The information about cellular metabolic activity contained by measured sensor data dynamics is superimposed by manifold sources of error. Careful consideration of data processing is necessary to eliminate these errors as much as possible and to avoid an incorrect interpretation of data.
The response of two well-characterized human breast cancer cell lines (MCF-7 and MDA-MB-231) to a series of nutrient deficiencies is investigated with a label-free cell assay platform. The motivation of the research is to analyze adaptive responses of tumor cell metabolism and to find limiting conditions for cell survival. The platform measures extracellular values of pH and dissolved oxygen saturation to provide data of extracellular acidification rates and oxygen uptake rates. Additional electric cell substrate impedance sensing and bright-field cell imaging supports the data interpretation by providing information about cell morphological parameters. A sequential administration of nutrient depletions does not cause metabolic reprogramming, since the ratios of oxygen uptake to acidification return to their basal values. While the extracellular acidification drops sharply upon reduction of glucose and glutamine, the oxygen uptake is not affected. In contrast to other published data, cell death is not observed when both glucose and glutamine are depleted and cell proliferation is not inhibited, at least in MCF-7 cultures. It is assumed that residual concentrations of nutrients from the serum component are able to maintain cell viability when delivered regularly by active flow like in the cell assay platform, and, in a similar way, under physiological conditions.
Using modeling and simulation, we quantify the influence of spatiotemporal dynamics on the accuracy of data obtained from sensors placed in microscaled reaction volumes. The model refers to cellular reaction (i.e. proton extrusion and oxygen consumption) in complex, buffering solutions. Whole cells or viable tissues cultured in such devices are monitored in real time with integrated sensors for pH and dissolved oxygen. A 3D finite element model of diffusion and metabolic reaction was set up. With respect to pH, the effect of buffering species on proton diffusion is analysed in detail. To account for the delayed time response of real sensors, the sensor impulse response time was implemented by linear convolution. A validation of the model has been achieved by an electrochemical approach. The model reveals significant deviations of measured pH and O2, and values of these parameters actually occurring at different sites of the cell culture volume. It is applicable to any setting of (bio-) sensors involving reaction and diffusion of dissolved gases and particularly H(+) ions in buffered solutions.
Flow-induced shear stress on adherent cells leads to biochemical signaling and mechanical responses of the cells. To determine the flow-induced shear stress on adherent cells cultured in a micro-scaled reaction chamber, we developed a suitable finite element method model. The influence of the most important parameters-cell shape, cell density, shear modulus and fluid velocity-was investigated. Notably, the cell shape strongly influences the resulting shear stress. Long and smooth cells undergo lower shear stress than more rounded cells. Changes in the curvature of the cells lead to stress peaks and single cells experience higher shear stress values than cells of a confluent monolayer. The computational results of the fluid flow simulation were validated experimentally. We also analyzed the influence of flow-induced shear stress on the metabolic activity and shape of L929, a mouse fibroblast cell line, experimentally. The results indicate that threshold stress values for continuous flow conditions cannot be transferred to quasi static flow conditions interrupted by short fluid exchange events.
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