← BioTransfer GEO Dataset Finder
GEO series

Single-cell analysis of the progeric vascular wall reveals progressive cell-type specific dysfunction and accumulation of somatic mutations

GSE317471 Mus musculus (mouse) Single-cell RNA-seq 30 samples Submitted 2026/06/30 Platform GPL21493
At a glance

Single-cell RNA-seq · mouse · 30 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
30 samples: 30 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)
Background: The premature aging disorder Hutchinson-Gilford Progeria Syndrome (HGPS) is caused by de novo LMNA mutations producing the aberrant Lamin A isoform progerin. HGPS patients die from cardiovascular disease, with their arteries showing extensive cellular and structural remodeling, but the mechanisms driving vascular dysfunction are not fully understood.
Read the full summary ↓
Methods: To characterize the molecular processes underlying vascular degeneration in HGPS, we used LmnaG609G/G609G mice, which carry the mouse equivalent of the HGPS-causing mutation and recapitulate the vascular phenotype without atheroprone stimuli. We performed single-cell RNA-sequencing on aortic arch cells from wild-type and LmnaG609G/G609G at different ages to capture time-dependent transcriptional changes. The Smart-seq2 protocol was used for sequencing, as it has high sensitivity and provides full-length transcript coverage. Results: We identified transcriptionally distinct VSMC and fibroblast populations that evolved over time. Disease-enriched VSMCs displayed high levels of endoplasmic reticulum (ER) stress and underwent phenotypic switching towards a fibroblast-like state, potentially expanding through intercellular communication. ER stress inhibition with TUDCA showed the ER stress and phenotypic switch develop independently. In addition, we observed increased DNA damage and somatic mutation accumulation in progeria VSMCs, correlating with high ER stress, a ROS response, and eventually increased expression of p53-related genes. Lastly, VSMC apoptosis occurred by 12 weeks of age. Progeria-enriched fibroblasts either became activated or increased cartilage production and accumulated somatic mutations, but to a lesser extent than VSMCs, suggesting cell-type somatic mutation susceptibility. Conclusions: We provide a detailed cellular and molecular atlas of VSMCs and fibroblasts with HGPS progression. We have developed a user-friendly searchable database for the research community to access the expression data, which will be of high relevance in preclinical research to ameliorate vascular aging.
This dataset
Open GSE317471 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 GSE317471_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 PRJNA1413226 and SRA study SRP665943. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
30 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 30 sample titles: 30 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

Search all mouse 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.