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Gene perturbation in human arterial smooth muscle cells under treatments promoting synthetic or contractile phenotype

GSE246269 Homo sapiens (human) Single-cell RNA-seq 55 samples Submitted 2024/04/11 Platform GPL30173
At a glance

Single-cell RNA-seq · human · 55 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
55 samples: 12 conditions, mostly with about 4 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 Gene-Regulatory Networks of Advanced Symptomatic AtherosclerosisCirculation research · 2024
Summary (written by the submitters)
Smooth muscle cell (SMC) gene regulatory networks are thought to play a role in atherosclerosis, a disease characterized by alterations to SMC phenotype. Based on analysis of multiple bulk and single-cell RNA-Seq atherosclerosis data sets, FRZB and ALCAM were prioritized as candidate regulators of a disease-associated gene expression program in SMC-s. The current RNA-Seq experiment was devised to study the contribution of these genes to SMC transcriptional phenotype under different treatment conditions.
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 GSE246269 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 GSE246269_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 PRJNA1032420 and SRA study SRP468516. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38639096) ↗
Study design
12 conditions, mostly with about 4 replicates each
ALCAM siRNA TGFbeta ×6 Negative Control siRNA Cholesterol ×6 Negative Control siRNA TGFbeta ×6 ALCAM siRNA Cholesterol ×5 control lentivirus Cholesterol ×4 control lentivirus No treatment ×4 control lentivirus TGFbeta ×4 FRZB lentiviral overexpression Cholesterol ×4 +4 more

Supports a case/control comparison: 12 samples read as cases, 28 as controls.

How we read this: 12 replicated groups read from 55 sample titles; they account for 55 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

+ 15 more — browse all 55 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.