Building a Methodological Framework for Cell Fate Engineering
Cell fate engineering has become an area of intense research in the last fifteen years. A useful framework of cell fate engineering should include three pillars: the discovery of new cell fate-reprogramming cocktails of factors, the evaluation of engineered cells, and the revelation of underlying molecular mechanisms. One major challenge has been the lack of a scalable screening approach in vitro for the performance of reprogramming cocktails. This limits the speed of discovering new cocktails that can efficiently reprogram diverse cell types. Such new cocktails are needed to unleash the full applicational potential of engineered cells in regenerative medicine, disease modeling, and drug discovery. Another challenge is that despite the advantages of in vivo reprogramming, such as more efficient and mature fate conversion, the underlying gene programs, and thereby the molecular mechanisms, have been largely unknown. This is in large part due to the difficulty of specifically isolating and analyzing reprogrammed cells, without contamination from their endogenous counterparts. To address these, in this thesis, I first develop Reprogram-seq, a method that screens thousands of transcription factor cocktails for their reprogramming performance by single-cell perturbation screens. Reprogram-seq found a cocktail of three factors that efficiently and functionally reprograms fibroblasts to epicardial-like cells. Thus, Reprogram-seq accelerates rational cell fate engineering. Next, I performed single-cell transcriptomic analysis of in vivo neurogenesis induced in astrocytes by a novel reprogramming factor, DLX2. This is enabled by a lineage tracer that highly specifically tracks all cells reprogrammed from astrocytes. My analysis reveals that DLX2 induces a neural stem cell-like behavior, transitioning from quiescence to activation, proliferation, and neurogenesis. Gene regulatory network analysis and mouse genetics identify and confirm key nodes mediating DLX2-dependent fate reprogramming. Therefore, this study dissects the gene programs of in vivo reprogramming with single-cell transcriptomics and paves the way for applying Reprogram-seq in vivo. Together, my thesis research has demonstrated that single-cell omic technologies accelerate the discovery of new reprogramming cocktails, streamline the transcriptional evaluation of engineered cells, and dissect gene programs that underlie reprogramming, contributing to all three pillars of the framework. I expect these methodologies to be generalizable to and useful for other cell fate engineering scenarios.