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Retinoic acid promotes tissue regeneration by resolving stem cell lineage plasticity

GSE231731 Mus musculus (mouse) RNA-seq (gene expression) 27 samples Submitted 2024/03/26 Platform GPL19057Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 27 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
27 samples: 27 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
Vitamin A resolves lineage plasticity to orchestrate stem cell lineage choicesScience (New York, N.Y.) · 2024
Summary (written by the submitters)
Stem cells upended from their niche upon injury display lineage plasticity, a transient multi-lineage state essential for tissue repair. Employing high-throughput approaches and three-dimensional cultures of hair follicle stem cells (HFSCs), we investigate the signals that govern the transition between homeostatic regeneration and lineage plasticity.
Read the full summary ↓
We identify retinoic acid (RA) as a master orchestrator of HFSC behavior during these two processes. In the hair follicle, RA signals within defined niches and interacts with WNT and BMP cues to drive hair regeneration. In wounded skin, reduced RA signaling prompts HFSCs to prioritize epidermal re-epithelialization and must be restored to promote hair regrowth. Substantiated in vivo, our findings have profound therapeutic implications for hair growth and for chronic wounds and cancers, where lineage plasticity is unresolved.
Published in
Vitamin A resolves lineage plasticity to orchestrate stem cell lineage choices
Tierney MT, Polak L, Yang Y et al. · Science (New York, N.Y.) 2024 · PMID 38452090 · doi:10.1126/science.adi7342
This dataset
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.

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 GSE231731_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA967382 and SRA study SRP435987. Searching any of these in the dataset finder brings you back here.

Study design
27 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 27 sample titles: 27 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.