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 -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 O(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.