PLX075060

GSE48085: Genome-scale analysis in blood progenitor and mast cells reveals how lineage-affiliated transcription factors control distinct regulatory programs [ChIP-Seq/RNA-Seq Experiments]

  • Organsim mouse
  • Type RNASEQ
  • Target gene
  • Project ARCHS4

Despite major advances in the generation of genome-wide binding maps, the mechanisms by which transcription factors (TFs) regulate cell type identity have remained largely obscure. Through comparative analysis of 10 key haematopoietic TFs in both mast cells and blood progenitors, we demonstrate that the largely cell-type specific binding profiles are not opportunistic, but instead contribute to cell-type specific transcriptional control, because (1) mathematical modelling of differential binding of shared TFs can explain differential gene expression, (2) cell-type specific binding is largely mediated through consensus binding sites, and (3) knock-down of blood stem cell regulators Gata2 and Erg in mast cells reveals mast cell specific genes as direct targets. Finally we show that the known mast cell regulators Mitf and c-Fos likely contribute to the global reorganisation of TF binding profiles. Taken together therefore, our study elucidates how key regulatory TFs contribute to transcriptional programmes in several distinct mammalian cell types. SOURCE: Felicia Ng Cambridge Institute for Medical Research

View on GEOView in Pluto

Key Features

Enhance your research with our curated data sets and powerful platform features. Pluto Bio makes it simple to find and use the data you need.

Learn More

14K+ Published Experiments

Access an extensive range of curated bioinformatics data sets, including genomic, transcriptomic, and proteomic data.

Easy Data Import

Request imports from GEO or TCGA directly within Pluto Bio. Seamlessly integrate external data sets into your workflow.

Advanced Search Capabilities

Utilize powerful search tools to quickly find the data sets relevant to your research. Filter by type, disease, gene, and more.

Analyze and visualize data for this experiment

Use Pluto's intuitive interface to analyze and visualize data for this experiment. Pluto's platform is equipped with an API & SDKs, making it easy to integrate into your internal bioinformatics processes.

Read about post-pipeline analysis

View QC data and experiment metadata

View quality control data and experiment metadata for this experiment.

Request import of other GEO data

Request imports from GEO or TCGA directly within Pluto Bio.

Chat with our Scientific Insights team