Affiliation:
1. Research Centre “E. Piaggio,” University of Pisa 1 , Pisa, Italy
2. Department of Information Engineering, University of Pisa 2 , Pisa, Italy
Abstract
Accurately modeling oxygen transport and consumption is crucial to predict metabolic dynamics in cell cultures and optimize the design of tissue and organ models. We present a methodology to characterize the Michaelis–Menten oxygen consumption parameters in vitro, integrating novel experimental techniques and computational tools. The parameters were derived for hepatic cell cultures with different dimensionality (i.e., 2D and 3D) and with different surface and volumetric densities. To quantify cell packing regardless of the dimensionality of cultures, we devised an image-based metric, referred to as the proximity index. The Michaelis–Menten parameters were related to the proximity index through an uptake coefficient, analogous to a diffusion constant, enabling the quantitative analysis of oxygen dynamics across dimensions. Our results show that Michaelis–Menten parameters are not constant for a given cell type but change with dimensionality and cell density. The maximum consumption rate per cell decreases significantly with cell surface and volumetric density, while the Michaelis–Menten constant tends to increase. In addition, the dependency of the uptake coefficient on the proximity index suggests that the oxygen consumption rate of hepatic cells is superadaptive, as they modulate their oxygen utilization according to its local availability and to the proximity of other cells. We describe, for the first time, how cells consume oxygen as a function of cell proximity, through a quantitative index, which combines cell density and dimensionality. This study enhances our understanding of how cell–cell interaction affects oxygen dynamics and enables better prediction of aerobic metabolism in tissue models, improving their translational value.
Funder
Swiss National Science Foundation
European Commission
Subject
Biomedical Engineering,Biomaterials,Biophysics,Bioengineering
Cited by
1 articles.
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