Data Processing in Cellular Microphysiometry.

GOAL: This contribution points out the need for well-defined and documented data processing protocols in microphysiometry, an evolving field of label-free cell assays. The sensitivity of the obtained cell metabolic rates toward different routines of raw data processing is evaluated. METHODS: A standard microphysiometric experiment structured in discrete measurement intervals was performed on a platform with a pH- and O 2-sensor readout. It is evaluated using three different data evaluation protocols, based on A) fast Fourier transformation of such dynamics, B) linear regression (LIN) of pH(t) and O2(t) dynamics, and C) numerical simulation (SIM) with a subsequent fitting of dynamics for parameter estimation. RESULTS: We propose a sequence of well documented steps for an organized processing of raw sensor data. Figures of merit for the quality of raw data and the performance of data processing are provided. To estimate metabolic rates, a reaction-diffusion modeling approach is recommended if the necessary model input parameters such as the distribution of the active biomass, sensor response time, and material properties are available. CONCLUSION: 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.

Estimation of dynamic metabolic activity in micro-tissue cultures from sensor recordings with an FEM model.

We estimated the dynamic cell metabolic activity and the distribution of the pH value and oxygen concentration in tissue samples cultured in vitro by using real-time sensor records and a numerical simulation of the underlying reaction-diffusion processes. As an experimental tissue model, we used chicken spleen slices. A finite element method model representing the biochemical processes and including the relevant sensor data was set up. By fitting the calculated results to the measured data, we derived the spatiotemporal values of the pH value, the oxygen concentration and the absolute metabolic activity (extracellular acidification and oxygen uptake rate) of the samples. Notably, the location of the samples in relation to the sensors has a great influence on the detectable metabolic rates. The long-term vitality of the tissue samples strongly depends on their size. We further discuss the benefits and limitations of the model.

Reaction-diffusion modelling for microphysiometry on cellular specimens.

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.