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Clearance of p21-highly-expressing senescent cells accelerates cutaneous wound healing

GSE278527 Mus musculus (mouse) Single-cell RNA-seq 36 samples Submitted 2024/10/01 Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 36 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
Mus musculus (mouse)
Samples
36 samples: 7 conditions, mostly with about 6 replicates eachGroups 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
Clearance of p21 highly expressing senescent cells accelerates cutaneous wound healingNature aging · 2025
Summary (written by the submitters)
Targeting senescent cells for therapeutic purposes is gaining momentum across various organ systems. However, concerns about potential off-target effects have been raised. Previous studies have shown that removing senescent cells expressing high levels of p16 (p16high) can hinder processes like wound healing. Here, we identify a distinct senescent cell population during dermal wound healing characterized by high expression level of p21 (p21high) using the p21-Cre mouse model.
Read the full summary ↓
Using a standard cutaneous injury model, we find that eliminating p21high cells can expedite wound closure, in contrast to the effects of removing p16high cells. Through Xenium, a single cell spatial imaging platform, we show that p21high cells are distinct from p16high cells, with p21high cells mainly comprising fibroblasts, immune cells, keratinocytes, and endothelial cells with a pro-inflammatory profile. Moreover, inhibition of NF-kB signaling specifically from p21high cells partially contributes to the accelerated wound healing rates. These findings highlight the heterogeneity of senescent cells during wound healing responses within the skin and likely in other conditions.
Published in
Clearance of p21 highly expressing senescent cells accelerates cutaneous wound healing
Gasek NS, Yan P, Zhu J et al. · Nature aging 2025 · PMID 39537987 · doi:10.1038/s43587-024-00755-4
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 GSE278527_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: 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 PRJNA1167732 and SRA study SRP536027. Searching any of these in the dataset finder brings you back here.

Study design
7 conditions, mostly with about 6 replicates each
Skin Biopsy, P, Day12, ×6 Skin Biopsy, PD, Day12, ×6 Skin Biopsy, PD, Day4, ×6 Skin Biopsy, P, Day4, ×5 Skin Biopsy, P, Day2, ×5 Skin Biopsy, PD, Day2, ×5 Skin Biopsy, unwounded skin, Day0, ×3

Supports a between-group comparison across 36 samples.

How we read this: 7 replicated groups read from 36 sample titles; they account for 36 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
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