phase contrast image analysis set up for continuous monitoring of neurite kinetics and growth

<meta charset="utf8"></head><body>Geavanceerde live-beeldvormingstechnieken voor nauwkeurige meting van de uitgroei van de neuriet

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.

<meta charset="utf8"></head><body>Auteur in de schijnwerpers: onthulling van de moleculaire basis van pijnperceptie en neuropathische pijn

Standaardisatie van een nieuwe semi-automatische software voor het meten van de uitgroei van je nauriet 

In our lab, we study how neurite outgrowth and branching can modulate pain perception using in vitro models. It is well known that pain responses vary between males and females during aging, so we are trying to dig out the molecular basis of this process. Recently we studied the involvement of importin alpha-3 and nucleocytoplasmic protein in the late phase of chronic pain.We studied importin alpha-3 mice that have a reduction in neuropath pain after spinal injury. After sciatic lesion, the missed target of fibers can cause neuropathic pain. Understanding how axon can regenerate and reach the correct target is a hot topic in the basic and the clinical research.This protocol allows for the quantification of neurite outgrowth and branching on both face contrast and immunocytochemistry images. The quantification is not limited to single neurons, but neurite nets are also doable. And lastly, since the software is semiautomatic, it represents a useful time saving tool for analysis.Measuring neurite outgrowth and branching allows for monitoring neuronal behavior in different conditions ranging from injury and regeneration to pathologies and drug efficacy. Hence, a standardized methodology for measuring these parameters is a key player in future research. Our future research, we focus on how importin alpha-3 with its neurite outgrowth and branching in different experimental setups ranging from dissociated cultures to whole explant cultures.Understanding the molecular basis underneath axonal retargeting would be a breakthrough in the neuropathic pain field.

Fasecontrastbeeldanalyse ingesteld voor continue monitoring van de kinetiek en groei van de nautiet

phase-contrast-image-analysis-set-up-for-continuous-monitoring

Research

JoVE Journal

Neuroscience

Phase Contrast Image Analysis Set Up for Continuous Monitoring of Neurite Kinetics and Growth

94 Views. To begin, scan the vessel and acquire images. Select the scanned vessel to analyze. Select Launch analysis, followed by Create new analysis definition.Select Neurotrack. Then select Image channels. After that, choose a representative set of images to perform the analysis on.Then go to Segmentation adjustment and use the slider to adjust the segmentation sensitivity toward more background or more cells. For cleanup, adjust the whole fill to remove any hole in the cell body mask s...