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RNA sequencing of slow-and fast-cycling epidermal stem cells vs hair follicle stem cells in murine back skin, tail skin vs oral epithelium

GSE208730 Mus musculus (mouse) RNA-seq (gene expression) 35 samples Submitted 2026/02/16 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 35 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
35 samples: 13 conditions, mostly in triplicateGroups 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
Spatial organization of epithelial heterogeneity through undulating structures of the skin and oral mucosaThe Journal of investigative dermatology · 2026
Summary (written by the submitters)
This study was performed to compare transcriptomic changes in the heterogeneous mouse skin epidermal stem cells and mouse oral epithelial stem cells during homeostasis at young age. Slow-cycling stem cells (label retaining cells, LRCs), fast-cycling stem cells (non-label retaining cells, nLRCs) express unique gene signatures in both skin and oral tissues and have independent stem cell identities.
Read the full summary ↓
Our data indicates that there are similarities and differences in gene expression patterns between LRC and non-LRC populations in different epithelial tissues, suggesting stem cell autonomous heterogeneity and environmental differences.
Published in
Spatial organization of epithelial heterogeneity through undulating structures of the skin and oral mucosa
Ishikawa M, Ngo YX, Nishikawa I et al. · The Journal of investigative dermatology 2026 · PMID 41786124 · doi:10.1016/j.jid.2026.02.014
This dataset
Open GSE208730 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 GSE208730_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 PRJNA860829 and SRA study SRP387402. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41786124) ↗
Study design
13 conditions, mostly in triplicate
BK-BL-LRC ×3 BK-BL-nLRC ×3 BK-SL-LRC ×3 BK-SL-nLRC ×3 BK-GL ×3 BK-Bulge ×3 TL-BL-LRC ×3 TL-BL-nLRC ×3 +5 more

Supports a between-group comparison across 35 samples.

How we read this: 13 replicated groups read from 35 sample titles; they account for 35 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
Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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