GEO series

A smooth muscle cell gene-regulatory network critical for the development of advanced-stage and symptomatic atherosclerosis [human]

GSE260657 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2024/03/05 Platform GPL21290

At a glance

Single-cell RNA-seq · human · 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: Homo sapiens (human)
Samples: 15 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.
Paper: Single-Cell Gene-Regulatory Networks of Advanced Symptomatic AtherosclerosisCirculation research · 2024

Summary (written by the submitters)

While our understanding of the single-cell gene expression patterns underlying the transformation of vascular cell types during the progression of atherosclerosis is rapidly improving, the clinical and pathophysiological relevance of these changes remain poorly understood. Single cell RNA sequencing (scRNAseq) data generated with SmartSeq2 (~8000 genes/cell) in nearly 19,000 single cells isolated during atherosclerosis progression in mice with human-like plasma lipoproteins and from humans with asymptomatic and symptomatic carotid plaques was clustered into multiple subtypes.

Read the full summary ↓

For clinical and pathophysiological context, the advanced-stage and symptomatic subtype clusters were integrated with 135 tissue-specific (atherosclerotic aortic wall, mammary artery, liver, skeletal muscle, and visceral and subcutaneous, fat) gene-regulatory networks (GRNs) inferred from 600 coronary artery disease (CAD) patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study.Advanced stages of atherosclerosis progression and symptomatic carotid plaques were largely characterized by three smooth-muscle cells (SMC), and three macrophage (MP) subtype clusters with extracellular matrix organization/osteogenic (SMC), and M1-type pro-inflammatory/Trem2-high lipid-associated (MP) phenotypes. Integrative analysis of these 6 clusters with STARNET revealed significant enrichments of three arterial wall GRNs: GRN33 (MP), GRN39 (SMC) and GRN122(MP) with major contributions to CAD heritability and strong associations with clinical scores of coronary atherosclerosis severity (SYNTAX/Duke scores). The presence and pathophysiological relevance of GRN39 was verified in five independent RNAseq datasets obtained from the human coronary and aortic artery, and primary SMCs and by targeting its top-key drivers, FRZB and ALCAM, in cultured human vascular SMCs.By identifying and integrating the most gene-rich single-cell subclusters of atherosclerosis to date with a CAD framework of GRNs, GRN39 was identified and independently validated as being critical for the transformation of contractile SMCs into an osteogenic phenotype promoting advanced-stage, symptomatic atherosclerosis.

Published in

Single-Cell Gene-Regulatory Networks of Advanced Symptomatic Atherosclerosis

Mocci G, Sukhavasi K, Örd T et al. · Circulation research 2024 · PMID 38639096 · doi:10.1161/CIRCRESAHA.123.323184

This dataset

Open GSE260657 in finder → Find more human 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 GSE260657_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 PRJNA1082625 and SRA study SRP492782. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38639096) ↗

Study design

A cohort of 15 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run athero_human_1 … athero_human_15 and carry no group information.

Samples in this study

GSM8121894 athero_human_1
GSM8121895 athero_human_2
GSM8121896 athero_human_3
GSM8121897 athero_human_4
GSM8121898 athero_human_5
GSM8121899 athero_human_6
GSM8121900 athero_human_7
GSM8121901 athero_human_8
GSM8121902 athero_human_9
GSM8121903 athero_human_10
GSM8121904 athero_human_11
GSM8121905 athero_human_12
GSM8121906 athero_human_13
GSM8121907 athero_human_14
GSM8121908 athero_human_15

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