Concentration dependency of nonequilibrium thermal dissociation curves in complex target samples

A revised design for microarray experiments to account for experimental noise and uncertainty of probe response

Background

Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance.

Results

Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements.

Conclusion

The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.

Identification of non-specific hybridization using an empirical equation fitted to non-equilibrium dissociation curves

Non-equilibrium dissociation curves (NEDCs) have the potential to identify non-specific hybridizations on high throughput, diagnostic microarrays. We report a simple method for the identification of non-specific signals by using a new parameter that does not rely on comparison of perfect match and mismatch dissociations. The parameter is the ratio of specific dissociation temperature (T(d-w)) to theoretical melting temperature (T(m)) and can be obtained by automated fitting of a four-parameter, sigmoid, empirical equation to the thousands of curves generated in a typical experiment. The curves fit perfect match NEDCs from an initial experiment with an R(2) of 0.998±0.006 and root mean square of 108±91 fluorescent units. Receiver operating characteristic curve analysis showed low temperature hybridization signals (20-48°C) to be as effective as area under the curve as primary data filters. Evaluation of three datasets that target 16S rRNA and functional genes with varying degrees of target sequence similarity showed that filtering out hybridizations with T(d-w)/T(m)<0.78 greatly reduced false positive results. In conclusion, T(d-w)/T(m) successfully screened many non-specific hybridizations that could not be identified using single temperature signal intensities alone, while the empirical modeling allowed a simplified approach to the high throughput analysis of thousands of NEDCs.

Was the Scanner Calibration Slide used for its intended purpose?

In the article, Scanner calibration revisited, BMC Bioinformatics 2010, 11:361, Dr. Pozhitkov used the Scanner Calibration Slide, a key product of Full Moon BioSystems to generate data in his study of microarray scanner PMT response and proposed a mathematic model for PMT response [1]. In the end, the author concluded that "Full Moon BioSystems calibration slides are inadequate for performing calibration," and recommended "against using these slides." We found these conclusions are seriously flawed and misleading, and his recommendation against using the Scanner Calibration Slide was not properly supported.

Beyond Affymetrix arrays: expanding the set of known hybridization isotherms and observing pre-wash signal intensities

Microarray hybridization studies have attributed the nonlinearity of hybridization isotherms to probe saturation and post-hybridization washing. Both processes are thought to distort ‘true’ target abundance because immobilized probes are saturated with excess target and stringent washing removes loosely bound targets. Yet the paucity of studies aimed at understanding hybridization and dissociation makes it difficult to align physicochemical theory to microarray results. To fill the void, we first examined hybridization isotherms generated on different microarray platforms using a ribosomal RNA target and then investigated hybridization signals at equilibrium and after stringent wash. Hybridization signal at equilibrium was achieved by treating the microarray with isopropanol, which prevents nucleic acids from dissolving into solution. Our results suggest that (i) the shape of hybridization isotherms varied by microarray platform with some being hyperbolic or linear, and others following a power-law; (ii) at equilibrium, fluorescent signal of different probes hybridized to the same target were not similar even with excess of target and (iii) the amount of target removed by stringent washing depended upon the hybridization time, the probe sequence and the presence/absence of nonspecific targets. Possible physicochemical interpretations of the results and future studies are discussed.

Real-time DNA microarrays: reality check

DNA microarrays are plagued with inconsistent quantifications and false-positive results. Using established mechanisms of surface reactions, we argue that these problems are inherent to the current technology. In particular, the problem of multiplex non-equilibrium reactions cannot be resolved within the framework of the existing paradigm. We discuss the advantages and limitations of changing the paradigm to real-time data acquisition similar to real-time PCR methodology. Our analysis suggests that the fundamental problem of multiplex reactions is not resolved by the real-time approach itself. However, by introducing new detection chemistries and analysis approaches, it is possible to extract target-specific quantitative information from real-time microarray data. The possible scope of applications for real-time microarrays is discussed.

Simultaneous quantification of multiple nucleic acid targets in complex rRNA mixtures using high density microarrays and nonspecific hybridization as a source of information

To date, it has been problematic to accurately quantify multiple nucleic acid sequences, representing microbial targets, in multi-target mixtures using oligonucleotide microarrays, primarily due to nonspecific target binding (i.e., cross-hybridization). While some studies ignore the effects of nonspecific binding, other studies have developed approaches to minimize nonspecific binding, such as physical modeling to design highly specific probes, subtracting nonspecific signal using mismatch probes, and/or removing nonspecific duplexes by scanning through a range of wash stringencies. We have developed an alternative approach that, in contrast to previous approaches, uses nonspecific target binding as a source of information. Specifically, the new approach uses hybridization patterns (fingerprints) to quantify specific nucleic acid targets in complex target mixtures. We evaluated the approach by mixing together in vitro transcribed 28S rRNA targets at varying concentrations (up to 1.0 nM), and hybridizing the 24 mixtures to microarrays (n=3160 probes, in duplicate). Three independent Latin-square-designed experiments revealed accurate quantification of the targets. The regression between actual concentration of targets and those determined by the approach were highly positively correlated with high R(2) values (e.g., R(2)=0.90, n=6 targets; R(2)=0.84, n=8 targets; R(2)=0.82, n=10 targets).