Precisely parameterized experimental and computational models of tissue organization

Abstract

Patterns of cellular organization in diverse tissues frequently display a complex geometry and topology tightly related to the tissue function. Progressive disorganization of tissue morphology can lead to pathologic remodeling, necessitating the development of experimental and theoretical methods of analysis of the tolerance of normal tissue function to structural alterations. A systematic way to investigate the relationship of diverse cell organization to tissue function is to engineer two-dimensional cell monolayers replicating key aspects of the in vivo tissue architecture. However, it is still not clear how this can be accomplished on a tissue level scale in a parameterized fashion, allowing for a mathematically precise definition of the model tissue organization and properties down to a cellular scale with a parameter dependent gradual change in model tissue organization. Here, we describe and use a method of designing precisely parameterized, geometrically complex patterns that are then used to control cell alignment and communication of model tissues. We demonstrate direct application of this method to guiding the growth of cardiac cell cultures and developing mathematical models of cell function that correspond to the underlying experimental patterns. Several anisotropic patterned cultures spanning a broad range of multicellular organization, mimicking the cardiac tissue organization of different regions of the heart, were found to be similar to each other and to isotropic cell monolayers in terms of local cell–cell interactions, reflected in similar confluency, morphology and connexin-43 expression. However, in agreement with the model predictions, different anisotropic patterns of cell organization, paralleling in vivo alterations of cardiac tissue morphology, resulted in variable and novel functional responses with important implications for the initiation and maintenance of cardiac arrhythmias. We conclude that variations of tissue geometry and topology can dramatically affect cardiac tissue function even if the constituent cells are themselves similar, and that the proposed method can provide a general strategy to experimentally and computationally investigate when such variation can lead to impaired tissue function.

Modeling Inherited Arrhythmia Disorders Using Induced Pluripotent Stem Cell-Derived Cardiomyocytes

“…Examples of these approaches have been used to model cardiomyopathies 29,30 and are being extended to arrhythmias. Incorporation of optogenetic techniques 109 will permit spatially precise optical stimulation and recording from the engineered tissues 110 . Combining iPSC-CMs with bioengineered cardiac tissues and optogenetics will certainly enhance IAD models and make it more likely that these models will be useful for predicting clinical outcomes.…”

Topology control of human fibroblast cells monolayer by liquid crystal elastomer

“…The living tissues designed in our work resemble the tissues with topological defects grown by Saw et al (5) at template-free isotropic substrates, with that difference that the defects in the LCE-patterned tissue are of a predetermined structure and appear at predetermined locations. Since the cells' alignment patterns, topological defects in them, and even the defect cores control many important biochemical processes at microscale, such as action potential propagation (8) and apoptosis (5), our study opens the possibility to engineer platforms for the controlled patterning of tissues and their design for specific functions.…”

Multiscale Characterization of Engineered Cardiac Tissue Architecture

“…However, it is unclear if this consistency holds overall length-scales, and no one has been able to reliably quantify this. Previously, quantifying orientation of architecture in cells has been done using various computational algorithms [19][20][21][22][23][24][25][26]. Also, prior studies used a variety of metrics to analyze their results that were based on different statistical distributions, such as Gaussian [12,20,21,24,26,27].…”