Measuring and modeling the dynamics of patterning in human stem cells

摘要 该项目的长期目标是了解复杂人体组织中的细胞如何感知、处理和 在人类正常发育和发育性疾病期间对信号作出反应。 发育生物学的一个基本问题是了解组织在发育过程中是如何形成模式的。 动物本申请将在人类多能性细胞的模式化的背景下解决该问题。 细胞分化为中胚层和内胚层。我们要研究的第一个问题是细胞如何感知信号， 然后仔细研究内部基因调控网络如何处理这一信号， 细胞选择命运时的转录反应。 该提案的第一个目的是使用新型微流体的组合来控制信号梯度，基因组 修饰技术来荧光标记关键转录因子， 荧光，图像处理和数学工具来分析数据。它进一步跟进 本申请人最近发现，感知信号的关键受体位于基底。本研究 证明了细胞如何（在）感知活性顶端信号的能力，由于其受体被定位 基本影响模式。这是申请人意识到的第一项试图定量分析 了解人类干细胞是如何形成的 第二个目标是了解细胞内基因调控网络的状态 影响人和小鼠胚层分化期间对TGF-β信号的应答。事实上，细胞 从胚胎的同一区域获得的发育即使相隔12小时， 对同一个信号的反应。在发育过程中获得的单细胞基因表达数据是 用于构建细胞内基因调控网络的预测数学模型。建立这样 预测数学模型在过去是非常具有挑战性的。利用这些模型， 目的是揭示细胞是否可以根据不同的方式对相同的形态发生信号做出反应， 核心基因调控回路的状态。在早期的背景下， 人类和小鼠的发展，使用成像和分子技术干扰基因表达。 该提案的发现将有助于更好地了解多能人类细胞如何 在发育过程中和癌症中对信号都有反应。此外，建立预测模型的能力 潜在基因调控网络的研究为理解疾病的潜在机制开辟了途径 国家在未来。

Abstract The long-term goal of this project is to understand how cells in complex human tissues sense, process and respond to signal during normal human development and developmental diseases. A fundamental question in developmental biology is to understand how tissues are patterned in a developing animal. This Application will address the question in the context of the patterning of the human pluripotent cells into mesoderm and endoderm. The first question that will be investigated is how cells sense signal, followed by a closer look at how the internal gene regulatory network processes this signal to launch a transcriptional response as the cell chooses its fate. The first aim of this proposal uses a combination of novel microfluidics to control gradients of signals, genome modification techniques to fluorescently tag key transcription factors to study their dynamics using epifluorescence, and image processing and mathematical tools to analyze the data. It further follows up on the applicant’s recent discovery that key receptors that sense signals are basally localized. This study demonstrates how the (in)ability of the cell to sense active apical signal due its receptors being localized basally affect patterning. This is the first study the applicants are aware of to attempt to quantitatively understand how human stem cells are patterned. The second aim focuses on understanding how the state of the gene regulatory network within the cell affects the response to TGF-beta signal during germ layer differentiation in human and mouse. Indeed, cells even twelve hours apart in development obtained from the same region of the embryo show digitally distinct responses to the same signal. Single cell gene expression data obtained during the course of development is used to build a predictive mathematical model of the intracellular gene regulatory network. Building such predictive mathematical models has been very challenging in the past. Using these models, the goal of this aim is to uncover whether cells can respond to the same morphogenetic signal in distinct ways depending on the state of a core gene regulatory circuit. The predictions are checked experimentally in the context of early human and mouse development using imaging and molecular techniques to perturb gene expression. The discoveries made by the proposal will lead to a better understanding of how multipotent human cells respond to signal both during development and in cancer. Furthermore, the ability to build predictive models of the underlying gene regulatory network opens avenues to understand the mechanisms underlying disease states in the future.