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Mitochondrial RNA cytosolic leakage drives the SASP [CRISPR/Cas9]

GSE306957 Mus musculus (mouse) RNA-seq (gene expression) 34 samples Submitted 2025/10/29 Platform GPL34284Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 34 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
34 samples: 11 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
Mitochondrial RNA cytosolic leakage drives the SASPNature communications · 2025
Summary (written by the submitters)
Senescent cells secrete proinflammatory factors known as the senescence-associated secretory phenotype (SASP), contributing to tissue dysfunction and aging. Mitochondrial dysfunction is a key feature of senescence, influencing SASP via mitochondrial DNA (mtDNA) release and cGAS/STING pathway activation. Here, we demonstrate that mitochondrial RNA (mtRNA) also accumulates in the cytosol of senescent cells, activating RNA sensors RIG-I and MDA5, leading to MAVS aggregation and SASP induction.
Read the full summary ↓
Inhibition of these RNA sensors significantly reduces SASP factors. Furthermore, BAX and BAK play a key role in mtRNA leakage during senescence, and their deletion diminishes SASP expression in vitro and in a mouse model of Metabolic Dysfunction Associated Steatohepatitis (MASH). These findings highlight mtRNA's role in SASP regulation and its potential as a therapeutic target for mitigating age-related inflammation.
Published in
Mitochondrial RNA cytosolic leakage drives the SASP
Victorelli S, Eppard M, Martini H et al. · Nature communications 2025 · PMID 41398033 · doi:10.1038/s41467-025-66159-z
This dataset
Open GSE306957 in finder → Find more 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 GSE306957_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 PRJNA1314047 and SRA study SRP616750. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41398033) ↗
Study design
11 conditions, mostly in triplicate
Senescent MRC5 fibroblast; siScramble; ×4 Liver, FFC diet, AAV8-TBG-Cre (control) ×4 Proliferating MRC5 fibroblast, pLenti CRISPR/… ×3 Proliferating MRC5 fibroblast, IFIH1 CRISPR/C… ×3 Proliferating MRC5 fibroblast, DDX58 CRISPR/C… ×3 Senescent MRC5 fibroblast, IFIH1 CRISPR/Cas9; ×3 Senescent MRC5 fibroblast, DDX58 CRISPR/Cas9; ×3 Proliferating MRC5 fibroblast; siScramble; ×3 +3 more

Supports a between-group comparison across 34 samples.

How we read this: 11 replicated groups read from 34 sample titles; they account for 34 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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