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Supervisors names
Stavros Loukogeorgakis and Mattia Gerli
Background
The amniotic fluid (AF) surrounds and protect the human fetus during gestation. This solution is rich in cells, shed from a number of developing fetal tissues. AF cells have been mostly ascribed to the epithelial, immune and mesenchymal lineages. These cells hold great regenerative potential both as a direct cell therapy tool, as well as for the development of advanced ex vivo models of congenital conditions. Despite this, a detailed atlas of the cells present in the human AF has yet to be reported. Our team has recently drafted the first single cell map of the human AF, covering parts of the second trimester of gestation. Thanks to this effort, we were able to demonstrate that the AF epithelial cells have multiple tissue origins, and are capable of forming three dimensional epithelial organoids resembling features of the fetal intestine, kidney and lung. In addition, we managed to identify the origin of the AF mesenchymal cell. Finally, in tight collaboration with the group of Dr Loukogeorgakis, secondary supervisor for this project, we found further potential therapeutic value in the AF’s immune cell fraction.
Aims and objectives
This new PhD project aims at using advanced bioinformatics and single cell biology technologies, to expand our atlas and investigate the dynamic changes happening to the AF cellular composition, across the whole gestational period. Our ultimate aim is to create a dynamic temporal map of each AF cell population and study its changes in healthy pregnancies and with the occurrence of congenital diseases. This exciting project will help us in advancing our knowledge of human development, investigate the possible use of AF cell mapping for diagnostic purpose and investigate innovative regenerative medicine uses for this precious resource.
Methods
This project will involve the use single cell biology techniques to create a dynamic multiomic map of the human AF. Building up on our currently available dataset, the student will focus on the wet lab techniques such as cell sorting, scRNAseq, CITEseq, scATACseq to generate novel single cell data. The project will also entail a significant bioinformatics data analysis phase, with extensive use of Seurat, and a number of R packages that will allow a thorough analysis of the data.
References
1 De Coppi et al., Nature Biotechnology 2007
2 Loukogeorgakis et al., Stem Cells 2016
3 Gerli, Calà et al., BiorXiv 2023 https://doi.org/10.1101/2023.05.31.539801 – Under revision
Contact
Mattia Francesco Maria Gerli, PhD - m.gerli@ucl.ac.uk