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Delineating cooperative effects of Notch and biomechanical signals on patterned liver differentiation.

Ishita JainIan C BergAyusha AcharyaMaddie BlaauwNicholas GosstolaPablo Perez-PineraGregory H Underhill
Published in: Communications biology (2022)
Controlled in vitro multicellular culture systems with defined biophysical microenvironment have been used to elucidate the role of Notch signaling in the spatiotemporal regulation of stem and progenitor cell differentiation. In addition, computational models incorporating features of Notch ligand-receptor interactions have provided important insights into Notch pathway signaling dynamics. However, the mechanistic relationship between Notch-mediated intercellular signaling and cooperative microenvironmental cues is less clear. Here, liver progenitor cell differentiation patterning was used as a model to systematically evaluate the complex interplay of cellular mechanics and Notch signaling along with identifying combinatorial mechanisms guiding progenitor fate. We present an integrated approach that pairs a computational intercellular signaling model with defined microscale culture configurations provided within a cell microarray platform. Specifically, the cell microarray-based experiments were used to validate and optimize parameters of the intercellular Notch signaling model. This model incorporated the experimentally established multicellular dimensions of the cellular microarray domains, mechanical stress-related activation parameters, and distinct Notch receptor-ligand interactions based on the roles of the Notch ligands Jagged-1 and Delta-like-1. Overall, these studies demonstrate the spatial control of mechanotransduction-associated components, key growth factor and Notch signaling interactions, and point towards a possible role of E-Cadherin in translating intercellular mechanical gradients to downstream Notch signaling.
Keyphrases
  • growth factor
  • cell proliferation
  • single cell
  • cell adhesion
  • stem cells
  • cell therapy
  • mesenchymal stem cells
  • case control
  • bioinformatics analysis