Tag Archives: DAPI

FISH-Quant: the sequel

As promised, I started using FISH-Quant to analyze my FISH images.

I must say that I enjoy using FQ much better than the previous program that was developed by one of my lab members. I find FQ more intuitive, more informative, and with a better range of parameters and tools for analysis.

In essence, the program goes through several steps:

In the first step, one must determine basic parameters (such as NA, pixel size, ex/em wavelengths). After further defining folders and loading the image file one needs to define an outline of the cell.

Determining cell outline, nucleus & transcription sites is fast & easy.

Determining cell outline, nucleus & transcription sites is fast & easy.

This is already very helpful – in the previous program, I either analyzed an entire image, or had to draw the outline (mask) in ImageJ, then use a third program to select spots only in the mask area. I could only use one mask per analysis. with FQ, I can use multiple outlines per image, and I can get automated detection of the nucleus (based on a DAPI image) and automated identification of transcription sites inside the nucleus.

At this point I should mentioned that I came across some bugs – which initiated a very productive work relations with Florian Mueller – the developer of FQ.

The next step is the filtering of the image. I’m not exactly sure how its done mathematically. From the little I understand, the program first blurs the image. This simulates the background, which is then subtracted from the original image. The program then enhances small features that remained.

Filtering: on the left - the "blurred" image. In the middle - after subtracting the background. On right - the final filtered image.

Filtering: on the left – the “blurred” image. In the middle – after subtracting the background. On right – the final filtered image.

Then comes a pre-detection step. In this step, a range of thresholds is tested for detecting the spots. After it is done, one chooses the best threshold to give “true spots”, based on a quality score of the spots.

Pre-detection of the spots. Several parameters to play with.

Pre-detection of the spots. Several parameters to play with.

Here, Florian implemented a new feature: in the original (published) program, this step was performed on the entire cell. Since I’m currently interested only in cytoplasmic mRNAs, and the nucleus gave lots of background, Florian added a feature that enables me to analyze only the cytoplasm or only nucleus (or both). This was very helpful.

The next step is fitting the parameters from the pre-detection to the original image and you get results.

Results! green circles - "good" spots. Blue circles: "bad" spots. the histograms on the lower left help determine which threshold to use

Results! green circles – “good” spots. Blue circles: “bad” spots. the histograms on the lower left help determine which threshold to use

Thresholding of the spots can be done on several paramaters: XY and Z sizes and amplitude of the signal. The histograms below help determine the best thresholds. The program also shows composite images of maximum projection in XY and XZ of all spots.

One can also take a closer look using the spot inspector.

Inspecting the spots is fun!

Inspecting the spots is fun!

This window allows inspection of each and every spot. At my request, Florian added another feature, of enabling the index number of each spot to appear next to the spot. Unfortunately, there is still a little bug there, which doesn’t really interfere:

Numbers, numbers all around me!

Numbers, numbers all around me!

A main advantage of FQ, is the ability to do batch analysis of images (from the filtering step onward. One still needs to draw outlines in each image individually. Here again Florian helped by adding a quick save button). Batch analysis allows better thresholding (the statistics are better since one can look at all spots in all images in one histogram).

In summary – this has been a good experience so far. I am still trying to find the best threshold for my FISH images but I’m learning a lot in the process.

The major advantage of FQ is actually its ability to analyze transcription sites more precisely than other programs (I elaborated on that in the previous post). I haven’t even started using FQ for that, but I will, once my yeast project will run smoothly…


The original paper for further reading on how this program works:
ResearchBlogging.org Mueller, F., Senecal, A., Tantale, K., Marie-Nelly, H., Ly, N., Collin, O., Basyuk, E., Bertrand, E., Darzacq, X., & Zimmer, C. (2013). FISH-quant: automatic counting of transcripts in 3D FISH images Nature Methods, 10 (4), 277-278 DOI: 10.1038/nmeth.2406

Basics in Confocal microscopy and image analysis

Nowadays, confocal microscopy is possibly the most widely used optical method in biological research. This methods creates better (and prettier) images than widefield microscopy (whether transmitted light or epifluorescence). The main advantages of confocal vs. widefield microscopy is the elimination of out-of-focus glare (thus increasing resolution and increasing signal-to-noise ratio) and the ability to collect serial optical sections of the specimen (z-sections).

The basic configuration of the optics is similar to that of the epifluorescnece microscope. The addition that created the confocal microscope, invented by Marvin Minsky in 1955, was to add two pinholes. The light produced by lamp (or laser) passes through the first pinhole on the way to the specimen. The light that is reflected (bright light) or emitted (fluorescent light) from the specimen passes through a second pinhole on the way to the detector (eyepiece, camera or any recording device). The two pinholes have the same focus – thus they are confocal. The light from other focal planes cannot go through the second pinhole, and this reduces the background “glare” of out-of-focus fluorescence seen in epifluorescence widefield microscopes.

Since biological samples usually have thickness of a few microns at least, one can get an image of a thin slice of the sample (e.g. 0.1 µm) without physically slicing the sample (optical section). We can then move the focus along the Z axis to get clear images of up or down sections. Thus, for a cell 3µm thick, we can have 30 hi-resolution images 0.1 µm thick from bottom to top (z-sections). These images can then be stacked one on top of the other (z-stacking) to create a single 2D image or to reconstruct a 3D image of the sample.

Here’s an example from an experiment I did last week (note that this is a widefield, not confocal microscope):

Above is a composite image of 31 Z sections of U2OS cells, create by the ImageJ program. The”pseudo-blue” represents the blue fluorescnce of a dye called DAPI (4′,6-diamidino-2-phenylindole)  which intercalates into DNA, and is therefore a popular nuclear dye. When bound to DNA, it is excited by UV light (peak at 358nm) and emits blue/cyan light (peak at 461nm). The “pseudo-red” color represents the fluorescence of mCherry-ZBP1 fusion protein. mCherry is an RFP.

You can see in the image that the first and last few images in the series are out of focus. You can therefore choose the best or sharpest Z-section according to your needs.

However, most people do not show a single Z section since then we miss a lot of information that is found in other sections. The available programs today allow “stacking” the section to create a projection of all the sections into a single image.

Here is the maximum projection of the Z sections shown above:

Maximum projection means that the algorithm chooses, for each pixel, the highest value found in any of the 31 Z sections. However, since we chose all 31 sections, we can still see a “glare” or halo. This is a result of the “halos” from the out of focus sections.

I therefore choose only a few sections to create the next image:

This image is now sharper and better looking.

The program allows you other options besides maximu projection: you can choose minimum, average, median, and even standrad deviation, seen in the next image (DAPI channel only):

It looks very cool. I stacked the entire 31 sections (of a differnt field), so you can see the halo from the out-of-focus sections sorounding the “black” rim of the nucleus (black since it has the minimal standrd deviation value for all the images). The blue zones, with high SD, suggest a larger differnce in fluorescence between the differnt sections.

Above, I mentions that the blue and red are pseudo colors. What actually happened was that the images I took with the microscope at each channel (range of wavelengths) is actually maintained as a greyscale image.

Using the image analysis program you can then merge the images of the differnt channels (up to 4 in ImageJ) to create a color image. When creating the merged image, you determine what color to assign to each channel. Here is the same image, but with the colors reversed:

You should take that into account when you see pretty pictures in sceintific journals.

The program also allows to creade 3D representations of your Z stack. but I haven’t learned how to do that.

There are many other tools that one can use with the image analysis program besides creating the image. One important feture is the ability to measure the intensity of the fluorescent signal (actually, the pixels) in certain areas within the cell. You can measure distances and angles between objects and probably many moer that I still have to learn.