Development of real-time assays for impedance-based detection of microbial double-stranded DNA targets: optimization and data analysis.

A real-time, label free assay was developed for microbial detection, utilizing double-stranded DNA targets and employing the next generation of an impedimetric sensor array platform designed by Sharp Laboratories of America (SLA). Real-time curves of the impedimetric signal response were obtained at fixed frequency and voltage for target binding to oligonucleotide probes attached to the sensor array surface. Kinetic parameters of these curves were analyzed by the integrated data analysis package for signal quantification. Non-specific binding presented a major challenge for assay development, and required assay optimization. For this, differences were maximized between binding curve kinetic parameters for probes binding to complementary targets versus non-target controls. Variables manipulated for assay optimization included target concentration, hybridization temperature, buffer concentration, and the use of surfactants. Our results showed that (i) different target-probe combinations required optimization of specific sets of variables; (ii) for each assay condition, the optimum range was relatively narrow, and had to be determined empirically; and (iii) outside of the optimum range, the assay could not distinguish between specific and non-specific binding. For each target-probe combination evaluated, conditions resulting in good separation between specific and non-specific binding signals were established, generating high confidence in the SLA impedimetric dsDNA assay results.

An improved algorithm for quantifying real-time impedance biosensor signals.

In previously published work [1] we presented a real-time electrochemical impedance biosensor prototype system and a state-space estimation algorithm for signal quantification. Experiments in the interim have revealed some algorithm failure modes which reduced the reliability and repeatability of quantification. The present work describes a related algorithm that introduces constraints based on a priori knowledge of the expected signals predicted by the biosensor signal model. The improvements in reliability and repeatability bring the system close to deployment for real-world trials.

Impedimetric biosignal analysis and quantification in a real-time biosensor system.

We describe our real-time, label-free, electrochemical impedance biosensor system with an emphasis on the use of an impedance response signal model to quantify assays. The signal processing for estimating model parameters from noisy data and the quantitative verification against target concentration and affinity are also presented.