Name: 높은 처리량 및 다중 파라 메트릭 정량화를위한 두 알고리즘 초파리 신경 근육 접합 형태학 (동영상) | JoVE Journal
Uploaded: 2017-05-03T12:30:00.000+00:00
Duration: 12 min 29 s
Description: 배우세요 Drosophila NMJ Morphometrics 알고리즘을 사용하여 초파리 신경근육접합 형태를 자동 정량화하는 방법을 익히세요. 이 기술은 고처리량 분석을 통해 시냅스 발달 조절인자를 효율적으로 식별하는 데 활용됩니다.

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We acknowledge the Vienna Drosophila Resource Center and Bloomington Drosophila stock center (NIH P40OD018537)for providing Drosophila strains. We thank Jack Fransen from the Microscopical Imaging Centre for expert support in imaging. This study was supported by VIDI and TOP grants (917-96-346, 912-12-109) from the Netherlands Organization for Scientific Research (NWO), by two DCN/Radboud University Medical Center PhD fellowships, by the German Mental Retardation Network funded by the NGFN+ program of the German Federal Ministry of Education and Research (BMBF) and by the European Union's FP7 large scale integrated network Gencodys (HEALTH-241995) to AS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

1. Requirements Prior to Image Processing
<ol>
	<li>Perform Drosophila open book preparations of third instar wandering larvae (L3), as previously described19.</li>
	<li>Co-immunolabel Drosophila NMJ terminals using a combination of two markers: Dlg-1 or Hrp together with Brp for analysis with &#34;Drosophila NMJ Morphometrics&#34;, and Syt or Csp together with Brp for analysis with &#34;Drosophila NMJ Bouton Morphometrics&#34;20. 
	NOTE: Antibodies from the same species can be combined by pre-labeling one with an antibody-conjugation kit such as the Zenon Alexa Labeling Kits17.</li>
	<li>Image NMJ terminals using a microscope of choice, e.g., fluorescence (with or without ApoTome) or confocal microscopy.
	<ol>
		<li>Acquire a 2-channel image stack of the NMJ terminal.
		<ol>
			<li>Adjust the microscope settings in a manner that channel 1 acquires the NMJ terminal immunolabeled with Dlg-1 (or Hrp, Syt, Csp) and channel 2 the NMJ terminal immunolabeled with Brp.</li>
			<li>Optionally, analyze one-channel images (of synapses immunolabeled with a single antibody) with the macros. Image NMJs immunolabeled uniquely with Dlg-1 or Hrp for analysis with &#34;Drosophila NMJ Morphometrics&#34;, or Syt or Csp for &#34;Drosophila NMJ Bouton Morphometrics&#34;. 
			NOTE: It is not possible to analyze synapses immunostained with only anti-Brp.</li>
		</ol>
		</li>
		<li>Export the obtained images as individual &#39;.tiff files. Invert the channel order prior to running the macros if not acquired as indicated.</li>
	</ol>
	</li>
</ol>
2. Software Requirements and Installation
<ol>
	<li>Download the macros: &#34;Drosophila NMJ Morphometrics&#34; and &#34;Drosophila NMJ Bouton Morphometrics&#34; from the following website: <a href="https://doi.org/10.6084/m9.figshare.2077399.v1">https://doi.org/10.6084/m9.figshare.2077399.v1</a>21.</li>
	<li>Move the cursor to the folder &#34;Macros update 1&#34;, and click on the appearing option &#34;view&#34;. A list with the content of this folder will appear. The folder contains the macros &#34;Drosophila NMJ Morphometrics&#34; and &#34;Drosophila NMJ Bouton Morphometrics&#34;. 
	NOTE: Both macros are compatible with Fiji versions 1.4, which is also provided in the same folder. The macros may not run on recent versions. Please utilize the provided version 1.4. It is unproblematic to start this version, even on computers with a more recent Fiji version available.</li>
	<li>Click on &#34;Download all&#34;. The folder content will be downloaded to the computer as a .zip file. Unzip the downloaded file.</li>
	<li>Copy the Drosophila_NMJ_Morphometrics.ijm and Drosophila_NMJ_Bouton Morphometrics.ijm files to Fiji.app/plugins/ directory. When restarting the program, the macros will appear at the bottom of the Plugins dropdown menu.</li>
</ol>
3. Run Sub-macro &#34;Convert to Stack&#34; to Create Z-projections and Hyperstacks of the NMJ Images
<ol>
	<li>Start the graphical interface by selecting Plugins in the toolbar and choose &#34;Drosophila NMJ Morphometrics&#34; in the dropdown menu.</li>
	<li>Define the &#34;Unique File String&#34; setting in the macro&#39;s graphical interface. 
	NOTE: The microscope software uses an identification signature to organize planes and channels when storing stacks as individual &#39;.tiff files. The entered unique file string setting needs to specify the signature assigned by the software to the first plane of the first channel (important: lowest plane and channel number needs to be indicated).</li>
	<li>Select only the sub-macro &#34;Convert to stack&#34; and click &#34;ok&#34; and select the folder where the images are located. If a main directory with several subfolders is selected, all individual &#39;.tiff files within the main directory and subfolder matching the unique file string criteria will be processed.
	<ol>
		<li>If the z-stack only contains one channel, select the box &#34;Channel 1 only&#34;.</li>
	</ol>
	</li>
	<li>Notice that two new files per NMJ image, by default termed as stack_image_name and flatstack_image_name will appear. Store only these stack and flatstack for further analysis. The .tiff file series can be deleted at this point, minimizing the required storage capacities and avoiding potential error sources.</li>
</ol>
4. Run Sub-macro &#34;Define ROI&#34; to Delineate the NMJ Terminal of Interest
<ol>
	<li>Start the graphical interface of &#34;Drosophila NMJ Morphometrics&#34;.</li>
	<li>Select only the checkbox &#34;Define ROI&#34; and press &#34;OK&#34; and select the main directory where the images entitled flatstack_name are stored and press &#34;Select&#34;. The sub-macro &#34;Define ROI&#34; automatically searches through all subfolders within the selected main directory.</li>
	<li>As the first projection opens, select the &#34;Freehand selections&#34; tool in the toolbar.</li>
	<li>Using the mouse draw a selection that exclusively contains the complete NMJ terminal of interest and click &#34;OK&#34; in the window &#34;Define terminal&#34;. The macro will proceed with the next projection.</li>
	<li>Delineate the next ROI and repeat until all ROIs are defined. The ROI image file, named &#34;roi_image_name&#34;, will be stored in the same directory as the previously generated stack and projection images for each of the processed images. The output of this sub-macro is a binary image of the ROI in white on a black background.</li>
</ol>
5. Run Sub-macro &#34;Analyze&#34; to Quantify NMJ Terminal Features
<ol>
	<li>Go to the toolbar, select &#34;Plugins&#34; and use: 
	&#34;Drosophila_NMJ_Morphometrics&#34; when analyzing synapses immunolabeled with anti-Dlg-1 or anti-Hrp (channel 1) together with anti-Brp (channel 2), or &#34;Drosophila_NMJ_Bouton_Morphometrics&#34; when analyzing synapses immunolabeled with anti-Syt or anti-Csp (channel 1) together with Brp (channel 2).
	<ol>
		<li>When one channel image stacks are to be analyzed (the structural channel Dlg-1 or HRP for &#34;Drosophila_NMJ_Morphometrics&#34;, or Syt or Csp for &#34;Drosophila_NMJ__Bouton_Morphometrics&#34;), select the box &#34;Channel 1 only&#34;.</li>
	</ol>
	</li>
	<li>Adjust the scale corresponding to the images to be analyzed.
	<ol>
		<li>If one pixel in the image corresponds to 2.5 &#181;m, indicate Scale-Pixels = 1, Scale-Distance in &#181;m = 2.5. In case both settings are left at 0, the NMJ area, perimeter, length and longest branch length will be expressed in number of pixels.</li>
	</ol>
	</li>
</ol>
<ol start="3">
	<li>If required, adjust the default analysis settings of the macro. Perform adjustments only if the sub-macro &#8220;Analyze&#8221; has previously been run with unsatisfactory results (see end of this section, and section 6 for instructions how to optimize the settings).</li>
	<li>Select the checkboxes &#34;Analyze&#34; and &#34;Wait&#34; and press &#34;OK&#34;.
	<ol>
		<li>Select the &#34;Wait&#34; check box when running the sub-macro &#34;Analyze&#34; on 2 channel images. Otherwise errors in active zone counting can occur due to limited computer capacities .</li>
	</ol>
	</li>
	<li>As a new window &#34;Choose a Directory&#34; opens, select the directory where the images are located and press &#34;select&#34;. The macro will analyze all images stored in the main directory and, if applicable, subsequent folders (using the three files from executing the previous sub-macros: stack_image_name, flatstack_image_name and roi_image_name). The macro processes each image individually and consecutively. This can take several minutes per image stack (depending on the computer capacity).</li>
	<li>After running the macro, note that a new image file named res_image_name for each analyzed synapse stored will be created in the parental folder. The quantitative measurements will be stored as &#34;results.txt&#34; file.</li>
	<li>Inspect all result images to detect and exclude pictures with segmentation errors. Possible segmentation errors are described in Table 3, along with advice how to adjust settings to circumvent these errors. Result images with such segmentation errors are provided as examples in Figure 4. 
	NOTE: When running the macro with the default settings observed in the user interface, there was an accuracy of approximately 95% when macro assessment was compared to manual evaluation17.</li>
</ol>
6. Adjust the Macro Settings to the Images
<ol start="1">
	<li>When more than 5% of images show segmentation errors, explore the different algorithms to define/choose the most suitable macro settings for the images.</li>
	<li>Adjust rolling ball radius value 
	NOTE: The rolling ball radius function subtracts the background of the image. This function is of crucial importance when working with images acquired on fluorescence microscopes and/or when images have high background noise. The subtraction of the background will help the macro&#39;s auto-thresholding steps to produce adequate segmentation of the NMJ terminals.
	<ol>
		<li>Select three NMJ stack_image_name images generated by the sub-macro &#8220;Convert to stack&#8221;. Choose images which are representative for the image dataset.</li>
		<li>In the toolbar, select Image | Color | Split channels. Two image stacks will be created, one representing channel 1 and the other channel 2, respectively and save them.</li>
		<li>Open the image stack belonging to channel 1 open, corresponding to Dlg-1, Hrp, Syt or Csp immunolabeling.</li>
		<li>Run the filter &#34;Subtract background&#34; by selecting &#34;Process&#34; in the toolbar followed by &#34;Subtract Background&#8230;&#34; in the dropdown menu.</li>
		<li>Click on the preview checkbox in the pop-up window and adjust the rolling ball radius to the value most appropriate for the images. The &#8220;Rolling ball radius&#8221; setting should be adjusted to values that increase the contrast between synapse and background (see Figure 5A&#8217;).
		<ol>
			<li>See Figure 5 for an example. In panel A, parts of the synapse show the same grey levels as the background, whereas in Figure 5 panel A&#8217; a &#8220;Rolling ball radius&#8221; of 500 results in strong contrast between the synapse and the background.</li>
		</ol>
		</li>
		<li>Create a z-projection by selecting in the toolbar Image | Stack| Z-projection, chose Projection type = Max Intensity and save the resulting image. When the appropriate value for the rolling ball radius is defined, run the &#8220;Subtract background&#8221; algorithm on the remaining representative images with the same rolling ball radius value. Create the Z-projections and save them (in any directory). 
		NOTE: The rolling ball radius value for 8-bit or RGB images should be at least as large as the radius of the largest object in the image that is not part of the background. For 16-bit and 32-bit images the radius should be inversely proportional to the pixel value range22.</li>
	</ol>
	</li>
	<li>Determine the different auto-thresholds that will be used
	<ol>
		<li>Open the Z-projections saved in the previous step (6.2.6) and Select Image | Adjust | AutoThreshold | Try all.</li>
		<li>As a binary thresholded result image will appear with all different auto-threshold algorithms, determine the most suitable algorithm for the images.
		<ol>
			<li>When running the macro later on, change the threshold in the macro settings accordingly.</li>
			<li>Use more restrictive thresholds such as &#34;RenyiEntrophy&#34; or &#34;Moments&#34; as the NMJ outline threshold and more permissive thresholds such as &#34;Li&#34; to determine the NMJ skeleton, and &#34;Huang&#34; to determine active zones. When images are very sharp with little to no background, use &#34;Huang&#34; as&#34; NMJ outline threshold. Otherwise parts of the synapse might be missing after image segmentation.</li>
			<li>See Figure 5B for an example. Appropriate segmentation of the synapse is obtained with auto-thresholds highlighted by green boxes. Some examples of non-suitable thresholds are highlighted by red boxes (check synapses at high magnification). In the latter, either parts of the synapse are missing or parts of the background are included. See reference 23 for more information.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
<ol start="4">
	<li>Determine the maximum size of the small particles 
	NOTE: This function will exclude all particles detected by the NMJ outline threshold and Skeleton threshold that are smaller than the defined value in the &#34;small particles setting&#34; from the analysis. This value is defined in pixels. This function serves as a noise filter and is very useful when high rates of non-uniform background (such as crystals/dust) are present in the obtained images.
	<ol>
		<li>Open the Z-projections saved in step 6.2.6. and set the scale to detect number of pixels via Analyze | Set Scale. Apply the following settings: distance in pixels=1, known distance=1, pixel aspect ratio=1, Unit of length=pixel and press &#34;Ok&#34;. Click on the &#34;Oval selection&#34; tool in the toolbar.</li>
		<li>Using the mouse draw a selection closely surrounding single particles that are present in the immunostaining but do not belong to the NMJ. Press Ctrl+m for a Windows user or cmd+m for Mac users. A result window will open, indicating the area of the particles selected in number of pixels.</li>
		<li>Repeat the previous step several times with several artifacts present in the images to determine the biggest contaminating particle/artifact area. This will be the value to set in the setting when running the macro later on. When running the macro set the &#34;Small Particles Size&#34; as the smallest particle size observed pus a margin of 25%.</li>
		<li>See Figure 5D for an example. The biggest crystal detected has an area of 112 pixels. The &#34;Small particles size&#34; setting, when processing this image with the macro, should be set to 125 - 150.</li>
	</ol>
	</li>
</ol>
<ol start="5">
	<li>Determine minimum bouton size 
	NOTE: This function will exclude all boutons detected by the NMJ outline threshold that are smaller than the defined value from the analysis. This value is defined in pixels.
	<ol>
		<li>Follow the same steps as described in section 6.4, but in this case draw a selection surrounding the smallest boutons present in the NMJ terminal. Choose the smallest area corresponding to the smallest bouton of the measured ones. This is the value to set in the minimum bouton size setting when running the macro later on.</li>
	</ol>
	</li>
</ol>
<ol start="6">
	<li>Define &#34;Maxima noise tolerance&#34; value
	<ol>
		<li>To define the &#8220;Find maxima noise tolerance&#8221; value for the macro, open the channel 2 Z-stack saved in section 6.2.2.</li>
		<li>Go to the plugins tab in the popup menu, select Process | Maximum(3D), and when the maximum_image_name appears (which can take up a few minutes), close the original image stack.</li>
		<li>Select the Maximum&#8230;_image_name (the newly obtained image stack) and select Plugins | Process | Minimum (3D), when the new image Minimum of Maximum&#8230;_image_name apears close the Maximum &#8230; _image_name stack.</li>
		<li>In the toolbar, select Process | Find maxima&#8230;. A new window &#34;Find maxima&#8230;&#34; will open. Click the checkbox &#34;Preview point selection&#8230;&#34; and fill the &#34;Noise tolerance&#34; box with the default macro setting 50. The maxima points will be indicated in the image as little crosses.
		<ol>
			<li>Increase the &#8220;Noise tolerance&#8221; value if observing an excess of annotated active zones, i.e. crosses that are not on top of active zones that are not in focus on the selected stack plane, or false active zones that are detected in the background.
			<ol>
				<li>On the other hand, if observing incompletely annotated active zones, i.e. active zones in focus not being recognized, decrease the &#8220;Maxima noise tolerance&#8221; value. Keep trying different values following this procedure till the crosses are appropriately labeling active zones in focus. Fill the &#8220;Find maxima noise tolerance&#8221; with this value.</li>
				<li>See Figure 5C for an example. Too many active zones are detected. In Figure 5C&#8217; only the active zones in focus are detected when increasing the &#8220;Maxima noise tolerance&#8221; value.</li>
			</ol>
			</li>
		</ol>
		</li>
		<li>Run the sub-macro &#34;Analyze&#34; for the representative images selected in step 5.1, with the settings defined in the all the previous steps.</li>
	</ol>
	</li>
	<li>Adjust Brp-puncta lower and upper threshold
	<ol>
		<li>Notice that a new file will be appearing after running the macro according to step 6.6, called 2_active_zone_stack_image_name. In this image stack the active zones detected by the &#34;Find maxima&#34; function are indicated by white dots in each plane.</li>
		<li>Open this file by dragging and dropping it into the toolbar and select Image | Stack | Z-project | Projection type = Sum slices. A projection of the 2_active_zone_stack_image_name will be obtained.</li>
		<li>Select Image | Adjust | Threshold. A new window &#34;Threshold&#34; will open. Slide the upper bar to choose a threshold value where, all desired foci/Brp-positive spots are visualized in red. 
		NOTE: If the threshold is set too low, an excess of active zones will be counted. If set too high, a fraction of the active zones will be missed.
		<ol>
			<li>See Figure 5E for an example. When threshold is set to 400, most of the active zones (symbolized as a 1 pixel foci) are not included in the segmentation, since they are not highlighted in red (Figure 5E). When threshold is set to a value of 50 all the active zones are highlighted in red (Figure 5E&#8217;).</li>
		</ol>
		</li>
		<li>Define this value as minimum threshold. Leave &#8220;Upper puncta threshold&#8221; on the maximum value.</li>
		<li>Re-run the sub-macro &#8220;Analyze&#8221; for the representative images with the settings defined in all the previous steps of this section. Critically evaluate the resulting image files and make sure that the segmentation is done properly. If this is not the case readjust the settings according to the nature of the segmentation errors (Figure 4, Table 3).</li>
	</ol>
	</li>
</ol>

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<li>Nijhof, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+New+Fiji-Based+Algorithm+That+Systematically+Quantifies+Nine+Synaptic+Parameters+Provides+Insights+into+Drosophila+NMJ+Morphometry%5BTitle%5D)+AND+%22PLoS+Comput+Biol%22%5BJournal%5D)">A New Fiji-Based Algorithm That Systematically Quantifies Nine Synaptic Parameters Provides Insights into Drosophila NMJ Morphometry.</a> PLoS Comput Biol. 12, e1004823(2016).</li>
<li>Schindelin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Fiji%3A+an+open-source+platform+for+biological-image+analysis%5BTitle%5D)+AND+%22Nat+Methods%22%5BJournal%5D)">Fiji: an open-source platform for biological-image analysis.</a> Nat Methods. 9, 676-682 (2012).</li>
<li>Brent, J. R., Werner, K. M., McCabe, B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Drosophila+larval+NMJ+dissection%5BTitle%5D)+AND+%22J+Vis+Exp%22%5BJournal%5D)">Drosophila larval NMJ dissection.</a> J Vis Exp. , (2009).</li>
<li>Dubos, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Conditional+depletion+of+intellectual+disability+and+Parkinsonism+candidate+gene+ATP6AP2+in+fly+and+mouse+induces+cognitive+impairment+and+neurodegeneration%5BTitle%5D)+AND+%22Hum+Mol+Genet%22%5BJournal%5D)">Conditional depletion of intellectual disability and Parkinsonism candidate gene ATP6AP2 in fly and mouse induces cognitive impairment and neurodegeneration.</a> Hum Mol Genet. 24, 6736-6755 (2015).</li>
<li>Nijhof, B., et al. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aNijhof%2C+B+%22Drosophila+NMJ+Morphometrics%22">Drosophila NMJ Morphometrics</a>. , figshare 
 
 <a target="_blank" href="https://doi.org/10.6084/m9.figshare.2077399.
">https://doi.org/10.6084/m9.figshare.2077399.
</a> (2017).</li>
<li>Ferreira, T., Rasband, W. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aFerreira%2C+T+%22ImageJ+User+Guide+IJ+1.46%22">ImageJ User Guide IJ 1.46</a>. , 
 <a target="_blank" href="http://rsbweb.nih.gov/ij/docs/guide/146-29.html">http://rsbweb.nih.gov/ij/docs/guide/146-29.html</a> (2014).</li>
<li>Ferreira, T., Rasband, W. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aFerreira%2C+T+%22ImageJ+User+Guide+IJ+1.46%22">ImageJ User Guide IJ 1.46</a>. , Available from: 
 <a target="_blank" href="http://fiji.sc/Auto_Threshold">http://fiji.sc/Auto_Threshold</a> (2014).</li>
<li>Pielage, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+presynaptic+giant+ankyrin+stabilizes+the+NMJ+through+regulation+of+presynaptic+microtubules+and+transsynaptic+cell+adhesion%5BTitle%5D)+AND+%22Neuron%22%5BJournal%5D)">A presynaptic giant ankyrin stabilizes the NMJ through regulation of presynaptic microtubules and transsynaptic cell adhesion.</a> Neuron. 58, 195-209 (2008).</li>
<li>Koch, I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Drosophila+ankyrin+2+is+required+for+synaptic+stability%5BTitle%5D)+AND+%22Neuron%22%5BJournal%5D)">Drosophila ankyrin 2 is required for synaptic stability.</a> Neuron. 58, 210-222 (2008).</li>
<li>Iqbal, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Homozygous+and+heterozygous+disruptions+of+ANK3%3A+at+the+crossroads+of+neurodevelopmental+and+psychiatric+disorders%5BTitle%5D)+AND+%22Hum+Mol+Genet%22%5BJournal%5D)">Homozygous and heterozygous disruptions of ANK3: at the crossroads of neurodevelopmental and psychiatric disorders.</a> Hum Mol Genet. 22, 1960-1970 (2013).</li>
<li>Prokop, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Organization+of+the+efferent+system+and+structure+of+neuromuscular+junctions+in+Drosophila%5BTitle%5D)+AND+%22Int+Rev+Neurobiol%22%5BJournal%5D)">Organization of the efferent system and structure of neuromuscular junctions in Drosophila.</a> Int Rev Neurobiol. 75, 71-90 (2006).</li>
<li>Wan, H. I., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Highwire+regulates+synaptic+growth+in+Drosophila%5BTitle%5D)+AND+%22Neuron%22%5BJournal%5D)">Highwire regulates synaptic growth in Drosophila.</a> Neuron. 26, 313-329 (2000).</li>
<li>Shi, L., Fu, A. K., Ip, N. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Molecular+mechanisms+underlying+maturation+and+maintenance+of+the+vertebrate+neuromuscular+junction%5BTitle%5D)+AND+%22Trends+Neurosci%22%5BJournal%5D)">Molecular mechanisms underlying maturation and maintenance of the vertebrate neuromuscular junction.</a> Trends Neurosci. 35, 441-453 (2012).</li>
<li>Sutcliffe, B., Forero, M. G., Zhu, B., Robinson, I. M., Hidalgo, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Neuron-type+specific+functions+of+DNT1%2C+DNT2+and+Spz+at+the+Drosophila+neuromuscular+junction%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">Neuron-type specific functions of DNT1, DNT2 and Spz at the Drosophila neuromuscular junction.</a> PLoS One. 8, e75902(2013).</li>
<li>Carpenter, A. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((CellProfiler%3A+image+analysis+software+for+identifying+and+quantifying+cell+phenotypes%5BTitle%5D)+AND+%22Genome+Biol%22%5BJournal%5D)">CellProfiler: image analysis software for identifying and quantifying cell phenotypes.</a> Genome Biol. 7, R100(2006).</li>
</ol>

The authors have no conflicts of interest to disclose.

&#34;Drosophila NMJ Morphometrics&#34; and &#34;Drosophila NMJ bouton Morphometrics&#34; are powerful tools for researchers interested in evaluating synapse morphology. Manual assessment of NMJ parameters is laborious; it is estimated that the macros would save an experienced researcher up to 15 min/NMJ spent on manual image segmentation. With one to two dozens of synapses evaluated per condition or genotype, this quickly sums up to considerable amounts of saved time, even in small scale studies. When performing large screens, the gain of using high throughput analysis, compared to manual assessment and quantification, can be immense. In addition to increased throughput, the macros readily provide objective analysis; they exclude personal biases that otherwise require blinded experiments as well as interpersonal differences that occur when multiple researchers are involved in the analysis. Finally, the macros provide a sensitive and accurate analysis of NMJ features, allowing the identification of synaptic regulators that cause rather subtle than dramatic NMJ defects and have so far remained unappreciated by the researcher&#39;s eye. Detailed information about validation procedures and the algorithms utilized in the macros are found in the publication Nijhof et al.17.
The functionality of the macros has been validated to appropriately measure morphological features of Drosophila melanogaster NMJs at muscle 4. Subsequently, it was demonstrated that the macros were also suitable to analyze synapses at other muscles in this organism. It is likely that the macros can also be used to measure morphological parameters of NMJ with similar structure in other species, including other Drosophila species and further insects. Even NMJs very distant in evolution, e.g., NMJs of mice, show a quite similar structural conformation29. The macros have not been tested on NMJ preparations from other species but potential users are encouraged to test the macros for such purposes.
It is very important that the user explores the different auto-thresholds and algorithms to define/choose the most suitable macro settings for the images. With these settings, an accuracy of approximately 95% is achieved when macro assessment was compared to manual evaluation. Adjusting the macro settings to properly segment 100% of the images can be a very laborious or even impossible procedure. Therefore, exclusion of images not properly segmented is recommended if their numbers are below 5%. Evidently, if the quality of the images is low, the macros will generate higher ratios of unsatisfactory image segmentations. Low quality images will similarly influence manual evaluation and can therefore not be linked to the performance of the macros. Nonetheless the macros are rather robust as they were designed for images generated on a high content microscope (an automated fluorescence microscope that allows imaging of large numbers of samples)17.
A critical point is that the user visually inspects all result images generated by the macros. This will allow to detect and exclude pictures with unsatisfying segmentation. In section 6 of this protocol, the user is guided how to adjust the settings for correct image segmentation when running the sub-macro &#34;Analyze&#34;. To quickly familiarize with the requirements of the macros and how to adjust the macro settings a folder called &#34;Examples_adjusting macro settings&#34; is included in the macro repository <a href="https://doi.org/10.6084/m9.figshare.2077399.v1">https://doi.org/10.6084/m9.figshare.2077399.v1</a>. Thirteen subfolders, each with examples images obtained at different microscope platforms (high content/confocal/fluorescence microscopes) and different immunostainings, are provided. A PDF entitled &#8220;Examples guide&#8221; is included in the same folder where the settings required for each example are provided, along with a text document providing the expected results and results images.
The macros were designed to process images saved as .tiff separated files, nonetheless some users might have saved their images in a different format. The following website <a href="https://doi.org/10.6084/m9.figshare.2077399.v1">https://doi.org/10.6084/m9.figshare.2077399.v1</a>21 contains a folder named &#34;Drosophila NMJ&#34; where three example files (Example 1 - 3) and the document &#34;Examples Guide&#34; with detailed instructions how to import images into the macro if not stored as .tiff separated files can also be found in the same folder.
Together, &#34;Drosophila NMJ Morphometrics&#34; and &#34;Drosophila NMJ bouton Morphometrics&#34; macros quantify ten different NMJ features: NMJ area, NMJ perimeter, number of boutons, NMJ bouton area, NMJ length, NMJ longest branch length, number of islands, number of branches, number of branching points and number of active zones. This provides a great advantage over so far available tools that can assess only one or a few synaptic features30,31. Multiparametric quantitative analysis bears great potential for new discoveries, e.g., to identify novel regulators that control one up to numerous aspects of synapse biology. It also provides the required resolution to determine genes that coregulate exactly the same or overlapping NMJ features and thus are likely to operate in common molecular pathways. Finally, it opens the possibility to investigate how different synaptic parameters correlate with one another in undisturbed conditions17 and which genes ensure such coordinated morphometric correlations.
Taken together, this protocol illustrates how to use the two macros &#34;Drosophila NMJ Morphometrics&#34; and &#34;Drosophila NMJ Bouton Morphometrics&#34;, which perform objective and sensitive quantification of ten morphological NMJ features in a high-throughput manner.

Cognitive disorders such as intellectual disability, autism spectrum disorder and schizophrenia are often characterized by abnormal synaptic function1,2,3. Synapse morphology and function are tightly intertwined; morphological defects can cause synaptic malfunction and, reversely, aberrant synaptic transmission will impact synaptic maturation and morphology4,5,6.
A number of model organisms have been employed in order to better understand synapse biology and shed light on how synaptic changes affect brain function in health and disease7,8,9. The Drosophila NMJ is an extensively studied and well-established in vivo model for glutamatergic synapse biology10,11. In the past decades, this model has been used for physiological and gene-focused studies, as well as for large scale genetic screens, with the aim to detect morphological differences between NMJs. In particular, forward genetic screens have identified many crucial regulators and mechanisms underlying synapse development and function12,13,14,15,16. However, most of these screens relied on visual assessment of NMJ terminal morphology and on qualitative detection of the synaptic abnormalities or semi-quantitative scoring of few morphological features. As a consequence, rather subtle synaptic morphological abnormalities that are non-obvious to the human eye are easily missed. In order to be able to comprehensively detect quantitative differences, the NMJ has to be accurately evaluated by systematic quantification of the morphological parameters of interest. Measuring NMJ features manually is laborious, especially when there are several NMJ features of interest and/or when performing large-scale genetic screenings. In order to support multiparametric, high-throughput morphological analysis and to achieve objective quantification, two macros &#34;Drosophila NMJ Morphometrics&#34; and &#34;Drosophila NMJ Bouton Morphometrics&#34; were developed17. Both macros run in the open source image analysis software Fiji18, and enable quantification of both confocal and nonconfocal images.
&#34;Drosophila NMJ Morphometrics&#34; measures NMJ terminals immunostained with the postsynaptic marker disc large-1 (Dlg-1) or the presynaptic horseradish peroxidase (Hrp), co-labeled with the active zone marker bruchpilot (Brp). It quantifies nine morphological parameters (further described below): NMJ area, NMJ perimeter, number of boutons, NMJ length, NMJ longest branch length, number of islands, number of branches, number of branching points and number of active zones in the synaptic terminal (Figure 1). Although an algorithm for determining the number of boutons is present in this macro, it did not meet the criteria for accuracy17. To properly assess the number of boutons, it is necessary to use the &#34;Drosophila NMJ Bouton Morphometrics&#34; macro, which is specifically designed to quantify boutons using NMJ preparations immunostained by anti-Synaptotagmin (Syt) or anti-Cysteine string protein (Csp), and co-immunolabeled with Brp. The &#34;Drosophila NMJ Bouton Morphometrics&#34; macro quantifies the following parameters: number of boutons, NMJ bouton area, NMJ length, NMJ longest branch length, number of islands, number of branches, number of branching points and number of active zones (Figure 2).
The macros consist of 3 sub-macros: (I) &#34;Convert to stack&#34; identifies all available image files and creates Z-hyperstacks and maximum intensity projections of both channels. As output, this macro will generate two new files per synapse called &#34;stack_image_name&#34; and &#34;flatstack_image_name&#34;. II) &#34;Define ROI&#34; will open all maximum projection images &#34;flatstack_image_name&#34; consecutively and present them with the request to manually define the region of interest (ROI) in which the specific synaptic terminal of interest is present. This was implemented to allow exclusion of synapses connecting to adjacent muscles and/or other types of synaptic terminals (such as 1s) that can be present in the images11. (III) &#34;Analyze&#34; applies fully automated analysis to the image area within the borders of the ROI. As a result of this step, the user will obtain two new files: &#34;results.txt&#34; where all the numerical measurement will be annotated and a &#34;res_image_name.tif&#34; where the underlying image segmentations produced by the macro will be illustrated. During image analysis three structures are derived from each synaptic terminal: the NMJ outline, the NMJ skeleton, and the number of Brp-positive active zones. The NMJ outline is used to determine the NMJ area and its perimeter and a subsequent watershed separation provides the number of boutons. From the skeleton, five NMJ features are deduced: the total NMJ length, the sum of the length of the longest continuous path connecting any two end points (longest branch length), the number of unconnected compartments per NMJ (referred to as &#34;islands&#34;), the number of branches, and the number of branching points (one branching point connects three or more branches). The number of active zones is determined in the Brp-channel by counting Brp-positive spots. The annotated NMJ outline (yellow line), the NMJ skeleton (blue line), and the number of Brp-positive active zones (indicated by white foci) are displayed in a results picture and the measurements of the parameters are processed to an (.txt) output file (Figure 3).
Drosophila NMJ Morphometrics&#34; and &#34;Drosophila NMJ Bouton Morphometrics&#34; were first described and extensively validated by Nijhof et al.17. This manuscript focuses on the methodology to analyze NMJ morphology using the macros &#34;Drosophila NMJ Morphometrics&#34; and &#34;Drosophila NMJ Bouton Morphometrics&#34;. Prior to macro-assisted analyses, NMJ dissections and immunostainings need to be performed. These are crucial steps, and the combination of markers used for immunohistochemistry needs to be suitable for macro analyses. These steps are briefly mentioned in section 1 of this protocol and direct the user to references describing in detail the protocols to execute these procedures.

<table><tbody><tr><td>&lt;strong&gt;Immunostaining&lt;/strong&gt;</td><td></td><td></td><td>&lt;strong&gt;Dilution&lt;/strong&gt;</td></tr><tr><td>Mouse anti-discs large 1</td><td>Developmental Studies Hybridoma Bank</td><td>AFFN-DLG1-4D6</td><td>1/25 (conjungated using the Zenon Alexa Fluor 528 Labeling Kit)</td></tr><tr><td>Rabbit anti-horseradish peroxidase</td><td>Jackson IR</td><td>323-005-021</td><td>1/500</td></tr><tr><td>Rabbit anti-Synaptotagmin</td><td>Gift from Hugo Bellen</td><td></td><td>Jan-00</td></tr><tr><td>Mouse anti-Cysteine string protein</td><td>Developmental Studies Hybridoma Bank</td><td>DCSP-1(ab49)</td><td>1/10 (conjungated using the Zenon Alexa Fluor 528 Labeling Kit)&amp;nbsp;</td></tr><tr><td>Mouse anti-Bruchpilot</td><td>Developmental Studies Hybridoma Bank</td><td>nc82</td><td>Jan-50</td></tr><tr><td>Goat anti-mouse Alexa Fluor 488</td><td>Life technologies</td><td>A11029</td><td>1/200</td></tr><tr><td>Goat anti-rabbit Alexa Fluor 568</td><td>Life technologies</td><td>A11011</td><td>1/500</td></tr><tr><td>Zenon Alexa Fluor 568 Mouse IgG1 Labeling Kit</td><td>ThermoFisher</td><td>Z25006</td><td></td></tr><tr><td>ProLong Gold Antifade Mountant</td><td>ThermoFisher</td><td>P36930</td><td></td></tr><tr><td>&lt;strong&gt;Material&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Equipment&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Confocal microscope or fluorescence microscope</td><td>Leica SP5</td><td></td><td></td></tr><tr><td>Zeiss Axio imager</td><td></td><td></td><td></td></tr><tr><td>Computer</td><td>Mac or Pc</td><td></td><td></td></tr><tr><td>&lt;strong&gt;Material&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Software&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>FIJI</td><td></td><td></td><td></td></tr></tbody></table>

two algorithms for high-throughput and multi-parametric quantification of drosophila neuromuscular junction morphology

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

Synaptic morphology is tightly related to synaptic efficacy, and in many cases morphological synapse defects ultimately lead to synaptic malfunction. The Drosophila larval neuromuscular junction (NMJ), a well-established model for glutamatergic synapses, has been extensively studied for decades. Identification of mutations causing NMJ morphological defects revealed a repertoire of genes that regulate synapse development and function. Many of these were identified in large-scale studies that focused on qualitative approaches to detect morphological abnormalities of the Drosophila NMJ. A drawback of qualitative analyses is that many subtle players contributing to NMJ morphology likely remain unnoticed. Whereas quantitative analyses are required to detect the subtler morphological differences, such analyses are not yet commonly performed because they are laborious. This protocol describes in detail two image analysis algorithms "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics", available as Fiji-compatible macros, for quantitative, accurate and objective morphometric analysis of the Drosophila NMJ. This methodology is developed to analyze NMJ terminals immunolabeled with the commonly used markers Dlg-1 and Brp. Additionally, its wider application to other markers such as Hrp, Csp and Syt is presented in this protocol. The macros are able to assess nine morphological NMJ features: NMJ area, NMJ perimeter, number of boutons, NMJ length, NMJ longest branch length, number of islands, number of branches, number of branching points and number of active zones in the NMJ terminal.

Two image analysis algorithms, "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics" were created, to automatically quantify nine morphological features of the Drosophila neuromuscular junction (NMJ).

Synaptic morphology is tightly related to synaptic efficacy, and in many cases morphological synapse defects ultimately lead to synaptic malfunction. The ...

The overall goal of this procedure is to automatically quantify morphological features of the Drosophila neuromuscular junction using morphometric software macros. This method can help to identify regulators of synapse development, a key question in neurobiology. The main advantage of this technique is the automatic quantification of multiple NMJ features.This allows for objective analysis of NMJ morphology in high through put. We decided to develop this methodology to be able to investigate NMJ morphology in a large amount of disease models. We thought of ways how to prevent in their personal differences and how to significantly speed up the quantification process.Visual demonstration of this method is very helpful. Appropriate macro settings are crucial and some steps might be difficult to new users, especially when not familiar with VT software. For this protocol, generate image stacks of NMJs and save them as individual TIFF files, where channel one shows DLG1 staining, or a similar marker, and channel two shows BRP staining.To begin, create C projections and hyper stacks of the NMJ image files. Open the plugin options and select Drosophila NMJ Morphometrics. Now, identify the unique file string that the microscope has assigned to the image series when storing them as TIFF.This will be at the end of the image name. Copy and paste the string given to the lowest plane and channel number into the unique file string setting window. Then, select the sub macro convert to stack and choose the directory or folder where the images are located.For each image file, two new files are made with the default names stack and flat stack, followed by the original image name. The original files can then be deleted to save storage space. Next, from the Drosophila NMJ Morphometrics interface select the define ROI sub macro, and choose the image file directory.As the first projection opens select the freehand selections tool. Then use the mouse to define a region containing a complete NMJ terminal of interest. Once selected, click OK in the define terminal window.Continue doing this until the NMJ terminals are defined in all of the projections. The macro advances the process automatically. For each image file one new file is made with the default names ROI followed by the original image name.To quantify the NMJ features first go to the Drosophila NMJ Morphometrics interface and set the scale. For example, if one pixel in the image corresponds to 0.72 microns set scale pixels to one and scale distance to 0.072. Then select the sub macro analyze and if there are two channel images, also toggle weight.Press OK and when prompted select the image file directory. The processing time can be several minutes per synapse. After the analysis, new image files for each analyzed synapse are stored in the parental folder and the quantitative measurements are the results.txt file. Inspect all the images and exclude pictures with segmentation errors. For example, parts of the synaptic terminal might not be included in the yellow outline.Parts of the background might be included in the synaptic terminal. A blue skeleton line may extend beyond the synaptic terminal. There may be too many active zones, or some active zones may remain undetected.If more than 5%of images have segmentation errors explore different analysis algorithms to improve the image processing. The upcoming video sections describe how to define many of these macro analysis settings. To adjust the rolling ball radius value for the macro select three NMJ Z projections that are representative of the image data set.Delete the result res image name and two active zones stack image name, previously created by the sub macro analyze. Open the stack image name files for each selected image and then from the tool bar select split channels to make an image of channel one and an image of channel two and save these files. Open the image belonging to channel one, which in this case corresponds to DLG one immuno labeling.Now, run the filter subtract background, found under the process tab. Set the rolling ball radius to a value that increases the contrast between the synapse and background. Create a Z projection with a projection type as max intensity and save the image.Then run the subtract background algorithm with the new rolling ball radius on all of the Z projections and save the results. To determine the best auto thresholds for the macro, open the saved C projections and run auto threshold with the try all option. From the resulting images, find the most suitable algorithm for the images and proceed using that threshold setting when running the macro for further images.Defining the different auto thresholds, it's critical for proper image segmentation by the macro. For this reason, to properly quantify eight of the signage parameters, so it's important to be familiar with the 16 auto threshold options offered by the software. Press the plus button to zoom into the auto threshold's result image.The algorithm names are located underneath each result image. In this example, the best auto threshold algorithm for the setting NMJ outline threshold is Huang. For skeleton threshold, the best setting is Li, and the best setting for the active zone threshold is Huang.To define the fine maxima noise tolerance value for the macro return to the original representative NMJ images. Open the BRP channel. Go to the plugins tab in the pop up menu and select maximum 3D.After a short while, a new image will appear, then close the original image. Next, use the minimum 3D command. Then close the maximum of C2 stacks synapse one image and select the newly created image minimum of maximum C2 stack synapse one.Now use the find maxima command. In the new window select preview point selection and set the noise tolerance to 50. The maxima points are then indicated with little crosses, which should only cover the synapse active zones.If too many crosses are made, increase the noise tolerance value. If some of the active zones are not annotated decrease the noise tolerance value. Use the derived threshold value for the find maxima noise tolerance field of the macro.Choosing the maxima noise tolerance value it's important to properly quantify the number of active zones. Sometimes it's necessary to try different values to define the proper one. Now adjust all the derived values in the threshold algorithms in the macro interface and run the sub macro analyze on the representative images that were originally used to define the macro settings.A new file with the active zones indicated by white dots will be created. Open this file by dragging and dropping it into the tool bar and selecting Z project with a projection type as some slices. Thus, a projection file is made.The next step is to adjust the threshold. In the new threshold window slide the upper bar to choose a threshold value where all the desired foci are read. These are the BRP positive spots.Use this value as BRP puncta lower threshold. Now, rerun the analysis on the original representative NMJ images using the previously defined values, algorithms, and then new BRP puncta lower threshold value. The resulting images should meet the criteria for critical analysis.Now use the define settings to run the macro on all NMJ images obtained under the same conditions. The Drosophila NMJ morphometrics macro was used to validate different known synaptic defects in three mutant genotypes. Ankyrin two mutants are known to have fused boutons and smaller NMJs.Using the macro the area and perimeter of panuronil and quirin two RNAI knock down NMJs were measured and found to be significantly smaller than controls. The GTPase Rab3 is required for proper bruckpila distribution. When disrupted there are fewer active zones.Using the macro panuronil Rab 3 knock down flies had an average of 138 active zones per NMJ terminal compared to 290 detected in controls. High wire is an important regulator of NMJ growth and when mutated NMJs have extended branching at their terminals. Using the macro panuronil high wire RNAI knock down lines showed significant increases in several skeleton derived parameters at their NMJs, including total length, longest branch length, number of branches, and number of branching points.After watching this video you should have a good understanding how to operate and adjust the settings of the Drosophila NMJ morphometrics macro. Once mastered the analysis of 50 synapses can be done within one hour. This saves approximately 15 minutes of quantification time per synapse.It's important to acquire good quality images of the NMJ. The better the images, the better the macro will perform. This technique will help researchers in the neurobiology field to efficiently quantify morphological parameters of the Drosophila NMJ.

two-algorithms-for-high-throughput-multi-parametric-quantification

Research

JoVE Journal

Neuroscience

The neuromuscular junction (NMJ) of Drosophila melanogaster is an important model system for studying normal synaptic function as well as perturbations to synaptic function found in certain neurological diseases. We present a protocol for dissection of the Drosophila larval motor system and immunostaining for active zone proteins within the NMJ.

Dissection and Imaging of Active Zones in the Drosophila Neuromuscular Junction

The Drosophila neuromuscular junction (NMJ) is used as a model to study the morphology and function of synapses. This video describes important features that researchers quantify to characterize the NMJ. The example protocol demonstrates a software able to perform the quantification automatically.

Drosophila Neuromuscular Junction (NMJ) Quantification: A Method to Assess Synaptic Morphology and Function

Morphology, size and location of intracellular organelles are evolutionarily conserved and appear to directly affect their function. Understanding the molecular mechanisms underlying these processes has become an important goal of modern biology. Here we show how these studies can be facilitated by the application of quantitative techniques.

Why Quantification Matters: Characterization of Phenotypes at the Drosophila Larval Neuromuscular Junction

Here we describe a simple and widely accessible method to injure segmental nerves in Drosophila larvae to visualize and quantify neurodegeneration of motor neurons at the neuromuscular junction (NMJ) of third instar larvae.

A Simple Neuronal Mechanical Injury Methodology to Study Drosophila Motor Neuron Degeneration

The ability to accurately detect neuromuscular junction components is crucial in evaluating modifications in its architecture because of pathological or developmental processes. Here we present a complete description of a straightforward method to obtain high-quality images of whole-mount neuromuscular junctions that can be used to perform quantitative measurements.

Dissection of Single Skeletal Muscle Fibers for Immunofluorescent and Morphometric Analyses of Whole-Mount Neuromuscular Junctions

We describe a dissection technique that preserves the architecture of the neuromuscular junction and enables a detailed immunocytochemical study of motor neurons in the adult Drosophila leg.

Dissection and Immunohistochemistry of the Drosophila Adult Leg to Detect Changes at the Neuromuscular Junction for an Identified Motor Neuron

This protocol describes a method for morphometric analysis of neuromuscular junctions by combined confocal and STED microscopy that is used to quantify pathological changes in mouse models of SMA and ColQ-related CMS.

Characterization of Neuromuscular Junctions in Mice by Combined Confocal and Super-Resolution Microscopy

This report provides a new sample preparation procedure for visualizing neuromuscular junctions in Drosophila Larvae. This method is more effective in preventing the curling of the samples compared to the traditional method and is particularly useful for Drosophila neuromuscular junction ultrastructural analysis.

Optimizing Sample Preparation Process for Transmission Electron Microscopy of Neuromuscular Junctions in Drosophila Larvae

Here, we present a detailed protocol to visualize the microtubule networks in neuromuscular junctions and muscle cells. Combined with the powerful genetic tools of Drosophila melanogaster, this protocol greatly facilitates genetic screening and microtubule-dynamics analysis for the role of microtubule network regulatory proteins in the nervous system.

Using Drosophila Larval Neuromuscular Junction and Muscle Cells to Visualize Microtubule Network

Local injections of a mitochondrial dye label terminal Schwann cell mitochondria in vivo for confocal microscopy. This method utilizes proof of concept demonstrated in skeletal muscle tissue from mice with healthy or diseased muscle to provide high-resolution mitochondrial visualization.

Methods for In situ Quantification of Mitochondrial Morphology in Muscle and Terminal Schwann Cells of Mice

배우세요 Drosophila NMJ Morphometrics 알고리즘을 사용하여 초파리 신경근육접합 형태를 자동 정량화하는 방법을 익히세요. 이 기술은 고처리량 분석을 통해 시냅스 발달 조절인자를 효율적으로 식별하는 데 활용됩니다.

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

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