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p53 maintains healthy aging [P53KO_PTENKO_RNAseq]

GSE314411 Mus musculus (mouse) Single-cell RNA-seq 31 samples Submitted 2026/05/01 Platform GPL34475
At a glance

Single-cell RNA-seq · mouse · 31 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
31 samples: 6 conditions, mostly with about 5 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.
Summary (written by the submitters)
The tumor suppressor p53 is a master regulator of cellar stress responses implicated in the aging process. As a tumor suppressor, the antagonistic pleiotropy (AP) theory predicts a beneficial cancer suppression function for p53 early in life that becomes detrimental late in life, for example by promoting accumulation of senescent cells, an inflammatory cell fate driven by tumor suppressors and linked to age-associated disease.
Read the full summary ↓
Although p53 signaling is generally activated in aging and disease, p53 is also known to suppress some phenotypes of cellular senescence, and the function of p53 in aged tissues, as well as its causal role in aging, is not well-defined. Here we show, contrary to the prediction of AP, that p53 promotes healthy aging in mice by suppressing inflammation and maintaining metabolic function. Multimodal RNA, protein, epigenetic, and spatial profiling at the single-cell level in aged mouse liver confirmed the expected age-dependent increase of immune infiltration, accumulation of senescent cells, and p53 pathway activation. Knockout of p53 in vivo in hepatocytes of aged, but not young, mice further increased inflammation and immune infiltration. Mechanistically, p53 suppressed DNA replication stress and damage, a precursor to inflammatory signaling, in senescent cells and in aged liver. In aged hepatocytes, p53 directly targeted and maintained expression of metabolic genes, and knockout of p53 sensitized aged mice to metabolic stress-induced liver damage. Conversely, pharmacological activation of p53 with a small molecule MDM2 inhibitor in aged mice reversed age-associated suppression of metabolic genes, decreased age-associated inflammation, delayed onset of frailty, (and extended lifespan) of aged mice. These data suggest that in mice, p53 delays phenotypes of aging, and that this function is targetable, potentially for the benefit of healthy aging in humans.
This dataset
Open GSE314411 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 GSE314411_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 PRJNA1390894 and SRA study SRP656573. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly with about 5 replicates each
Old_PTENKO ×6 Yng_Rosa26KO ×5 Yng_PTENKO ×5 Old_Rosa26KO ×5 Yng_Trp53KO ×5 Old_Trp53KO ×5

Supports a between-group comparison across 31 samples.

How we read this: 6 replicated groups read from 31 sample titles; they account for 31 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

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