Compressive Sensing DNA Microarrays

Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in a CSM, each sensor responds to a set of targets. We study the problem of designing CSMs that simultaneously account for both the constraints from CS theory and the biochemistry of probe-target DNA hybridization. An appropriate cross-hybridization model is proposed for CSMs, and several methods are developed for probe design and CS signal recovery based on the new model. Lab experiments suggest that in order to achieve accurate hybridization profiling, consensus probe sequences are required to have sequence homology of at least 80% with all targets to be detected. Furthermore, out-of-equilibrium datasets are usually as accurate as those obtained from equilibrium conditions. Consequently, one can use CSMs in applications in which only short hybridization times are allowed

Real-time DNA microarray analysis

We present a quantification method for affinity-based DNA microarrays which is based on the real-time measurements of hybridization kinetics. This method, i.e. real-time DNA microarrays, enhances the detection dynamic range of conventional systems by being impervious to probe saturation in the capturing spots, washing artifacts, microarray spot-to-spot variations, and other signal amplitude-affecting non-idealities. We demonstrate in both theory and practice that the time-constant of target capturing in microarrays, similar to all affinity-based biosensors, is inversely proportional to the concentration of the target analyte, which we subsequently use as the fundamental parameter to estimate the concentration of the analytes. Furthermore, to empirically validate the capabilities of this method in practical applications, we present a FRET-based assay which enables the real-time detection in gene expression DNA microarrays

The effects of mismatches on hybridization in DNA microarrays: determination of nearest neighbor parameters

Quantifying interactions in DNA microarrays is of central importance for a better understanding of their functioning. Hybridization thermodynamics for nucleic acid strands in aqueous solution can be described by the so-called nearest-neighbor model, which estimates the hybridization free energy of a given sequence as a sum of dinucleotide terms. Compared with its solution counterparts, hybridization in DNA microarrays may be hindered due to the presence of a solid surface and of a high density of DNA strands. We present here a study aimed at the determination of hybridization free energies in DNA microarrays. Experiments are performed on custom Agilent slides. The solution contains a single oligonucleotide. The microarray contains spots with a perfect matching complementary sequence and other spots with one or two mismatches: in total 1006 different probe spots, each replicated 15 times per microarray. The free energy parameters are directly fitted from microarray data. The experiments demonstrate a clear correlation between hybridization free energies in the microarray and in solution. The experiments are fully consistent with the Langmuir model at low intensities, but show a clear deviation at intermediate (non-saturating) intensities. These results provide new interesting insights for the quantification of molecular interactions in DNA microarrays.Comment: 31 pages, 5 figure

Recovering Sparse Signals Using Sparse Measurement Matrices in Compressed DNA Microarrays

Microarrays (DNA, protein, etc.) are massively parallel affinity-based biosensors capable of detecting and quantifying a large number of different genomic particles simultaneously. Among them, DNA microarrays comprising tens of thousands of probe spots are currently being employed to test multitude of targets in a single experiment. In conventional microarrays, each spot contains a large number of copies of a single probe designed to capture a single target, and, hence, collects only a single data point. This is a wasteful use of the sensing resources in comparative DNA microarray experiments, where a test sample is measured relative to a reference sample. Typically, only a fraction of the total number of genes represented by the two samples is differentially expressed, and, thus, a vast number of probe spots may not provide any useful information. To this end, we propose an alternative design, the so-called compressed microarrays, wherein each spot contains copies of several different probes and the total number of spots is potentially much smaller than the number of targets being tested. Fewer spots directly translates to significantly lower costs due to cheaper array manufacturing, simpler image acquisition and processing, and smaller amount of genomic material needed for experiments. To recover signals from compressed microarray measurements, we leverage ideas from compressive sampling. For sparse measurement matrices, we propose an algorithm that has significantly lower computational complexity than the widely used linear-programming-based methods, and can also recover signals with less sparsity

Modeling the kinetics of hybridization in microarrays

Conventional fluorescent-based microarrays acquire data after the hybridization phase. In this phase the targets analytes (i.e., DNA fragments) bind to the capturing probes on the array and supposedly reach a steady state. Accordingly, microarray experiments essentially provide only a single, steady-state data point of the hybridization process. On the other hand, a novel technique (i.e., realtime microarrays) capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed in [5]. The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to the conventional microarrays. In the current paper, we develop a probabilistic model of the kinetics of hybridization and describe a procedure for the estimation of its parameters which include the binding rate and target concentration. This probabilistic model is an important step towards developing optimal detection algorithms for the microarrays which measure the kinetics of hybridization, and to understanding their fundamental limitations

Modeling and Estimation for Real-Time Microarrays

Microarrays are used for collecting information about a large number of different genomic particles simultaneously. Conventional fluorescent-based microarrays acquire data after the hybridization phase. During this phase, the target analytes (e.g., DNA fragments) bind to the capturing probes on the array and, by the end of it, supposedly reach a steady state. Therefore, conventional microarrays attempt to detect and quantify the targets with a single data point taken in the steady state. On the other hand, a novel technique, the so-called real-time microarray, capable of recording the kinetics of hybridization in fluorescent-based microarrays has recently been proposed. The richness of the information obtained therein promises higher signal-to-noise ratio, smaller estimation error, and broader assay detection dynamic range compared to conventional microarrays. In this paper, we study the signal processing aspects of the real-time microarray system design. In particular, we develop a probabilistic model for real-time microarrays and describe a procedure for the estimation of target amounts therein. Moreover, leveraging on system identification ideas, we propose a novel technique for the elimination of cross hybridization. These are important steps toward developing optimal detection algorithms for real-time microarrays, and to understanding their fundamental limitations

On Limits of Performance of DNA Microarrays

DNA microarray technology relies on the hybridization process which is stochastic in nature. Probabilistic cross-hybridization of non-specific targets, as well as the shot-noise originating from specific targets binding, are among the many obstacles for achieving high accuracy in DNA microarray analysis. In this paper, we use statistical model of hybridization and cross-hybridization processes to derive a lower bound (viz., the Cramer-Rao bound) on the minimum mean-square error of the target concentrations estimation. A preliminary study of the Cramer-Rao bound for estimating the target concentrations suggests that, in some regimes, cross-hybridization may, in fact, be beneficial—a result with potential ramifications for probe design, which is currently focused on minimizing cross-hybridization

Breakdown of thermodynamic equilibrium for DNA hybridization in microarrays

Test experiments of hybridization in DNA microarrays show systematic deviations from the equilibrium isotherms. We argue that these deviations are due to the presence of a partially hybridized long-lived state, which we include in a kinetic model. Experiments confirm the model predictions for the intensity vs. free energy behavior. The existence of slow relaxation phenomena has important consequences for the specificity of microarrays as devices for the detection of a target sequence from a complex mixture of nucleic acids.Comment: 4 pages, 4 figure

DNA microarrays on a dendron-modified surface improve significantly the detection of single nucleotide variations in the p53 gene

Selectivity and sensitivity in the detection of single nucleotide polymorphisms (SNPs) are among most important attributes to determine the performance of DNA microarrays. We previously reported the generation of a novel mesospaced surface prepared by applying dendron molecules on the solid surface. DNA microarrays that were fabricated on the dendron-modified surface exhibited outstanding performance for the detection of single nucleotide variation in the synthetic oligonucleotide DNA. DNA microarrays on the dendron-modified surface were subjected to the detection of single nucleotide variations in the exons 5–8 of the p53 gene in genomic DNAs from cancer cell lines. DNA microarrays on the dendron-modified surface clearly discriminated single nucleotide variations in hotspot codons with high selectivity and sensitivity. The ratio between the fluorescence intensity of perfectly matched duplexes and that of single nucleotide mismatched duplexes was >5–100 without sacrificing signal intensity. Our results showed that the outstanding performance of DNA microarrays fabricated on the dendron-modified surface is strongly related to novel properties of the dendron molecule, which has the conical structure allowing mesospacing between the capture probes. Our microarrays on the dendron-modified surface can reduce the steric hindrance not only between the solid surface and target DNA, but also among immobilized capture probes enabling the hybridization process on the surface to be very effective. Our DNA microarrays on the dendron-modified surface could be applied to various analyses that require accurate detection of SNPs

Limits of performance of real-time DNA microarrays

DNA microarrays rely on chemical attraction between the nucleic acid sequences of interest (mRNA and DNA sequences, referred to as targets) and their molecular complements which serve as biological sensing elements (probes). The attraction between the complementary sequences leads to binding, in which probes capture target molecules. Molecular binding is a stochastic process and hence the number of captured analytes at any time is a random variable. Today, majority of DNA microarrays acquire only a single measurement of the binding process, essentially taking one sample from the steady-state distribution of the binding process. Real-time DNA microarrays provide much more: they can take multiple temporal measurements which not only allow more precise characterization of the steady-state but also enable faster detection based on the early kinetics of the binding process. In this paper, we derive the Cramer-Rao lower bound on the mean-square error of estimating the target amounts in real-time DNA microarrays, and compare it to that of conventional microarrays. The results suggest that a few temporal samples collected in the early phase of the binding process are often sufficient to enable significant performance improvement of the real-time microarrays over the conventional ones