Adult stem cells have the ability to turn into any cell type in their organ. Guiding their differentiation to specific cell types holds great promise in modeling the genetic underpinnings of disease and providing regenerative therapies. This differentiation occurs like a progressively branching tree, as cells are coaxed by a sequence and combination of signals into the many progeny cell types. Yet, we currently have a patchy understanding of this differentiation tree and as a result cannot make most cell types from adult stem cells. Building this stem cell differentiation tree by discovering what signals guide cells along each branch would unlock the ability to produce a vast number of cell types in a dish that are currently inaccessible or inefficient to derive. We have developed a new technology that promises to transform our ability to map stem cell trees and achieve new clinically valuable differentiated cell types. The key to this technology is a method to introduce barcode tags into hundreds of cell populations at a time that we have guided to sample all possible branches of a particular step of stem cell differentiation. We use a high-throughput technology called single cell RNA sequencing to read out what each of thousands of cells turned into, using the barcode tag to connect that fate to the sequence and combination of signals that gave rise to it. We can then create a map of that particular step that reveals the optimal sequence and combination of signals needed to turn adult stem cells into many types of cells, all from a single experiment. In this proposal, we will focus on developing our exhaustive profiling approach using adult airway stem cells from humans and mice as our model system. Airway cell types have high clinical utility in modeling and providing regenerative therapy for diseases such as Cystic Fibrosis, asthma, bronchitis, and chronic obstructive pulmonary disease.
In Aim 1 of this project, we will optimize and explore the resolution of our barcoding technology and will develop computational analysis and visualization tools to make sense of the data.
In Aim 2, we will focus our efforts on differentiating airway stem cells to the mature cell types in the airway, which are currently inaccessible to study. This project promises to expedite stem cell disease modeling and regenerative approaches.
Stem cells, because they can turn into any cell type in a given organ, hold great promise in modeling the genetic underpinnings of disease and providing regenerative therapies. We will pioneer a high-throughput technique to determine what drives stem cells into specific cell types, mapping hundreds of differentiation routes in a single experiment. We will apply this technique to studying differentiation in the adult airway, whose dysfunction is integral to Cystic Fibrosis, asthma, bronchitis, and chronic obstructive pulmonary disease.
