University of Dundee

EASTBIO: Machine learning based analysis of cell and tissue dynamics during gastrulation

This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership This opportunity is open to UK and EU nationals.

Applicants should apply by completing the EASTBIO application form (downloadable from the EASTBIO website) and e-mail to Candidates should also include their academic transcripts and ensure that they ask their referees to send completed references to Applicants may wish to explain their motivation for joining the EASTBIO training programme.

This aim of this highly interdisciplinary PhD project is to combine state-of-the-art machine learning and artificial intelligence (AI) based image analysis with sophisticated biophysical modelling to quantitatively analyse the cell behaviours that drive gastrulation in the chick embryo, a model system for human development. Gastrulation is characterised by large-scale tissue deformations and differential tissue movements that result in the formation and positioning of the three primary germ layers, the ectoderm that will give rise to the skin and nervous system, the mesoderm that will form the muscles an skeleton and the endoderm that will form the lining of the gut and associated glands in the embryo. These complex time varying three dimensional coordinated tissue deformations derive from the motility and contractility of hundreds of thousands of individual cells interacting with their neighbours. The main challenge is to understand how these complex cell behaviours are controlled in space and time through chemical and mechanical cell-cell signalling mechanisms and how these cell movements and rearrangements feedback on cell-cell signalling.This project will focus on the detailed analysis of key changes in cells behaviours of the epithelial epiblast cells that drive the formation of the primitive streak, their ingression through the streak and their subsequent movement inside the embryo to form different organs. This will involve analysis of distinct cell behaviours of fluorescently labelled cells during normal development and under conditions where candidate cell-cell signalling systems coordinating these behaviours have been disturbed through molecular genetic and direct mechanical manipulations. To image the over 200,000 cells in the gastrulation stage chick embryo we have designed and built a unique and highly innovative Fluorescence Light Sheet Microscope that for first time has allowed the detailed live imaging the all the complex cell behaviours, i.e. cell shape change, division, movement and ingression in the developing chick embryos where the membranes have been labelled with green fluorescent proteins (Rozbicki et al, 2015). This method is used in combination with high-resolution multi-photon confocal microscopy to study how individual cell behaviours are integrated in the context to produce more complex tissues during gastrulation.

These experiments produce enormous quantities of high-quality image data (>2TB/experiment) that require a detailed and sophisticated computational analysis. Therefore this project will make extensive use of advanced computational image processing and AI based data analysis methods. Analysis and interpretation of the data will be supported by detailed modelling of gastrulation as collective cell motion using concepts and methods from the physics of soft and active matter (Barton et al.,2017): The PhD candidate will closely collaborate with both the experimental (Weijer) and theoretical (Sknepnek) groups. This highly interdisciplinary project will provide training in state-of-the-art machine learning methods for image analysis, advanced cell and developmental biology, molecular genetics, live imaging using advanced lightsheet and Multiphoton confocal microscopy as well as provide the opportunity to learn/use advanced biophysical and mathematical modelling techniques.

References Rozbicki, al.Myosin-II-mediated cell shape changes and cell intercalation contribute to primitive streak formation. Nat Cell Biol17, 397-408 (2015).

Barton DL, Henkes S, Weijer CJ, & Sknepnek R (2017) Active Vertex Model for cell-resolution description of epithelial tissue mechanics. PLOS Computational Biology13(6):e1005569