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

项目摘要 这项提案是一项名为“从scRNA-seq重建眼睛中的位置信息”的新应用 并将其与信号转导联系起来”。 细胞命运如何被准确地指定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 对基因表达如何在多细胞生物体中跨时间和空间协调的理解非常有限。 组织，使细胞准确地和可重复地协商从多能性到分化性的过渡。 状态单细胞RNA测序（scRNA-seq）有望描述组织中每个细胞的状态， 定义为细胞内mRNA表达的整体模式。将细胞从组织中解离， 进行单独测序。一个主要的限制是难以重建每个空间位置 组织中的测序细胞这项提案的目的是开发一种全新的方法来实现 scRNA-seq数据的精确空间映射，从而推断细胞状态的数量和构型。的 方法依赖于物理原理，将数据数学排序到模拟 组织.在我们的病例中，相关组织是发育中的视网膜。果蝇的视网膜提供了 一个易于处理和定义明确的实验模型来解决这个问题。特别是 视网膜的定型模式和结构有助于识别和跟踪单个细胞 类型在空间和时间上的变化。因此，我们有一个严格的“地面真相”，以判断我们的方法，提炼 它和修改它，以获得scRNA-seq数据到空间坐标的最佳和准确的映射， 组织.我们的方法可以帮助扩展scRNA-seq的使用，以发现新的细胞类型及其在细胞中的位置。 较少特征化的组织和器官，包括哺乳动物的眼睛。 目标1将生成scRNA-seq数据，定量数据集将用于构建空间模型 它整合了细胞在时间和空间上的状态信息。 目的2将研究细胞间信号传导的特定特征如何影响细胞的定位， 眼睛发育中的差异。因为信号机制已经被证明是非常重要的 保守，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.