Identification of Master Transcription Factors of Dental Epithelial Stem Cell by Computational Method

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PROJECT SUMMARY Stem cell based treatments are promising approaches for tooth repair, restoration and replacement. However, there is no available source of dental epithelial stem cells (DESCs), which is essential for these approaches. Fortunately, recent development in cell reprogramming technology has made it possible to generate desired cell type from other available cell types by ectopic expression of only a handful master transcription factors (MTFs). However, experimental identification of MTFs is time- consuming and expensive. Hence a comprehensive identification of MTFs for DESCs is lacking and presents a critical barrier for understanding molecular biology of DESCs and realization its therapeutic potential. The objective of this application is to generate transcriptome (RNA-seq) and epigenome (H3K27ac ChIP-seq) profiles of DESCs and comprehensively identify MTFs of DESCs based on its molecular profiles. Our preliminary study shows that by fluorescence activated cell sorting (FACS) we can isolate DESCs from incisors of Sox2-GFP transgenic mouse, in which GFP expression is driven by DESCs specific marker Sox2. In addition, we developed a computational MTFs prediction method termed “MTFinder” (Master Transcription Factor-Finder) that incorporates both transcriptome and epigenome data into a Bayesian statistical model. In this project, we will first define dynamic transcriptome and epigenome profiles of mouse DESCs and its progeny (Aim 1). And then apply our recently developed computational model MTFinder to comprehensively identify MTFs for mouse DESCs (Aim 2). At the completion of this project, we will generate molecular profiles of mouse DESCs. This will significantly expand our understanding of transcriptional and epigenetics landscape of DESCs. We expect that a list of known and novel MTFs of DESCs will be identified. This provides foundation for future cell reprogramming research. Furthermore, this project will validate the performance of MTFinder computational method, which can be applied to other cell types of interests.