Investigating the Mechanochemical Basis of Cell Fate Decisions Using Multiplexed Protein Mapping of Developing Embryo
Shayan Shami Pour
Embryogenesis relies on the precise spatiotemporal coordination of morphogen gradients along the embryonic axes, driving cell fate specification and large-scale morphogenetic movements. While existing paradigms of tissue patterning have primarily focused on static tissues, the mechanisms by which cellular decisions are obtained in developing embryos, where multiple dynamic morphogen gradients are concurrently established and extensive morphogenesis occurs, remain poorly understood. To address this gap, we developed a high-throughput 3D iterative indirect immunofluorescence imaging (3D-4i) technology that captures cellular signaling, mechanochemical states, and fate decisions at subcellular resolution in a unified, cell-centric framework. Applying 3D-4i to hundreds of zebrafish embryos undergoing gastrulation, we generated comprehensive cell signaling, mechanochemical, and fate landscapes of early development. By linking these landscapes, we discovered that the segregation of cells in the fate space aligns with their separation in the signaling space, suggestive of their mutual information, and that cellular signaling and mechanochemical properties can partially predict cell fate decisions at the observed developmental snapshots. To explore these relationships over time, we generated a continuous pseudotime trajectory of cells across signaling, mechanochemical and fate landscapes as lineages segregate. We achieved this by inferring the developmental timing of each embryo with refined temporal resolution through the application of Spherical Harmonics Expansion to embryo morphology. Finally, using a mathematical modeling approach on the pseudodynamic data, we investigated the logic by which multiple signaling pathways coordinate to govern cell fate decisions during embryonic development. Collectively, these experimental and computational advances provide a system-level framework for unraveling the complex interplay of multimodal factors that drive multicellular self-organization.
