GEO series

Metabolite-induced DNA damage drives stochastic stem cell loss and clonal hematopoiesis

GSE316966 Mus musculus (mouse) RNA-seq (gene expression) 9 samples Submitted 2026/03/23 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 9 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: Mus musculus (mouse)
Samples: 9 samples: 9 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: Metabolite-induced DNA damage drives stochastic stem cell loss and clonal hematopoiesisCell stem cell · 2026

Summary (written by the submitters)

DNA damage and mutations in hematopoietic stem cells (HSC) enable clonal hematopoiesis (CH). Such damage occurs throughout life, but its origins remain unknown. Here, we demonstrate that endogenous formaldehyde causes HSC attrition and subsequently CH. We generated conditional mouse models lacking formaldehyde detoxification and Fanconi anemia (FA) DNA repair in blood.

Read the full summary ↓

Formaldehyde protection was crucial from embryonic HSC emergence and throughout life. Despite a severe deficiency in HSCs, these mice produce blood for many months. To determine what enables this, we employed a new unbiased method for detecting clones which exploits somatic variant data. This revealed initial polyclonal hematopoiesis which diminishes to monoclonal CH, devoid of known genetic selection. Furthermore, in FA children, we find the same transition to monoclonal hematopoiesis. Therefore, DNA damage-induced attrition down to a last functional cell can be a driving force for CH, representing an alternative route to CH other than purely by fitness-enhancing selection.

Published in

Metabolite-induced DNA damage drives stochastic stem cell loss and clonal hematopoiesis

Kamimae-Lanning AN, Brown JM, Günther M et al. · Cell stem cell 2026 · PMID 41875892 · doi:10.1016/j.stem.2026.02.011

This dataset

Open GSE316966 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 GSE316966_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 PRJNA1405608 and SRA study SRP664757. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41875892) ↗

Study design

9 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 9 sample titles: 9 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

GSM9463200 WT_spleen
GSM9463201 Adh5_spleen
GSM9463202 Fancd2_spleen
GSM9463203 No_Vav_iCre_spleen
GSM9463204 DKO_spleen
GSM9463205 WT_BM
GSM9463206 Adh5_BM
GSM9463207 Fancd2_BM
GSM9463208 DKO_BM

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