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

项目总结 干细胞治疗是一种很有前途的牙齿修复、修复和替换方法。 然而，目前还没有可用的牙科上皮干细胞(DESCs)来源，这对 这些方法。幸运的是，最近细胞重新编程技术的发展使 可以从其他可用单元格类型生成所需的单元格类型，方法仅为 少数主要转录因子(MTF)。然而，MTF的实验鉴定是时间- 既耗费人力又昂贵。因此，缺乏对DESCs的MTF的全面识别，并且 为了解DESCs的分子生物学和实现其治疗提供了关键障碍 潜力。该应用目的是产生转录组(RNA-SEQ)和表观基因组 (H3K27ac CHIP-SEQ)DESCs的配置文件，并基于ITS全面识别DESCs的MTF 分子图谱。我们的初步研究表明，通过荧光激活细胞分选(FACS)，我们 可以从Sox2-GFP转基因小鼠的切牙中分离出DESCs，其中GFP的表达是由 DESCS特异标记Sox2。此外，我们还开发了一种计算型MTF预测方法 称为“MTFinder”(主要转录因子-发现者)，它结合了转录组和 将表观基因组数据转换为贝叶斯统计模型。在本项目中，我们将首先定义动态 小鼠DESCs及其后代的转录组和表观基因组图谱(目标1)。然后应用我们的 最近开发的计算模型MTFinder，用于全面识别小鼠的MTF DESCS(目标2)。在这个项目完成后，我们将生成小鼠DESCs的分子图谱。 这将大大扩展我们对DESCs转录和表观遗传学图景的理解。 我们预计将确定一份已知的和新的DESCs的MTF清单。这为以下方面提供了基础 未来的细胞重新编程研究。此外，本项目还将验证MTFinder的性能 计算方法，可以应用于其他类型的兴趣单元。

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.