Compare fluorescent intensity between different images

Hello everyone,

I am still new to Image J, I may need your help on analysing and comparing fluorescent intensity between three samples (mutant, wildtype and control) which may include in a paper. It will be highly appreciated if any of you could help me regarding these questions.

The rodent tissues were first stained with monoclonal antibodies, then with Alexa Flur 488 secondary antibody and DAPI. It gave the brightest green signal in mutant, medium brightness in wildtype and very low signal in control. Those three samples were taken under the same magnification and identical settings under the microscope. I would like to pick five random cells from each sample, calculating their mean value and comparing the mean value among the mutant, wildtype and control.

Since the images were saved in RGB, should I

  1. convert the RGB image into separate channel (8-bit) and use only the green channel for more accurate grey value comparison?
  2. draw identical circle on 5 selected cells (e.g. width 50, height 50) on each sample, and get their grey values by adding them in ROI manager–>measure?
  3. use mean grey value or integrated density for comparing the intensity between the samples?
  4. substrate the background value from each sample (i.e. mutant grey value - mutant background value) before comparing among the samples?

It would be great if any of you could give answers on any questions above, or even give a better suggestion on how I should compare in a more reliable or accurate way.

Thank you for all your help in advance!

Here I attached the three images for your reference.
wildtype.tif (3.6 MB)
mutant.tif (3.6 MB)
control.tif (3.6 MB)

If you want quantitative statements in your paper, we need more info before we can judge and help.
How did you record the images? What microscope, what camera?
Did you take a photograph with a DSLR? Do identical settings include exposure time, illumination setting, camera gain etc?
There is a scale bar, so this is not the original data I presume?

Did you check basic parameters, like equal amounts of DAPI signal across the three samples (which I would expect if the cells have the same amount of DNA), the same number of cells per area?
To find ‘random cells’, throw a random grid on your image and take every cell hitting a grid point.

Hi, there, I have quite similar questions, could give a look at my post please? :slight_smile:

Hi everyone,

I’m a new user of ImageJ, and I’m interested in measuring and comparing the fluorescence intensity as part of a time course analysis on embryos at different developmental stages. For my experiments I have antibody-stained cells across a variety of conditions (wild-type vs. Knock out, developmental days etc.). I’ve read several ImageJ articles and forum posts about this topic, but I’m having difficulty connecting what I’ve read to create a “tailored” analysis protocol for my experiment. I’ve included a sample image below of the fluorescence I’m hoping to measure for each of the first 3 channels (ch 4 is my Hoechst). There are around 150 cells in each image. I’d like to count and measure the fluorescence intensity of each nuclei (which I am manually counting using cellcounter tool) in order to get an average intensity per cell. I have struggled having a good nuclear segmentation even when tried clearing my samples and changing acquisition parameters ( Z stack 5uM to 2uM) . My images are acquired with identical settings meaning exposure time, illumination setting, camera gain, etc. My images are currently in a .nd2 format, 16 bits, but can not upload them here in such format. I uploaded a sample using .tif.

Q1: I am not sure about the value I should use to represent my data. In brief, I open, substract background, despeckle, and set B&C prior to splitting channels and doing Max IP. Does this preprocessing have any influence somewhow in my Intensity measurements?

Q2: After generating a MaxIP of the merged 4 channels, I define the ROI for each nuclei of interest, I also define 5-10 ROI of the background. I export my results to an excel tablewhere I paste Area, IntDen, and RawIntDen as results per channel. According to Fiji tutorial, Integrated Density - Calculates and displays two values: “IntDen” (the product of Area and Mean Gray Value) and “RawIntDen” (the sum of the values of the pixels in the image or selection). Is it Okay to just use in a XY graph, Int Den vs Nuclei?
I am not sure If I should work with RawIntDen instead. I am actually working with the later but do not really understand the difference. I calculate using the averages (RawInt /Area)-(AvgRawIntCtrl/AvgAreaCtrl). Were the first terms (RawInt /Area), have to do with my nuclei of interest its respective area, and I substract the second (AvgRawIntCtrl/AvgAreaCtrl) that come from my Background control spots. Which method is the proper way of representing data?

Q3: My images are currently in a .nd2 format, 16bits, therefore my arbitraty units range between 86 and 16000. I’ve seen people having A.U ranging between 0-255 which I assume comes from converting to 8 bits image. Do I need to additionally convert my images 8-bit formats prior to analyzing them. Is this something that I would need to do to analyze my images? What purpose does converting the image to a different bit type serve?

Thank you in advance for your help!

Best wishes
Nicolas

Here I attached the metadata and one image for your reference.