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Imaging and Analyzing Your Wound Healing Assay

Now that you’ve optimized your setup, you are all set for imaging and analyzing your wound healing assay. Let’s take a look at what you need to consider to get started.

Imaging Your Assay: Tips and Tricks to Get It Looking Perfect

Phase Contrast or Fluorescence?

A standard wound healing assay uses phase-contrast images acquired via an inverted microscope. These are good enough to track gap closure over time. In some specialized cases, fluorescent labelled dyes for tracking two different cell populations or using a transgenic labelled cell line to track cell organelles are also used to study cell migration. In such cases, long-term imaging with a fluorescence microscope leads to photobleaching and phototoxicity. This can affect cellular behaviour and migration. Therefore, where possible transmitted-light techniques like phase contrast are preferred. Phase contrast is also available on most of the standard and advanced imaging systems. It is easy to implement and compatible with plastic dishes, making it a popular choice for imaging wound healing assay.

Choosing the Right Microscope

You can use a basic inverted transmitted-light cell-culture based microscope or a motorized automated microscope that is environmentally controlled.

Using a cell culture-based microscope

You can record the size of the gaps by inspecting at regular time points. When you plan to do it manually, mark the outer bottom of the dish to be used as reference points while taking pictures, since analysis involves matching the first image with the second. You can use a permanent marker or a razor blade to make the marking.

Using an automated microscope

Since this assay needs to be monitored for several hours, it is easier if you use a motorized automated system. Such a system also allows you to monitor the cell migration closely by taking images at regular intervals such as every 15 mins or 30 mins. To increase the chances of a successful experiment, the automated microscope should be equipped with:

  • A digital camera.
  • An environmental chamber for temperature, humidity and CO2 control.
  • A motorized stage for multi-position acquisition.
  • A motorized focus with autofocus capability to minimize drift over time.

Key Features for Ensuring Good Quality Images

Kohler Illumination

For optimal transmitted-light imaging, it is necessary to ensure even illumination across the field of view. This can be achieved by making sure that the microscope is set up for Kohler illumination.

Phase Contrast

Contrast is created based on the differences in the optical path length through the sample. The objectives contain a ring and will be denoted as PH1, PH2, etc. with the numbers denoting the size of the ring. Make sure this ring is in alignment with the bright ring from the condenser using a Bertrand lens or by looking into the empty ocular position after removing the eyepieces. Avoid using positions very close to the side of a dish or well plate, since phase is not optimal at these locations. The light gets reflected from the side of the walls resulting in a lower contrast that interfere in the image analysis.

Choice of Objective

You should ensure that your field of view allows you to visualize both sides of the wound and this is critical when choosing an objective. Usually, a gap of 500 µM fits in the field of view of a 10x objective. You can also use 4x or 5x objective for a larger field of view, while still maintaining a reasonable resolution. It is also important to consider the working distance of the objective you use. Working distance is the distance between the front lens of the objective and the surface of the dish when your specimen is in focus. The working distance of your objective can be found on the company’s website. If the working distance is too close to the thickness of the base of the well, you will never be able to get your cells in focus. Most of the cell culture microplates have a well base thickness of around 1.27mm and a working distance of 10X or 5x objective will easily be able to focus on your cells.

Analyzing Your Wound Healing Assay Data

Software to Use for Analysis

The gap can be measured using any image analysis software package (such as ImagePro, Metamorph or the open source Fiji/ImageJ). Manually tracing the leading edges can also be done but it is time consuming and the edges may be ill-defined in many cases. Hence a computational measurement of gap area using image analysis software is preferred. Furthermore, you can outsource the analysis by uploading your wound healing datasets using commercial vendors. For example, ibidi provides image analysis for wound healing assays, just be sure to use their culture inserts. Such vendors have a fixed pipeline for analysis and the cost would depend on the number and complexity of analysis. You can also try the free trial before approaching the vendors with a large number of datasets.

Important Notes to Remember When Analyzing Your Wound Healing Assay

Choose the Right File Format

Before you begin analysis, check that the data files are in a format compatible with your image analysis software. It is usually recommended to export files in a standard format such as TIFF, as opposed to JPEG or other formats that may compromise the quality of the image.

Dealing With Uneven Background

Uneven background can be due to:

  1. Uneven illumination: if the Kohler illumination is not ensured before imaging
  2. Imaging towards the edge of the well plates: sidewalls of the plates can reflect the light and cause uneven illumination.

Images with an uneven background will require flatfield or background subtraction. A plate containing media without cell growth can be imaged and used for background subtraction. Some of the most common filters available in the image analysis software such as rolling ball or flat-field filter can be applied to the images to overcome this issue.

Increase the Contrast

For analysis purpose, it is also helpful to increase the contrast between the gap (object to be analyzed) and the area containing cells (background). This step is optional and is required only when the contrast between cell boundary and gap is not distinct. Filter functions, such as edge detection or variance filters, work best in this case. These commonly used filters can be found in open-source software like ImageJ/ FIJI or commercial software like Image Pro premier.

Image Segmentation

Thresholding is the process of using a histogram to identify an intensity cut-off that separates features of interest (in this case the gap) from the background (in this case it’s the cells). Software packages provide a tool for interactively choosing a threshold, allowing one to slide the threshold value higher or lower until the gap area is selected. More sophisticated segmentation, such as smart segmentation in Image Pro Premier, use texture and pattern recognition for image segmentation. Such features are more useful when cell migration into the gap is not collective. The software splits the image into objects based on the threshold criterion, in this case – intensity. The largest object is the area of the gap and smaller objects will generally include smaller regions in the cell with similar intensity (Figure 1).

Figure 1: In-vitro wound healing assay setup showing gap area/ area of wound after scratch (t=0) and endpoint (t= 16 hr). Red color indicates the area of wound after edge detection and image segmentation.

Metrics for Quantification

Once you have measured the gap area for each time frame, there are multiple ways to present your data. You can also use a combination of the following metrics depending on the goal of your experiment.

  1. The simplest way to calculate cell migration rate is to present it as a plot of the gap area as a function of time. You can compare the dynamics of migration in different cell populations (e.g WT Vs mutant or knockdown cells). The migration rate can be expressed as the percentage of wound closure. The closure percentage will increase as cells migrate over time:

Wound Closure % = {(At=0  – At=Δh )/At=0} *100

At=0  = area of the wound measured immediately after scratching
At=Δh  = area of the wound measured h hours after the scratch is performed

2. If you see a clean migrating front where you can manually calculate wound width (average distance between the edges of the scratch). This can get time-consuming if you have a large dataset. The rate of cell migration can be calculated as follows:

RM = W–Wf /t

RM = Rate of cell migration nm/h
Wi = initial wound width (nm)
Wf = final wound width (nm)
t = duration of migration (hour)

How to Present Your Wound Healing Assay Data?

Numbers

Don’t get overwhelmed with all the numbers you get.  The numbers can be exported to an Excel sheet to plot a graph of migration rate versus time or wound closure percentage versus time. When comparing different cell types, treatments or knockdown cells the line plots will reveal any differences in the migration rates.

Images

You can also display the images at time t=0 and the endpoint of the analysis (Figure 2). In the case of a time-lapse setup, one can also consider making a video to show the dynamics of migration. Consider compressing such videos for playing in a PowerPoint presentation. Compression of videos can be done using ImageJ, FIJI or Image pro-premier and the options will vary depending on the quality and format of the file.

Figure 2: In-vitro wound healing assay setup showing gap after scratch (t=0) and once the wound is healed (t=16 hr). Dotted yellow lines at t=0 show the area of wound and double arrow line indicates initial wound width.

Hopefully, these tips will alleviate your fears and help you when you’re imaging and analyzing your wound healing assay.

References

  1. Jonkman JE, Cathcart JA, Xu F, Bartolini ME, Amon JE, Stevens KM, et al. An introduction to the wound healing assay using live-cell microscopy. Cell Adhes Migr. (2014) 8(5):440–51. doi: 10.4161/cam.36224
  2. Grada A, Otero-Vinas M, Prieto-Castrillo F, Obagi Z, Falanga V. Research Techniques Made Simple: Analysis of Collective Cell Migration Using the Wound Healing Assay. J Invest Dermatol. 2017 Feb;137(2):e11-e16. doi: 10.1016/j.jid.2016.11.020

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