Systems Biology-based tissue engineering

背景：细胞命运的决定是由外源性信号和内源性效应子整合到基因调控网络（GRNs）中来控制的。因此，通过理解GRNs的拓扑结构，可以从给定的输入信号预测和操纵细胞命运。人多能干细胞（hPSC）代表仅有的非转化的人细胞系。因此，结合基于CRISPR的基因组工程，hPSC也是基础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型。最新进展：在我们目前的NSERC发现资助周期中，我们起草了人类中内胚层规范的GRNs，我们使用它来优化人类骨骼肌，软骨和血管组织工程。此外，为了开发更精确的GRNs，我们开发了一种新的算法和基于R语言的应用程序ExCluster，以从RNA-seq数据中识别和量化差异剪接的转录本。目标：神经嵴是一种进化上保守的迁移性多能祖细胞的短暂群体。使用低等脊椎动物模式生物研究神经嵴已经超过150年。我们的目标是描绘人类神经嵴GRNs，并使用它们来驱动定向分化和重编程为神经嵴衍生物。过度假设：1）神经嵴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，从而设计人类神经嵴衍生物。2)整合转录和转录后网络将产生高分辨率的预测人类神经嵴的规范和分化的GRNs。实验设计：目标1：我们将整合公开可用的鸡，青蛙，斑马鱼和小鼠胚胎组学数据集，这些数据集将用于起草参考神经嵴GRN。目的2：我们将使用hPSC神经嵴分化的单细胞组学分析来独立地绘制转录网络。目的3：我们将建立NCC谱系的转录后GRNs。目的4：使用定向分化和CRISPR激活的遗传重编程进行组织工程神经嵴谱系。培训：拟议的研究计划将为至少3名博士生提供跨学科培训的绝佳平台，其中包括目前在我实验室的两名博士生。 影响：15年来，我们的NSERC发现研究计划推动了技术开发，组织工程和GRN建模方法，然后我们和其他人将其应用于转化研究。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，如合成生物学和生命与进化科学。

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.