266141 Oxygen Sensing and Control of Engineered Tissue

Wednesday, October 31, 2012
Hall B (Convention Center )
Seema M. Ehsan, Chemical Engineering and Materials Science, University of California, Irvine, Irvine, CA and Steven C. George, Biomedical Engineering, Chemical Engineering, University of California, Irvine, Irvine, CA


To properly model physiological processes with heterogeneous oxygenation conditions, an experimental platform is needed that can dynamically control and measure the oxygenation of 3D cell culture. Many studies have demonstrated that ambient oxygen concentrations can differ significantly from the concentrations that cells actually experience in 2D culture. The inherent diffusion limitation of 3D culture, particularly at clinically relevant dimensions (> 1 mm), adds an additional hurdle for oxygen transport. Here we present an experimental platform that offers simultaneous dynamic oxygen control and measurement of oxygen diffusion through 3D engineered tissue. The platform, in conjunction with a non-steady state mathematical model for oxygen diffusion, enables quantification of the oxygen diffusion coefficient and cellular oxygen consumption rate, while also predicting the temporal and spatial distributions of oxygen in a thick tissue subject to time-varying oxygenation conditions.

Materials and Methods

An oxygen control chamber system was fabricated to function as a miniature cell culture incubator with programmable oxygen tension in the gas phase. An oxygen sensor patch (Presens, Germany) was placed at the base of a cylindrical well, and a 2.5mg/mL fibrin tissue (3 million normal human lung fibroblasts per mL) was constructed on top of the patch (Figure 1). The well was then placed inside the oxygen control chamber, where an external oxygen probe non-invasively provides real-time measurements of the oxygen concentration at the base of the tissue. Michaelis-Menten kinetics was used to model oxygen consumption, and numerical techniques were performed to determine values for the oxygen consumption rate. Finite element analysis (FEA) software was then used to predict detailed spatial and temporal oxygen concentration.


The oxygen control system achieves gas equilibration (90% response time) within 60 seconds, and can be programmed to deliver 0-100% O2. The Michaelis Menten parameters were found to be: Vmax= 1.1e-4 mol/m3/s and Km= 0.036 mol/m3. FEA modeling provides temporal (Figure 2A) and spatial (Figure 2B) oxygen diffusion information.

Discussion and Conclusions

Through the simultaneous control and measurement of local tissue oxygen levels at the boundaries (top and base), a system has been developed that can predict the temporal and spatial oxygenation of 3D culture. While most mathematical models of 3D culture have relied on steady state assumptions and constant boundary conditions, the present model employs non-steady state analysis and allows for time varying boundary conditions consistent with in vivo observations of intermittent hypoxia.

A)                                                                                       B)

Figure 2: A) A square wave oxygen exposure profile at the top of the tissue (dashed line) has a significantly attenuated effect on the oxygenation at the base of a 2mm thick cellular tissue (solid, dotted lines). B) Sample spatial distribution of oxygen in model tissue (top layer: ambient gas, bottom layer: fibrin gel).

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