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

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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.