GEO series

Sex differences in DNMT3A-mutant clonal hematopoiesis and the effects of estrogen [scRNA-Seq]

GSE272266 Mus musculus (mouse) Single-cell RNA-seq 20 samples Submitted 2025/04/04 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 20 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 20 samples: 20 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: Sex differences in DNMT3A-mutant clonal hematopoiesis and the effects of estrogenCell reports · 2025

Summary (written by the submitters)

Blood cancers are generally more common in males, and the prevalence of most mutations that drive clonal hematopoiesis and myeloid malignancies is higher in males. In contrast, hematopoietic DNMT3A mutations are more common in females. We sought to investigate if sex hormones might contribute to the female predominance of DNMT3A mutations. We used single-cell RNA- and ATAC-sequencing in murine models to examine genotype-specific sex differences and sex hormone effects on hematopoietic stem and progenitor cells.

Published in

Sex differences in DNMT3A-mutant clonal hematopoiesis and the effects of estrogen

Stomper J, Niroula A, Belizaire R et al. · Cell reports 2025 · PMID 40178977 · doi:10.1016/j.celrep.2025.115494

This dataset

Open GSE272266 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 GSE272266_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 PRJNA1135977 and SRA study SRP519964. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40178977) ↗

Study design

20 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 20 sample titles: 20 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

GSM8397470 Bone marrow, WT, male, biol rep 1, scRNAseq
GSM8397471 Bone marrow, WT, male, biol rep 2, scRNAseq
GSM8397472 Bone marrow, WT, female, biol rep 1, scRNAseq
GSM8397473 Bone marrow, WT, female, biol rep 2, scRNAseq
GSM8397474 Bone marrow, Dnmt3a, male, biol rep 1, scRNAseq
GSM8397475 Bone marrow, Dnmt3a, male, biol rep 2, scRNAseq
GSM8397476 Bone marrow, Dnmt3a, female, biol rep 1, scRNAseq
GSM8397477 Bone marrow, Dnmt3a, female, biol rep 2, scRNAseq
GSM8397478 Bone marrow, WT, vehicle, biol rep 1, scRNAseq
GSM8397479 Bone marrow, WT, vehicle, biol rep 2, scRNAseq
GSM8397480 Bone marrow, WT, estradiol, biol rep 1, scRNAseq
GSM8397481 Bone marrow, WT, estradiol, biol rep 2, scRNAseq
GSM8397482 Bone marrow, WT, DHT, biol rep 1, scRNAseq
GSM8397483 Bone marrow, WT, DHT, biol rep 2, scRNAseq
GSM8397484 Bone marrow, Dnmt3a, vehicle, biol rep 1, scRNAseq
GSM8397485 Bone marrow, Dnmt3a, vehicle, biol rep 2, scRNAseq
GSM8397486 Bone marrow, Dnmt3a, estradiol, biol rep 1, scRNAseq
GSM8397487 Bone marrow, Dnmt3a, estradiol, biol rep 2, scRNAseq
GSM8397488 Bone marrow, Dnmt3a, DHT, biol rep 1, scRNAseq
GSM8397489 Bone marrow, Dnmt3a, DHT, biol rep 2, scRNAseq

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