Dataset Information

GMR biosensor arrays: correction techniques for reproducibility and enhanced sensitivity.

ABSTRACT: Giant magnetoresistive biosensors possess great potential in biomedical applications for quantitatively detecting magnetically tagged biomolecules. Magnetic sensing does not suffer from the high background levels found in optical sensing modalities such as the enzyme linked immunosorbent assay translating into a technology with higher sensitivity. However, to reveal the full potential of these sensors and compensate for non-idealities such as temperature dependence, digital correction and calibration techniques are not only useful but imperative. Using these calibration techniques to correct for process variations and dynamic changes in the sensing environment (such as temperature and magnetic field), we are able to obtain extremely sensitive and, more importantly, reproducible results for quantifiable biomolecular reorganization. The reproducibility of the system was improved by over 3 x using digital correction techniques and the sensors are made temperature independent by using a novel background correction technique.

SUBMITTER: Hall DA

PROVIDER: S-EPMC2881564 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GMR biosensor arrays: correction techniques for reproducibility and enhanced sensitivity.

Hall D A DA Gaster R S RS Osterfeld S J SJ Murmann B B Wang S X SX

Biosensors & bioelectronics 20100206 9

Giant magnetoresistive biosensors possess great potential in biomedical applications for quantitatively detecting magnetically tagged biomolecules. Magnetic sensing does not suffer from the high background levels found in optical sensing modalities such as the enzyme linked immunosorbent assay translating into a technology with higher sensitivity. However, to reveal the full potential of these sensors and compensate for non-idealities such as temperature dependence, digital correction and calibr ...[more]

PMID: 20219342

Similar Datasets

Project description:BackgroundExpression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples.ResultsWe describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that interarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log(2) units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators.ConclusionsThis study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells.

Dataset Information

GMR biosensor arrays: correction techniques for reproducibility and enhanced sensitivity.

Publications

GMR biosensor arrays: correction techniques for reproducibility and enhanced sensitivity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets