Systems Biology-based tissue engineering

背景：细胞命运的决定是由外部信号和内在效应物整合到基因调控网络（GRNs）中来控制的。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的命运。人多能干细胞（hPSCs）是目前唯一未经转化的人类细胞系。因此，结合基于crispr的基因组工程，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型。最近进展：在我们当前的NSERC发现资助周期中，我们起草了人类中胚层grn规范，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grn，我们开发了一种新的算法和基于r语言的应用程序ExCluster，用于从RNA-seq数据中识别和量化差异剪接转录物。目的：神经嵴是一个进化上保守的迁移多能祖细胞的瞬时群体。神经嵴已经用低等脊椎动物模式生物研究了150多年。我们的目标是描绘人类神经嵴grn，并使用它们来驱动神经嵴衍生物的定向分化和重编程。总体假设：1)神经嵴发育的高度进化守恒将使我们能够推断出高分辨率的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生成人类神经嵴规范和分化的高分辨率预测grn。实验设计：目标1：我们将整合公开可用的小鸡、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起草转录网络。目标3：我们将构建NCC谱系的转录后grn。目标4：使用定向分化和crispr激活基因重编程的组织工程神经嵴谱系。培养：本研究项目将为至少3名博士生提供跨学科培养的良好平台，其中2名博士生目前在我的实验室。影响：15年来，我们的NSERC发现研究项目推动了技术发展，组织工程和GRN建模方法，我们和其他人随后将其应用于转化研究。除了对组织工程的影响之外，我们作为这个项目的一部分开发的工具和方法应该影响其他领域，如合成生物学、生命和进化科学。

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.