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Determining Fluorescence Intensity and Signal

Fluorescence Area: This method can be used for a quick determination of fluorescent labeling area.

  1. To threshold your image, go to Image  > Adjust > Color threshold
    • Slide the Hue slider to match the color- so that the fluorescent areas are selected
  2. Go to Analyze  > Analyze Particles  > Display results. This will give you the area of fluorescent regions of your image.
  3. Add areas for all fluorescent regions. This is the total fluorescent area.

Fluorescence Intensity: This method determines the corrected total fluorescence by subtracting out background signal, which is useful for comparing the fluorescence intensity between cells or regions.

  1. Make a grayscale image by going to Image > Type > 8-bit.
  2. Outline desired cell with Freehand ROI tool.
  3. Set desired parameters by going to Analyze > Set Measurements. Make sure Area, Integrated Density and Mean Grey Value are checked.
  4. Now you can analyze by going to Analyze > Measure. A window will pop up with your measurements.
  5. Copy data into a spreadsheet.
  6. Now select a small area of your image that has no fluorescence. This will be your background.
  7. Analyze > Measure for that region. Copy data into spreadsheet.
  8. Repeat for several more cells and background regions.
  9. Calculate the mean fluorescence of background readings.
  10. Now calculate corrected total cell fluorescence (CTCF) = Integrated Density – (Area of Selected Cell x Mean Fluorescence of Background readings)
  11. Calculate for each cell.


Trainable Classification Plugin: This plugin is used for classifying positive and negative areas of signal in a large sample and determining the % pixels that are positive within a region of interest, a useful application for live/dead cell experiments.

  1. Open control and experimental images being analyzed.
  2. Convert to 8-bit image by selecting Image > Type > 8 bit.
  3. Edit > Selection > Specify to define a region that encompasses a large portion of your sample. Start with around 1500×900 pixels and adjust. As you will need to apply this same ROI to your experimental condition image, make sure it works for both. Save the ROI by using the Region of Interest (ROI) Manager tool (Analyze > Tools > ROI Manager).
  4. Apply this ROI to each image, center ROI over tissue, then crop the image to fit the ROI using Shift-X.
  5. Save the newly sized ROIs using the ROI Manager tool.
  6. Run the Trainable Weka Segmentation plug-in (Plugins > Segmentation > Trainable Weka Segmentation):
    • Trace MANY regions of negative signal and then select “Add to class 1”
    • Trace MANY regions of positive signal and then select “Add to class 2”
    • After numerous traces are labeled as either class 1 or class 2, select “Train classifier.”
    • Repeat steps to label additional traced areas as either class 1 or class 2 until the classifier’s segmentation reaches sufficient accuracy.
    • Once the classifier reaches sufficient accuracy, select “Create result” to create a classified image
  7. Apply the newly sized ROI to the classified image.
  8. Crop further by making sure ROI is selected and selecting Edit > Clear Outside. This will set the background to black.
  9. Determine percent area of positive signal:
    • With the final classified image with ROI open, open the histogram tool (Analyze > Histogram) and select “list” to get pixel counts. Record the number of Value 0 (red) and Value 1 (green) pixels.
    • The percent area of signal is calculated by dividing the number of red pixels by the total number of red and green pixels, multiplied by 100.
    • Repeat steps for all images being imaged and analyzed. Record all results in excel.