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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Results
  • Discussion
  • Disclosures
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

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.

Abstract

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.

Introduction

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 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 or MetaMorph21,23 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.

Protocol

1. Scanning the vessel on the machine

NOTE: The detection is performed by the built-in Basler Ace 1920-155 µm camera.

  1. Open the program by clicking Connect to Device and selecting Schedule - To Acquire. Then click the + sign.
  2. Specify whether the vessel will be scanned repeatedly or only 1x by choosing the option Scan on Schedule or Scan Once Now, respectively.
  3. 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.
    1. 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.
    2. Select Add Scan to restore a previously scanned vessel for additional scans.
  4. Select the scan type based on the assay and application. For the analysis, select Standard.
  5. 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.
  6. Select the microscope objective (4x, 10x, 20x). Acquire with the higher magnification (20x) in primary cultures, while in cell lines, 10x is sufficient.
  7. Select the type of vessel to scan from the options provided. Indicate the vessel's location in the drawer by selecting its position on the virtual map of the drawer.
  8. 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.
  9. Provide information about the vessel by typing the name and specify the plate map by clicking on the + sign.
  10. 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.

2. Setup for phase contrast image analysis

NOTE: Neurotrack analysis can only be performed on images previously acquired by the machine.

  1. Select the Scanned Vessel to analyze. Select Launch Analysis. Select Create New Analysis Definition.
  2. 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.
  3. 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.
    1. For cell-body cluster segmentation, adjust the following as described below.
      1. Segmentation mode: The images' 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.
      2. 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).
    2. For cleanup, adjust the following as described below.
      1. Hole Fill (µm2): Adjust this to remove any hole in the cell body mask smaller than the area specified by the user.
      2. 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.
      3. Min cell width (µm): Choose a value to define the size at which cell bodies will be considered as neurites.
    3. For cell-body cluster filters, adjust the following as described below.
      1. Area (µm2): 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.
    4. For neurite parameters, adjust the following as described below.
      1. 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.
      2. 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'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.
      3. Neurite width (µm): 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.
  4. 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).
  5. Repeat steps 2.3 and 2.4 for all the selected images. Click Next.
  6. Select the Scan time and well to analyze. Assign a Definition Name and, if needed, Analysis Notes. Click Next > Finish.

3. Setup for immunocytochemistry (ICC) image analysis

  1. 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.
  2. 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.
    1. 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.
      1. 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).
      2. 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.
    2. Cleanup: Use the following option to perform this.
      1. Hole fill (µm2): Use this to remove any holes in the cell body mask that are smaller than the area specified.
      2. 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.
      3. Min cell width (µm): Use this to define the size at which cell bodies will be considered as neurites.
    3. Cell-body cluster filters: Use the following option to apply the filters.
      1. Area (µm2): 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.
    4. Neurite parameters: Use the following option to set these.
      1. Neurite coarse sensitivity: Use this to adjust for neurite brightness. Sensitivity should be increased if the neurites' 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.
      2. 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.
      3. Neurite width (µm): 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.

4. Data export

  1. Open the analysis. Click on Graph Metrics.
  2. Select the Metric, Timepoints, and Wells of interest.
  3. Select the grouping option between All, None, Columns, Rows, and Plate Map Replicates.
  4. 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.

5. Image export

  1. Open the vessel. Click on Export Images and Movies.
  2. Select the export type for the images.
    1. Select As Displayed to export images as displayed. Click Next and select the Images of Interest for export. Click Next.
      1. Select the Sequence Type between a Single Movie or Series of Images and the time points of interest. Click Next.
      2. Adjust the export options as needed and click Next. Set the output folder, file format, and name of the file, then click Export.
    2. Select As Stored to export images in the raw format. Select the Image Type.
      1. Select the Timepoints and Wells of interest. Click Next. Set the output folder, file format and name of the file, then click Export.
  3. Image export with the segmentation masks
    1. 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.

Results

The neurite outgrowth measurement algorithm is robustly capable of detecting neurites in both neural networks and single neurons. It generates a yellow mask that segments objects with high contrast, such as cell bodies, cellular debris, dead cells, tissue explants, and shadows. Additionally, a magenta mask appears on neurites of various thicknesses. Neurite length values are provided in mm/mm2, indicating that the axonal length has been divided by the area of the image, which is 0.282739 mm2 and con...

Discussion

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 cells

Disclosures

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

Acknowledgements

We want to thank Alessandro Vercelli for the critical comments and Sartorius'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'Istruzione dell'Università e della Ricerca MIUR project Dipartimenti di Eccellenza 2023-2027 to Department of Neuroscience Rita Levi Montalcini. D.M.R.'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).

Materials

NameCompanyCatalog NumberComments
Collagenase AMerck / Roche10103586001
Dispase II (neutral protease, grade II)Merck / Roche4942078001
Dulbecco's modified eagle's mediumMerck / SigmaD5796
Fetal bovin serum Merck / SigmaF7524
Ham's F-12 Nutrient Mix (1X)ThermoFisher Scientific21765029
Ham's F12 w/ L-GlutamineEurocloneECM0135L
Hanks' Balanced Salt SolutionEurocloneECM0507L
HBSS (10X), no calcium, no magnesium, no phenol redThermoFisher Scientific14185045
HyClone Characterized Fetal Bovine Serum (U.S.)CytivaSH30071.03
Incucyte, Neurotrack Analysis Software Sartorius9600-0010
L-15 Medium (Leibovitz)Millipore/SigmaL5520
Laminin Mouse Protein, NaturalThermoFisher Scientific23017015
L-CysteineMerck / SigmaC7352
Leibovitz's L-15 medium w/o L-glutamineEurocloneECB0020L
mouse NGF 2.5S (>95%)Alomone LabsN-100
Neurobasal Medium [-] GlutamineThermoFisher Scientific21103049
NSC-34CELLutions Biosystems Inc (Ontario, Canada)CLU140
Papain from papaya latexSigmaP4762
Penicillin-Streptomycin (5,000 U/mL)ThermoFisher Scientific15070063
Percoll (Density 1.130 g/mL)Cytiva17089101
Poly-D-Lysine Solution (1mg/mL)EMD Millipore/MerckA-003-E
Poly-L-Lysine Solution (0-01%)SigmaP4832
Recombinant Human NT-3PeproTech450-03
Retinoic AcidMerck / SigmaR2625
Trypsin-EDTA solutionSigmaT3924
β-Tubulin III (Tuj1) antibodyMerck / SigmaT8660

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