← BioTransfer GEO Dataset Finder
GEO series

Age-associated chromatin reorganization drives overexpression of a novel Btaf1 variant regulating HSC self-renewal and MkP differentiation in male mice

GSE204933 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 82 samples Submitted 2026/02/19 Platform GPL30172Platform GPL24247Platform GPL17021
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 82 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
82 samples: 33 conditions, mostly in duplicateGroups 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.
Paper
Chromatin reorganization drives overexpression of a Btaf1 variant underpinning hematopoietic agingNature communications · 2026
Summary (written by the submitters)
Age-associated hematopoietic stem cell (HSC) dysfunction is accompanied by dramatic transcription changes, but it remains unclear whether specific transcripts could orchestrate these HSC aging phenotypes. Here, we performed epigenetic profiling in male mice to investigate the regulatory mechanisms underlying the HSC aging transcriptome and screen for potential aging driver genes.
Read the full summary ↓
We show dysregulated 3D chromatin organization, altered histone modifications and changed chromatin accessibility shape the HSC aging transcriptome. From the compilation of these data, we identified a new looping structure formed between part of the Btaf1 gene and the whole Ide gene in old HSCs (OHSCs) which is accompanied by overexpression of a novel, shorter variant of Btaf1 (nBtaf1). Mechanistically, elevated expression of nBtaf1 drives the aging-associated overexpression of HSC- and megakaryocyte progenitor (MkP)-related genes by regulating TBP binding at their promoters which contributes to HSC expansion and elevated MkP production in aged mice. ShRNA mediated knockdown of nBtaf1 restores a younger HSC transcriptome and specifically represses aging-associated HSC expansion and elevated MkP production. In summary, our data provide high resolution analysis of a dysregulated HSC aging epigenome and reveal a novel Btaf1 variant that drives HSC aging phenotypes in mice.
Published in
Chromatin reorganization drives overexpression of a Btaf1 variant underpinning hematopoietic aging
Zong L, Park B, Cao Y et al. · Nature communications 2026 · PMID 41851123 · doi:10.1038/s41467-026-70787-4
This dataset
Open GSE204933 in finder → Find more mouse 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 GSE204933_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 peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA842744 and SRA study SRP377291. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41851123) ↗
Study design
33 conditions, mostly in duplicate
Old HSC RNAseq ×8 Young HSC RNAseq ×4 Young HSC ATACseq ×4 Old HSC ATACseq ×4 Young HSC H3K4me3 ×2 Young HSC H3K27me3 ×2 Young HSC H3K36me3 ×2 Old HSC H3K36me3 ×2 +29 more

Supports a between-group comparison across 78 samples.

How we read this: 33 replicated groups read from 82 sample titles; they account for 78 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

+ 42 more — browse all 82 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.