Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcriptome analysis of differentiating normal and leukemic erythroid progenitors

ABSTRACT: We compared the transcriptomes of differentiating cultures of ES cell derived erythroid progentor cells (ES-EP) and murine erythroleukemia (MEL) cells stably transfected with GATA-1 fused to ER. RNA was isolated from duplicate differntiating cultures of MEL and ES-EP using Affymetrix GeneChip Mouse Gene 1.0 ST.

ORGANISM(S): Mus musculus

SUBMITTER: Sandeep Wontakal

PROVIDER: E-GEOD-35384 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

A core erythroid transcriptional network is repressed by a master regulator of myelo-lymphoid differentiation.

Wontakal Sandeep N SN Guo Xingyi X Smith Cameron C MacCarthy Thomas T Bresnick Emery H EH Bergman Aviv A Snyder Michael P MP Weissman Sherman M SM Zheng Deyou D Skoultchi Arthur I AI

Proceedings of the National Academy of Sciences of the United States of America 20120222 10

Two mechanisms that play important roles in cell fate decisions are control of a "core transcriptional network" and repression of alternative transcriptional programs by antagonizing transcription factors. Whether these two mechanisms operate together is not known. Here we report that GATA-1, SCL, and Klf1 form an erythroid core transcriptional network by co-occupying >300 genes. Importantly, we find that PU.1, a negative regulator of terminal erythroid differentiation, is a highly integrated co ...[more]

PMID: 22357756

Similar Datasets

Project description:Hepatocytes that have differentiated from human embryonic stem cells have great potential for the treatment of liver disease as well as for drug testing. Moreover, in vitro hepatogenesis is a powerful model system for studying the molecular mechanisms underlying liver development. DNA methylation is an important epigenetic mechanism that influences differential gene expression during embryonic development. We profiled gene expression and DNA methylation of three cell states of in vitro hepatogenesis—human embryonic stem cells, endoderm progenitors, and mature hepatocytes—using microarray analysis. Among 525 state-specific expressed genes, 67 showed significant negative correlation between gene expression and DNA methylation. State-specific expression and methylation of target genes were validated by quantitative reverse transcription–PCR and pyrosequencing, respectively. To elucidate genome-scale methylation changes beyond the promoter, we also performed high-throughput sequencing of methylated DNA captured by MBD2 protein [see SRA link below]. We found dynamic methylation changes in intergenic regions of the human genome during differentiation. Conclusion: This study provides valuable methylation markers for the lineage commitment of in vitro hepatogenesis and should help elucidate the molecular mechanisms underlying stem cell differentiation and liver development. ES, EP, and MH expression assays were run in quadruplicate. RNA isolated from the cell line at the indicated time points was used for gene expression analysis using the Human-6 Whole-Genome Expression BeadChip (Illumina, San Diego, CA), which generates expression profiles for more than 46,000 human transcripts. Biotin-labeled cRNA was produced using a linear amplification kit (Ambion, Austin, TX) using 300 ng of quality-checked total RNA as input. Chip hybridizations, washing, Cy3-streptavidin staining, and scanning were performed on a BeadArray Reader (Illumina) platform using reagents and following protocols supplied by the manufacturer. The detection score was used to determine expression

Project description:Hepatocytes that have differentiated from human embryonic stem cells have great potential for the treatment of liver disease as well as for drug testing. Moreover, in vitro hepatogenesis is a powerful model system for studying the molecular mechanisms underlying liver development. DNA methylation is an important epigenetic mechanism that influences differential gene expression during embryonic development. We profiled gene expression and DNA methylation of three cell states of in vitro hepatogenesis—human embryonic stem cells, endoderm progenitors, and mature hepatocytes—using microarray analysis. Among 525 state-specific expressed genes, 67 showed significant negative correlation between gene expression and DNA methylation. State-specific expression and methylation of target genes were validated by quantitative reverse transcription–PCR and pyrosequencing, respectively. To elucidate genome-scale methylation changes beyond the promoter, we also performed high-throughput sequencing of methylated DNA captured by MBD2 protein [see SRA link below]. We found dynamic methylation changes in intergenic regions of the human genome during differentiation. Conclusion: This study provides valuable methylation markers for the lineage commitment of in vitro hepatogenesis and should help elucidate the molecular mechanisms underlying stem cell differentiation and liver development. ES, EP, and MH methylation assays were run in triplicate. Genomic DNA (1 µg) from each sample was bisulfite converted using the EZ DNA methylation kit (Zymo Research, Orange, CA), and 200 ng of the converted DNA was used for amplification. Amplified DNA was hybridized to the Infinium HumanMethylation27 BeadChip (Illumina), which analyzes the methylation status of 27,578 CpG sites selected from more than 14,495 well-annotated genes. The arrays were imaged using the BeadArray Reader, and image processing and extraction of intensity data were performed according to Illumina’s instructions. Each methylation signal was used to compute the β value, which is a quantitative measure of DNA methylation ranging from 0 (for completely unmethylated cytosines) to 1 (for completely methylated cytosines).

Dataset Information

Transcriptome analysis of differentiating normal and leukemic erythroid progenitors

Publications

A core erythroid transcriptional network is repressed by a master regulator of myelo-lymphoid differentiation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure