Soft matter is a broad field, ranging from colloidal particles to micelles, from proteins to cellular tissues. As I mentioned in a previous post, a physics-based approach to characterising the morphology of soft biological matter can be very insightful. It provides simple, geometrical and structural metrics to identify variation in tissues. We recently demonstrated this approach in a rich article in Nature’s Bone Research, a journal dedicated to the quantitative study of bone properties in different species.
A few years ago, Erika Kague, from Physiology at the University of Bristol, asked me to think about her problem. By inducing genetic mutations, she can generate individual zebrafish (Danio rerio) – a common model organism in biology – with various malformations. These she can investigate to understand the effect of bone mineral density on the insurgence of osteoporosis. The issue is that one may want to rapidly, systematically and quantify the amount of malformation.
To do that, I worked with talented PhD student Yushi Yang to automatise a computer vision workflow of segmentation of three-dimensional images, detection of relevant anchor points and determination of several geometrical and structural metrics. Some of these quantities have been inspired by thermal soft matter analogues, such as the porosity of gels; others rely on graph theory metrics. The entire manually tagged database can be augmented using deep learning U-nets that Stephen Cross (Wolfson Bioimaging Facility, Bristol) optimised for our use case.
The result is a detailed characterisation of which genes promote certain kinds of changes in the bone structure. The data reveal a delicate balance between too little and too much bone mineral density to minimise the chances of osteoporosis.
The full article is here
Francesco Turci 