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

<meta charset="utf8"></head><body>正確な神経突起伸長測定のための高度なライブイメージング技術

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>著者スポットライト:痛みの知覚と神経因性疼痛の分子基盤を明らかにする

神経突起伸長測定のための新しい半自動ソフトウェアの標準化 

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.

神経突起の動態と成長を連続的にモニタリングするための位相差画像解析のセットアップ

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

103 ビュー. まず、船舶をスキャンして画像を取得します。スキャンした容器を選択して分析します。[分析の起動] を選択し、続いて [新しい分析定義の作成] を選択します。[ニューロトラック] を選択します。次に、[画像チャネル] を選択します。その後、解析を実行する代表的な画像セットを選択します。次に、[セグメンテーション調整]に移?...

ビデオ: 神経突起の動態と成長を連続的にモニタリングするための位相差画像解析のセットアップ

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.

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.

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

神経科学

Vessel Scanning Set Up to Measure Neurite Outgrowth in Real-Time

Begin by clicking Connect to Device to open the program. Select Schedule to acquire. Then, click the plus sign.Choose the options Scan on Schedule or Scan Once Now to specify whether the vessel will be scanned repeatedly or only once. Select New to create a brand-new vessel to scan. Based on the assay and application, select the scan type.For the analysis, select Standard. Then, to specify scan settings, choose one of the cell-by-cell options from None, Adherent Cell-by-Cell, or Non-Adherent Cell-by-Cell. Specify the image channels depending on the fluorescent molecules utilized.Now, select the microscope objective. From the options provided, select the type of vessel to scan. To indicate the vessel's location in the drawer, select its position on the virtual map of the drawer.Select the wells to scan to specify the scan pattern for the image acquisition. Select the desired number of images per well. Afterward, an estimation of the scan duration will appear.To retrieve information about the vessel, type its name in the provided space and click on the plus sign to specify the plate map. Click Next. Choose the analysis type and click Next again.A summary screen of the selected options will appear. If correct, click on Scan Now and the scan will start.

神経突起の成長をリアルタイムで測定するための容器スキャン設定

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 smaller than the area specified by the user. To adjust the size, increase or shrink the yellow mask by the specified number of pixels.Choose the minimum cell width value to define the size at which cell bodies will be considered as neurites. Set a minimum and a maximum cell body area value. To reduce the masking of small vessel imperfections and debris choose between None, Better, and Best options.Then increase sensitivity to detect finer neurites. Click Preview current to visualize the segmented image. After refining the setting for all the selected images, click Next.Select the scan time and well to analyze. Assign a definition name, and if needed, analysis notes. Click Next and Finish.