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RUNX1-Induced DNA Demethylation Drives Hematopoietic Stem and Progenitor Cell Development

GSE300808 Homo sapiens (human) DNA methylation 29 samples Submitted 2025/07/01 Platform GPL20795
At a glance

DNA methylation · human · 29 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
29 samples: 26 distinct titles, 3 of them repeatedGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
This study investigates the role of RUNX1’s DNA demethylation function in hematopoietic differentiation, revealing its necessity for generating hematopoietic stem and progenitor cells (HSPCs). Using CRISPR-edited iPSC lines—TADΔ (truncated transactivation domain) and Runt-VP64 (TAD replaced with VP64)—we demonstrated that RUNX1’s ability to induce DNA demethylation is critical for HSPC emergence.
Read the full summary ↓
While wild-type RUNX1 enabled robust HSPC production (41.1% efficiency), Runt-VP64 and TADΔ severely impaired differentiation (13.5% and 0.024% efficiency, respectively). Methylome analysis linked RUNX1-binding motifs to differentially methylated regions (DMRs) in wild-type cells, absent in mutants, highlighting RUNX1’s role in site-specific demethylation during progenitor cell emergence. These findings establish RUNX1’s DNA demethylation activity as essential for initiating hematopoietic lineage commitment, providing novel insights into epigenetic regulation of hematopoiesis.
This dataset
Open GSE300808 in finder → Find more human methylation datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE300808_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA1282140 and SRA study SRP594604. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
26 distinct titles, 3 of them repeated
WT_Day ×2 KO_Day ×2 VP64_Day ×2

How we read this: Read from 29 sample titles: 26 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.