Much of the posts on this blog deal with converting Images to numbers, which is what I would consider Image Analysis. This is ususally followed by some method of data analysis (converting numbers into results or other meaningful output.
This post will deal with the latter, and chiefly using Pivot tables to easily summarise the sorts of data that will frequently come out of Imaging experiments.
As part of my job, I find myself writing lots of bits of code for people. Until quite recently, my version control system was renaming the files and commenting in the header to keep track of changes.
Not the tidiest system
I say “quite recently” as I started using git as my version control system and have not looked back. I’m by no means an expert, but in this post, I’m going to give an introduction to using git in the context of scripting.
This post is really aimed at people who have no experience with version control systems or have heard about git but have never really used it (or have tried and failed to get the hang of it as I did…twice).
The Western blot is a staple of many Research Labs. Proteins are separated based on their size then labelled and identified using antibodies. Instead of using fluorescent labelled antibodies (although this can be done), most WBs use ChemiLuminescence to detect the amount of protein present. In this post we’ll look at the best way to acquire and analyse the humble Western blot.
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).
In the first part we used Fiji to measure the size and shape of our SmartFlare Nanoparticles. After having done this with a couple of different TEM images we want to analyse these data and get some useful numbers and graphs.