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Cells self-organize into functional, ordered structures during tissue morphogenesis, a process that is evocative of colloidal self-assembly into engineered soft materials. Understanding how intercellular mechanical interactions may drive the formation of ordered and functional multicellular structures is important in developmental biology and tissue engineering. Here, by combining an agent-based model for contractile cells on elastic substrates with endothelial cell culture experiments, we show that substrate deformation–mediated mechanical interactions between cells can cluster and align them into branched networks. Motivated by the structure and function of vasculogenic networks, we predict how measures of network connectivity like percolation probability and fractal dimension as well as local morphological features including junctions, branches, and rings depend on cell contractility and density and on substrate elastic properties including stiffness and compressibility. We predict and confirm with experiments that cell network formation is substrate stiffness dependent, being optimal at intermediate stiffness. We also show the agreement between experimental data and predicted cell cluster types by mapping a combined phase diagram in cell density substrate stiffness. Overall, we show that long-range, mechanical interactions provide an optimal and general strategy for multicellular self-organization, leading to more robust and efficient realizations of space-spanning networks than through just local intercellular interactions.
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This content will become publicly available on December 15, 2026
MultiCell: geometric learning in multicellular development
During developmental processes such as embryogenesis, how a group of cells self-organizes into specific structures is a central question in biology. However, it remains a major challenge to understand and predict the behavior of every cell within the living tissue over time during such intricate processes. Here we present MultiCell, a geometric deep learning method that can accurately capture the highly convoluted interactions among cells. We demonstrate that multicellular data can be represented with both granular and foam-like physical pictures through a unified graph data structure, considering both cellular interactions and cell junction networks. Using this method, we achieve interpretable four-dimensional morphological sequence alignment and predict single-cell behaviors before they occur at single-cell resolution during Drosophila embryogenesis. Furthermore, using neural activation map and model ablation studies, we demonstrate that cell geometry and cell junction networks are essential features for predicting cell behaviors during morphogenesis. This method sets the stage for data-driven quantitative studies of dynamic multicellular developmental processes at single-cell precision, offering a proof-of-concept pathway toward a unified morphodynamic atlas.
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- Award ID(s):
- 2046683
- PAR ID:
- 10692034
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- Nature Methods
- ISSN:
- 1548-7091
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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