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Mitochondrial metabolism and epigenetic crosstalk drive the SASP (RNA-Seq)

GSE279411 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/05/19 Platform GPL15433
At a glance

RNA-seq (gene expression) · human · 12 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
Homo sapiens (human)
Samples
12 samples: 3 × Proliferative vs 3 × Senescence irradiation Sen(IR) vs 3 × Senescence irradiation + CTPI2, Sen(IR+CTPI2) vs 3 × Senescence irradiation + UK5099, Sen(IR+UK5099)Groups 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)
Senescent cells drive age-related tissue dysfunction largely through the persistent activation of the senescence-associated secretory phenotype (SASP). Here, we identify key mitochondrial metabolic pathways, including the mitochondrial citrate carrier (SLC25A1) and ATP-citrate lyase (ACLY), as critical regulators of the SASP. These pathways fuel histone acetylation at SASP gene loci, promoting their expression.
Read the full summary ↓
Targeted inhibition of SLC25A1 or ACLY suppresses SASP expression without affecting cell cycle arrest, underscoring their potential as selective therapeutic targets for mitigating age-related inflammation. Furthermore, pharmacological inhibition of SLC25A1 reduces systemic inflammation and extends healthspan in aged mice. Our findings suggest that disrupting metabolic reprogramming in senescent cells offers a promising strategy to ameliorate aging-associated pathologies.
This dataset
Open GSE279411 in finder → Find more human 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 GSE279411_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 PRJNA1172665 and SRA study SRP538512. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
3 × Proliferative vs 3 × Senescence irradiation Sen(IR) vs 3 × Senescence irradiation + CTPI2, Sen(IR+CTPI2) vs 3 × Senescence irradiation + UK5099, Sen(IR+UK5099)

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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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.