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.     

Scanner calibration revisited

Background  

Calibration of a microarray scanner is critical for accurate interpretation of microarray results. Shi et al. (BMC Bioinformatics, 2005, 6, Art. No. S11 Suppl. 2.) reported usage of a Full Moon BioSystems slide for calibration. Inspired by the Shi et al. work, we have calibrated microarray scanners in our previous research. We were puzzled however, that most of the signal intensities from a biological sample fell below the sensitivity threshold level determined by the calibration slide. This conundrum led us to re-investigate the quality of calibration provided by the Full Moon BioSystems slide as well as the accuracy of the analysis performed by Shi et al.     

Interruption of Electrical Conductivity of Titanium Dental Implants Suggests a Path Towards Elimination Of Corrosion

Peri-implantitis is an inflammatory disease that results in the destruction of soft tissue and bone around the implant. Titanium implant corrosion has been attributed to the implant failure and cytotoxic effects to the alveolar bone. We have documented the extent of titanium release into surrounding plaque in patients with and without peri-implantitis. An in vitro model was designed to represent the actual environment of an implant in a patient’s mouth. The model uses actual oral microbiota from a volunteer, allows monitoring electrochemical processes generated by biofilms growing on implants and permits control of biocorrosion electrical current. As determined by next generation DNA sequencing, microbial compositions in experiments with the in vitro model were comparable with the compositions found in patients with implants. It was determined that the electrical conductivity of titanium implants was the key factor responsible for the biocorrosion process. The interruption of the biocorrosion current resulted in a 4–5 fold reduction of corrosion. We propose a new design of dental implant that combines titanium in zero oxidation state for osseointegration and strength, interlaid with a nonconductive ceramic. In addition, we propose electrotherapy for manipulation of microbial biofilms and to induce bone healing in peri-implantitis patients.     

Evaluation of gel-pad oligonucleotide microarray technology by using artificial neural networks

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.     

Development of a statistically robust quantification method for microorganisms in mixtures using oligonucleotide microarrays

High-density oligonucleotide arrays can be extremely useful for identifying and quantifying specific targets (i.e., ribosomal RNA of microorganisms) in mixtures. However, current array identification schemes are severely compromised by nonspecific hybridization, resulting in numerous false-positive and false-negative calls, they lack an adequate internal control for assessing the quality of identification, and are dependent on amplification of specific target sequences which introduce biases. We have developed a novel approach for the routine quantification and identification of metabolically active microorganisms in mixed samples. The advantage of our approach over conventional ones is that it avoids designing, optimizing, validating, and selecting oligonucleotide probes for arrays; also, nonspecific hybridization is no longer a problem. The basic principle of the approach is that a fluorescence pattern of a mixed sample is a superposition of the fluorescent patterns for each target. The superposition can be quantitatively deconvoluted in terms of concentrations of each microbe. We demonstrated the utility of our approach by extracting rRNA from three microorganisms, making test mixtures, labeling the rRNA, and hybridizing each test mixture to DNA oligonucleotide (20-mers, n=346,608) arrays. Comparison of known concentrations of individual targets in mixtures to those estimated by the solution revealed highly consistent results. The goodness-of-fit of the solution revealed that about 90% of the variability in the data could be explained. A new analytical approach for microbial identification and quantification has been presented in this report. Our findings demonstrate that including signal intensity values from all duplexes on the array, which are essentially nonspecific to the target organisms, significantly improved predictions of known microbial targets. To our knowledge, this is the first study to report this phenomenon. In addition, we demonstrate that the method is a self-sufficient analytical procedure since it provides statistical confidence of the quantification.     

Oligonucleotide microarrays: widely applied--poorly understood.

Microarray technology, which has been around for almost two decades, now provides an indispensable service to the biomedical research community. Soaring demand for high-throughput screening of genes potentially associated with cancer and other diseases, as well as the increased need for identifying microorganisms, have substantially opened up the application of this technology to many fields of science, including new ones such as array-based comparisons of whole genomes. Yet, despite this significant progress, the fundamental understanding of the pillars of this technology, have been largely unexplored, in particular for oligonucleotide-based microarrays. In fact, most of the current approaches for the design of microarrays are based on 'common-sense' parameters, such as guanine-cytosine content, secondary structure, melting temperature or possibility of minimizing the effects of nonspecific hybridization. However, recent experiments suggest that these are inadequate. Here we discuss these results, which challenge the basic principles and assumptions of oligonucleotide microarray technology. It is clear that more systematic physicochemical studies will be required to better understand the hybridization and dissociation behaviour of oligonucleotides.     

Developing a low-cost milliliter-scale chemostat array for precise control of cellular growth

Background: Multiplexed milliliter-scale chemostats are useful for measuring cell physiology under various degrees of nutrient limitation and for carrying out evolution experiments. In each chemostat, fresh medium containing a growth rate-limiting metabolite is pumped into the culturing chamber at a constant rate, while culture effluent exits at an equal rate. Although such devices have been developed by various labs, key parameters — the accuracy, precision, and operational range of flow rate — are not explicitly characterized. Methods: Here we re-purpose a published multiplexed culturing device to develop a multiplexed milliliter-scale chemostat. Flow rates for eight chambers can be independently controlled to a wide range, corresponding to population doubling times of 3~13 h, without the use of expensive feedback systems. Results: Flow rates are precise, with the maximal coefficient of variation among eight chambers being less than 3%. Flow rates are accurate, with average flow rates being only slightly below targets, i.e., 3%–6% for 13-h and 0.6%–1.0% for 3-h doubling times. This deficit is largely due to evaporation and should be correctable. We experimentally demonstrate that our device allows accurate and precise quantification of population phenotypes. Conclusions: We achieve precise control of cellular growth in a low-cost milliliter-scale chemostat array, and show that the achieved precision reduces the error when measuring biological processes.     

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).