Stem cells have yet to contribute to their full potential in the field of regenerative medicine and further understanding of the underlying kinetics of cell differentiation could be the step forward. Various methods have been used to characterise stem cell lineage commitment. However, most of these techniques are end-point assays and provide very little information about the changes occurring in the early stages of the differentiation process. This project aims to explore if the structural and geometrical specificity of the cytoskeletal components (actin in particular) encode information regarding cell lineage. Adipogenic and osteogenic differentiation lineages were selected, as they have been extensively studied over the past few decades. We have developed a novel approach to describe cells by defining their cytoskeletal and nuclear morphology in terms of 19 geometric measurements. This set of parameters has a range of complexity, extending from one dimensional (e.g. fibre length, fibre thickness) to compound geometrical readings (e.g. chirality and fibre alignment), while some estimate morphological and mechanical properties of the nucleus i.e. Poisson ratio and chromatin condensation. A proprietary image analysis algorithm is used to analyse fluorescent images of cells biochemically and mechanically stimulated to differentiate for a period of up to 10 days. Our analysis pipeline is currently optimised for images acquired at x20 magnification using epi-fluorescence but can be further adapted for high throughput live cell imaging. Factorial analysis of the measured features showed that some parameters change markedly in the early stages of differentiation. More interestingly we observed these changes to be non-linear and non-monotonic. This analysis, in light with previously published literature on the subject has allowed us to more intricately hypothesise probable mechanisms involved with mechanotransduction which direct the lineage commitments. As our technique quantifies the morphology of individual cells, we used our extracted feature data to characterise each cell using a multivariate predictive model (LDA).
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:766234 |
| Date | January 2018 |
| Creators | Dodhy, Asad |
| Publisher | Queen Mary, University of London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://qmro.qmul.ac.uk/xmlui/handle/123456789/46030 |
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