BMP Signaling and the Robustness of In Vivo Stem Cell Decisions

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，干细胞根据从邻近细胞接收到的信号做出决定。对这一过程的定量理解是人类对干细胞分化的可靠控制所必需的。然而，以预测数学模型的形式出现的这一关键拼图目前尚不存在。为了解决这一概念上的差距，来自德克萨斯农工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。目标是创建这种关系的数学模型，该模型可用于设计有效且可靠的协议来控制分化。重点研究骨形态发生蛋白（Bone Morphogenetic Protein， BMP）信号通路及其在果蝇卵巢生殖系干细胞（germline stem cell， GSC）决策中的作用。高度保守的BMP通路是动物王国中干细胞决策的许多主要调节因子之一，在果蝇雌性GSCs中，它是决定分化与自我更新决策的中心枢纽。在GSCs和分化细胞中，即成囊细胞（CBs），正反馈回路和负反馈回路调节BMP通路。假设是，这些反馈回路增强了干细胞决策的稳健性，因此，在设计控制分化的方案时必须考虑到这一点。因此，该项目将研究gsc和cb中的这些反馈回路，并将其纳入模型。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途径组分浓度的时间过程。这些测量将用作模型约束或测试模型预测。实验将在野生型和基因紊乱的条件下进行，例如失去反馈回路组件。通过光遗传学可以实现对该通路的精确扰动。该项目的结果有望成为BMP通路调控干细胞决策的预测性机制模型，这将产生几个积极影响。首先，由于该通路的高度保守性，详细的机制模型影响了对其他生物中BMP信号传导的理解。其次，该系统的定量、机制描述将为干细胞决策的外部操纵奠定基础，例如设计去分化。最后，该结果将为干细胞培养提供体内补充，因此，将成为在本地环境中推进干细胞生物学知识的模型系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.