Measuring and modeling the dynamics of patterning in human stem cells

人类干细胞模式动态的测量和建模

• 批准号: 10084170
• 负责人: Sharad Ramanathan
• 金额: $ 32.52万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-11 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10084170
• 关键词: Address; Affect; Amino Acid Motifs; Animals; Anterior; Apical; Awareness; BMP4; Cell Differentiation process; Cells; Complex; Congenital Abnormality; Data; Data Analyses; Defect; Development; Developmental Biology; Disease; Embryo; Endoderm; Epiblast; Epithelial; Exposure to; Family; Future; Gene Expression; Genes; Genetic Transcription; Genome; Geometry; Germ Layers; Goals; Hour; Human; Human Development; Image; In Vitro; Individual; Ligands; Literature; Location; Malignant Neoplasms; Mathematics; Measures; Mesoderm; Methods; Microfluidic Microchips; Microfluidics; Microscopy; Modeling; Modification; Molecular; Molecular Biology; Mus; Pattern; Positioning Attribute; Primitive Streaks; Process; Publishing; Regulator Genes; Signal Transduction; Smad Proteins; Statistical Methods; Statistical Models; System; Techniques; Testing; Time; Tissues; Transforming Growth Factor beta; Transforming Growth Factor beta Receptors; Work; apical membrane; base; basolateral membrane; blastomere structure; bone morphogenetic protein receptors; cell type; course development; developmental disease; digital; endodermal progenitor; experimental study; germline stem cells; human RNA sequencing; human embryonic stem cell; human embryonic stem cell line; human stem cells; human tissue; image processing; in vivo; knock-down; mathematical model; microscopic imaging; mouse development; multipotent cell; mutant; novel; predicting response; predictive modeling; prevent; progenitor; receptor; response; single cell analysis; single cell sequencing; single-cell RNA sequencing; stem cells; tool; transcription factor

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.

抽象的 该项目的长期目标是了解复杂的人体组织中的细胞如何感知，过程和 在正常的人类发育和发育疾病期间对信号做出响应。 发育生物学的一个基本问题是了解如何在发育中形成组织 动物。该应用程序将在人类多元化的构图中解决这个问题 细胞进入中胚层和内胚层。将要研究的第一个问题是细胞感应信号， 然后仔细研究内部基因调节网络如何处理此信号以启动 转录响应当细胞选择其命运时。 该提案的第一个目的使用了新型微流体的组合来控制信号的梯度，基因组 修改技术以荧光标记关键转录因子，以研究其动力学使用 落荧光以及图像处理和数学工具来分析数据。它进一步跟进 申请人最近发现，关键接收器基本上是局部的。这项研究 演示细胞因其受体被局部的（IN）感知活性顶信号的（IN）的能力如何 基本影响图案。这是申请人意识到尝试进行定量尝试的第一项研究 了解人类干细胞的图案。 第二个目的是了解细胞中基因调节网络状态如何 在人和小鼠的生殖层分化过程中影响对TGF-β信号的响应。确实，细胞 从胚胎的同一区域获得的开发中，甚至相隔十二个小时也显示了数字不同的 对同一信号的响应。在开发过程中获得的单细胞基因表达数据是 用于建立细胞内基因调节网络的预测数学模型。建造这样的 过去，预测性数学模型一直受到挑战。使用这些模型，目的 目的是揭示细胞是否可以根据ON的不同方式响应相同的形态发生信号 核心基因调节电路的状态。在早期的背景下对预测进行实验检查 人类和小鼠的发育使用成像和分子技术来扰动基因表达。 该提案提出的发现将使人们更好地理解多能人类细胞 负责在发育和癌症期间发出信号。此外，建立预测模型的能力 基因调节网络的基因调节网络为了解疾病的机制提供了途径 未来的国家。