Tag Archives: spot analysis

FISH spot analysis

In the previous post I introduced the RNA FISH protocol at the end of that we have a slide which we can image. If all goes well, that image should show “spots” of the RNA we FISHed for.

What data do we want to get from the spot analysis?

1) Number of the specific RNAs in the cell.

2) The localization of said RNA.

3) spot intensity will tell us how many RNAs are in a particular spot (especially important for transcription analysis).

The simplest most straight forward way is to manually count spots, using the “cell count” option in ImageJ program. However, not only is it very tedious, it is also wrong, because our eyes cannot easily distinguish RNA spots to “noise” of the assay.

Many labs developed computational tools to analyze such spots. The most common method is using a Gaussian fit algorithm that finds single sources of light using Airy discs calculations. I am unfamiliar with the math behind it. I just try to use the program correctly. The program I currently use was developed by Timothée Lionnet in our lab. It is yet unpublished, but is briefly described here. (Tim is helping me alot with the analysis!)

In the figure below you can see a simplification of how the program works:

figure 1: simaplification of the spot analysis program. shown are FISH image, two zoomed-in regions (A, B) and an intensity plot of along the line in each zoomed region. red trace - fitted Gaussian plot (illustration)

Fig 1: simaplification of the spot analysis program. shown are FISH image, two zoomed-in regions (A, B) and an intensity plot of along the line in each zoomed region. red trace – fitted Gaussian plot (illustration)

Basically, I load the image to the program and choose the best z section with sharpest foci. I then use the mouse to navigate across the image to seek for spots that may show a fit. A zoomed in section of the image is shown in a small window to assist. The x, y & z profiles of a line across the zoomed-in window show the intensity along the x, y & z. What I’m looking for are nice high peaks which co-localize on the x, y & z. I then press a button that calculates the Gaussian fit which is shown as a red overlay. If I get a nice bell-shape in all three axes, I’m happy and I press “record”. I collect ~10 such good records.  If there is no Gaussian fit (e.g. plot B), I move on. This can be tedious, but is usually required just once per set of images.

The next step is determining the threshold (not an easy task) and then the output of the program gives three files. One is an image file with the spots at the positions identified by the program. Another is a text file with the location (x,y,z) and intensity of each spot. Last is a file with all the parameters used by the program. The most useful to me are the xy & z parameters. I can use those to do batch analysis of all images from the same set (i.e. same experiment/imaging conditions).

So what is the problem with the threshold?

Well, we do not want to set it too low (so it will detect “noise”) but not too high so we will miss low intensity “real” spots. From what I was told, the best way to determine if I am using the correct threshold is to plot a histogram of all the spots based on their intensity, and compare plots of low and high threshold.

Here is a nice set of histograms which demonstrate the usefulness of it:

Fig 2: histograms of spot analysis at three different thresholds. x axis – spot intensity (arbitrary units). y axis – occurrence.

The histograms of the low threshold include a lot of low-intensity spots which are separate from the “true” spots (the green histogram at higher threshold) which shows a nice Gaussian distribution (no relation to the Gaussian fit).

A second test to the threshold setting is to do the spot analysis on cells which do not express your RNA (i.e. you should get zero signal). You never get zero signal. But the threshold should be set so that you are satisfied with the background in negative cells, compared to the positive cells. Are you satisfied with 10 spots per cell? 1 spot per cell? 0.1 spots per cell? That really depends on the experimental question.

[Edit dec. 3, 2012: after further discussion with Tim, another important issue came out: we need to take into account also the number of cells in the frame; sort of to normalize on the number of cells, to get an estimate of the number of spots per cell.

As to the background level in the negative control – I understand that there is no standard “background level” that is accepted. It really does depend only on the experimental needs, and the ratio with the positive control. For me, though the pos. ctrl has 1000’s of spots and the negative ~1 spot per cell, this background I stil consider not low enough for my purposes.]

How to improve the data?

My initial imaging was done with the 60x objective. The resolution is enough to see the single spots, and I could have several cells in each image. However, using the 100x objective improves the resolution and hence the spot analysis (particularly for spots that lie one near another). The downside is that the field is smaller so more images are needed to have data on more cells.

Second, more photons is better: increase exposure time. However, increasing too much will also increase the background, so a balance should be found.

Hybridize with more probes – the more probes you have (different probes to same RNA, not greater amount of same probe), the better the signal/noise ratio. Also, performing double-FISH with two different probe set with different dyes to the same mRNA will definitely reduce the background when doing co-localization analysis (which is not easy).

The histogram mentioned above will also be useful for later analysis of spot intensity, particularly for transcription analysis. But that will be a separate post.

[Edit: those of you who read this post when published noticed that I now removed the screen-shots of the spot analysis program and replaced them with an illustration. this has to do with future publications and/or patent applications. Sorry.]