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Multi-omics integrative approach of senescent endothelial cells and derived extracellular vesicles in a replicative senescence model [RNA-seq]

GSE311038 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/12/01 Platform GPL34281
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: 12 samples, each with a distinct title — no replicated groupsGroups 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)
Aging is a major unmodifiable risk factor for cardiovascular disease (CVD). Replicative endothelial senescence (RES), characterized by permanent cell-cycle arrest and a senescence-associated secretory phenotype (SASP), is a hallmark of vascular aging and contributes to endothelial dysfunction. However, RES molecular mechanisms, particularly the role of extracellular vesicles (EVs), remain poorly understood.
Read the full summary ↓
To elucidate these mechanisms, an integrated multi-omics approach (proteomics, transcriptomics, and miRNA profiling) was used to characterize senescent endothelial cells (ECs) and their secreted EVs compared with early-passage counterparts. Senescent ECs and EVs displayed canonical senescence signatures, exhibiting enrichment of cell cycle, DNA damage response, p53 and NF-κB signaling, and SASP components, which are strongly linked to CVD. Senescent EVs also showed a marked suppression of RNA metabolism, chromatin organization, and repair machinery, while carrying pro-inflammatory, adipogenic, and angiogenic mediators, reinforcing their role as vectors of secondary cellular senescence. Multi-omics integration revealed a coordinated regulatory network involving transcription factors (e.g., REST) and miRNAs. Angiogenic regulators miR-22-3p and miR-126-5p also emerged as RES mediators in both ECs and EVs, with miR-22-3p exhibiting compartment-specific regulation. Downregulation of key miRNAs (miR-335-3p, miR-590-3p, miR-29, and miR-17 families) promoted the derepression of downstream targets, driving endothelial phenotypic drift and dysfunction, primarily characterized by impaired PI3K–Akt signaling, maladaptive angiogenesis, and extracellular matrix remodeling. Our study provides a comprehensive map of RES-driven molecular alterations, identifying potential candidate biomarkers and therapeutic targets for age-related CVD. These findings also emphasize the role of EVs in propagating senescence and CVD risk, opening perspectives for EV-based interventions to counteract vascular aging.
This dataset
Open GSE311038 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 GSE311038_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 PRJNA1368555 and SRA study SRP648053. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 12 sample titles: 12 distinct titles with little repetition. 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.