Decoding the Spatial Grammar of Developmental Signaling

项目摘要 胚胎用一种复杂的化学信号语言向细胞传达指令。一个决定性 这种语言的特点是它在空间中的使用;指令被编码在空间分辨的信号“模式”中 分子。学习翻译这种模式的语言-阅读和编写干细胞的指令 是现代发育生物学的一个明确目标。这种能力将具有变革性的 通过引导替代组织的发展， 实验室然而，要实现这一目标，必须克服两个关键挑战。首先，我们需要新的方法 来创造和测试发育信号模式。第二，我们需要能够预测 由任意信号模式编码的发展结果。在这里，我们提出了一种新的方法， 解决了这两个挑战，使用高通量光遗传学操纵发育 发信号。我们的策略结合了用于人类发育信号的光遗传学控制的新试剂 具有用于空间分辨光刺激的平台的干细胞。我们的模式平台将允许我们创造 在一次实验中，超过500万个细胞的单细胞分辨率信号模式。我们将利用 这种方法提供了独特的规模，以建立发育信号的深度采样配对库 图案和产生的组织结构。该数据集将为建模创造令人兴奋的新机会 发育信号传导和合理引导组织体外发育。在这个提案中，我们首先提出了一个 将这种方法应用于Nodal信号通路，该通路协调内胚层和中胚层 在早期脊椎动物胚胎中形成。首先，我们将从深度学习中导入计算方法， 预测由任意节点信号模式产生的组织结构的模型。第二，我们将使用 这些模型可以指导三维组织的合理设计， 体外类器官。最后，我们将使用高通量光遗传学操作来严格构建和 约束Nodal patterning的机械模型。据我们所知，这项工作将构成第一个高- 在空间分辨信号模式上的吞吐量筛选。通过对模式空间进行经验采样， 通过前所未有的深度，我们的目标是建立一条新的道路，以合理的工程复杂的组织。

Project Summary Embryos communicate instructions to their cells using an elaborate language of chemical signals. A defining feature of this language is its use in space; instructions are encoded in spatially-resolved ‘patterns’ of signaling molecules. Learning to translate this language of patterns—to read and write instructions that stem cells understand— is a defining aim of modern developmental biology. This ability would have transformative implications for regenerative medicine by making it possible to guide the development of replacement tissues in the laboratory. However, two key challenges must be overcome to realize this goal. First, we need new methods to create and test developmental signaling patterns. Second, we need modeling frameworks that can predict the developmental outcomes encoded by arbitrary patterns of signaling. Here, we propose a new approach that addresses both of these challenges using high-throughput optogenetic manipulation of developmental signaling. Our strategy combines new reagents for optogenetic control of developmental signaling in human stem cells with a platform for spatially-resolved light stimulation. Our patterning platform will allow us to create patterns signaling with single-cell resolution for over 5 million cells in a single experiment. We will leverage the unique scale afforded by this approach to build deeply-sampled paired libraries of developmental signaling patterns and resulting tissue structures. This dataset will create exciting new opportunities for modeling developmental signaling and rationally guiding tissue development in vitro. In this proposal, we lay out a first application of this approach to the Nodal signaling pathway, which orchestrates endoderm and mesoderm formation in early vertebrate embryos. First, we will import computational methods from deep learning to build models that predict the tissue structures resulting from arbitrary Nodal signaling patterns. Second, we will use these models to guide the rational design of three-dimensional tissues that can be used to grow endodermal organoids in vitro. Finally, we will use high-throughput optogenetic manipulation to rigorously construct and constrain mechanistic models of Nodal patterning. To our knowledge, this work will constitute the first high- throughput screen on spatially-resolved signaling patterns. By empirically sampling pattern space with unprecedented depth, we aim to establish a new path to the rational engineering of complex tissues.