GEO series

Vascular Cell Senescence and Age-Related Endothelial Dysfunction: The Role of the Senescence-Associated Secretory Phenotype and CXCL12

GSE296698 Mus musculus (mouse) Single-cell RNA-seq 15 samples Submitted 2026/05/15 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 15 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 15 samples: 7 conditions, mostly in duplicateGroups 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: Senolytic Treatment With Fisetin Reverses Age-Related Endothelial Dysfunction Partially Mediated by SASP Factor CXCL12Aging cell · 2026

Summary (written by the submitters)

Cellular senescence and the senescence-associated secretory phenotype (SASP) broadly contribute to age-related endothelial dysfunction, in part, by promoting oxidative stress (a key source being mitochondria) and inflammation, which reduce the bioavailability of the vasodilatory molecule nitric oxide (NO). In the present study, we leveraged aortic single-cell transcriptomics to characterize specific vascular senescent cells and SASP profiles in young (6mo) vs.

Read the full summary ↓

old (27mo) mice. In vivo supplementation with the natural senolytic compound fisetin allowed for the assessment of senescent cell signatures with aging and senolytic intervention. Both aging and senolytic treatment had the greatest effect on endothelial cell populations. As such, we focused on senescent-specific molecular changes in endothelial cells. Senescent endothelial cells demonstrated increased abundance of transcripts related to cardiovascular pathology and high expression of SASP factors. Of the identified SASP factors that had higher expression with aging, Cxcl12 mRNA was the most highly upregulated with old age and downregulated with senolytic treatment and this pattern persisted in circulating CXCL12 protein concentrations in plasma. We next sought to determine the causal role of the circulating SASP milieu and CXCL12 in regulating endothelial function with aging and fisetin supplementation. Ex vivo exposure of isolated arteries and cultured endothelial cells to plasma from young, old and old fisetin-supplemented mice revealed that the aged circulating SASP milieu directly induced endothelial dysfunction through induction of cellular senescence induction, reduced NO bioactivity, and increased mitochondrial oxidative stress. The circulating SASP milieu-mediated impairments were in part mediated by CXCL12 and subsequently alleviated by fisetin supplementation. These findings establish the circulating SASP milieu as an underlying mechanism of age-related endothelial dysfunction and identify CXCL12 as a novel mediator and therapeutic target of cellular senescence-induced endothelial dysfunction with aging.

Published in

Senolytic Treatment With Fisetin Reverses Age-Related Endothelial Dysfunction Partially Mediated by SASP Factor CXCL12

Mahoney SA, Mazan-Mamczarz K, Tsitsipatis D et al. · Aging cell 2026 · PMID 42021544 · doi:10.1111/acel.70500

This dataset

Open GSE296698 in finder → Find more mouse 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 GSE296698_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1260553 and SRA study SRP583983. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42021544) ↗

Study design

7 conditions, mostly in duplicate

Young_Female_Vehicle ×2 Young_Female_Fisetin ×2 Young_Male_Vehicle ×2 Young_Male_Fisetin ×2 Old_Male_Fisetin ×2 Old_Female_Fisetin ×2 Old_Female_Vehicle ×2 Old_Male_Vehicle ×1

Supports a between-group comparison across 14 samples.

How we read this: 7 replicated groups read from 15 sample titles; they account for 14 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

GSM8974828 Young_Female_Vehicle_1 Young_Female_Vehicle
GSM8974829 Young_Female_Vehicle_2 Young_Female_Vehicle
GSM8974830 Young_Female_Fisetin_1 Young_Female_Fisetin
GSM8974831 Young_Female_Fisetin_2 Young_Female_Fisetin
GSM8974832 Young_Male_Vehicle_1 Young_Male_Vehicle
GSM8974833 Young_Male_Vehicle_2 Young_Male_Vehicle
GSM8974834 Young_Male_Fisetin_1 Young_Male_Fisetin
GSM8974835 Young_Male_Fisetin_2 Young_Male_Fisetin
GSM8974836 Old_Male_Fisetin_1 Old_Male_Fisetin
GSM8974837 Old_Male_Fisetin_2 Old_Male_Fisetin
GSM8974838 Old_Male_Vehicle_1
GSM8974839 Old_Female_Fisetin_1 Old_Female_Fisetin
GSM8974840 Old_Female_Fisetin_2 Old_Female_Fisetin
GSM8974841 Old_Female_Vehicle_1 Old_Female_Vehicle
GSM8974842 Old_Female_Vehicle_2 Old_Female_Vehicle

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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.