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Single-cell and spatial transcriptomics uncovers the role of senescent vascular cells in pathological arterial remodeling during murine atherosclerosis

GSE239602 Mus musculus (mouse) Spatial transcriptomics 82 samples Submitted 2024/07/28 Platform GPL30172
At a glance

Spatial transcriptomics · mouse · 82 samples

What it is
Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism
Mus musculus (mouse)
Samples
82 samples: 12 conditions, mostly with about 10 replicates eachGroups 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
Single-cell and spatial transcriptomics map senescent vascular cells in arterial remodeling during atherosclerosis in miceNature aging · 2025
Summary (written by the submitters)
Studying cardiovascular senescence is crucial in understanding the diverse manifestations of disease-related changes in the cardiovascular system and their implications for overall health and disease. To systematically investigate the heterogeneity of senescent vascular cells in atherosclerosis, we employed the senescence reporter mouse p16TdTomato+/-, injected with an adeno-associated virus expressing PCSK9 to induce atherosclerosis when fed a high-fat diet (HFD).
Read the full summary ↓
We then treated this mouse with the senolytic drug ABT-737 or vehicle. Whole-aorta single-cell RNA-sequencing (scRNA-seq) and Gene Set Enrichment Analysis (GSEA) using the SenMayo panel, uncovered 10 cell clusters displaying increased senescent features that were reduced by treatment with ABT-737. Interestingly, these clusters do not express ‘classical’ senescence markers such as p16 and p21, but were senescent based on SenMayo GSEA, indicating that the senescence transcriptomic signature in this niche requires deeper characterization. Using unbiased subclustering of the top four potentially senescent clusters, we identified subsets of cells increasing by HFD and reduced by ABT-737 treatment. Accordingly, unique transcripts associated with each subpopulation, such as Spp1, Ctsb, and Tnfrsf11b, implicated these cells in senescence-related signaling pathways. We then validated these findings by spatial transcriptomic profiling, locating senescent cells in the cap and core of brachiocephalic arteries from atherosclerotic mice. Our results uncover a new vascular-specific transcriptomic signature that may be exploited for therapeutic targeting in age-related vascular diseases.
Published in
Single-cell and spatial transcriptomics map senescent vascular cells in arterial remodeling during atherosclerosis in mice
Mazan-Mamczarz K, Tsitsipatis D, Childs BG et al. · Nature aging 2025 · PMID 40660002 · doi:10.1038/s43587-025-00889-z
This dataset
Open GSE239602 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 GSE239602_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: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA999750 and SRA study SRP452082. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40660002) ↗
Study design
12 conditions, mostly with about 10 replicates each
mWTA Slide5 121522 DSP-1001660013726-B-A ×11 mWTA Slide2 121522 DSP-1001660013726-B-C ×10 mWTA Slide1 121422 DSP-1001660013725-A-A ×10 mWTA Slide7 121522 DSP-1001660013726-B-B ×9 mWTA Slide4 121522 DSP-1001660013726-B-D ×9 mWTA Slide3 121422 DSP-1001660013725-A-C ×8 mWTA Slide1 121422 DSP-1001660013725-A-B ×6 mWTA Slide4 121522 DSP-1001660013726-B-E ×5 +5 more

Supports a between-group comparison across 81 samples.

How we read this: 12 replicated groups read from 82 sample titles; they account for 81 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

+ 42 more — browse all 82 samples with per-sample file links →

Datasets on a similar topic

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