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促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，干细胞是根据它们从邻近细胞接收到的信号做出决定的。对这一过程的定量理解对于人类可靠地控制干细胞分化是必要的。然而，以预测性数学模型的形式，这块拼图的关键部分目前还没有找到。为了解决这一概念上的差距，德克萨斯农工大学的研究人员将使用光学显微镜、遗传学和数学方法来定量探索信号与干细胞分化之间的关系。其目标是建立一种数学模型，用于设计有效和可靠的方案来控制分化。研究将集中在骨形态发生蛋白(BMP)信号通路及其在果蝇卵巢生殖系干细胞(GSC)决策中的作用。高度保守的BMP途径是整个动物界干细胞决定的许多主要调节因子之一，在果蝇雌性GSCs中，它是决定分化和自我更新决定的中心枢纽。在GSCs和分化细胞，即成囊细胞(CBS)中，正反馈环和负反馈环调节BMP途径。假设是这些反馈环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，该项目将在GSC和CBS中调查这些反馈循环，以将它们纳入模型。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)，将被用来测量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。实验将在野生型和遗传扰动的条件下进行，例如反馈环组件的丢失。对这一途径的精确扰动将通过光遗传学实现。该项目的结果有望成为BMP途径调节干细胞决策的预测性、机械性模型，这将产生几个积极的影响。首先，由于该途径的高度保守性，一个详细的机制模型影响了对其他生物体中BMP信号的理解。其次，对该系统的定量、机械性描述将为干细胞决策的外部操纵奠定基础，例如设计的去分化。最后，这些结果将为干细胞培养提供活体补充，并因此将成为在本土背景下促进干细胞生物学知识的模型系统。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.