We want to thank Alessandro Vercelli for the critical comments and Sartorius&#39;s technical support for the help. Our research on these topics has been generously supported by the Rita-Levi Montalcini Grant 2021 (MIUR, Italy). This research was funded by Ministero dell&#39;Istruzione dell&#39;Universit&#224; e della Ricerca MIUR project Dipartimenti di Eccellenza 2023-2027 to Department of Neuroscience Rita Levi Montalcini. D.M.R.&#39;s research has been conducted during and with the support of the Italian national inter-university PhD course in Sustainable Development and Climate Change (link: www.phd-sdc.it).

1. Scanning the vessel on the machine
NOTE: The detection is performed by the built-in Basler Ace 1920-155 &#181;m camera.
<ol>
	<li>Open the program by clicking Connect to Device and selecting Schedule - To Acquire. Then click the + sign.</li>
	<li>Specify whether the vessel will be scanned repeatedly or only 1x by choosing the option Scan on Schedule or Scan Once Now, respectively.</li>
	<li>Select New to create a brand-new vessel to scan. If a new vessel is not added, create a new scan using one of the options described below.
	<ol>
		<li>Select Copy Current to create a new vessel by copying a vessel from the current schedule. Select Copy Previous to create a new vessel by copying a previously scanned vessel.</li>
		<li>Select Add Scan to restore a previously scanned vessel for additional scans.</li>
	</ol>
	</li>
	<li>Select the scan type based on the assay and application. For the analysis, select Standard.</li>
	<li>Specify scan settings. Choose one of the Cell-by-Cell options, either None, Adherent Cell-by-Cell, or Non-adherent Cell-by-Cell, and specify the image channels depending on the fluorescent molecules utilized. Select the None option for adult and embryonic sensory neuron cultures. For cell lines, select None or Adherent Cell-by-Cell options.</li>
	<li>Select the microscope objective (4x, 10x, 20x). Acquire with the higher magnification (20x) in primary cultures, while in cell lines, 10x is sufficient.</li>
	<li>Select the type of vessel to scan from the options provided. Indicate the vessel&#39;s location in the drawer by selecting its position on the virtual map of the drawer.</li>
	<li>Specify the scan pattern for the image acquisition by selecting the wells to scan. Select the desired number of images per well. An estimation of the scan duration will appear.</li>
	<li>Provide information about the vessel by typing the name and specify the plate map by clicking on the + sign.</li>
	<li>Click Next. Choose the Analysis Type, and click Next. A summary screen of the selected options will appear. If correct, click on Scan Now and the scan will start.</li>
</ol>
2. Setup for phase contrast image analysis
NOTE: Neurotrack analysis can only be performed on images previously acquired by the machine.
<ol>
	<li>Select the Scanned Vessel to analyze. Select Launch Analysis. Select Create New Analysis Definition.</li>
	<li>Select Neurotrack. Select Image Channels. Select a representative set of images to perform the analysis on. Select all images for each well to train the algorithm.</li>
	<li>Refine the Analysis Definition settings by adjusting the following parameters. 
	NOTE: By default, neurites are segmented in magenta, whereas cell bodies in yellow. It is possible to modify the colors as desired.
	<ol>
		<li>For cell-body cluster segmentation, adjust the following as described below.
		<ol>
			<li>Segmentation mode: The images&#39; segmentation is done to distinguish cell bodies from background and neurites. Choose between Brightness and Texture. For primary cultures and cell lines select Brightness mode.</li>
			<li>Segmentation adjustment: Use the slider to adjust the segmentation sensitivity toward more background or more cells. It ranges from 0 (Background) to 2 (Cells). It increases or decreases the size of the yellow mask. Move the slider towards 0 (Background) so the size of the yellow mask will gradually reduce leaving space for the magenta mask. The opposite occurs if the slider is moved towards 2 (Cells).</li>
		</ol>
		</li>
		<li>For cleanup, adjust the following as described below.
		<ol>
			<li>Hole Fill (&#181;m2): Adjust this to remove any hole in the cell body mask smaller than the area specified by the user.</li>
			<li>Adjust size (pixels): Increase (if positive) or shrink (if negative) the yellow mask by the specified number of pixels. It ranges from -10 to +10. Adjust this to add or remove yellow segmentation on the high contrast objects such as dead cells and cellular debris.</li>
			<li>Min cell width (&#181;m): Choose a value to define the size at which cell bodies will be considered as neurites.</li>
		</ol>
		</li>
		<li>For cell-body cluster filters, adjust the following as described below.
		<ol>
			<li>Area (&#181;m2): Set a minimum and a maximum value of cell body area. Values above and below the set values will not be considered as cell bodies.</li>
		</ol>
		</li>
		<li>For neurite parameters, adjust the following as described below.
		<ol>
			<li>Filtering: It reduces the masking of small vessel imperfections and debris. Choose between None, Better, and Best options. Choose None only for very clean cultures and vessels. Choose Better for faster processing at the expense of losing detection of very fine neurites. It may be sufficient for cells with thick or high-contrast neurites; also, it can be useful for vessels with many imperfections or debris. Choose Best for longer processing time, but it is the most sensitive filter setting to ensure the detection of very fine neurites.</li>
			<li>Neurite sensitivity: Use to adjust the detection sensitivity. Increase sensitivity to detect finer neurites. It ranges from 0.25 (Less) to 0.75 (More). 
			NOTE: It increases or decreases the software&#39;s sensitivity to recognize neurites. If the slider is moved towards 0.25 (Less), the software will be stricter in its recognition of neurites. Instead, if the slider is moved towards 0.75 (More), the software will be less strict in this detection, and therefore, more imperfections (i.e., cell debris, dirt) will be considered neurites.</li>
			<li>Neurite width (&#181;m): Use it to tune the detection to the size of the neurons. It can be 1, 2 or 4. By increasing it, the thinner neurites will not be considered. Set it to 1 for primary adult sensory neuron cultures and 2 for cell lines and embryonic sensory neuron cultures.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Click Preview Current to visualize the segmented image. The following measurements will be provided for each image: Neurite Length (mm/mm2), Neurite Branch Points (Per mm2), Cell-Body Clusters (Per mm2), and Cell-Body Cluster Area (mm2/mm2).</li>
	<li>Repeat steps 2.3 and 2.4 for all the selected images. Click Next.</li>
	<li>Select the Scan time and well to analyze. Assign a Definition Name and, if needed, Analysis Notes. Click Next &#62; Finish.</li>
</ol>
3. Setup for immunocytochemistry (ICC) image analysis
<ol>
	<li>Follow the same steps illustrated from 2.1 to 2.4. Select the Image Channels for neurites and nuclei. Select the Set of images; select All images for each well to train the algorithm.</li>
	<li>Refine the Analysis Definition settings by adjusting the following parameters as described below. 
	NOTE: By default, neurites are segmented in blue, whereas cell bodies are purple. However, the colors can be modified as desired.
	<ol>
		<li>Cell-body cluster segmentation: Use this to segment the image into objects of interest. Estimate the background brightness at every pixel in the image. After the background is found, perform one of the following options.
		<ol>
			<li>No background subtraction: Use this to segment without altering the original picture. Choose between Adaptive or Fixed Threshold. Adaptive: the background is used to find objects but not explicitly subtracted; with this option, it is possible to set the Threshold Adjustment (GCU). Fixed Threshold: objects that are brighter than this threshold are detected in the original image; with this option, it is possible to set the Threshold (GCU).</li>
			<li>Background subtraction: Use this option to subtract the background from the image using a Top-Hat transform, then apply a threshold to it. With this option, set Radius and Threshold. Radius: a disk of this radius is used; the disk should be large enough that it does not fit entirely within any object in the image. Threshold: objects that are brighter than this threshold are detected in the background-subtracted image.</li>
		</ol>
		</li>
		<li>Cleanup: Use the following option to perform this.
		<ol>
			<li>Hole fill (&#181;m2): Use this to remove any holes in the cell body mask that are smaller than the area specified.</li>
			<li>Adjust size (pixels): Use this to increase (if positive) or shrink (if negative) the purple mask by the specified number of pixels. The range is -10 to +10. It adds or removes purple on high-contrast objects such as dead cells and cellular debris.</li>
			<li>Min cell width (&#181;m): Use this to define the size at which cell bodies will be considered as neurites.</li>
		</ol>
		</li>
		<li>Cell-body cluster filters: Use the following option to apply the filters.
		<ol>
			<li>Area (&#181;m2): Set a minimum and a maximum value of cell body area. Values above and below the set values will not be considered as cell bodies.</li>
		</ol>
		</li>
		<li>Neurite parameters: Use the following option to set these.
		<ol>
			<li>Neurite coarse sensitivity: Use this to adjust for neurite brightness. Sensitivity should be increased if the neurites&#39; fluorescence intensity is low. It ranges from 0 (Less) to 10 (More). It increases or decreases the sensitivity of the software to recognize less bright neurites. Optimal values range from 7 to 10; note that if it is set to 10, it is very likely that also background will be considered in the neurite measurement.</li>
			<li>Neurite fine sensitivity: Use this to adjust the detection sensitivity. Sensitivity should be increased to detect finer neurites. It ranges from 0.25 (Less) to 0.75 (More). It increases or decreases the sensitivity of the software to recognize fine and less bright neurites. If the slider is moved towards 0.25 (Less) the software will not consider faint neurites. Instead, if the slider is moved towards 0.75 (More) the software will detect also very faint (almost background) neurites.</li>
			<li>Neurite width (&#181;m): Use this to tune the detection to the size of the neurons. It can be 1, 2 or 4. By increasing it, the thinner neurites will not be considered. Set it on 1 for primary adult sensory neurons cultures, on 2 for cell lines and embryonic sensory neurons cultures.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Data export
<ol>
	<li>Open the analysis. Click on Graph Metrics.</li>
	<li>Select the Metric, Timepoints, and Wells of interest.</li>
	<li>Select the grouping option between All, None, Columns, Rows, and Plate Map Replicates.</li>
	<li>Click on Export Data and select the folder of destination and, if needed, other options. A .txt file will be created. 
	NOTE: The machine will provide a single average value of the chosen metric for each well. Manual annotation is required during the analysis to retrieve single image values.</li>
</ol>
5. Image export
<ol>
	<li>Open the vessel. Click on Export Images and Movies.</li>
	<li>Select the export type for the images.
	<ol>
		<li>Select As Displayed to export images as displayed. Click Next and select the Images of Interest for export. Click Next.
		<ol>
			<li>Select the Sequence Type between a Single Movie or Series of Images and the time points of interest. Click Next.</li>
			<li>Adjust the export options as needed and click Next. Set the output folder, file format, and name of the file, then click Export.</li>
		</ol>
		</li>
		<li>Select As Stored to export images in the raw format. Select the Image Type.
		<ol>
			<li>Select the Timepoints and Wells of interest. Click Next. Set the output folder, file format and name of the file, then click Export.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Image export with the segmentation masks
	<ol>
		<li>Open the analysis. Click on the Icon of Image Layers. Select the Desired Channels Masks (Phase Neurite and Phase Cell-Body Cluster). Follow step 5.2.</li>
	</ol>
	</li>
</ol>

Advanced Live-Imaging Techniques for Accurate Neurite Outgrowth Measurement

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<li>Zhu, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Loss+of+WIPI4+in+neurodegeneration+causes+autophagy-independent+ferroptosis%5BTitle%5D)+AND+%22Nat+Cell+Biol%22%5BJournal%5D)">Loss of WIPI4 in neurodegeneration causes autophagy-independent ferroptosis.</a> Nat Cell Biol. 26, 542-551 (2024).</li>
<li>Reggiani, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((BET+inhibitors+drive+Natural+Killer+activation+in+non-small+cell+lung+cancer+via+BRD4+and+SMAD3%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">BET inhibitors drive Natural Killer activation in non-small cell lung cancer via BRD4 and SMAD3.</a> Nat Commun. 15, 2567(2024).</li>
<li>Ackerman, H. D., Gerhard, G. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Bile+acids+induce+neurite+outgrowth+in+NSC-34+cells+via+TGR5+and+a+distinct+transcriptional+profile%5BTitle%5D)+AND+%22Pharmaceuticals%22%5BJournal%5D)">Bile acids induce neurite outgrowth in NSC-34 cells via TGR5 and a distinct transcriptional profile.</a> Pharmaceuticals. 16, 174(2023).</li>
<li>Tuttle, R., Matthew, W. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Neurotrophins+affect+the+pattern+of+DRG+neurite+growth+in+a+bioassay+that+presents+a+choice+of+CNS+and+PNS+substrates%5BTitle%5D)+AND+%22Development%22%5BJournal%5D)">Neurotrophins affect the pattern of DRG neurite growth in a bioassay that presents a choice of CNS and PNS substrates.</a> Development. 121, 1301-1309 (1995).</li>
<li>Wurster, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Live+monitoring+and+analysis+of+fungal+growth%2C+viability%2C+and+mycelial+morphology+using+the+IncuCyte+NeuroTrack+processing+module%5BTitle%5D)+AND+%22mBio%22%5BJournal%5D)">Live monitoring and analysis of fungal growth, viability, and mycelial morphology using the IncuCyte NeuroTrack processing module.</a> mBio. 10 (3), e00673-e00619 (2019).</li>
</ol>

The authors declare that the research was conducted without any commercial or financial relationships that could potentially create a conflict of interest.

Accurately measuring how neurons grow in healthy, injured, and diseased conditions is a critical parameter in many experimental setups within the neuroscience field. Whether working with organotypic cultures of whole DRG explants or dissociated cultures, properly measuring axonal outgrowth has been a significant challenge over the last 20 years. Without reliable and accurate quantification of neurite outgrowth, it is impossible to assess if a specific treatment, such as retinoic acid (for 4 days) for NSC-34 cells34 or neurotrophins (for 1-2 days) for embryonic DRG neurons14,35, has been effective. Neurons typically exhibit continuous growth when healthy; however, following injury the axonal growth rate increases12,13. Timing is crucial when measuring axonal growth; therefore, before commencing an experiment, it is essential to conduct a trial test to determine the optimal time for fixing the cells based on their growth rate plateau curve.
The choice of the method, manual or automatic, marks a watershed in terms of time expenditure and accuracy. Some of the most common manual methods include NeuronJ18,20&#160;and Metamorph (Visitron)2,22,28. Manual methods require users to manually trace neurites, which is extremely time-consuming and requires single-cell images. With manual segmentation of neurites, neural networks are completely out of reach. Typically, these methodologies measure only the longest axon or use the vector distance measurement, thereby losing important information such as the number&#160;of branch points and the total axonal length. Somewhat of an improvement is provided by the Sholl analysis30, which anyway is limited to single-cell conditions. Single-cell analysis presents several challenges, starting with the seeding of cells. Neurons must be plated at a very low concentration, which may not be the most suitable growth condition for every cell type. Another issue arises from image acquisition. Usually, the confocal microscope is utilized for imaging, which requires trained users and fluorescent neurons and is neither time nor cost-efficient. With the confocal microscope high-resolution images are obtained, but very few neurons get imaged from each experiment. This represents a limitation as more biological replicates are necessary to reach an adequate number of neurons.
Some automatic methodologies such as NeuroMath2,28&#160;solve the issue of the time-consuming neurite segmentation which is performed in an automatic way.
However, due to time constraints, this neurite outgrowth measurement module provides faster results when acquiring images at the time-lapse microscopy machine. The latter, together with this software, significantly improves time and cost efficiency for both students and principal investigators.
The acquisition machine allows the creation of quite a complete map of the well, acquiring a variable number of images based on the diameter of the well. This represents a significant advantage as multiple neurons get imaged at the same time. However, reaching only a 20x magnification is sufficient and suitable to analyze the images with the software. Its power relies in its capability to train on a set of images and perform a semi-automatic measurement of the total axonal length. Additionally, the software can work on both single neurons and neuronal networks. The software is able to segment neurites and cell bodies with two different masks. A yellow mask segments all the high-contrast objects in the image, such as cell bodies, cell debris, and shadows. A magenta mask, instead, segments neurites. The precision and accuracy of the software were proven by the segmentation of the same neurons with both the software and NeuronJ. From the statistical analysis point of view, the values of neurite lengths nicely correlated with a high Spearman correlation coefficient.
After assessing the reliability of the method, we moved to the analysis of various test conditions. For an optimal analysis, some precautions are required. First, neurons have to be uniformly seeded in the well, avoiding spots with high cell concentration. The clustering of neurons causes the software to lose precision in the neurites&#39; detection. Another variable that influences the accuracy and precision of the analysis is the cleanliness of the culture. A clean culture with few cellular debris and dead cells is preferable. However, the software is able to compensate for such issues by modulation of the neurite sensitivity and the cell body segmentation mask. As aforementioned, the yellow mask segments high-contrast objects, among which there are also shadows caused by the movement of the medium in the well. The shadows might cover neurites, thereby resulting in the loss of neurite length. However, such an issue is easily resolved by allowing the medium to settle down before image acquisition.
The algorithm is able to perform neurite length quantification on both phase contrast images and immunocytochemistry images. When fluorescence is involved, other precautions have to be taken in order to obtain a reliable analysis. Firstly, the quality of the staining profoundly influences the outcome of the analysis. If neurites are broken or segmented and cell bodies are torn away during washes, the analysis loses strength and reliability. Additionally, the fluorescence itself represents a potentially disturbing element for the analysis. Since the focus is performed automatically by the machine, the objective puts on focus very bright objects such as artifacts, cell bodies, or thick neurites. As a result, thinner and less bright neurites are left behind, making it difficult for the user to properly measure the neurite length.
As a consequence, the analysis of phase images might be more well-founded compared to the analysis of immunocytochemistry images. The software is capable of working on many different neuronal morphologies from the most intricate and elaborate to the simplest ones, either as single-cells or in neuronal networks. Therefore, it is utilizable in many different research fields, ranging from primary cultures of neurons coming from the central or peripheral nervous system of various developmental stages to iPSC-derived neurons and cell lines such as NSC-34.
Despite the significant potential of the software, some limitations can be noted. Firstly, the precision of cell body segmentation is suboptimal. Consequently, parameters such as cell body cluster and cell body cluster area cannot be reliably used for cell counting. Secondly, in addition to the necessary precautions for optimal neurite segmentation, insufficiently thick axons may be excluded from the neurite length measurement, thereby resulting in data loss.
The branch point parameter encounters issues related to both types of segmentation. Shadows, dead cells, or debris in the culture that localize&#160;on branch points obscure them as they get covered by the cell body segmentation. Moreover, in the case of thin neurites, the reliability of the branch point parameter is again severely compromised.
Furthermore, the automatic focus during image acquisition can sometimes be suboptimal. The machine&#39;s maximal&#160;magnification is limited to 20x, which may be inadequate for observing finer details or fluorescence in slender structures such as neurites. Additionally, the machine performs best with homogeneous plastic substrates. If a glass coverslip is inserted in the well, the focus on the glass may fail, resulting in partially blurred images. However, this software not only applies to neurons but also to completely different fields of research, such as fungal growth36.
All things considered, we believe this neurite outgrowth measurement module to be a reliable tool for measuring neurites quickly, unbiasedly and efficiently.

In sciatic nerves, it is possible to measure axonal regeneration1. Additionally, in vitro studies have shown the feasibility of monitoring axonal outgrowth2,3 to comprehend its various phases, from axonal sprouting to axonal degeneration, in both healthy and injured neurons. By tracking these processes, it is possible to measure parameters such as axonal polarity, initiation, stability, and branching. The last parameter is crucial to understand neuropathic pain perception4,5,6. Similarly, axonal degeneration can be monitored in vivo7&#160;or in vitro8,9. During neurite outgrowth, actin and microtubule cytoskeletal networks stabilize or change according to the needs of the cell10. The actin cytoskeleton reorganizes to allow the formation of the axonal growth cone, and the microtubules re-align into bundles to stabilize the growing neurite11. In order to study neurite outgrowth of central and peripheral neurons in vitro, three common parameters are quantified: total axonal length, maximal distance, and branch points. These parameters are used to study the neuronal outgrowth response to treatment (i.e., neurotrophins, compounds, inhibitors, retinoic acid, siRNA, shRNA) or in genetically modified animals12,13,14. In order to assess if neurons have more elongated neurites and/or more branching, these three parameters allow us to assess the morphology of a neuron. Neurite length measurement is the top-interest parameter in several in vitro experimental setups. From dorsal root ganglia, mainly two types of cultures are performed: dissociated in vitro culture or organotypic culture of whole DRG explants. In either case, neurite length is a gold parameter to assess the outcome of the experiment. In a motor neuron-like cell line (NSC-34), axonal outgrowth and branching are measured after differentiation induced by retinoic acid15,16. In fact, by measuring the neurite outgrowth, it is possible to determine if a specific treatment has worked17, the growth rate18, or the regeneration capacity after an injury procedure19.
How to properly assess neurite outgrowth has posed a significant number of challenges over the years in the research field. However, there is no standardization of neurite length measurements. Some of the most utilized methods for in vitro cell cultures are, for example, the manual NeuronJ plug-in on Fiji18,20&#160;or MetaMorph21,23&#160;and the semi-automatic Neurolucida23,24. Other than manual methodologies, there are automatic methods, too, such as the NeuriteTracer plug-in on Fiji25, HCA Vision software26,27, or WIS-NeuroMath2,28. Other less accurate methodologies rely on the measurement of the overall dimension of the neurons. These methods include the measurement of the vector distance from the cell body to the tip of the longest axon29 or the Sholl analysis30. However, these measurement methods are suitable for very low-density cultures or single neurons. Moreover, all these methodologies are mainly utilized on stained neurons or neurons that are expressing genetically encoded fluorophores (i.e., GFP, Venus, mCherry). The type of neuron and the density of the cell culture deeply affect the choice of measurement methodology. For example, manually segmenting neurons with very intricate and complicated morphologies, such as DRG neurons, can easily become an impossible task. If convoluted neurons are already a challenge to segment, neural nets are completely out of reach for manual approaches due to their highly complex organization.
On the one hand, manual segmentation is very precise because it is performed by human eyes and intelligence; on the other hand, it is really time-consuming. The elevated time expenditure required by manual methods is the main drawback. For this reason, only a few neurons are acquired for analysis, making it less accurate and costly in terms of time. Automatic or semi-automatic approaches, on the other hand, partially reduce the time expenditure. However, they also have some disadvantages. Automatic methods need to be trained in order to work properly, and if the software is not interactive enough with the user, the segmentation can be wrong.
Other than neurite outgrowth measurement, the number of branch points is also valuable information. With manual segmentation, the number of branch points can be calculated, whereas this is not possible with a vector distance. With automatic methods, the number of branch points is usually provided, whereas with the Sholl analysis, it has to be calculated with a mathematical formula.
In this methods paper, we aim to describe the functionality and effectiveness of this semi-automatic software in measuring the total axonal length and other parameters. The machine allows for the automatic acquisition of images at defined time points or for conducting long-term studies (days, weeks, months), preserving a physiological environment for live cells. Measuring neurite outgrowth using phase-contrast time-lapse imaging has the benefit of enabling continuous monitoring of neurite kinetics and growth. Additionally, it is also possible to monitor cell death through the addition in the media of specific dyes that target dead cells31,32,33. Although the software has been released in 2012, we are the first to standardize this methodology in a reproducible and unbiased way for the accurate quantification of neurite outgrowth. However, it is important to note that the software is not included with the purchase of the machine. Despite this additional expense, its use offers significant advantages in measuring total axonal length and other parameters, thereby contributing to research in the field of neuroscience.

<table><tbody><tr><td>Collagenase A</td><td>Merck / Roche</td><td>10103586001</td><td></td></tr><tr><td>Dispase II (neutral protease, grade II)</td><td>Merck / Roche</td><td>4942078001</td><td></td></tr><tr><td>Dulbecco&#039;s modified eagle&#039;s medium</td><td>Merck / Sigma</td><td>D5796</td><td></td></tr><tr><td>Fetal bovin serum&amp;nbsp;</td><td>Merck / Sigma</td><td>F7524</td><td></td></tr><tr><td>Ham&#039;s F-12 Nutrient Mix (1X)</td><td>ThermoFisher Scientific</td><td>21765029</td><td></td></tr><tr><td>Ham&#039;s F12 w/ L-Glutamine</td><td>Euroclone</td><td>ECM0135L</td><td></td></tr><tr><td>Hanks&#039; Balanced Salt Solution</td><td>Euroclone</td><td>ECM0507L</td><td></td></tr><tr><td>HBSS (10X), no calcium, no magnesium, no phenol red</td><td>ThermoFisher Scientific</td><td>14185045</td><td></td></tr><tr><td>HyClone Characterized Fetal Bovine Serum (U.S.)</td><td>Cytiva</td><td>SH30071.03</td><td></td></tr><tr><td>Incucyte, Neurotrack Analysis Software&amp;nbsp;</td><td>Sartorius</td><td>9600-0010</td><td></td></tr><tr><td>L-15 Medium (Leibovitz)</td><td>Millipore/Sigma</td><td>L5520</td><td></td></tr><tr><td>Laminin Mouse Protein, Natural</td><td>ThermoFisher Scientific</td><td>23017015</td><td></td></tr><tr><td>L-Cysteine</td><td>Merck / Sigma</td><td>C7352</td><td></td></tr><tr><td>Leibovitz&#039;s L-15 medium w/o L-glutamine</td><td>Euroclone</td><td>ECB0020L</td><td></td></tr><tr><td>mouse NGF 2.5S (&gt;95%)</td><td>Alomone Labs</td><td>N-100</td><td></td></tr><tr><td>Neurobasal Medium [-] Glutamine</td><td>ThermoFisher Scientific</td><td>21103049</td><td></td></tr><tr><td>NSC-34</td><td>CELLutions Biosystems Inc (Ontario, Canada)</td><td>CLU140</td><td></td></tr><tr><td>Papain from papaya latex</td><td>Sigma</td><td>P4762</td><td></td></tr><tr><td>Penicillin-Streptomycin (5,000 U/mL)</td><td>ThermoFisher Scientific</td><td>15070063</td><td></td></tr><tr><td>Percoll (Density 1.130 g/mL)</td><td>Cytiva</td><td>17089101</td><td></td></tr><tr><td>Poly-D-Lysine Solution (1mg/mL)</td><td>EMD Millipore/Merck</td><td>A-003-E</td><td></td></tr><tr><td>Poly-L-Lysine Solution (0-01%)</td><td>Sigma</td><td>P4832</td><td></td></tr><tr><td>Recombinant Human NT-3</td><td>PeproTech</td><td>450-03</td><td></td></tr><tr><td>Retinoic Acid</td><td>Merck / Sigma</td><td>R2625</td><td></td></tr><tr><td>Trypsin-EDTA solution</td><td>Sigma</td><td>T3924</td><td></td></tr><tr><td>&amp;beta;-Tubulin III (Tuj1) antibody</td><td>Merck / Sigma</td><td>T8660</td><td></td></tr></tbody></table>

standardization of a novel semi-automatic software for neurite outgrowth measurement

Effective live-imaging techniques are crucial to assess neuronal morphology in order to measure neurite outgrowth in real time. The proper measurement of neurite outgrowth has been a long-standing challenge over the years in the neuroscience research field. This parameter serves as a cornerstone in numerous in vitro experimental setups, ranging from dissociated cultures and organotypic cultures to cell lines. By quantifying the neurite length, it is possible to determine if a specific treatment worked or if axonal regeneration is enhanced in different experimental groups. In this study, the aim is to demonstrate the robustness and accuracy of the Incucyte Neurotrack neurite outgrowth analysis software. This semi-automatic software is available in a time-lapse microscopy system which offers several advantages over commonly used methodologies in the quantification of the neurite length in phase contrast images. The algorithm masks and quantifies several parameters in each image and returns neuronal cell metrics, including neurite length, branch points, cell-body clusters, and cell-body cluster areas. Firstly, we validated the robustness and accuracy of the software by correlating its values with those of the manual NeuronJ, a Fiji plug-in. Secondly, we used the algorithm which is able to work both on phase contrast images as well as on immunocytochemistry images. Using specific neuronal markers, we validated the feasibility of the fluorescence-based neurite outgrowth analysis on sensory neurons in vitro cultures. Additionally, this software can measure neurite length across various seeding conditions, ranging from individual cells to complex neuronal nets. In conclusion, the software provides an innovative and time-effective platform for neurite outgrowth assays, paving the way for faster and more reliable quantifications.

Author Spotlight: Unveiling the Molecular Basis of Pain Perception and Neuropathic Pain

Neurite outgrowth assays provide a quantitative value about regenerative neuronal processes. The advantage of this semi-automatic software is that it segments cell bodies and neurites separately by creating a mask and measures various parameters such as neurite length, number of branch points, cell-body cluster area, and number of cell clusters.

Standardization of a Novel Semi-Automatic Software for Neurite Outgrowth Measurement

Effective live-imaging techniques are crucial to assess neuronal morphology in order to measure neurite outgrowth in real time. The proper measurement of ...

standardization-novel-semi-automatic-software-for-neurite-outgrowth

Nanyang Technological University

Research

JoVE Journal

Neuroscience

972 Views.  University of Turin. In ons lab bestuderen we hoe uitgroei en vertakking van neurieten pijnperceptie kunnen moduleren met behulp van in vitro modellen. Het is algemeen bekend dat pijnreacties variëren tussen mannen en vrouwen tijdens het ouder worden, dus we proberen de moleculaire basis van dit proces te achterhalen. Onlangs hebben we de betrokkenheid van importine alfa-3 en nucleocytoplasmisch eiwit in de late fase van chronische pijn bestudeerd.We hebben geïmporteerde...

Video: Auteur in de schijnwerpers: onthulling van de moleculaire basis van pijnperceptie en neuropathische pijn

Automated Quantification of Synaptic Fluorescence in C. elegans

Synapse strength refers to the amplitude of postsynaptic responses to presynaptic neurotransmitter release events, and has a major impact on overall neural circuit function. Synapse strength critically depends on the abundance of neurotransmitter receptors clustered at synaptic sites on the postsynaptic membrane. Receptor levels are established developmentally, and can be altered by receptor trafficking between surface-localized, subsynaptic, and intracellular pools, representing important mechanisms of synaptic plasticity and neuromodulation. Rigorous methods to quantify synaptically-localized neurotransmitter receptor abundance are essential to study synaptic development and plasticity. Fluorescence microscopy is an optimal approach because it preserves spatial information, distinguishing synaptic from non-synaptic pools, and discriminating among receptor populations localized to different types of synapses. The genetic model organism Caenorhabditis elegans is particularly well suited for these studies due to the small size and relative simplicity of its nervous system, its transparency, and the availability of powerful genetic techniques, allowing examination of native synapses in intact animals.
Here we present a method for quantifying fluorescently-labeled synaptic neurotransmitter receptors in C. elegans. Its key feature is the automated identification and analysis of individual synapses in three dimensions in multi-plane confocal microscope output files, tabulating position, volume, fluorescence intensity, and total fluorescence for each synapse. This approach has two principal advantages over manual analysis of z-plane projections of confocal data. First, because every plane of the confocal data set is included, no data are lost through z-plane projection, typically based on pixel intensity averages or maxima. Second, identification of synapses is automated, but can be inspected by the experimenter as the data analysis proceeds, allowing fast and accurate extraction of data from large numbers of synapses. Hundreds to thousands of synapses per sample can easily be obtained, producing large data sets to maximize statistical power. Considerations for preparing C. elegans for analysis, and performing confocal imaging to minimize variability between animals within treatment groups are also discussed. Although developed to analyze C. elegans postsynaptic receptors, this method is generally useful for any type of synaptically-localized protein, or indeed, any fluorescence signal that is localized to discrete clusters, puncta, or organelles.
The procedure is performed in three steps: 1) preparation of samples, 2) confocal imaging, and 3) image analysis. Steps 1 and 2 are specific to C. elegans, while step 3 is generally applicable to any punctate fluorescence signal in confocal micrographs.

Automated Quantification of Synaptic Fluorescence in C. elegans

The abundance of neurotransmitter receptors clustered at synapses strongly influences synaptic strength. This method quantifies fluorescently-labeled neurotransmitter receptors in three dimensions with single-synapse resolution in C. elegans, allowing hundreds of synapses to be rapidly characterized within a single sample without distortions introduced by z-plane projection.

Synapse strength refers to the amplitude of postsynaptic responses to presynaptic neurotransmitter release events, and has a major impact on overall ...

Simulating Pancreatic Neuroplasticity: In Vitro Dual-neuron Plasticity Assay

Neuroplasticity is an inherent feature of the enteric nervous system and gastrointestinal (GI) innervation under pathological conditions. However, the pathophysiological role of neuroplasticity in GI disorders remains unknown. Novel experimental models which allow simulation and modulation of GI neuroplasticity may enable enhanced appreciation of the contribution of neuroplasticity in particular GI diseases such as pancreatic cancer (PCa) and chronic pancreatitis (CP). Here, we present a protocol for simulation of pancreatic neuroplasticity under in vitro conditions using newborn rat dorsal root ganglia (DRG) and myenteric plexus (MP) neurons. This dual-neuron approach not only permits monitoring of both organ-intrinsic and -extrinsic neuroplasticity, but also represents a valuable tool to assess neuronal and glial morphology and electrophysiology. Moreover, it allows functional modulation of supplied microenvironmental contents for studying their impact on neuroplasticity. Once established, the present neuroplasticity assay bears the potential of being applicable to the study of neuroplasticity in any GI organ.

Simulating Pancreatic Neuroplasticity: In Vitro Dual-neuron Plasticity Assay

Neuronal plasticity is an increasingly recognized, but insufficiently understood feature of the gastrointestinal (GI) tract. Here, in the example of human pancreatic disorders, we present an in vitro neuroplasticity assay for the study of neuronal plasticity in the GI tract at both morphological and functional level.

Neuroplasticity is an inherent feature of the enteric nervous system and gastrointestinal (GI) innervation under pathological conditions. However, the ...

Medicine

A Neurite Outgrowth Assay and Neurotoxicity Assessment with Human Neural Progenitor Cell-Derived Neurons

Neurite outgrowth assay and neurotoxicity assessment are two major studies that can be performed using the presented method herein. This protocol provides reliable analysis of neuronal morphology together with quantitative measurements of modifications on neurite length and synaptic protein localization and abundance upon treatment with small molecule compounds. In addition to the application of the presented method in neurite outgrowth studies, neurotoxicity assessment can be performed to assess, distinguish and rank commercial chemical compounds based on their potential developmental neurotoxicity effect.
Even though cell lines are nowadays widely used in compound screening assays in neuroscience, they often differ genetically and phenotypically from their tissue origin. Primary cells, on the other hand, maintain important markers and functions observed in vivo. Therefore, due to the translation potential and physiological relevance that these cells could offer neurite outgrowth assay and neurotoxicity assessment can considerably benefit from using human neural progenitor cells (hNPCs) as the primary human cell model.
The presented method herein can be utilized to screen for the ability of compounds to induce neurite outgrowth and neurotoxicity by taking advantage of the human neural progenitor cell-derived neurons, a cell model closely representing human biology.&#34;

The presented protocol describes a method for a neurite outgrowth assay and neurotoxicity assessment of small molecule compounds.

Neurite outgrowth assay and neurotoxicity assessment are two major studies that can be performed using the presented method herein. This protocol provides ...

Developmental Biology

Preparation of Tunable Extracellular Matrix Microenvironments to Evaluate Schwann Cell Phenotype Specification

Traumatic peripheral nervous system (PNS) injuries currently lack suitable treatments to regain full functional recovery. Schwann cells (SCs), as the major glial cells of the PNS, play a vital role in promoting PNS regeneration by dedifferentiating into a regenerative cell phenotype following injury. However, the dedifferentiated state of SCs is challenging to maintain through the time-period needed for regeneration and is impacted by changes in the surrounding extracellular matrix (ECM). Therefore, determining the complex interplay between SCs and differing ECM to provide cues of regenerative potential of SCs is essential. To address this, a strategy was created where different ECM proteins were adsorbed onto a tunable polydimethylsiloxane (PDMS) substrate which provided a platform where stiffness and protein composition can be modulated. SCs were seeded onto the tunable substrates and critical cellular functions representing the dynamics of SC phenotype were measured. To illustrate the interplay between SC protein expression and cellular morphology, differing seeding densities of SCs in addition to individual microcontact printed cellular patterns were utilized and characterized by immunofluorescence staining and western blot. Results showed that cells with a smaller spreading area and higher extent of cellular elongation promoted higher levels of SC regenerative phenotypic markers. This methodology not only begins to unravel the significant relationship between the ECM and cellular function of SCs, but also provides guidelines for the future optimization of biomaterials in peripheral nerve repair.

This methodology aims to illustrate the mechanisms by which extracellular matrix cues such as substrate stiffness, protein composition and cell morphology regulate Schwann cell (SC) phenotype.

Traumatic peripheral nervous system (PNS) injuries currently lack suitable treatments to regain full functional recovery. Schwann cells (SCs), as the ...

Bioengineering

High-resolution Volume Imaging of Neurons by the Use of Fluorescence eXclusion Method and Dedicated Microfluidic Devices

Volume is an important parameter regarding physiological and pathological characteristics of neurons at different time scales. Neurons are quite unique cells regarding their extended ramified morphologies and consequently raise several methodological challenges for volume measurement. In the particular case of in vitro neuronal growth, the chosen methodology should include sub-micrometric axial resolution combined with full-field observation on time scales from minutes to hours or days. Unlike other methods like cell shape reconstruction using confocal imaging, electrically-based measurements or Atomic Force Microscopy, the recently developed Fluorescence eXclusion method (FXm) has the potential to fulfill these challenges. However, although being simple in its principle, implementation of a high-resolution FXm for neurons requires multiple adjustments and a dedicated methodology. We present here a method based on the combination of fluorescence exclusion, low-roughness multi-compartments microfluidic devices, and finally micropatterning to achieve in vitro measurements of local neuronal volume. The high resolution provided by the device allowed us to measure the local volume of neuronal processes (neurites) and the volume of some specific structures involved in neuronal growth, such as growth cones (GCs).

Volume is an important parameter regarding physiological and pathological characteristics of cells. We describe a fluorescent exclusion method allowing full-field measurement of in vitro neuronal volume with sub-micrometric axial resolution required for the analysis of neurites and dynamic structures implied in neuronal growth.

Volume is an important parameter regarding physiological and pathological characteristics of neurons at different time scales. Neurons are quite unique ...

Post-differentiation Replating of Human Pluripotent Stem Cell-derived Neurons for High-content Screening of Neuritogenesis and Synapse Maturation

Neurons differentiated in two-dimensional culture from human pluripotent stem-cell-derived neural progenitor cells (NPCs) represent a powerful model system to explore disease mechanisms and carry out high content screening (HCS) to interrogate compound libraries or identify gene mutation phenotypes. However, with human cells the transition from NPC to functional, mature neuron requires several weeks. Synapses typically start to form after 3 weeks of differentiation in monolayer culture, and several neuron-specific proteins, for example the later expressing pan-neuronal marker NeuN, or the layer 5/6 cerebral cortical neuron marker CTIP2, begin to express around 4-5 weeks post-differentiation. This lengthy differentiation time can be incompatible with optimal culture conditions used for small volume, multi-well HCS platforms. Among the many challenges are the need for well-adhered, uniformly distributed cells with minimal cell clustering, and culture procedures that foster long-term viability and functional synapse maturation. One approach is to differentiate neurons in a large volume format, then replate them at a later time point in HCS-compatible multi-wells. Some main challenges when using this replating approach concern reproducibility and cell viability, due to the stressful disruption of the dendritic and axonal network. Here we demonstrate a detailed and reliable procedure for enzymatically resuspending human induced pluripotent stem cell (hiPSC)-derived neurons after their differentiation for 4-8 weeks in a large-volume format, transferring them to 384-well microtiter plates, and culturing them for a further 1-3 weeks with excellent cell survival. This replating of human neurons not only allows the study of synapse assembly and maturation within two weeks from replating, but also enables studies of neurite regeneration and growth cone characteristics. We provide examples of scalable assays for neuritogenesis and synaptogenesis using a 384-well platform.

This protocol describes a detailed procedure for resuspending and culturing human stem cell derived neurons that were previously differentiated from neural progenitors in vitro for multiple weeks. The procedure facilitates imaging-based assays of neurites, synapses, and late-expressing neuronal markers in a format compatible with light microscopy and high-content screening.

Neurons differentiated in two-dimensional culture from human pluripotent stem-cell-derived neural progenitor cells (NPCs) represent a powerful model ...

Studying Pre-formed Fibril Induced &#945;-Synuclein Accumulation in Primary Embryonic Mouse Midbrain Dopamine Neurons

The goal of this protocol is to establish a robust and reproducible model of &#945;-synuclein accumulation in primary dopamine neurons. Combined with immunostaining and unbiased automated image analysis, this model allows for the analysis of the effects of drugs and genetic manipulations on &#945;-synuclein aggregation in neuronal cultures. Primary midbrain cultures provide a reliable source of bona fide embryonic dopamine neurons. In this protocol, the hallmark histopathology of Parkinson&#8217;s disease, Lewy bodies (LB), is mimicked by the addition of &#945;-synuclein pre-formed fibrils (PFFs) directly to neuronal culture media. Accumulation of endogenous phosphorylated &#945;-synuclein in the soma of dopamine neurons is detected by immunostaining already at 7 days after the PFF addition. In vitro cell culture conditions are also suitable for the application and evaluation of treatments preventing &#945;-synuclein accumulation, such as small molecule drugs and neurotrophic factors, as well as lentivirus vectors for genetic manipulation (e.g., with CRISPR/Cas9). Culturing the neurons in 96 well plates increases the robustness and power of the experimental setups. At the end of the experiment, the cells are fixed with paraformaldehyde for immunocytochemistry and fluorescence microscopy imaging. Multispectral fluorescence images are obtained via automated microscopy of 96 well plates. These data are quantified (e.g., counting the number of phospho-&#945;-synuclein-containing dopamine neurons per well) with the use of free software that provides a platform for unbiased high-content phenotype analysis. PFF-induced modeling of phosphorylated &#945;-synuclein accumulation in primary dopamine neurons provides a reliable tool to study the underlying mechanisms mediating formation and elimination of &#945;-synuclein inclusions, with the opportunity for high-throughput drug screening and cellular phenotype analysis.

Here, we present a detailed protocol to study neuronal &#945;-synuclein accumulation in primary mouse dopamine neurons. Phosphorylated &#945;-synuclein aggregates in neurons are induced with pre-formed &#945;-synuclein fibrils. Automated imaging of immunofluorescently labeled cells and unbiased image analysis make this robust protocol suitable for medium-to-high throughput screening of drugs that inhibit &#945;-synuclein accumulation.

The goal of this protocol is to establish a robust and reproducible model of &#945;-synuclein accumulation in primary dopamine neurons. Combined with ...

Semi-Quantitative Determination of Dopaminergic Neuron Density in the Substantia Nigra of Rodent Models using Automated Image Analysis

Estimation of the number of dopaminergic neurons in the substantia nigra is a key method in pre-clinical Parkinson's disease research. Currently, unbiased stereological counting is the standard for quantification of these cells, but it remains a laborious and time-consuming process, which may not be feasible for all projects. Here, we describe the use of an image analysis platform, which can accurately estimate the quantity of labeled cells in a pre-defined region of interest. We describe a step-by-step protocol for this method of analysis in rat brain and demonstrate it can identify a significant reduction in tyrosine hydroxylase positive neurons due to expression of mutant &#945;-synuclein in the substantia nigra. We validated this methodology by comparing with results obtained by unbiased stereology. Taken together, this method provides a time-efficient and accurate process for detecting changes in dopaminergic neuron number, and thus is suitable for efficient determination of the effect of interventions on cell survival.

Here we present an automated method for semi-quantitative determination of dopaminergic neuron number in the rat substantia nigra pars compacta.

Estimation of the number of dopaminergic neurons in the substantia nigra is a key method in pre-clinical Parkinson's disease research. Currently, unbiased ...

Automatic Identification of Dendritic Branches and their Orientation

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

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

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

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Fluorescent genetically encoded calcium indicators have contributed greatly to our understanding of neural dynamics from the level of individual neurons to entire brain circuits. However, neural responses may vary due to prior experience, internal states, or stochastic factors, thus generating the need for methods that can assess neural function across many individuals at once. Whereas most recording techniques examine a single animal at a time, we describe the use of wide-field microscopy to scale up neuronal recordings to dozens of Caenorhabditis elegans or other sub-millimeter-scale organisms at once. Open-source hardware and software allow great flexibility in programming fully automated experiments that control the intensity and timing of various stimulus types, including chemical, optical, mechanical, thermal, and electromagnetic stimuli. In particular, microfluidic flow devices provide precise, repeatable, and quantitative control of chemosensory stimuli with sub-second time resolution. The NeuroTracker semi-automated data analysis pipeline then extracts individual and population-wide neural responses to uncover functional changes in neural excitability and dynamics. This paper presents examples of measuring neuronal adaptation, temporal inhibition, and stimulus crosstalk. These techniques increase the precision and repeatability of stimulation, allow the exploration of population variability, and are generalizable to other dynamic fluorescent signals in small biosystems from cells and organoids to whole organisms and plants.

We present a method for the flexible chemical and multimodal stimulation and recording of simultaneous neural activity from many Caenorhabditis elegans worms. This method uses microfluidics, open-source hardware and software, and supervised automated data analysis to enable the measurement of neuronal phenomena such as adaptation, temporal inhibition, and stimulus crosstalk.

Fluorescent genetically encoded calcium indicators have contributed greatly to our understanding of neural dynamics from the level of individual neurons ...

Standardization of a Novel Semi-Automatic Software for Neurite Outgrowth Measurement

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