GEO series

Human iPSC-based modeling unveils chromatin remodeling induced by SETBP1 mutation as a potential initiating factor in GATA2 deficiency.

GSE264140 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2025/10/02 Platform GPL16791

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 14 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: Homo sapiens (human)
Samples: 14 samples: 7 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: Human iPSCs-based modeling unveils SETBP1 as a driver of chromatin rewiring in GATA2 deficiencyNature communications · 2025

Summary (written by the submitters)

GATA2 deficient patients are prone to develop myelodysplastic syndrome (MDS) that can evolve to acute myeloid leukemia (AML). The MDS progression is usually associated with the acquisition of cytogenetic and/or additional somatic alterations. Mutations in SETBP1 and ASXL1 genes are frequently observed in pediatric GATA2 patients, although how they contribute to the disease progression remains poorly understood.

Read the full summary ↓

Here we develop a human induced pluripotent stem cells (hiPSCs)-based model to study the impact of germline GATA2 mutation along with selected somatic mutations on GATA2 deficiency progression. We found that, while germline heterozygous GATA2 mutation alone is insufficient to induce an impairment of myeloid development, SETBP1 and ASXL1 mutations lead to a significant decrease of myeloid differentiation potential with a complete monocytopenia. The prominent loss of myeloid progenitor was associated with a down regulation of myeloid-specific genes and an up-regulation of leukemic stem cell markers. Through epigenomic profiling, we found that mutation in SETBP1 promotes chromatin remodeling in genes involved in myeloid neoplasms that preceded the blockage of myeloid differentiation, a state that persisted after the acquisition of the ASXL1 mutation. Finally, transcription factors motif analysis revealed an enrichment for MEIS1, PU.1, RUNX1 and HOXA9 motifs in more accessible chromatin regions, suggesting their cooperation in disease progression. Beyond establishing a valuable tool for studying the molecular mechanisms underlying GATA2 deficiency, our findings suggest that mutated SETBP1 primes the onset of hematopoietic impairment at transcriptomic and epigenomic level in GATA2 deficiency, a process exacerbated by the acquisition of the ASXL1 mutation.

Published in

Human iPSCs-based modeling unveils SETBP1 as a driver of chromatin rewiring in GATA2 deficiency

Pera J, Romero-Moya D, Torralba-Sales E et al. · Nature communications 2025 · PMID 41249190 · doi:10.1038/s41467-025-65806-9

This dataset

Open GSE264140 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE264140_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 PRJNA1101045 and SRA study SRP502077. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41249190) ↗

Study design

7 conditions, mostly in duplicate

CTRL_biol ×2 G_biol ×2 GS_biol ×2 GSA_biol ×2 A_biol ×2 S_biol ×2 GA_biol ×2

Supports a between-group comparison across 14 samples.

How we read this: 7 replicated groups read from 14 sample titles; they account for 14 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

GSM8212735 CTRL_biol rep1 CTRL_biol
GSM8212736 CTRL_biol rep2 CTRL_biol
GSM8212737 G_biol rep1 G_biol
GSM8212738 G_biol rep2 G_biol
GSM8212739 GS_biol rep1 GS_biol
GSM8212740 GS_biol rep2 GS_biol
GSM8212741 GSA_biol rep1 GSA_biol
GSM8212742 GSA_biol rep2 GSA_biol
GSM9116214 A_biol rep1 A_biol
GSM9116215 A_biol rep2 A_biol
GSM9116216 S_biol rep1 S_biol
GSM9116217 S_biol rep2 S_biol
GSM9116218 GA_biol rep1 GA_biol
GSM9116219 GA_biol rep2 GA_biol

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