Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Chromatin state maps (H3K4me3 and H3K27me3) from partially and fully reprogrammed mouse cell lines

ABSTRACT: Chromatin state maps (H3K4me3 and H3K27me3) from partially and fully reprogrammed mouse cell lines obtained by ectopic expression of Oct4, Sox2, Klf4 and c-Myc using constitutive retroviral infection of MEFs (MCV6, MCV8, MCV8.1) or induction of lentivirus in secondary B lymphocytes obtained from iPS-derived chimeric mice (BIV8). Keywords: High-throughput ChIP-sequencing, Illumina, cell type comparison H3K4me3 and H3K27me3 ChIP-Seq in singlicate from three partially reprogrammed cell lines (BIV1, MCV8, MCV6), one iPS cell line (MCV8.1) and MEFs (subsampled from Mikkelsen et al, Nature, 2007)) Raw sequence data files for this study are available for download from the SRA FTP site at ftp://ftp.ncbi.nlm.nih.gov/sra/Studies/SRP000/SRP000215

ORGANISM(S): Mus musculus

SUBMITTER: Tarjei Mikkelsen

PROVIDER: E-GEOD-11074 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Dissecting direct reprogramming through integrative genomic analysis.

Mikkelsen Tarjei S TS Hanna Jacob J Zhang Xiaolan X Ku Manching M Wernig Marius M Schorderet Patrick P Bernstein Bradley E BE Jaenisch Rudolf R Lander Eric S ES Meissner Alexander A

Nature 20080528 7200

Somatic cells can be reprogrammed to a pluripotent state through the ectopic expression of defined transcription factors. Understanding the mechanism and kinetics of this transformation may shed light on the nature of developmental potency and suggest strategies with improved efficiency or safety. Here we report an integrative genomic analysis of reprogramming of mouse fibroblasts and B lymphocytes. Lineage-committed cells show a complex response to the ectopic expression involving induction of ...[more]

PMID: 18509334

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Project description:RNA-Seq has been increasingly used for the quantification and characterization of transcriptomes. The ongoing development of the technology promises the more accurate measurement of gene expression. However, its benefits over widely accepted microarray technologies have not been adequately assessed, especially in toxicogenomics studies. The goal of this study is to enhance the scientific community's understanding of the advantages and challenges of RNA-Seq in the quantification of gene expression by comparing analysis results from RNA-Seq and microarray data on a toxicogenomics study. A typical toxicogenomics study design was used to compare the performance of an RNA-Seq approach (Illumina Genome Analyzer II) to a microarray-based approach (Affymetrix Rat Genome 230 2.0 arrays) for detecting differentially expressed genes (DEGs) in the kidneys of rats treated with aristolochic acid (AA), a carcinogenic and nephrotoxic chemical most notably used for weight loss. We studied the comparability of the RNA-Seq and microarray data in terms of absolute gene expression, gene expression patterns, differentially expressed genes, and biological interpretation. We found that RNA-Seq was more sensitive in detecting genes with low expression levels, while similar gene expression patterns were observed for both platforms. Moreover, although the overlap of the DEGs was only 40-50%, the biological interpretation was largely consistent between the RNA-Seq and microarray data. RNA-Seq maintained a consistent biological interpretation with time-tested microarray platforms while generating more sensitive results. However, there is clearly a need for future investigations to better understand the advantages and limitations of RNA-Seq in toxicogenomics studies and environmental health research. Eight rats were randomly divided into two groups: four rats were administered with aristolochic acid (AA), and four rats were treated with the control vehicle. RNA samples were extracted from the kidney tissue of each rat and were independently assayed with both the NGS (Illumina Genome Analyzer II) and the microarray (Affymetrix Rat Genome 230 2.0) platforms. The RNA-Seq and microarray data were compared in terms of absolute gene expression, gene expression patterns, differentially expressed genes, and biological interpretation.

Dataset Information

Chromatin state maps (H3K4me3 and H3K27me3) from partially and fully reprogrammed mouse cell lines

Publications

Dissecting direct reprogramming through integrative genomic analysis.

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