 A computational modeling approach for predicting multicell spheroid patterns based on signaling-induced differential adhesionNikita Sivakumar, Helen V Warner, Shayn M Peirce and Matthew J Lazzara PLOS Computational Biology, 2022, vol. 18, issue 11, 1-29 Abstract: Physiological and pathological processes including embryogenesis and tumorigenesis rely on the ability of individual cells to work collectively to form multicell patterns. In these heterogeneous multicell systems, cell-cell signaling induces differential adhesion between cells that leads to tissue-level patterning. However, the sensitivity of pattern formation to changes in the strengths of signaling or cell adhesion processes is not well understood. Prior work has explored these issues using synthetically engineered heterogeneous multicell spheroid systems, in which cell subpopulations engage in bidirectional intercellular signaling to regulate the expression of different cadherins. While engineered cell systems provide excellent experimental tools to observe pattern formation in cell populations, computational models of these systems may be leveraged to explore more systematically how specific combinations of signaling and adhesion parameters can drive the emergence of unique patterns. We developed and validated two- and three-dimensional agent-based models (ABMs) of spheroid patterning for previously described cells engineered with a bidirectional signaling circuit that regulates N- and P-cadherin expression. Systematic exploration of model predictions, some of which were experimentally validated, revealed how cell seeding parameters, the order of signaling events, probabilities of induced cadherin expression, and homotypic adhesion strengths affect pattern formation. Unsupervised clustering was also used to map combinations of signaling and adhesion parameters to these unique spheroid patterns predicted by the ABM. Finally, we demonstrated how the model may be deployed to design new synthetic cell signaling circuits based on a desired final multicell pattern.Author summary: The remarkable ability of cells to self-organize is critical for the assembly of functional tissues during embryogenesis and is impaired by the molecular aberrations that lead to tumorigenesis and metastasis. Learning the rules of cellular self-assembly will provide a new way to understand such physiological and pathophysiological processes and create an instruction manual for designing tissues from scratch for therapeutic applications. Biologists have begun to learn the rules of self-assembly through reverse engineering–that is, through engineering biochemical circuits that control how cells adhere to one another. These approaches can yield simple, multi-cell structures that self-assemble into a core of one cell type surrounded by a shell of another cell type, for example. However, engineering more complex tissue patterns requires exploring a large domain of circuit structures and parameters. To facilitate this exploration in a guided and systematic manner, we created a computational model that predicts how mixtures of cells with different circuity for expressing adhesion proteins will interact to form varied patterns in spheroidal tissues comprised of hundreds of cells. Our model enables the design of internal molecular signaling circuitry that permits cells to be used as building blocks for self-assembled tissues with specific structures, and ultimately functions. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1010701 DOI: 10.1371/journal.pcbi.1010701 Access Statistics for this article More articles in PLOS Computational Biology from Public Library of Science |
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