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Human iPSCs-based modeling unveils a dominant role of SETBP1 mutation in shaping stable chromatin accessibility landscape in GATA2 deficiency

GSE265824 Homo sapiens (human) RNA-seq (gene expression) 25 samples Submitted 2025/10/02 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 25 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)
Topic
Myelodysplastic syndromesThis series is part of our curated briefing on it.
Samples
25 samples: 11 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)
Patients with GATA2 deficiency are predisposed to developing myelodysplastic syndrome (MDS), which can progress to acute myeloid leukemia (AML). This progression is often associated with the acquisition of additional cytogenetic and somatic alterations. Mutations in SETBP1 and ASXL1 genes are recurrently observed in GATA2 patients, but their precise roles in disease progression remain poorly understood.
Read the full summary ↓
Here we developed a hiPSC-based system to investigate the functional impact of SETBP1 and ASXL1 mutations in the context of germline GATA2 haploinsufficiency. Using precise genome editing, we recreated patient-relevant combinations of these mutations to model distinct premalignant stages of GATA2 deficiency. We demonstrate that heterozygous GATA2 mutation alone has a limited impact on early hematopoietic progenitors, without disrupting myeloid differentiation. In contrast, acquisition of SETBP1 or ASXL1 mutations impairs hematopoietic differentiation in a GATA2 deficient background, leading to a premalignant state marked by reduced myeloid progenitor output. Strikingly, the combination of all three mutations results in a severe depletion of myeloid progenitors, closely recapitulating hematopoietic defects observed in GATA2-related MDS and highlighting a synergistic interplay among the mutations. We provide new insights into the molecular mechanism underlying GATA2 deficiency progression, revealing that SETBP1 mutation plays a dominant role in establishing a stable chromatin accessibility landscape, even when co-occurring with ASXL1. Our study establishes a novel humanized model system for studying GATA2 deficiency. This model provides new insights into the cellular and molecular events underlying the progression of myeloid impairment in GATA2 deficiency and represents a platform for testing future therapeutic strategies.
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 GSE265824 in finder → Find more human RNA-seq datasets → Myelodysplastic syndromes landscape →
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 GSE265824_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 PRJNA1104265 and SRA study SRP503865. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41249190) ↗
Study design
11 conditions, mostly in duplicate
GA_biol ×4 CTRL_biol_rep_2 ×2 GSA_biol_rep_1 ×2 GSA_biol_rep_2 ×2 GS_biol_rep_1 ×2 G_biol_rep_1 ×2 G_biol_rep_2 ×2 CTRL_biol ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 11 replicated groups read from 25 sample titles; they account for 24 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
More Myelodysplastic syndromes datasets

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