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PURPL bidirectionally controls senescence: depletion rejuvenates fibroblasts while overexpression accelerates aging [RNA-Seq2]

GSE301166 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/12/02 Platform GPL29480
At a glance

RNA-seq (gene expression) · human · 18 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
18 samples: 6 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
Targeting PURPL RNA enabled rejuvenation of senescence cells via epigenetic reprogrammingJournal of translational medicine · 2025
Summary (written by the submitters)
ellular senescence is a fundamental driver of aging and age-related diseases, with long non-coding RNAs (lncRNAs) emerging as critical regulators of this process. Here, we identify PURPL as a bidirectional modulator of senescence in human fibroblasts. Through comparative transcriptomic analysis of replicative and doxorubicin-induced senescence models, we discovered PURPL as the most significantly upregulated lncRNA common to both systems.
Read the full summary ↓
CRISPR interference (CRISPRi)-mediated PURPL depletion in senescent cells reversed hallmark aging phenotypes, including restoration of LMNB1 expression, suppression of p21, and reduced SA-β-gal activity. Conversely, PURPL overexpression in young fibroblasts recapitulated senescence signatures, inducing extracellular matrix reorganization and cell cycle arrest pathways. RNA-seq revealed PURPL overexpression dysregulated 1,633 genes, mirroring natural aging transcriptomes. These opposing effects establish PURPL as a central regulator of cellular senescence, suggesting its therapeutic potential for age-related conditions. Our findings provide mechanistic insights into lncRNA-mediated senescence control and highlight PURPL as a novel target for aging intervention strategies.
Published in
Targeting PURPL RNA enabled rejuvenation of senescence cells via epigenetic reprogramming
Wang J, Yang X, Su X et al. · Journal of translational medicine 2025 · PMID 41107828 · doi:10.1186/s12967-025-07208-5
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 GSE301166_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 PRJNA1284215 and SRA study SRP596045. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in triplicate
BJ sgCtrl, ×3 BJ sgRNA-PURPL#1, ×3 BJ sgRNA-PURPL#2, ×3 BJ Ctrl, ×3 BJ PURPL#1-OE, ×3 BJ PURPL#2-OE, ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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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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.