In situ synthesis of DNA microarray on functionalized cyclic olefin copolymer substrate.

Thermoplastic materials such as cyclic-olefin copolymers (COC) provide a versatile and cost-effective alternative to the traditional glass or silicon substrate for rapid prototyping and industrial scale fabrication of microdevices. To extend the utility of COC as an effective microarray substrate, we developed a new method that enabled for the first time in situ synthesis of DNA oligonucleotide microarrays on the COC substrate. To achieve high-quality DNA synthesis, a SiO(2) thin film array was prepatterned on the inert and hydrophobic COC surface using RF sputtering technique. The subsequent in situ DNA synthesis was confined to the surface of the prepatterned hydrophilic SiO(2) thin film features by precision delivery of the phosphoramidite chemistry using an inkjet DNA synthesizer. The in situ SiO(2)-COC DNA microarray demonstrated superior quality and stability in hybridization assays and thermal cycling reactions. Furthermore, we demonstrate that pools of high-quality mixed-oligos could be cleaved off the SiO(2)-COC microarrays and used directly for construction of DNA origami nanostructures. It is believed that this method will not only enable synthesis of high-quality and low-cost COC DNA microarrays but also provide a basis for further development of integrated microfluidics microarrays for a broad range of bioanalytical and biofabrication applications.

Comparison of pattern detection methods in microarray time series of the segmentation clock.

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns.