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

计算方法鉴定牙上皮干细胞主转录因子

基本信息

批准号：
10239100
负责人：
Huojun Cao
金额：
$ 23.04万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10239100
关键词：
Address Adult Animals Bayesian Modeling Cells ChIP-seq Computer Models Computing Methodologies Consumption Data Dental Dental Libraries Development Ectopic Expression Epigenetic Process Epithelial Fibroblasts Fluorescence-Activated Cell Sorting Foundations Future Genetic Transcription Goals Hepatocyte Human Incisor Lead Life Literature Liver Mesenchymal Stem Cells Methods Modeling Molecular Molecular Biology Molecular Profiling Mus Mutant Strains Mice Pattern Performance Process Protocols documentation Research Source Technology Testing Therapeutic Time Tooth regeneration Tooth structure Transgenic Mice Validation base cell type embryonic stem cell epigenome epithelial stem cell experimental study in silico induced pluripotent stem cell interest novel repaired restoration self renewing cell stem cell biomarkers stem cell therapy stem cells transcription factor transcriptome transcriptome sequencing

项目摘要

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.

项目概要基于干细胞的治疗是牙齿修复、修复和替换的有前景的方法。然而，目前还没有牙上皮干细胞 (DESC) 的可用来源，而这对于牙齿发育至关重要。幸运的是，细胞重编程技术的最新发展已经使这些方法得以实现。可以通过仅异位表达从其他可用细胞类型生成所需的细胞类型然而，MTF 的实验鉴定需要时间。因此，缺乏且昂贵的 DESC 的 MTF 的全面识别。为理解 DESC 的分子生物学和实现其治疗提出了一个关键障碍该应用程序的目标是生成转录组 (RNA-seq) 和表观基因组。 (H3K27ac ChIP-seq) DESC 概况，并根据其综合鉴定 DESC 的 MTF 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 转基因小鼠的门牙中分离出 DESC，其中 GFP 的表达由 DESCs 特异性标记 Sox2 此外，我们开发了一种计算 MTF 预测方法。称为“MTFinder”（主转录因子查找器），包含转录组和表观基因组数据转化为贝叶斯统计模型在这个项目中，我们将首先定义动态。小鼠 DESC 及其后代的转录组和表观基因组谱（目标 1）。最近开发的计算模型 MTFinder 可全面识别小鼠的 MTF DESC（目标 2）。该项目完成后，我们将生成小鼠 DESC 的分子图谱。这将显着扩展我们对 DESC 转录和表观遗传学景观的理解。我们预计将确定一份已知的和新颖的 DESC MTF 列表，这为以下方面提供了基础。此外，该项目还将验证 MTFinder 的性能。计算方法，可应用于其他感兴趣的细胞类型。