In previous posts, we’ve almost taken for granted how Fiji/ImageJ deals with multidimensional data. Whenever you have more than one image, be it a second channel all the way up to massive 5D datasets, you’re dealing with stacks.
This concept is so fundamental, I thought it deserving of it’s own post. Let’s stack ’em up.
In the last post, I covered the basics of wound-healing assays and how to quantify and visualise them. This is great, but is the ability of cells to heal an artificial wound really what you want to measure? Probably not…
One of the major advantages of having environmental control on our microscopes at the CCI, is that you can get cells to behave a little more like they do in an animal or plant. A great example of this is when looking at cardiomyocytes that can spontaneously start to contract when grown in culture. You can see a load of these on YouTube.
These types of culture are a great model with which to study the effect of drugs or other treatments on the cells of the heart. This post will look at one way to quantify contractile motion without the need for exogenous stains or dyes (which can themselves perturb physiological behaviour).
One of the biggest challenges in modern Imaging Research lies in how to handle big datasets. This is a particular issue when undertaking multidimensional acquisition. In this post, I’ll be covering some ideas on how to sensibly work with large datasets as well as some neat tricks on how to downsize the biggest ones.
Multidimensional datasets can have multiple timepoints, channels, slices, positions or combinations of all of these.