Understanding the heterogeneity and inefficiencies of human pluripotent stem cell (hPSC) differentiation are key to robust human tissue design. This thesis combines live multi-day, multi-colour high content fluorescence microscopy movies of hPSCs stably co-expressing fluorescently tagged histone H2B and the two-colour FUCCI cell cycle reporter with quantitative image analysis to inform both top-down and bottom-up mathematical approaches of studying hPSC behaviour and cell fate decisions. Using CHASTE, an agent based cell modelling toolkit, I implemented in silico agent-based simulations to model hPSC proliferation, colony formation and migration in 2D informed by experimentally observed cell cycle and motility parameters derived from manually quantitated fluorescent hPSC timelapse image sequences. I developed a computational image processing pipeline to quantitatively characterise hPSC populations and dynamics and to obtain high-dimensional quantitative single-cell level feature information from ’live’ hPSCs in order to inform top-down modelling of hPSC behaviour. This pipeline captures several hundred morphological, proliferation and fluorescence associated features for hundreds of thousands of cells across different experimental conditions for downstream analysis and prediction of cell fate. Using the image analysis tools we developed, I helped quantitatively characterise ORACLE, a novel class of nuclear-rim localised fluorescent cell fate reporters recently developed in our group. Using that pipeline I morphologically profiled hPSC cell populations during pluripotency maintenance as well as during early differentiation toward early germ layer (ectoderm, mesoderm, endoderm) cell fates. Coupling this approach with deep learning approaches allowed us to achieve accurate and robust cell fate phenotyping ’live’. We were able to demonstrate that this cell phenotyping method is capable of predicting cell fate changes as early as fluorescent transcription factor based cell fate reporters on an individual cell and sub-colony level. We demonstrate this approach can be used to quantitatively identify cell proliferative properties that potentially predict cell fate transitions in an unbiased fashion.
- Stem cells
- Computational biology
Computational approaches to study human pluripotent stem cell proliferation and fate dynamics
in vitroRen, E. (Author). 21 Jun 2022
Student thesis: Doctoral Thesis › Doctor of Philosophy (PhD)