Evaluation of Gel-Pad Oligonucleotide Microarray Technology by Using Artificial Neural Networks

Abstract

Past studies have suggested that thermal dissociation analysis of nucleic acids hybridized to DNA microarrays would improve discrimination among duplex types by scanning through a broad range of stringency conditions. To more fully constrain the utility of this approach using a previously described gel-pad microarray format, artificial neural networks (NNs) were trained to recognize noisy or low-quality data, as might derive from nonspecific fluorescence, poor hybridization, or compromised data collection. The NNs were trained to classify dissociation profiles (melts) into groups based on selected characteristics (e.g., initial signal intensity, area under the curve) using a data set of 21,044 profiles derived from 186 probes hybridized to a study set of RNA extracted from 32 microbes common to the human oral cavity. Three melt profile groups were identified: one group consisted mostly of ideal melt profiles; another group consisted mostly of poor melt profiles; and, the remainder were difficult to classify. Screening of melting profiles of perfect-match hybrids revealed inconsistencies in the form of melting profiles even for identical probes on the same microarray hybridized to same target rRNA. Approximately 18% of perfect-match duplex types were correctly classified as poor. Experimental variability and deviation from ideal melt behavior were shown to be attributable primarily to a method of local background subtraction that was very sensitive to displacement of the grid frames used for image capture (both determined by the image analysis system) and duplexes with low binding constants. Additional results showed that long RNA fragments limit the discriminating power among duplex types.

Revision of the nonequilibrium thermal dissociation and stringent washing approaches for identification of mixed nucleic acid targets by microarrays

“…One source of confusion might be the visible similarity of equilibrium dissociation curves in solution (30,31) to nonequilibrium dissociation curves, since both are sigmoid-shaped, but follow fundamentally different physical laws. Another source might be due to image acquisition software and processing errors, which have been shown to grossly distort signal intensity values, leading to misinterpretation of array data (10,32,33). Previously published literature does not provide any physicochemical proof that the NTD approach can be effectively used to distinguish between specific and nonspecific hybridizations for mixtures of targets.…”

“…We did not perform quantitative analysis of the curves published by Siripong et al . (35), since we have previously shown that the image processing algorithm used in their study significantly alters the signal intensities (10,32,33), making it impossible to analyze the chemical kinetics.…”

“…An alternative method is to perform nonequilibrium thermal dissociation (NTD) directly on an array by increasing the temperature of a buffer solution at constant ionic strength (10,11,14). The foundation of this method lies on two assumptions.…”

Tween-modified suspension array for sensitive biomolecular detection

“…In order to get a lower limit of detection, many methods have been developed, including three-dimensional (3D) matrixes modification, such as Noble PA group and Bavykin S group developed ployacrylamide for surface modification [11,12], and Gershwin ME group and Dufva M group used agarose for surface coated and polymer [13,14], which could increase the efficiency of biomolecules immobilization. And some groups chose signal amplification technology [15][16][17][18][19], or used more sensitive signal detection and collection apparatus for signal collection.…”

Introduction to Microarray‐Based Detection Methods

“…This enables rapid establishment of the melting curves for all probes on the microarray, facilitating development of validated probe sets. Three-dimensional microarray systems include gel-pads [73,104,136], flow-through systems such as PamGene (www.pamgene.com) [192] or MetriGenix (www.metrigenix.com) [88].…”