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SM22α-lineage stromal cells in perivascular adipose tissue contribute to abdominal aortic aneurysm

GSE293558 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2025/05/09 Platform GPL17021
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. 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
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)
Background: Perivascular adipose tissue (PVAT) is a key regulator of vascular dysfunction. Impairment of PVAT phenotypic plasticity with aging may play a role in vascular pathology including abdominal aortic aneurysms (AAA). Yet, the mechanisms underlying PVAT plasticity in aneurysm pathogenesis remain elusive. Methods: Single-cell RNA sequencing (scRNA-seq) was performed on perivascular stromal cells (PVSCs) from young (2-3-month-old) and aged (18-20-month-old) mice.
Read the full summary ↓
The expression of peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) was measured in PVAT of aged mice and human aneurysm samples. Loss- and gain-of-function approaches were employed to investigate the role of SM22α-lineage PVSCs-derived PGC-1α in aneurysm development. Molecular mechanisms were explored through transcriptome and functional studies in young and aged mice, SM22αCre; Rosa26RFP/+; PGC1αf/f and SM22αCre; Rosa26RFP/+ mice with angiotensin II (AngII)- and DOCA/salt- induced AAA models. Results: SM22α+ cells accumulated in PVAT of angiotensin II-treated aged mice and patients with aortic aneurysms. scRNA-seq analysis revealed that aging disrupted the differentiation potential of SM22α-lineage PVSCs and led to reduced PGC-1α level. PGC1α downregulation in PVAT was observed in both mice AAA models and human aneurysm lesions. In mice with SM22α-driven PGC-1α-deletion AngII induced AAA formation was accompanied by PVSC-to-myofibroblast differentiation. in vitro PGC1α-knockdown suppressed nuclear YAP signaling, reducing adipocyte differentiation, while increasing MMP2-secreting myofibroblasts. Furthermore, PGC-1α overexpression in aged mice or administration of the YAP signaling inhibitor Verteporfin in SM22αCre; Rosa26RFP/+; PGC1αf/f mice restored PVAT function and conferred protection against aneurysm formation. Lastly, we employed the radiomics analysis to non-invasively evaluate PVAT in the context of AAA severity in humans. Conclusions: PGC-1α deficiency in SM22α-lineage stromal cells disrupts the balance between adipogenic and myofibrogenic differentiation through regulating the YAP signaling, ultimately promoting aneurysm development. Radiomics assessment may present a promising noninvasive approach for PVAT evaluation in aneurysms, offering valuable potential for clinical research.
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 GSE293558_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 PRJNA1245172 and SRA study SRP576146. Searching any of these in the dataset finder brings you back here.

Samples in this study

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