1st year MSc (Interdisciplinary Biomedical Research). The student will be assigned to a PhD project prior to the 2nd year of study.

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Pregnancy in placental mammals involves bi-directional communication between the implanting embryo and the uterine mucosa or endometrium.1 Intrinsic chromosome instability renders human embryo extremely diverse in their genetic makeup and developmental potential.2 Our previous studies indicated that, when prepared optimally for pregnancy, the endometrium acts as a biosensor for embryo fitness, capable of simultaneously supporting the development of high-fitness embryos and eliminating low-quality embryos through a menstruation-like mechanism.1,2 This phenomenon has been termed the 'implantation checkpoint'. Growing evidence suggests that failure to establish a robust implantation checkpoint in a conception cycle creates an endometrial implantation environment that neither selects against low-fitness embryos nor supports high-quality embryos; both scenarios lead to miscarriage.2,3 The aims of the project are (i) to gain granular insights into the receptor-ligand interactions that control embryo-endometrial communication at implantation and (ii) to define how pathological endometrial states associated with reproductive failure impact on embryo biosensing and selection at implantation. This project, which aligns with the molecular and cellular medicine theme of the MRC, is designed to uncover novel pre-pregnancy diagnostic biomarkers and therapeutic target to mitigate the high physical, psychological, and economical burden associated with recurrent miscarriage. The project capitalises on the unique resources available in the Brosens' lab, including access to fresh endometrial tissues and bio-banked samples (>5000); access to spent culture medium from IVF embryos that resulted in implantation failure, miscarriage, or live birth; and single cell transcriptomic data (including spatial transcriptomics) on 35 endometrial biopsies obtained across the implantation window from patients with and without prior reproductive failure. In addition, Professor Ott will provide expertise in the analysis of complex large data sets.