Decoding the Spatial Grammar of Developmental Signaling

University Of Pittsburgh At Pittsburgh, Pittsburgh PA

Investigators

Abstract

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.