BMP Signaling and the Robustness of In Vivo Stem Cell Decisions

BMP 信号传导和体内干细胞决策的稳健性

• 批准号: 2313692
• 负责人: Gregory Reeves
• 金额: $ 60.11万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313692&HistoricalAwards=false
• 关键词: BMP; Signaling; Robustness; Vivo; Stem

Gaining the ability to reproducibly control stem cell differentiation would advance fundamental knowledge of cell and tissue biology. This would have broad impacts on society and human health. It is well-known that stem cells make decisions based on signals they receive from neighboring cells. A quantitative understanding of this process is required for reliable human control of stem cell differentiation. However, this crucial piece of the puzzle, in the form of a predictive mathematical model, is currently missing. To address this conceptual gap, researchers from Texas A&M University will employ light microscopy, genetics, and mathematical approaches to quantitatively probe the relationship between signaling and stem cell differentiation. The goal is to create a mathematical model of this relationship that can be used to design efficient and reliable protocols for controlling differentiation.The research will focus on the Bone Morphogenetic Protein (BMP) signaling pathway and its role in germline stem cell (GSC) decisions in the Drosophila ovary. The highly-conserved BMP pathway is one of many major regulators of stem cell decisions across the animal kingdom, and in Drosophila female GSCs, it is the central hub in dictating differentiation vs self-renewal decisions. In GSCs and in differentiating cells, known as cystoblasts (CBs), positive and negative feedback loops regulate the BMP pathway. The hypothesis is that these feedback loops enhance the robustness of stem cell decisions, and as such, must be accounted for when designing protocols to control differentiation. Therefore, the project will investigate these feedback loops in both GSCs and CBs to incorporate them into the model. Advanced confocal microscopy techniques, such as raster image correlation spectroscopy (RICS) and fluorescence recovery after photobleaching (FRAP), will be used to measure biophysical parameters of the BMP pathway and to obtain time courses of concentrations of fluorescently-tagged BMP pathway components. These measurements will be used as model constraints or to test model predictions. Experiments will be done under wildtype and genetically perturbed conditions, such as loss of feedback loop components. Precise perturbations to the pathway will be achieved through optogenetics. The outcome of the project is expected to be a predictive, mechanistic model of BMP pathway regulation of stem cell decisions, which will have several positive impacts. First, due to the high conservation of the pathway, a detailed mechanistic model impacts the understanding of BMP signaling in other organisms. Second, the quantitative, mechanistic description of the system will form the foundation for external manipulation of stem cell decisions, such as designed de-differentiation. Finally, the results will provide an in vivo complement to stem cell cultures, and as such, will be a model system to advance knowledge of stem cell biology within native context.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

获得可重复控制干细胞分化的能力将推进细胞和组织生物学的基础知识。这将对社会和人类健康产生广泛影响。众所周知，干细胞根据它们从邻近细胞接收的信号做出决定。对这一过程的定量理解是可靠的人类控制干细胞分化所必需的。然而，这个关键的拼图，以预测数学模型的形式，目前还没有。为了解决这一概念上的差距，来自德克萨斯A M大学的研究人员将采用光学显微镜，遗传学和数学方法来定量探索信号传导和干细胞分化之间的关系。我们的目标是建立一个数学模型，这种关系可以用来设计有效和可靠的协议，控制分化。研究将集中在骨形态发生蛋白（BMP）信号通路及其在生殖系干细胞（GSC）的决定在果蝇卵巢的作用。高度保守的BMP通路是动物界干细胞决策的许多主要调节因子之一，在果蝇雌性GSC中，它是决定分化与自我更新决策的中心枢纽。在GSC和分化细胞中，称为成囊细胞（CB），正反馈和负反馈回路调节BMP通路。假设是这些反馈回路增强了干细胞决策的鲁棒性，因此，在设计控制分化的方案时必须考虑到这一点。因此，该项目将调查GSC和CB中的这些反馈回路，以将其纳入模型。先进的共聚焦显微镜技术，如光栅图像相关光谱（RICS）和光漂白后的荧光恢复（FRAP），将用于测量BMP途径的生物物理参数，并获得荧光标记的BMP途径成分的浓度的时间过程。这些测量值将用作模型约束条件或测试模型预测。实验将在野生型和遗传干扰条件下进行，例如反馈环组件丢失。对该途径的精确扰动将通过光遗传学实现。该项目的成果预计将是BMP通路调节干细胞决策的预测性机制模型，这将产生一些积极的影响。首先，由于该途径的高度保守性，详细的机制模型会影响对其他生物体中BMP信号传导的理解。其次，系统的定量、机械描述将为干细胞决策的外部操纵（如设计的去分化）奠定基础。最后，研究结果将为干细胞培养提供体内补充，因此，将成为在自然环境中推进干细胞生物学知识的模型系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。