Hi Sumit, in order to produce reliable quantitative results, you should automate the image analysis. In your case, this should be fairly straightforward. The two key steps are thresholding and object detection. Before that, you should optimize the dynamic range (Image>Adjust>Brightness/contrast). Be sure to use exactly the same max and min values for each image. For the first step is, thresholding: (Image>Adjust>Threshold). Note that color images generally have to split into their red, green and blue channels (Image>color>Split channels). Various additional commands, such as erode and dilate, are available to help the computer to recognize objects. In ImageJ, object detection and quantification can be performed via the “Analyze particles” command (Analyze>Analyze Particles).

Dear Sumit,

I have few other suggestions:

1) I think that one key important thing is that the acquisition parameters are the same across the images. I know that is an obvious point, but I saw so many people measuring and comparing fluorescence from image acquired with completely different parameters.

2) Be sure you are not saturating the pixels on your CCD, otherwise you will lose power in your quantification. It does not seems the case in the sample image you posted, but I thought it was worth to mention.

finally for the quantification... I think that the answer on your question depends on what you would like to quantify.

1) The answer above are all valid, however, personally I would stay away from normalisation, Adam is correct in saying that there is variation between coverslips and samples, but I usually deal with this variability making (when possible) more sample for each condition and averaging them across.

2) another point to consider is if you are interested in the total amount of fluorescence, I mean a bigger mitocondrium with the same amount of marker will have a lower mean intensity... You can use the total (or integrated) fluorescence intensity but in this case you have to be careful and remove the background, (a bigger object have a bigger integrated background).

Hope this help,

good luck

Max

I think all the points above are very good.

In practice, after taking images with same settings etc from all the samples, perhaps just substract from each image the background by calculating mean intensity in an area of the image where no cells exist. So first draw a box/rectangle (same size in all images, maybe always at the left or right corner of the image where no cells would be), measure the mean intenisty of that box and substract that mean value from the whole image (ie from each pixel).

Then from there background substracted images calculate both the mean and total mitotracker intensity per cell, either by using software to automatically segment the cells or by manually sketching out the cell boundaries based either on the mitotracker dye or - even better, but not neccessary based on the image you had - another dye which would dye the whole cells/ boundaries.

In imageJ you can set the measurements, so measure both mean and total (integrated) intensities, it is difficult to say which parameter would be better to analyse in the end. I think would be good idea to have a control (say a drug treated parasite which should for sure decrease/increase the mitotracker dye) for example FCCP or similar. This would also give some kind of indicatioin of the dynamic range of this method.