(I-332) Configurational entropy is an intrinsic driver of tissue structural heterogeneity

Introduction:: Tissues have recognizable and reproducible structural features that are critical to their function, but these same features can also be highly variable in their details. While this property of tissues is well appreciated, the mechanisms by which it emerges are not understood. Here we use human mammary organoids as a model to demonstrate that structural heterogeneity is an intrinsic property of self-organizing tissues, emerging from the cellular mechanics and dynamics.

Materials and Methods:: Patient-derived mammary epithelial cells were reconstituted into organoids of defined composition, geometry, and microenvironment. The proportion of luminal cells at the organoid boundary was used as a quantitative descriptor of tissue structure. The interfacial tensions (mechanics) of single cells were measured using a combination of micropipette aspiration and contact angle analysis. The cell motility (dynamics) was estimated by tracking nuclei within organoids. The structural distribution of organoids was modeled as a canonical ensemble, where the structural probability was defined as a function of interfacial mechanics, cell dynamics, and structural degeneracy.

Results, Conclusions, and Discussions::

We show that mammary organoids behave as a dynamic structural ensemble at the steady state, having a reproducible average and variance. This feature of the system allowed us to apply the principle of maximum entropy to link the probability of observing different structural configurations with three parameters: the tissue’s mechanical potential, the degeneracy of possible cellular configurations in space, and the mechanical energy associated with fluctuations in cell position. We then map these tissue-level state variables to the average properties of single cells and their microenvironment, providing a means to make surprisingly accurate predictions for how perturbations to cells and their interactions alter the distribution of structures at the tissue-scale. Our analysis offers several important and broadly applicable conceptual insights into the self-organization of tissues. First, it explains why the average structure of many tissues differs significantly from the predictions of existing energy-based models of cell sorting, as entropy favors the occupancy of otherwise higher energy tissue configurations because they are more numerous compared to lower energy ordered configurations. Second, it suggests that in all self-renewing tissues, a certain baseline level of structural heterogeneity should be expected because tissues must engage in homeostatic processes that are known to increase their activity, such as cell motility, cell division, apoptosis, immune cell trafficking, and the endocytosis of material from their environment.We anticipate these conceptual and mathematical tools will find applications as diverse as regenerative medicine, tissue modeling, and disease prevention.

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