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Dual-species microarray for assessing gene expression in stromal microenvironment, cancer cells and their interactions (v2 array)

ABSTRACT: Dual-species microarray for assessing gene expression in stromal microenvironment, cancer cells and their interactions (v2 array)

PROVIDER: PRJNA133309 | ENA |

REPOSITORIES: ENA

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Dual-species microarray for assessing gene expression in stromal microenvironment, cancer cells and their interactions

Project description:This SuperSeries is composed of the following subset Series: GSE23054: Dual-species microarray for assessing gene expression in stromal microenvironment, cancer cells and their interactions (v1 array) GSE23364: Dual-species microarray for assessing gene expression in stromal microenvironment, cancer cells and their interactions (v2 array) Refer to individual Series

2010-08-04 | E-GEOD-23377 | biostudies-arrayexpress

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Project description:Design a human-mouse dual-species microarray to provide a technology for investigation of cancer-stromal interaction in xenograft model. The 2 reference RNA were used to evaluate and identify probes with potential the cross-species hybridization, so that we can label these probes and exclude them before analysis of biological samples/data. Reference RNA from human and mouse were labeled using Cy3. Samples were hybridized on customized-commercial array with Agilent probes and user-designed species-specific probes (Human-mouse dual-species array HomoMus_v2). This array is designed and improved from hte first version GPL10714

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Dual-species microarray for assessing gene expression in stromal microenvironment, cancer cells and their interactions

Project description:This SuperSeries is composed of the SubSeries listed below.

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MMASS: an optimised array-based method for assessing CpG island

Project description:We describe an optimized microarray method for identifying genome-wide CpG island methylation called Microarray-based Methylation Assessment of Single Samples (MMASS) which directly compares methylated to unmethylated sequences within a single sample. To improve previous methods we used bioinformatic analysis to predict an optimised combination of methylation-sensitive enzymes that had the highest utility for CpG-island probes and different methods to produce unmethylated representations of test DNA for more sensitive detection of differential methylation by hybridization. Subtraction or methylation-dependent digestion with McrBC was used with optimized (MMASS-v2) or previously described (MMASS-v1, MMASS-sub) methylation-sensitive enzyme combinations and compared to a published McrBC method. Comparison was performed using DNA from the cell line HCT116. We show that the distribution of methylation microarray data is inherently skewed and requires exogenous spiked controls for normalization and that analysis of digestion of methylated and unmethylated control sequences together with linear fit models of replicate data showed superior statistical power for the MMASS-v2 method. Comparison to previous methylation data for HCT116 and validation of CpG islands from PXMP4, SFRP2, DCC, RARB and TSEN2 confirmed the accuracy of MMASS-v2 results. The MMASS-v2 method offers improved sensitivity and statistical power for high-throughput microarray identification of differential methylation. Keywords: Methylation genomic hybridizations.

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