A sense of proximity: Cell packing modulates oxygen consumption.

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.

A Millifluidic Chamber for Controlled Shear Stress Testing: Application to Microbial Cultures.

In vitro platforms such as bioreactors and microfluidic devices are commonly designed to engineer tissue models as well as to replicate the crosstalk between cells and microorganisms hosted in the human body. These systems promote nutrient supply and waste removal through culture medium recirculation; consequently, they intrinsically expose cellular structures to shear stress, be it a desired mechanical stimulus to drive the cell fate or a potential inhibitor for the model maturation. Assessing the impact of shear stress on cellular or microbial cultures thus represents a crucial step to define proper environmental conditions for in vitro models. In this light, the aim of this study was to develop a millifluidic device enabling to generate fully controlled shear stress profiles for quantitatively probing its influence on tissue or bacterial models, overcoming the limitations of previous reports proposing similar devices. Relying on this millifluidic tool, we present a systematic methodology to test how adherent cellular structures react to shear forces, which was applied to the case of microbial biofilms as a proof of concept. The results obtained suggest our approach as a suitable testbench to evaluate culture conditions in terms of shear stress faced by cells or microorganisms.

Scaling of joint mass and metabolism fluctuations in in silico cell-laden spheroids.

Variations and fluctuations are characteristic features of biological systems and are also manifested in cell cultures. Here, we describe a computational pipeline for identifying the range of three-dimensional (3D) cell-aggregate sizes in which nonisometric scaling emerges in the presence of joint mass and metabolic rate fluctuations. The 3D cell-laden spheroids with size and single-cell metabolic rates described by probability density functions were randomly generated in silico. The distributions of the resulting metabolic rates of the spheroids were computed by modeling oxygen diffusion and reaction. Then, a method for estimating scaling exponents of correlated variables through statistically significant data collapse of joint probability distributions was developed. The method was used to identify a physiologically relevant range of spheroid sizes, where both nonisometric scaling and a minimum oxygen concentration (0.04 mol⋅m-3) is maintained. The in silico pipeline described enables the prediction of the number of experiments needed for an acceptable collapse and, thus, a consistent estimate of scaling parameters. Using the pipeline, we also show that scaling exponents may be significantly different in the presence of joint mass and metabolic-rate variations typically found in cells. Our study highlights the importance of incorporating fluctuations and variability in size and metabolic rates when estimating scaling exponents. It also suggests the need for taking into account their covariations for better understanding and interpreting experimental observations both in vitro and in vivo and brings insights for the design of more predictive and physiologically relevant in vitro models.