← BioTransfer GEO Dataset Finder
GEO series

Cellular Quiescence Displays a Transcriptional Inflammatory Phenotype Similar to the One in Cellular Senescence [bulk RNA-seq]

GSE287058 Homo sapiens (human) RNA-seq (gene expression) 40 samples Submitted 2026/01/15 Platform GPL20301Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 40 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
40 samples: 12 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.
Summary (written by the submitters)
Cellular senescence, traditionally viewed as the irreversible arrest of proliferating cells in response to stress and DNA damage, is a key mechanism underlying cellular aging. Several studies have identified a process called ”quiescence-origin senescence,” where quiescent cells bypass proliferation and transition directly into senescence.
Read the full summary ↓
In this context, we introduce the concept of the transcriptional Quiescence-Associated Secretory Phenotype (tQASP), a putative inflammatory secretory profile that arises in human quiescent cells and may serve as a precursor to the full senescence-associated secretory phenotype (SASP). We report that quiescent cells express tQASP genes in a cell-type-, induction-dependent manner and that this response is influenced by environmental oxygen levels. tQASP expression is also increased in tissue affected by Idiopathic Pulmonary Fibrosis (IPF) and shows a positive correlation with myofibroblast activation. Together, these data support the concept of a continuum between quiescence and senescence and suggest that the tQASP may be an early marker of cellular aging, with significant implications for tissue homeostasis, aging, cancer, and disease progression.
This dataset
Open GSE287058 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 GSE287058_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 PRJNA1211075 and SRA study SRP557575. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 conditions, mostly in triplicate
BJ Contact Inhibition High Ox ×3 BJ Proliferating High Ox ×3 BJ Serum Withdrawal High Ox ×3 IMR90 Contact Inhibition High Ox ×3 IMR90 Proliferating High Ox ×3 IMR90 Serum Withdrawal High Ox ×3 IMR90 Contact inhibition Low Ox ×3 IMR90 Proliferating Low Ox ×3 +8 more

Supports a between-group comparison across 36 samples.

How we read this: 12 replicated groups read from 40 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
Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.