Image Analysis Case Studies

Reproducible research requires good quantification. Our users perform wide and varied projects which often require bespoke analysis solutions. Below are some examples of the type of analysis we do and teach on a regular basis.


Counting and measuring objects

Whether you’re interested in the number of cells in your piece of tissue, how many spots you have in per cell or the number of colonies or zebrafish in a dish, the counting of objects works in fundamentally similar ways. Objects are segmented, then contiguous pixels are considered to be single objects. These objects can then be analysed for area, shape, intensity or any number of other features. To find out more about segmentation, check out our learning resources.

This tissue section has be analysed using CellProfiler to give the number of gamma H2AX foci per nucleus
Credit: Jilly Hope

Tracking

Biology doesn’t stand still, so sometimes your scientific question requires you follow objects through time. These objects then need tracked and measured. To find out more about tracking, check out our learning resources.

Cell migration assays are common experiments. This was analysed using FIJI.
Credit: Anna Maria Ochocka-Fox.

Movement of neuronal mitochondria analysed usinga FIJI plugin, Trackmate.
Credit: Laura Murphy


Workflow automation

Even the most trivial of tasks, for example, brightness and contrast adjustments, can become arduous when applied to hundreds of images. Many of the software packages available in the facility can be automated so if you find you are going through image after image clicking the same few buttons then please get in touch. To find out more about automation, check out the relevant page for your software of choice.

Macros produced in the facility are designed to work on output of all of our microscopes.

Visualisation

Obviously, quantification is important but communicating your findings is also important. A good figure or video can save 1000 words.

Statistical colour-coding objects allows clusters to be identified easily.
Different visualisation modes can be used to represent the 3D nature of microscopy data.

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