Analysis of Stem Cell Interactions At Single Cell Resolution

Abstract

As a stem cell divides, two daughter cells are produced that either possess stem cell characteristics or become a cell with a more specialized function. Cellular markers have been identified that can be used to describe the phenotype of stem cell progeny. However, the development of tools that allow the description of cellular phenotypes remains a challenge due to the complexity of cellular heterogeneity. In my thesis, I address this problem by developing a phenotyping platform that stimulates cells to either remain in a stem cell state or become a cell with a more specialized func-tion. Multi-well plates were used to create microenvironments that resemble the natural surroundings of stem cells in tissues. High-resolution imaging technology was used to capture the cellular state after exposure to these microenvironments. High-content analysis was applied to generate a detailed description of every cell in the assay. Subsequently, mathematical modelling was used to describe the cell phenotyping profile in each environment. These profiles were then compared to characterise the cellular response to each environment. High-throughput technology was used to understand how topography features affect proliferation of pluripotent stem cells in a xeno-free environment. An algorithm was used to predict whether any given topography will support the pluripotent state. A second algorithm was used to predict an optimised topography. In the final part of my thesis, primary mouse skin fibroblasts and neural progenitor cells that were derived from human iPS cells were exposed to extrinsic cues in cellular assays. While distinct fibroblast subpopulations from neonatal mouse back skin responded to the assay irrespective of media conditions, human iPS cell-derived neural progenitor cells required the ad-dition of extrinsic cues to induce a self-renewal response. Therefore, it is possible to produce neural progenitor cells in vitro and study stem cell characteristics that are known to occur in an in vivo context.

Date of Award	2015
Original language	English
Awarding Institution	King's College London
Supervisor	Fiona Watt (Supervisor) & Gillian Bates (Supervisor)