Long non-coding RNA expression profiling in the NCI60 cancer cell line panel using high-throughput RT-qPCR

Long non-coding RNAs (lncRNAs) form a new class of RNA molecules implicated in various aspects of protein coding gene expression regulation. To study lncRNAs in cancer, we generated expression profiles for 1707 human lncRNAs in the NCI60 cancer cell line panel using a high-throughput nanowell RT-qPCR platform. We describe how qPCR assays were designed and validated and provide processed and normalized expression data for further analysis. Data quality is demonstrated by matching the lncRNA expression profiles with phenotypic and genomic characteristics of the cancer cell lines. This data set can be integrated with publicly available omics and pharmacological data sets to uncover novel associations between lncRNA expression and mRNA expression, miRNA expression, DNA copy number, protein coding gene mutation status or drug response

Target enrichment using parallel nanoliter quantitative PCR amplification

Background: Next generation targeted resequencing is replacing Sanger sequencing at high pace in routine genetic diagnosis. The need for well validated, high quality enrichment platforms to complement the bench-top next generation sequencing devices is high. Results: We used the WaferGen Smartchip platform to perform highly parallelized PCR based target enrichment for a set of known cancer genes in a well characterized set of cancer cell lines from the NCI60 panel. Optimization of PCR assay design and cycling conditions resulted in a high enrichment efficiency. We provide proof of a high mutation rediscovery rate and have included technical replicates to enable SNP calling validation demonstrating the high reproducibility of our enrichment platform. Conclusions: Here we present our custom developed quantitative PCR based target enrichment platform. Using highly parallel nanoliter singleplex PCR reactions makes this a flexible and efficient platform. The high mutation validation rate shows this platform’s promise as a targeted resequencing method for multi-gene routine sequencing diagnostics

Development of a sensitive diagnostic multiplex platform based on digitally encoded microcarriers

In answer to the ever-increasing need in biomolecular research and clinical diagnostics to carry out many assays simultaneously in on tube, several microcarrier-based multiplex technologies (suspension arrays) have arisen in the past few years. Simultaneous detection of different target molecules that are present in one sample is possible by incubating the sample with a mixture of differently encoded microcarriers, each carrying another probe which can specifically interact with one of the targets. This means that each target will bind to a differently encoded microcarrier. When the targets are caught, several methods exist to label those 'positive' microcarriers. By means of this label, and by means of the code, it becomes possible to verify whether a target was caught at its surface, and which target was caught, respectively. Those multiplex measurements work quantitatively, because the more a target is present in the sample, the more targets will bind to their corresponding microcarrier. Five years ago, our research group proposed the use of spatial selective photobleaching, as an alternative method for the development of digitally encoded microcarriers, which were called 'memobeads'. It was suggested that this method could overcome the multiplexing limitations of existing technologies. The present study aimed to optimize the surface characteristics of t hose memobeads, and to verify whether they could then be applied to multiplex protein tests and nucleic acid tests. Furthermore, it was investigated in which way these memobead assays (and in general the assays performed with every kind of suspension arrays) could be improved to make them more efficient and sensitive

Diagnosis of Lung Cancer: What Metabolomics Can Contribute

The reprogrammed metabolism of cancer cells reflects itself in an alteration of metabolite concentrations, which in turn can be used to define a specific metabolic phenotype or fingerprint for cancer. In this contribution, a metabolism-based discrimination between lung cancer patients and healthy controls, derived from an analysis of human blood plasma by proton nuclear magnetic resonance (1H-NMR) spectroscopy, is described. This technique is becoming widely used in the field of metabolomics because of its ability to provide a highly informative spectrum, representing the relative metabolite concentrations. Cancer types are characterized by decreased or increased levels of specific plasma metabolites, such as glucose or lactate, compared to controls. Data analysis by multivariate statistics provides a classification model with high levels of sensitivity and specificity. Nuclear magnetic resonance (NMR) metabolomics might not only contribute to the diagnosis of lung cancer but also shows potential for treatment follow-up as well as for paving the way to a better understanding of disease-related diverting biochemical pathways

Compressive Imaging of Subwavelength Structures II. Periodic Rough Surfaces

A compressed sensing scheme for near-field imaging of corrugations of relative sparse Fourier components is proposed. The scheme employs random sparse measurement of near field to recover the angular spectrum of the scattered field. It is shown heuristically and numerically that under the Rayleigh hypothesis the angular spectrum is compressible and amenable to compressed sensing techniques. Iteration schemes are developed for recovering the surface profile from the angular spectrum. The proposed nonlinear least squares in the Fourier basis produces accurate reconstructions even when the Rayleigh hypothesis is known to be false

Intérêt du temps total de traite pour la sélection des vaches sur la vitesse de traite. I. – Le testage des taureaux

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