Lung diseases such as chronic obstructive pulmonary disease, pulmonary fibrosis, and cystic fibrosis involve dysfunction of cells of the lung and airways. A promising strategy for treating these diseases is to replace damaged tissues with patient-derived, pluripotent stem cells (PSCs). Using current methods, it is possible to generate human lung progenitor cells from PSCs by using a specific combination of three defined signaling factors. However, like most differentiation protocols, this treatment scheme leads to incomplete, inefficient, and heterogeneous production of the desired cell types. Why do ?identical? PSCs make different cell fate decisions? What are the sources of cell-to-cell heterogeneity? How can we better manipulate PSCs to produce desired cell types? To answer these questions, it is critical to monitor the fate decisions of individual cells over both time and space. Here, I propose to use state-of-the-art computational approaches to study the differentiation of human stem cells to lung progenitors both in real time and at single-cell resolution. Specifically, I will investigate how OCT4, a major pluripotency transcription factor, controls the expression of downstream target GATA6, an early marker of the endodermal lineage that precedes lung progenitors. Our preliminary data shows that GATA6 has a binding site for OCT4; shows bimodal expression during differentiation; and is inversely expressed with OCT4. Thus, my central hypothesis is that variation in OCT4 dynamics among individual stem cells influences the heterogeneity of stem cell fates by controlling GATA6 expression.
In Aim 1, I will use time-lapse microscopy to monitor OCT4 dynamics in single cells and identify specific sources of heterogeneity in OCT4 protein levels. I will develop new image analysis methods to track the basal dynamics, cell cycle dependency, and ?heritability? between mother and daughter cells in proliferating PSC populations.
In Aim 2, I will simultaneously image OCT4 and GATA6 levels in single cells to ask how OCT4 dynamics control expression of GATA6 during differentiation. To this end, I have developed a computational modeling strategy that uses time-lapse measurements of two signaling proteins to determine the precise regulatory relationship between the protein pair (i.e., OCT4 and GATA6). The model will be used to predict why some cells induce GATA6 expression during differentiation while others do not. Finally, I will test model predictions by perturbing OCT4 levels in differentiating stem cells to accept/reject competing mechanistic hypotheses. This study will accomplish three important purposes. First, it will provide new computational strategies for studying stem cell fate decisions with a specific focus on the OCT4-GATA6 signaling axis; second, it will advance our knowledge of lung progenitor cell biology that could spur the development of new differentiation strategies to replace lung tissue; finally, it will provide the unique interdisciplinary training I need to make meaningful future contributions to the field of quantitative single-cell biology.!
Stem cell differentiation protocols are imperfect in that stem cells appear to self-organize and replicate the cellular heterogeneity of a developing embryo. To better understand the heterogeneity of stem cells as they are differentiating, it is critical to use live-cell imaging experiments to follow the real-time decisions of individual cells. This project will determine the sources of heterogeneity for stem cells and develop a predictive model to determine how individual stem cells acquire their fates during differentiation.