Reconstructing positional information in the eye from scRNA-seq and relating it to signal transduction

项目总结 该提案是一项新的应用程序，名为“根据scRNA-seq重建眼睛中的位置信息” 并将其与信号转导联系起来。 如何准确地指定细胞命运的问题对于理解两者都是至关重要的 正常的发育进程和改变细胞命运的疾病机制。然而，我们目前有 对多细胞中基因表达如何跨越时间和空间协调的理解非常有限 组织，以便细胞准确和可重复地协商从多能细胞到分化细胞的转变 州政府。单细胞RNA测序(scRNA-seq)承诺根据状态描述组织中的每个细胞， 定义为细胞内信使核糖核酸表达的整体模式。细胞从组织中分离出来， 以个体测序为准。一个主要的限制是很难重建每个 对组织中的细胞进行了测序。这项提议的目标是开发一种全新的方法来获得 ScRNA-seq数据的准确空间映射，从而推断细胞状态的数量和配置。这个 方法依赖于物理原理以数学方式将数据排序到2D网格上，该网格模拟 组织。在我们的例子中，相关的组织是发育中的视网膜。果蝇视网膜提供了一种极好的 用于解决这一问题的易于处理和定义良好的实验模型。尤其是， 视网膜的刻板图案和结构有助于识别和跟踪单个细胞 在空间和时间上打字。因此，我们有一个严谨的“地道”来判断我们的方法，提炼 并对其进行修改，以实现将scrna-seq数据最佳且准确地映射到 组织。我们的方法可以帮助扩大scRNA-seq的使用，以发现新的细胞类型及其在 特征较少的组织和器官，包括哺乳动物的眼睛。 目标1将生成scRNA-seq数据，量化数据集将用于构建空间模型 它集成了有关细胞状态的信息，跨越时间和空间。 目标2将研究细胞-细胞信号的特定特征如何影响细胞的定位 发育期眼睛的分化。因为信号机制已经被证明是非同寻常的 保守地说，我们对驱动分化的信号相互作用的探索可能与 哺乳动物的发育和疾病。

PROJECT SUMMARY This proposal is a new application entitled "Reconstructing positional information in the eye from scRNA-seq and relating it to signal transduction". The question of how cell fates are accurately specified is of fundamental importance to understanding both normal developmental progression and disease mechanisms that alter cell fates. However we currently have very limited understanding of how gene expression is coordinated across time and space in a multicellular tissue so that cells accurately and reproducibly negotiate the transition from a multipotent to a differentiated state. Single-cell RNA sequencing (scRNA-seq) promises to describe each cell in a tissue in terms of a state, defined as the global pattern of mRNA expression inside the cell. Cells are dissociated from a tissue and are subject to individual sequencing. A major limitation is the difficulty in reconstructing the spatial position of each sequenced cell in the tissue. The goal of this proposal is to develop a radically new method to acheive accurate spatial mapping of scRNA-seq data, thereby inferring the number and configuration of cell states. The method relies on physical principles to mathematically order the data onto a 2D lattice that simulates the tissue. In our case, the relevant tissue is the developing retina. The Drosophila retina provides a superbly tractable and well-defined experimental model with which to address this problem. In particular, the stereotyped patterning and architecture of the retina facilitates the identification and tracking of individual cell types over space and time. Therefore, we have a rigorous “ground truth” to which to judge our method, refining it and modifying it to attain optimal and accurate mapping of scRNA-seq data onto spatial coordinates of a tissue. Our method could help expand the use of scRNA-seq to discover new cell types and their positions in less characterized tissues and organs, including the mammalian eye. Aim 1 will generate scRNA-seq data, and the quantitative datasets will be used to construct a spatial model that integrates information about cell states across both time and space. Aim 2 will investigate how specific features of cell-cell signaling contribute to the positioning of cell differentiation in the developing eye. Because signaling mechanisms have proven to be extraordinarily conserved, our exploration of the signaling interactions that drive differentiation are likely to be relevant to mammalian development and disease.