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RNASeq of DNMT3A mutant K562

GSE268825 Homo sapiens (human) Single-cell RNA-seq 32 samples Submitted 2025/10/29 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 32 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
32 samples: 32 samples, each with a distinct title — no replicated groupsGroups 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
Clonal hematopoiesis landscape in frequent blood donorsBlood · 2025
Summary (written by the submitters)
Donor blood saves lives, yet the potential impact of recurrent large-volume phlebotomy on donor health and hematopoietic stem cells (HSCs) remains largely unexplored. In our study, we conducted a comprehensive screening of 217 older male volunteer donors with a history of extensive blood donation (> 100 life-time donations) to investigate the phenomenon of clonal hematopoiesis (CH).
Read the full summary ↓
No significant difference in the overall incidence of CH was found in frequent donors (FD) compared to sporadic donors (< 10 life-time donations, 212 donors). However, upon deeper analysis of mutations in DNMT3A, the most commonly affected gene in CH, we observed distinct mutational patterns between the FD and control donor (CD) cohorts. Functional analysis of FD enriched DNMT3A variants examined in CRISPR-edited human HSCs demonstrated their competitive outgrowth potential upon stimulation with erythropoietin (EPO). In contrast, clones harboring leukemogenic DNMT3A R882 mutations exhibited a stronger response to inflammatory cues rather than EPO. Through concurrent mutational and immunophenotypic profiling of primary samples at single cell resolution as well as experiments in humanized mice, a myeloid bias of R882 mutant HSCs was detected. Remarkably, no significant lineage bias was observed in HSCs harboring EPO responsive DNMT3A variants. Our data demonstrate a nuanced ongoing Darwinian evolution at the somatic stem cell level, with EPO identified as a novel environmental factor that favors HSCs carrying certain non-malignant DNMT3A mutations. Crucially, our findings robustly demonstrate that frequent blood donation over many years does not promote the selection of malignant clones, thus strongly reaffirming its safety.
Published in
Clonal hematopoiesis landscape in frequent blood donors
Karpova D, Huerga Encabo H, Donato E et al. · Blood 2025 · PMID 40067117 · doi:10.1182/blood.2024027999
This dataset
Open GSE268825 in finder → Find more human 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 GSE268825_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1118805 and SRA study SRP511110. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40067117) ↗
Study design
32 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 32 sample titles: 32 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
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