CLIJ2 allows you to use ImageJ/Fiji on GPU instead if CPU processing, so much faster! 🙂
Here is a nice article about what CLIJ2 can do. By the way, this article is published on a new imaging forum called FocalPlane. Check it out! And here is the presentation of how to use CLIJ2 at one of the recent Neubias event in May 2020.
If you have an analysis pipeline built in Fiji, Icy or Matlab and processing takes a long time, CLIJ2 will help you a lot.
Do you know that clearing is not just about light sheet microscopy? Even if you have done your job well and your sample is directly on the coverslip (not on the slide), as soon as your sample is thicker than 10 um (1 cell diameter), you will see the effect of the refraction index mismatch.
What is that? Your sample and the mounting medium around it have a certain refraction index (or likely several). The objective you are using is designed for a certain refraction index (e.g. air, water or oil). If these refraction indices do not match what happens? as soon as you image a tiny bit away from the coverslip, the sample will look elongated, the intensity and contrast will drop very fast.
Sounds familiar? If yes, changing your mounting medium to match the objective will solve the problem. It works for light sheet but it also works for wide field or confocal imaging! Just change your mounting medium and you will see an enormous difference!
Here is an article describing a one-step clearing protocol. This basically is about using a different mounting medium. Easy, cheap and non-toxic! Give it a try!
Have a look at this post for more info.
It is not easy to find enough antibodies that work together to be able to label a sample with more than 4 antibodies at the same time. And even 4 is pushing it.
This paper describes a new immunostaining multiplexing method called 4i. The method is based on a special imaging buffer that prevents the antibody from being strongly bound to the sample due to the imaging process. This allows the authors to detach the antibody with gentle treatments, leaving the sample in a good shape and ready for another round of labeling and imaging.
Using this method they have successfully labelled the same sample with 40 different primary/secondary of the shelf antibody pairs!
Measuring the volume of a cell is often done by labelling the cell membrane or its cytoplasm. Analysing large flat cells this way is easy but it is much harder for tiny cells like blood cells, yeast or bacteria.
Another way to measure volumes is to use a negative stain, i.e. where the medium is made fluorescent with a dye that does not go into the cell. The cell appears as a black hole in fluorescent images and unlike lipid-based membrane labelling, borders are even and easy to segment.
While many dyes can be used for live cells, one must choose large dyes when negatively imaging cells that have been fixed and permeabilized.
This paper and this one use high molecular weight (2000 KDa) Dextran to achieve these results and measure the size of bacteria.
This recent paper optimizes the technique.
The microscopy field is moving away from blue dyes. This is because red light, used to excited far red and deep red fluorophores, is less damaging to live cells than near UV light which is used to excite blue fluorophores.
On top of that, red light penetrates deeper into thick samples.
So as the trend in microscopy is to move to thicker samples and use more live samples, far red and deep red fluorophores are becoming more attractive.
Here is an article describing 3 new fluorescent protein in the far red to deep red range. One can excite them with 640 nm or a 685 nm lasers or LEDs.
To image a thick sample, it is crucial to match the refraction index of the sample with that of the immersion medium between the sample and the objective. Typically, life samples are in an aqueous solution like culture medium which has a refraction index of 1.33. Unfortunately organoids often have a higher refraction index closer to 1.44 therefore as one images deeper into the organoids, light scatters due to the refraction index mismatch and the images become blurry.
This paper presents a product that has a high RI and is compatible with cell culture. Good to keep in mind for those who image organoids over time.
NucleAlzer is a great new deep learning tool to identify roundish objects like nuclei and cells in fluorescent or bright field images.
To test if the tool works for you before you download it, you can simply upload one of your images and check the result. Easy! 😀
Here you can see very nice video tutorials on the Alveole website and this is a cool article by Viasnoff et al about making 3D microniches with 1 um resolution! And you can do this at the LCI facility!! 🙂