← BioTransfer GEO Dataset Finder
GEO series

Transcriptional changes and epigenetic landscapes in fetal hematopoietic stem and progenitor cells with DDX21 knockout

GSE296783 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/07/31 Platform GPL24676Platform GPL24247
At a glance

RNA-seq (gene expression) · human · 12 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
12 samples: 5 × E13.5 LK WT_RNAseq vs 5 × E13.5 LK Ddx21-cKO_RNAseq vs 3 × H1-HSC_shNC_RNAseq vs 3 × H1-HSC_shDDX21_RNAseqGroups 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)
Fetal hematopoietic stem cells and their committed progenitors (HSPCs) are highly proliferative cells that rely on active ribosome biogenesis to support their rapid expansion during fetal hematopoiesis. Insufficient ribosome biogenesis, a pathological condition known as ribosomopathy, activates the p53 pathway and induces cell cycle arrest in immature progenitor cells, leading to disrupted hematopoiesis and erythroid aplasia (anemia).
Read the full summary ↓
DDX21 is an RNA helicase that regulates ribosome biogenesis by modulating rRNA splicing, processing, and ribosomal protein transcription. To elucidate the role of DDX21 in hematopoiesis, we generated Ddx21 conditional knockout (Ddx21cKO) in hematopoietic cells by crossing Ddx21f/f with Vav1-Cre mice. The Ddx21cKO embryos showed defective hematopoiesis and decreased HSPC numbers and their committed progenitors (CMP, GMP, and MEP). In this study, transcriptional changes in E13.5 HSPCs were analyzed by RNA-seq. Epigenetic marks associated active chromatin (H3K4me3, H3K27ac) and repressive chromatin (H3K27me3, H4K4me1) were analyzed by CUT&Tag. Chromatin accessibility was analyzed by ATAC-seq, whereas chromatin occupancies by Ddx21 and Kdm5a were analyzed by CUT&Tag.
This dataset
Open GSE296783 in finder → Find more RNA-seq 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 GSE296783_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1258612 and SRA study SRP582967. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 × E13.5 LK WT_RNAseq vs 5 × E13.5 LK Ddx21-cKO_RNAseq vs 3 × H1-HSC_shNC_RNAseq vs 3 × H1-HSC_shDDX21_RNAseq

Supports a between-group comparison across 16 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 16 of them. 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

Search all RNA-seq datasets in GEO →

Share this dataset

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.