/boomerang_catcher

Doysabas, K.C., et al. (2020) "ATeam technology for detecting early signs of viral cytopathic effect" J. Vet. Med. Sci.

Boomerang Catcher (BC) is the ImageJ & python program set to analyze FRET in viral infected cells.

Download latest version

Please cite the paper when you use this program

Doysabas, K.C., et al. (2020) "ATeam technology for detecting early signs of viral cytopathic effect" J. Vet. Med. Sci. in press https://doi.org/10.1292/jvms.20-0021

How to Use

Image Preparation

BC tries to analyze an image file named as img.tif.
BC requires img.tif to follow the rules;

  1. File name should be img.tif
  2. img.tif should be hyper-stacked
    1. Different images should be stacked into Z-direction
    2. In each Z-stack, two channels (channel 1 and 2) should be set
    3. img.tif should contain at least two Z-stacks
  3. All images should be taken in same condition

Making Hyperstack

There is a lecture video (1_how_to_make_hyperstack.mov).

  1. Gather image files into one directory
    • I recommend to follow the naming rule;
      • 1_1.tif for image-1, channel-1
      • 1_2.tif for image-1, channel-2
      • 2_1.tif for image-2, channel-1
      • ...
  2. Open ImageJ, and click File -> Import -> Image Sequence...
  3. Select the directory containing the images described in step 1
  4. Click Open
  5. A new window will open, and click OK
  6. A stacked image will be imported
  7. Select Image -> Hyperstacks -> Stack to Hyperstack...
  8. Select Order as xyczt (default)
  9. Change Channels (c): as 2
  10. Change Slices (z): as the half number of the total images
  11. Click OK
  12. Select Image -> Rename..., change file name as img.tif and click OK
  13. Save the img.tif by selecting File -> Save As... -> Tiff...
  14. Select the directory where you want to save the img.tif, and click Save

Run ImageJ Program

  1. Open the img.tif by dragging and dropping the file into the ImageJ control bar
  2. Select Plugins -> Macros -> Run...
  3. Select 190918_masking_measure.ijm and click Open
  4. Program will run and detecting cells
  5. You will find windows named as C1-img.tif, C2-img.tif, Result of filter2.tif, and Results
    • C1-img.tif shows the detected cells in channel 1
    • C2-img.tif shows the detected cells in channel 2
    • Result of filter2.tif shows the detected area and area-ID
    • Results shows the 1st-step data
  6. Save the table file, Results, by selecting File -> Save As...
  7. Click Save
  8. If you need, save the images in your proper directories

Run Python Program

2nd-step analysis will be performed by Google Colaboratory. This is the link to briefly understand Google Colaboratory.

In this step, you need to mount Google Colaboratory to your Google Drive. If you have not mounted it yet, follow this instruction.

  1. Access to Boomerang Catcher Python Program
  2. Click the Connect in the upper right
  3. Click the > sign on the left side of the window
  4. New tab will appear, and click File
  5. By clicking Upload or dropping the Results to the left side tab
  6. After finishing the upload, click the grey triangle mark of the top row
  7. After running the first row, click the next grey triangle
  8. After running the second row, click Refresh in the left side tab, and you will find out.csv
  9. Click right button of your mouse on the out.csv, and click Download
  10. Save the file your directory
  11. In the out.csv, you can find Ch2/Ch1-Ratio column: this is the final data

Analyzing Algorithm

  1. Subtracting Background to enhance contrast from channel-2 image

  2. Next, enhance contrast until saturating 0.3 % of pixels

  3. Apply Gaussian Blur, sigma equals to 4

  4. Apply threshold by Minimum method to detect cell body without nucleus (Filter 1)

  5. Select the area with 50 - 500 $\mu$m^2^

  6. Thresholded image of step 4 is duplicated

  7. Apply threshold by Otsu method to detect cell body including nucleus (Filter 2)

  8. Select the area with 50- 800 $\mu$m^2^

  9. By multiplying the inverted Filter 1 and Filter 2, and generate nuclear mask (Filter 3)

  10. Measuring the average intensity of channel-1 and channel-2 by detecting Filter 1 (data calculating cell body including nucleus) (Data 1)

  11. Measuring the average intensity of channel-1 and channel-2 by detecting Filter 3 (data calculating only nucleus) (Data 2)

  12. Calculate ((Area of Data 1) * (Mean Intensity of Data 1) - ((Area of Data 2) * (Mean Intensity of Data 2))) / ((Area of Data 1) - (Area of Data 2)) for each channel

  13. Calculate (Mean Intensity of Channel-2) / (Mean Intensity of Channel-1)