Arguably one of the best things about doing Research using microscopes is the awesome power of the Image for getting your point across. Whether it’s for public outreach, presentation at a conference or just showing off your work at group meeting, a picture says… well you know the rest.
But what if you’re looking at a temporal phenomenon? Sure you could always use a montage but there’s nothing like a moving picture to wow your audience and get pulses racing. In this post we’ll look at a few different ways of turning your multidimensional data into movies.
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…
The wound healing assay is a fairly common technique in the Cell Biology Research toolkit. The idea is simple: grow cells to cover a surface, scratch a ‘wound’ into the cells then see how well the cells can close the wound (either by replication, migration or a combination of both).
The blood! The horror!
In this post, we’ll be using Fiji to analyse this type of assay which will give us lots of flexibility in both methodology and visualisation options.
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.