Understanding Cell State Transitions in Development and Disease

Abstract

During development, eukaryotic cells undergo a series of state transitions that transform a stem cell into a diverse set of differentiated progenitors with distinctive biological functions. Disruption of these processes can drive cancer and autoimmune diseases. Yet it is incompletely understood what molecular mechanisms lead to maintenance and transition of cellular states and how scientists can effectively identify these states in novel biological systems. This thesis aims to address these challenges using hematopoietic cells as a model system. I start by investigating an epigenetic mechanism controlling activation timing of the fate determining gene Bcl11b during early T-cell development (Chapter I). I then explore the potential of such mechanism to generate diverse temporal schedules and patterns of population size control during development (Chapter II). Lastly, I build a machine learning workflow for the discovery and exploration of phenotypic states and their dynamics from brightfield movies of unmanipulated cells. This technique is applied to identify phenotypic states in primary patient acute myeloid leukemia stem cells during differentiation (Chapter III). This work lays the foundation for further understanding of how the timing of mammalian gene expression is regulated. Finally, it provides a robust method for recognizing important cellular phenotypes associated with development and disease.