensity in the other channel. Any plugin can only use the look-up tables and image display internally coded
in FIJI/ImageJ.In the plugin, it is offered to have the image in either blue or gray levels.Nevertheless, the user can go to Image>Look-up tables and apply any of those while running the plugin.In some cases, changing brightness&contrast will help as well (Image>Adjust>Brightness&Contrast) 4) deleting a spine made it so you could not add a spine back at the same location.Often trying to add a spine somewhere else would lead to error "needs a point".
Response:In an earlier version of the plugin, it was not possible to add a spine in a head that was previously deleted.We have updated the plugin so it is know possible.The message « needs a point » indicates that it is first required to define a region of interest before clicking on the « Add » button.As written in the dialog window, the user holds the SHIFT key and click in the image.Then a region of interest appears (as a square selection), and then clicking on « Add » button will prompt the plugin to find a local maxima within the region, and segment the object.5) it was hard to tell when spines are selected-often leading to deleting multiple spines by mistake.There is no undo so this means starting over.Most mistakes I made led to needing to start over.
Response: Undo function is not build in FIJI/ImageJ, it is a known limitations of the program which is not in our hands.Also build in FIJI/ImageJ, selection is shown as yellow line around the selected objet.It is true that once selecting a spine head, to select another will not unselect the previously selected one.The simple reason is that is allows to merge objects when two objects are found in one single spine head (a needed function since in some large spine heads, more than one local maximum can be found, hence the function allows to overcome oversegmentation).Once the merge is performed, no spine head is selected.To unsure no objects are selected, the user can click outside of a spine head.This will by default unselect all selected spines.It is a build in function of FIJI/ImageJ, that when clicking outside a selection or an object, a « select none » function is applied.
6) When getting into the spine neck path, clicking update caused the log to show errors.
Response: It is difficult to respond without knowing the content of the error message.For our previously published plugin DiAna, as well as for Spot Spine, our email adress is on the webpage of the plugin and in the publication.We keep answering to all request and help users.In addition, we answer to questions posted on the widely used FIJI/ImageJ oriented « scientific community image forum » : https://forum.image.sc.Concerning updating the necks.First delete a neck by clicking on (or near) the head or the neck and then click on « delete path ».Then click again in head while holding SHIFT key, a yellow dot appears.Then click on « Update path ».A new path starting from the dot will be created.Please note that the algorithm computes best path it can detect, so the new path may be same as first.Neck detection is very challenging because intensity of the neck in the image is usually very low.Anytime delete or update path function is applied, the results Table is updated.7) I could not tell which spines were which.I know from the FAQ they are coded by intensity but I could not find how to figure out which was which on the screen.This made it difficult to see how my manual edits were correlating with actual data output.
Response: We acknowledge this would help, but annotation on the output image is not straightforward.If in the image stack, it would be visible only in one plane ; if on the maximum projection numbers would overlap if spines are close to each other.Nonetheless, the user has several options because the ID of the spine in the table is color coded in the image.For a spine with ID 25, the voxels in the image will have an intensity of 250.Therefore : The user can point on the spine in the image and read the intensity (= the ID) in the FIJI/ImageJ menu window.Since the image is a composite with four channels, the user needs to select the spine head channel (i.e.first channel) otherwise the intensity given may be 0 (in necks and dendrite channels) or a number which is the gray level of the orignial image (fourth channel).To select a channel see the « C » sliding bar at the bottom of the image.The user can read an ID in the table and find it by checking the intensities of the spines in the image.The ID numbers increase with stack depth.Another way is to use the 3DManager of the plugin 3DImageSuite (that plugin is necessarily installed for the proper functioning of Spot spine).In short, the steps to follow are : Go to plugin menu, 3DSuite, 3DManager.Once the manager is open, click on the composite image containing the spine heads, necks, dendrite and image to activate.Choose the spine head channel (i.e.first channel).In the 3DManager, click on « Add Image ».All objects will be listed in the manager.ObjX is same as in « Name » column of the Result Table .ValX is the same as the spine head intensity in the image.Click on « Live ROI ».Click on one ROI in the list to make it encircled in yellow in the image stack.
8) Any amount of fussing to try to add or improve a spine manually led to bits of spine all over the dendrite that I couldn't figure out how to edit.
Response: It is difficult to respond without seeing an image on which the phenomenon you described appears.Again, for our previously published plugin DiAna, as well as for Spot Spine, our email adress is on the webpage of the plugin and in the publication.We keep answering to all request and help users.In addition, we answer to questions posted on the widely used FIJI/ImageJ oriented « scientific community image forum » : https://forum.image.sc.
analysis; however, they fail to provide a clear rationale behind the development of the Spot Spine Plugin, despite the existence of numerous available techniques.Please provide a brief introduction here regarding the purpose and significance of developing this plugin.2. On the interface for selecting spine detection parameters, in order to enhance users' comprehension of parameter definitions, could you please specify the units for "Minimum distance to dendrite" and "Maximum distance to dendrite" (in pixels or microns)?3. The "Result Table" generated by the plugin presents a wide range of parameters; however, it has come to my attention that the commonly utilized parameter of dendritic spine density is conspicuously absent.It is strongly recommended to incorporate statistical data for this particular parameter.4. The statistical parameters include the identification (ID) of each identified spine; however, the corresponding ID is not annotated on the output images, which poses challenges for traceability.It is recommended to incorporate ID markers in the output images to facilitate referencing.5.The statistical analysis of dendritic spines often necessitates the processing of a large number of dendrites, and batch analysis proves to be highly efficient in this regard.It has been observed that Spot Spine lacks a batch analysis feature, thus it is recommended to incorporate this functionality into the plugin.6.The article utilizes the analysis of spines on a single dendrite as an illustrative example.It would be beneficial to provide clarification and discussion regarding the applicability of the plugin to dendrites with multiple branches.

Is the rationale for developing the new software tool clearly explained? Partly
Is the description of the software tool technically sound?Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?Yes

Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?Yes Competing Interests: No competing interests were disclosed.

Reviewer Expertise: neuroscience
We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.section : « Dendrite length, number of spines and spine density is given in a log window ».
4. The statistical parameters include the identification (ID) of each identified spine; however, the corresponding ID is not annotated on the output images, which poses challenges for traceability.It is recommended to incorporate ID markers in the output images to facilitate referencing.
Response: We acknowledge this would help, but annotation on the output image is not straightforward.If in the image stack, it would be visible only in one plane ; if on the maximum projection numbers would overlap if spines are close to each other.Nonetheless, the user has several options because the ID of the spine in the table is color coded in the image.For a spine with ID 25, the voxels in the image will have an intensity of 250.Therefore : The user can point on the spine in the image and read the intensity (= the ID) in the FIJI/ImageJ menu window.Since the image is a composite with four channels, the user needs to select the spine head channel (i.e.first channel) otherwise the intensity given may be 0 (in necks and dendrite channels) or a number which is the gray level of the orignial image (fourth channel).To select a channel see the « C » sliding bar at the bottom of the image.The user can read an ID in the table and find it by checking the intensities of the spines in the image.The ID numbers increase with stack depth.Another way is to use the 3DManager of the plugin 3DImageSuite (that plugin is necessarily installed for the proper functioning of Spot spine).In short, the steps to follow are : Go to plugin menu, 3DSuite, 3DManager.Once the manager is open, click on the composite image containing the spine heads, necks, dendrite and image to activate.Choose the spine head channel (i.e.first channel).In the 3DManager, click on « Add Image ».All objects will be listed in the manager.Names are obj1-val10 ; obj2-val20 ; obj3-val30 and so on.ObjX is same as in « Name » column of the Result Table .ValX is the same as the spine head intensity in the image.Click on « Live ROI ».Click on one ROI in the list to make it encircled in yellow in the image stack.
5. The statistical analysis of dendritic spines often necessitates the processing of a large number of dendrites, and batch analysis proves to be highly efficient in this regard.It has been observed that Spot Spine lacks a batch analysis feature, thus it is recommended to incorporate this functionality into the plugin.
Response: In nearly all cases, a fully automated procedure would bring false positives and false negatives.Hence we made the choice to design a tool that allows manual editing at each step of the process.Therefore a batch mode would not be of much help.Nevertheless, if several users would contact us for expressing that need, it could be implemented.
6.The article utilizes the analysis of spines on a single dendrite as an illustrative example.It would be beneficial to provide clarification and discussion regarding the applicability of the plugin to dendrites with multiple branches.
Response: If the swc file encode a dendrite splitting in two branches (or more), the plugin will detect spines all along the dendrite.In principle, the plugin could be used to detect spines on a complete dendritic tree if the image has the sufficient resolution.However, the purpose of the plugin not to analyse dendritic tree morphology, hence the spine density will not be given for each branch, but as one value for a total dendrite length.
C57BL/6J male mice were maintained in a 12-hour light/12-hour dark cycle, under stable temperature (22°C) and humidity (60%) conditions with ad libitum access to food and water.All experiments were carried out in accordance with the standard ethical guidelines [European Community Council Directive on the Care and Use of Laboratory Animals (86/609/EEC) and the French National Committee (2010/63)].

Figure 2 .
Figure 2. Examples of images illustrating the main steps of the process.A. Maximum intensity projection showing the detected local maxima.B. Maximum intensity projection showing the segmented spine heads.C. Maximum intensity projection showing traced necks and spine heads.D. 3D volume rendering of the dendrite encoded in swc file, segmented spine heads and traced necks.

I
was not able to open any of my own 2-photon imaging data sets (despite having an associated SWC file that I made specifically to test this tool).Trying to open them immediately caused an error.The error was not specific so I am unclear what I was doing wrong.