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Single-cell RNA sequencing of aortic cellular in ApoE-/- mice and MSC_ITGB3-treated ApoE-/- mice

GSE238011 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2026/07/11 Platform GPL9185
At a glance

Single-cell RNA-seq · mouse · 2 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
2 samples
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)
We reports 33,782 individual cells from HFD-fed mouse aortas with or without MSC_ITGB3 treatment were subjected to single-cell RNA sequencing. We generated a compendium of 30 cell clusters, mainly smooth muscle cells (SMCs), endothelial cells (ECs), and immune cells. In the HFD group, genes associated with proliferation, migration, and collagen were highly expressed in the major SMC subpopulations.
Read the full summary ↓
However, the expression of contraction-related genes in SMC subpopulations was significantly higher in the MSC_ITGB3 group than in the HFD group. After HFD consumption, EC subpopulations with active PI3K-Akt signaling pathways, extracellular matrix-receptor interaction, and contraction-related genes were significantly enriched. In the MSC_ITGB3 group, the number of dendritic cells positively correlated with atherosclerotic lesion progression and contributed to lipid accumulation, and inflammatory factor levels decreased. These data on the composition, signaling pathways, and cellular communication of the aorta cells following stem cell treatment and atherosclerotic disease progression may help improve atherosclerosis treatment.
This dataset
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.

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 GSE238011_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA997372 and SRA study SRP450857. Searching any of these in the dataset finder brings you back here.

Samples in this study

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