Investigating the connection between early embryo architecture and embryo viability

About the Project

In humans, approximately a quarter of all pregnancies end before 6 weeks’ gestation, often around the time of implantation, before the pregnancy has become apparent. Reasons behind early pregnancy losses are still poorly understood. Therefore, identifying factors that contribute to successful development and establishment of functional mother-foetus connection is so important to shedding light on causes of early miscarriages and early pregnancy pathologies.

Dynamic relations between local cell neighbourhood and the changing architecture of developing organisms are a crucial part of the developmental process, albeit rarely investigated in detail due to the complexity of the issue and the lack of appropriate tools. As a result, little is known about what constitutes correct organisation of early mammalian embryo.

The overarching aim in this project is to understand the link between changing architecture of preimplantation mammalian embryo and the genetic factors that controls the development. Specifically, this project will involve:

• identifying the critical stages in early mammalian development that can be linked to embryo viability with the use of machine learning pipeline created in the lab
• create interactive 4D map of the developing mammalian embryo using newly developed in the lab architecture assessment tool
• testing the correlation between embryo architecture, cell micro-environment and embryo viability in order to identify markers of health vs disrupted development
• describe the link between differences in embryo architecture and genetic control of the early development.

This work will provide an understanding of the fundamental processes during early development and contribute to improvements in the safety and efficiency of IVF techniques.

Candidates are expected to hold (or be about to obtain) a minimum 2:1 Bachelors Degree with Honours (or equivalent) in biology, biomedical research or physics. Candidates with experience in machine learning or with an interest in image analysis are encouraged to apply.

Eligibility

Applicants must have obtained or be about to obtain a minimum Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in a relevant discipline.

Before you Apply

Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.

How to Apply

To be considered for this project you MUST submit a formal online application form – on the application form select PhD Developmental Biology Programme. Full details on how to apply can be found on the Website: How to apply for postgraduate research at The University of Manchester

If you have any queries regarding making an application please contact our admissions team FBMH.doctoralacademy.admissions@manchester.ac.uk

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website: Equality, diversity and inclusion (EDI | Postgraduate Research | Biology, Medicine and Health | University of Manchester)

Funding Notes

Applications are invited from self-funded students. This project has a Band 3 (high) fee. Details of our different fee bands can be found on our website View Website

References

The student will be instructed how to perform all necessary lab techniques by myself and subsequently will be working closely with me. We will be meeting weekly to discuss progress of the project.
Recent publications:
1. Forsyth, J.E., Al-Anbaki, A.H., Plusa, B., Cotter, S.L. Application to cell matching across imaging modalities.
Statistics and Computing, 2023, 33(5), 100
2. Forsyth J.E, Al-Anbaki A.H, de la Fuente R, Modare N, Perez-Cortes D, Rivera I, Seaton Kelly R, Cotter S, Plusa B. IVEN: A quantitative tool to describe 3D cell position and neighbourhood reveals architectural changes in FGF4-treated preimplantation embryos. PLoS Biology, 2021, 19(7), e3001345. doi: 10.1371/journal.pbio.3001345
3. Plusa B, Piliszek A, Common principles of early mammalian embryo self-organisation. Development (Cambridge), 2020, 147(14), dev183079. doi.org/10.1242/dev.183079