Kevin Schwarzkopf

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.

Real-Time Biosensor Platform: Fully Integrated Device for Impedimetric Assays

An impedimetric biosensor platform for bioaffinity assays has been developed that is based on real-time, label-free electrochemical detection performed via a direct interface to electronic digital data processing. The sensor array consists of 15 gold microelectrode pairs (Fig. 1) that are enclosed in three reaction chambers and biofunctionalized with specific probes. The impedance change caused by specific capture of target analyte molecules on the functionalized electrode surface is recorded in real time. The measuring instrument is capable of continuous and simultaneous stimulation and recording of all electrodes on the array. A corresponding mathematical algorithm and a software package for data analysis have been developed. The software performs (i) filtering of the instrument noise, and (ii) extraction of the exponential component of the impedance signal. Thus, the algorithm can quantify both rate of target to probe binding, and target to probe affinity. The described fully integrated platform can be used as a basic research tool for development of various bio-affinity impedimetric assays. To facilitate such applications, we have developed a streamlined manufacturing technology, and a set of assay protocols for detection of microbes based on nucleic acid hybridization. The assay was shown to detect and distinguish between two closely related but different Escherichia coli strains. The assay sensitivity was sufficient for reliable measurements of specific PCR products amplified from microbial genomic DNA. The sensor array platform is adaptable for detection of a wide range of analytes of practical significance, and it has potential for further integration with amplification (i.e. PCR) and sample preparation modules.

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.

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.