Organisation data: Research Groups
Job Number: EBI02056
Contract Type: Postdoctoral Fellow
Contract Duration-Length of Time (years/months): 2.5 years
Advertised Grade-Grading: Year 1 stipend - £2,952.45 per month
Closing date: 7 March 2023
Gonadal development is poorly understood, principally because it starts in utero and is ongoing in many mammals for several decades. Due to obvious ethical reasons, there is a limited understanding of ovarian development in humans, especially in childhood. The Single-Cell Map of the Pediatric Ovary in 3D project aims to generate paired single-cell transcriptomics and chromatin accessibility data of pediatric ovaries to define cell states and their regulatory networks. The roadmap of pediatric ovarian maturation generated in this work will be an essential resource to better understand gonadal pathophysiology, as well as illuminate the development of novel in vitro models of follicle development, with important clinical implications for infertility treatments.
The Uhlmann group develops methods to quantify morphology from microscopy images, whether they are 2D, 3D, static, dynamic, and of any imaging modality. Our overarching aim is to provide general quantification frameworks for bioimages to investigate living systems across scales and build bridges between mathematical modeling and image data.
The unique resource we consider in this project offers a challenging opportunity froman image analysis and quantification perspective since the ovary is a highly dynamic tissue with very specific cellular compositions and tissue architectures. The Uhlmann group will lead efforts towards generating a 3D map of gonadal tissue, teaming up with our research and clinician partners from the Sanger Institute (project lead), the University of Oxford, and the Oxford University Hospitals NHS Foundation Trust.
The successful candidate will develop computational tools to quantitatively characterize tissue microenvironments, and identify changes in tissue architecture across the lifespan of gonads.
The project will involve the design of a pipeline to quantify single-cell gene expression data in 3D image datasets (stitching, registration, and quantification), followed by data analysis to reconstruct and quantify follicles during development. Tissue architecture will then be characterized in 3D by combining representation learning strategies (to extract key features of tissue microenvironments in an unbiased manner) and classical algorithms for multiscale morphology description. The joint consideration of learned features and visual descriptors will help construct an interpretable and robust characterization of tissue microenvironments, which will then allow identifying and quantifying architectural changes.
We expect all code produced in this project to be developed in Python, released as fully open source and made publicly available to the research community along with analysis results following reproducible research practices.
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