Systems Biology-based tissue engineering

背景：细胞命运的决定是由外在信号和内在效应分子整合到基因调控网络(GRN)中控制的。因此，通过了解GRN的拓扑结构，可以根据给定的输入信号预测和操纵细胞命运。人类多能干细胞(HPSCs)是唯一未转化的人类细胞系。因此，与基于CRISPR的基因组工程相结合，hPSCs也是基础科学和应用科学的强大工具，也是应用系统方法和开发合成生物学平台的理想模式。最新进展：在我们当前的NSERC发现资金周期中，我们起草了人中胚层规范的GRN，用于优化人类骨骼肌、软骨和血管组织工程。此外，为了开发更精确的GRN，我们开发了一个新的算法和基于R语言的应用程序ExCluver，用于从RNA-seq数据中识别和量化差异剪接转录本。目的：神经脊是一个进化上保守的迁移性多能祖细胞的暂时性群体。使用低等脊椎动物模型生物研究神经脊已有150多年的历史。我们的目标是描绘人类神经脊GRN，并使用它们来驱动定向分化和重新编程到神经峰衍生物。最重要的假设：1)神经脊发育的高度进化保守性将使我们能够推导出高分辨率的GRN，从而设计出人类神经脊的衍生品。2)转录和转录后网络的集成将产生高分辨率的预测GRN，用于人类神经脊的指定和分化。实验设计：目标1：我们将整合公开可用的鸡、蛙、斑马鱼和小鼠胚胎组学数据，这些数据将被用来起草一个参考神经峰GRN。目的2：我们将利用hPSC神经脊区分化的单细胞组学图谱来独立绘制转录网络。目的3：我们将构建NCC谱系的转录后GRN。目的4：应用定向分化和CRISPR激活的遗传重编程技术构建组织工程神经脊谱系。培训：拟议的研究计划将为至少3名博士生提供一个很好的跨学科培训平台，其中包括目前在我的实验室的两名博士生。影响：15年来，我们的NSERC Discovery研究计划推动了技术开发、组织工程和GRN建模方法，我们和其他人随后将这些方法应用于翻译研究。除了对组织工程的影响外，我们作为该计划的一部分开发的工具和方法应该会影响其他领域，如合成生物学、生命科学和进化科学。

BACKGROUND: Cell fate decisions are controlled by the integration of extrinsic signals and intrinsic effectors into gene regulatory networks (GRNs). Thus, by understanding the topology of GRNs, it is possible to predict and manipulate cell fate from a given input signal. Human pluripotent stem cells (hPSCs) represent the only non-transformed human cell lines. Thus, in combination with CRISPR-based genome engineering, hPSCs are also a powerful tool for fundamental and applied science and an ideal model in which to apply systems approaches and develop synthetic biology platforms. RECENT PROGRESS: During our current NSERC Discovery funding cycle, we drafted GRNs of human mesendoderm specification, which we used to optimize human skeletal muscle, cartilage, and vascular tissue engineering. Moreover, to develop more precise GRNs, we developed a novel algorithm and R-language based application, ExCluster, to identify and quantify differentially spliced transcripts from RNA-seq data. GOAL: The neural crest is an evolutionarily conserved transient population of migratory multipotent progenitor cells. The neural crest has been studied using lower vertebrate model organisms for over 150 years. Our goal is to delineate human neural crest GRNs and use them to drive directed differentiation and reprogramming to neural crest derivatives. OVERARCHING HYPOTHESES: 1) The high degree of evolutionary conservation in neural crest development will allow us to deduce high-resolution GRNs from which to engineer human neural crest derivatives. 2) Integration of transcriptional and post-transcriptional networks will generate high-resolution predictive GRNs of human neural crest specification and differentiation. EXPERIMENTAL DESIGN: Aim 1: We will integrate publicly available chick, frog, zebrafish, and mouse embryonic omics datasets which will be used to draft a reference neural crest GRN. Aim 2: We will use single cell omics profiling of hPSC neural crest differentiation to independently draft transcriptional networks. Aim 3: We will build post-transcriptional GRNs of NCC lineages. Aim 4: Tissue engineer neural crest lineages using directed differentiation and CRISPR-activated genetic reprogramming. TRAINING: The proposed research program will provide an excellent platform for interdisciplinary training of at least 3 PhD students, including two currently in my lab. IMPACT: For 15 years, our NSERC Discovery research program has driven technology development, tissue engineering and GRN modelling approaches, which we and others have then applied to translational research. Beyond the impact on tissue engineering, the tools and approaches that we develop as part of this program should impact additional fields such as synthetic biology and life and evolutionary sciences.