Probing the importance of dynamic cell micro-environments on cell fate choices

Abstract

The first few days within mammalian development are arguably some of the most important moments in an organism's lifetime. Between the time of fertilisation of the egg by the sperm and the implantation of the embryo into the uterus wall, a series of morphological and biochemical changes take place that are crucial to the advancement of the pregnancy. This period is known as the preimplantation period and during this time three distinct cell lineages are specified and organised. The trophectoderm (TE), primitive endoderm (PrE) and epiblast (Epi) cell lineages are crucial for the formation of extraembryonic tissues that support the development of the embryo, as well as the formation of the embryo proper itself. The morphological changes within the murine embryo mirror key stages within human development and so the murine embryo offers a promising avenue for understanding our own development and improving assisted reproductive therapies. In this thesis, we work towards the better understanding of the Epi versus PrE specification event through quantitative assessments of embryo architecture and cell neighbourhood in both fixed and live embryos. We first present a novel, user-friendly neighbourhood assessment tool, IVEN, and apply it to murine preimplantation embryos at various fixed stages of development. Through the application of IVEN, we show for the first time a quantitative description of the morphology and cell microenvironment for all cells within the embryo at key stages within the murine preimplantation period. In order to investigate the spatiotemporal dynamics of cells within the embryo, and the corresponding levels of Epi-associated proteins, we present a novel Bayesian landmark matching approach dependent only on the spatial coordinates of the points to be registered. We apply our method to the registration of cells between real time imaging studies with corresponding immunostained images. Within this work, we also present a novel approach to 'Bayesian data selection' which facilitates the inference of cells that can be registered via the chosen model, thus removing the requirement for manual data curation prior to registration. More generally, this data selection approach could be applied to a multitude of statistical problems where subsections of data may be subject to higher noise or corrupted. We therefore envisage that this method could change data handling in the presence of noisy observations. Finally, we introduce sequential neighbourhood analyses of real time imaging data and present preliminary tracking and quantification of neighbourhood dynamics within developing embryos. Through cell registration between real time imaging and corresponding immunostaining images, we then compare the neighbourhood dynamics of Epi and PrE precursors via the comparison of SOX2 positive and negative cells. Within this thesis we demonstrate the value of quantitative approaches within developmental biology and the resulting wealth of information that can be used to progress our understanding of mammalian preimplantation development.

Date of Award	22 Aug 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Berenika Plusa (Main Supervisor) & Simon Cotter (Co Supervisor)