title: Structural Characterization and Statistical-Mechanical Model of Epidermal Patterns.
abstract: In proliferating epithelia of mammalian skin, cells of irregular polygon-like shapes pack into complex, nearly flat two-dimensional structures that are pliable to deformations. In this work, we employ various sensitive correlation functions to quantitatively characterize structural features of evolving packings of epithelial cells across length scales in mouse skin. We find that the pair statistics in direct space (correlation function) and Fourier space (structure factor) of the cell centroids in the early stages of embryonic development show structural directional dependence (statistical anisotropy), which is a reflection of the fact that cells are stretched, which promotes uniaxial growth along the epithelial plane. In the late stages, the patterns tend toward statistically isotropic states, as cells attain global polarization and epidermal growth shifts to produce the skin's outer stratified layers. We construct a minimalist four-component statistical-mechanical model involving effective isotropic pair interactions consisting of hard-core repulsion and extra short-range soft-core repulsion beyond the hard core, whose length scale is roughly the same as the hard core. The model parameters are optimized to match the sample pair statistics in both direct and Fourier spaces. By doing this, the parameters are biologically constrained. In contrast with many vertex-based models, our statistical-mechanical model does not explicitly incorporate information about the cell shapes and interfacial energy between cells; nonetheless, our model predicts essentially the same polygonal shape distribution and size disparity of cells found in experiments, as measured by Voronoi statistics. Moreover, our simulated equilibrium liquid-like configurations are able to match other nontrivial unconstrained statistics, which is a testament to the power and novelty of the model. The array of structural descriptors that we deploy enable us to distinguish between normal, mechanically deformed, and pathological skin tissues. Our statistical-mechanical model enables one to generate tissue microstructure at will for further analysis. We also discuss ways in which our model might be extended to better understand morphogenesis (in particular the emergence of planar cell polarity), wound healing, and disease-progression processes in skin, and how it could be applied to the design of synthetic tissues.
authors.affiliation: Department of Chemistry, Princeton University, Princeton, New Jersey.
authors.forename: Duyu
authors.initials: D
authors.lastname: Chen
authors.affiliation: Department of Molecular Biology, Princeton University, Princeton, New Jersey.
authors.forename: Wen Yih
authors.initials: WY
authors.lastname: Aw
authors.affiliation: Department of Molecular Biology, Princeton University, Princeton, New Jersey.
authors.forename: Danelle
authors.initials: D
authors.lastname: Devenport
authors.affiliation: Department of Chemistry, Princeton University, Princeton, New Jersey; Department of Physics, Princeton University, Princeton, New Jersey; Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey; Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey. Electronic address: torquato@princeton.edu.
authors.forename: Salvatore
authors.initials: S
authors.lastname: Torquato
mesh_terms: D000818:Animals; D016880:Anisotropy; D000078404:Epidermal Cells; D004817:Epidermis / Q000196:embryology; D007091:Image Processing, Computer-Assisted; D051379:Mice; D008954:Models, Biological; D013223:Statistics as Topic