Difference between revisions of "20.109(F22):M1D4"

From Course Wiki
Jump to: navigation, search
(Part 3: Image γH2AX experiment)
Line 112: Line 112:
 
*How does the data obtained in the two analysis approaches compare?  Are the results the same?  Different?
 
*How does the data obtained in the two analysis approaches compare?  Are the results the same?  Different?
 
*Which analysis approach best represents the raw data images?  Why?
 
*Which analysis approach best represents the raw data images?  Why?
 +
 +
==Navigation links==
 +
Next day: [[20.109(F22):M1D5 | Placeholder]]<br>
 +
Previous day: [[20.109(F22):M1D3 | Placeholder]]<br>

Revision as of 00:43, 1 September 2022

20.109(F22): Laboratory Fundamentals of Biological Engineering

Fa22 banner image v3.png

Fall 2022 schedule        FYI        Assignments        Homework        Class data        Communication        Accessibility

       M1: Genomic instability        M2: Drug discovery        M3: Project design       



Part 4: Analyze γH2AX images by measuring fluorescence intensity

Please obtain the raw γH2AX images from the Class Dropbox folder. Find your group subfolder which contains images taken from your coverslips stained by the teaching faculty. Six sets of images (i.e. image stacks) were taken per experimental condition, and each image stack contains images from two channels: DAPI (blue) and FITC (green). Remember that the secondary antibody used for the γH2AX staining was conjugated to an Alexa488 fluorophore, which emits green light. For each image stack, you will use ImageJ to 1) identify the location of the nuclei using the DAPI channel and 2) quantify the total γH2AX fluorescence in the FITC channel at locations specified by the DAPI channel.

Identify intensity thresholds for DAPI channel

Example of thresholding cell nuclei using ImageJ

First, you will identify intensity thresholds that will properly identify the cell nuclei in all the images. To be consistent and fair in analyzing fluorescence images, it is good practice to use the same intensity thresholds on all the images.

  1. Open ImageJ (most recent download is here: https://imagej.nih.gov/ij/download.html).
  2. Open one image stack (drag and drop the file into the ImageJ toolbar) from the no treatment condition.
    • The first image you see is the DAPI channel
    • If you scroll to the right, the second image in the stack is the FITC (γH2AX) channel.
  3. While the image is on the DAPI channel, go to Image -> Adjust -> Threshold.
    • A threshold window should pop up
    • Check the box for "Dark Background"
    • Make sure the cell nuclei are highlighted in red.
    • Adjust the threshold values to properly identify the majority of the cells' nuclei.
    • Record the threshold values.
  4. Repeat this process for one image from each condition and cell line, and settle on threshold values for the DAPI channel that you will then use to analyze all the images. Write these values in your notebook.
    • It is best to define the lower threshold value based on your images, and set the upper threshold value as 255, which is the maximum possible intensity value for a 8-bit image.
    • You can type in threshold values by clicking on the "Set" button in the Threshold window.
  5. Close all open images (File -> Close All).

Test γH2AX quantification on one representative image

  1. In ImageJ, open one image to test the FITC quantification protocol.
  2. Split the image stack into two separate images.
    • Go to Image -> Stacks -> Stack to Images.
    • The DAPI image will have "-0001" as a suffix in its title.
    • The FITC (gamma-H2AX) image will have "-0002" as a suffix in its title.
  3. Duplicate the DAPI image and turn it into a mask to identify nuclei locations.
    • Click on the DAPI image.
    • Go to Image -> Duplicate, and click OK on the default title.
    • Set the thresholds you chose on the duplicated DAPI image to identify nuclei.
      • Go to Image -> Adjust -> Threshold.
      • Check the box for "Dark Background".
      • Click on the "Set" button and type in your threshold values (use 255 for the upper threshold level).
    • Go to Process -> Binary -> Convert to Mask.
      • This makes the image black and white, where the white areas should correspond to nuclei locations.
  4. Use the newly created mask to identify locations on the DAPI channel in which to quantify the gamma-H2AX signal.
    • Go to Analyze -> Set Measurements.
      • In the Set Measurements window, make sure the following boxes are checked: Area, Mean gray value, Min & max gray value, Shape descriptors, Integrated density, Display label.
      • In the "Redirect to" field, scroll and select the FITC image (suffix -0002). Then press OK.
        • This will direct ImageJ to the FITC image to analyze the metrics you selected in the areas identified by your mask. This will give you information about the gamma-H2AX signal in each nucleus.
  5. Run the analysis by selecting Analyze -> Analyze Particles.
    Example of areas identified by intensity thresholds for DAPI channel in ImageJ
    • In the "Size" field, type 200-Infinity. This will eliminate small, extraneous particles that do not correspond to nuclei.
    • "Circularity" can remain at default values: 0-1.
    • "Show" should say "Outlines".
    • Click the following options: Display results, Exclude on edges, Summarize.
    • Press OK to complete.
  6. A window will pop up showing outlines of each nucleus the software identified based on the thresholds you defined. Each identified area is labeled with a red number, corresponding to the left column of the data shown in the "Results" window.
  7. Take a look at the "Results" window to see the results of the analysis. It is good practice to validate the numerical results by comparing them to what you see in the images.
    • The definition of the various measurements performed can be found on the ImageJ website (linked here).
    • Does the nucleus with the largest "Area" correspond to the biggest nucleus you see in the drawing? The area here is in units of square pixels.
    • The RawIntDens field is the total intensity (sum of the intensity of all the pixels) of the corresponding region. Does a region with a high total intensity value correspond to a cell with a high gamma-H2AX signal? Click on the FITC image to double check.
  8. Close the "Results" window and do not save the data, as you will run the analysis on all the files together next.
  9. Close all open windows in ImageJ (File -> Close All).

Quantify γH2AX signal in all images

  1. Create four folders on your desktop for each experimental condition: No treatment, 100uM H2O2, 2uM Arsenic and 2uM Arsenic+100uM H2O2.
  2. Move the image files from both experimental dates into the appropriate folder according to the experimental condition in the file name.
    • Ensure that all of your images from one experimental condition are in one folder together.
  3. Download AnalyzeH2AX_FITCintensityBatch_Fa21script (linked here).
    • Right click on the link and download the file into a folder where you can find it.
  4. In ImageJ, go to Plugins--> Macros--> Run, and click on the AnalyzeH2AX_FITCintensityBatch_Fa21 script that you downloaded.
  5. When the script prompts you to "Choose input folder," choose the folder containing all your .tif image stacks (folder named by one experimental condition), and click "Open."
  6. In the dialog box titled "Choose Intensity Threshold Values," type in the corresponding DAPI threshold values you have chosen, and click "OK."
  7. You will be prompted to name the resulting Excel file next.
  8. Please wait for the script to run through all your images for one condition. In the end all the image files will pop up, along with the "drawings" that show where it identified cells in your images.
  9. The script will output one Excel file into your image folder.
  10. Before closing any images, validate the results in the Excel file with the images in ImageJ.
    • Choose a few representative images to verify.
    • Check the "Drawing" images and DAPI images to see if the nuclei were called correctly by your threshold values.
  11. Repeat this analysis for each folder containing images from an experimental condition.
  12. To calculate total intensity of FITC signal per nucleus divide the RawIntDens (sum of the intensity of all the pixels in arbitrary units, a.u.) by area (pixels2) for each outline that represents a nucleus.
  13. Finally, calculate the average of the total FITC intensity(a.u.) per area (px2) for all nuclei in each experimental condition.

Part 5: Analyze γH2AX images by counting foci

In addition to the above analysis, you will also use ImageJ to enumerate the γH2AX foci present in the nuclei of the treated cells. If you would like to review concepts used in this code, please review a protocol written by researchers at Duke University outlined here.

In this analysis, you will calculate the average foci per nuclei, indicating double strand breaks (γH2AX staining).

  1. To begin, ensure that all of your H2AX images are in individual folders indicating the condition.
  2. Download AnalyzeH2AX_batch script here.
  3. Identify fluorescence intensity threshold values that will identify the nucleus in the DAPI channel.
    • Open a representative image in ImageJ.
    • Examine the first image in the stack, which corresponds to the DAPI channel.
    • Go to Image--> Adjust --> Threshold
      • Check the box for "Dark Background"
      • Make sure the cell nuclei are highlighted in red.
      • Adjust the threshold values to properly identify the majority of the cells' nuclei.
      • Record the threshold values, and repeat with other conditions to identify a representative threshold value.
  4. Run the ImageJ macros to use fluorescence intensity to identify all the cells (DAPI channel) and cells with a significant amount of γH2AX sites (FITC channel).
    • In ImageJ, go to Plugins--> Macros--> Run, and click on the AnalyzeH2AX_CountMaximabatch script that you downloaded.
    • In the next window, choose the folder containing your .tif image stacks for the chosen condition, and click "Open."
    • You will be prompted to "Enter result file name" to name your spreadsheet for this analysis. Do so, and click "Ok".
    • In the dialog box titled "Choose Intensity Threshold Values," type in all the corresponding threshold values you have chosen, and click "OK."
    • Please wait for the script to run through all your images. In the end all the image files will pop up, along with the "drawings" that show where it identified cells in your images
    • On the "Drawing" windows that popped up, similar to the images shown here, each area identified by your fluorescence thresholds are assigned a number. Each of those numbers are listed in the corresponding Excel spreadsheets, followed by the information assessed by the ImageJ script.
    • For the DAPI channel, most of the identified regions correspond to a single nuclei. For the FITC channel, one may argue that for a cell having a significant amount of double strand breaks, the entire nucleus (rather than a few spots) shows up as being identified by the intensity thresholds provided.
    • The script will output resulting Excel files into your image folder. The Excel files contain raw data of each region of interest identified by the fluorescence intensity thresholds you chose.
    • You may use the Excel files to assess how foci are identified in each nuclei. To do this, divide the Raw Integrated Density (RawIntDen) for each nuclei by 255 (each maxima representing a foci has a value of 255) to identify the number of foci in each nucleus. You can now average the number for each image in the condition.
    • Repeat these steps for the additional conditions to quantify your results.


In your laboratory notebook, complete the following:

  • How does the data obtained in the two analysis approaches compare? Are the results the same? Different?
  • Which analysis approach best represents the raw data images? Why?

Navigation links

Next day: Placeholder

Previous day: Placeholder