Endothelial BMP6 Drives Hemodynamic-Dependent VSMCs Calcification in Carotid Atherosclerosis
Single-cell RNA-seq · human · 14 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
- 14 samples: 14 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.
- Paper
- Endothelial BMP6 Drives Hemodynamic-Dependent VSMCs Calcification in Carotid AtherosclerosisAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE309462_RAW.tar (it can run to tens of gigabytes).
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 PRJNA1336120 and SRA study SRP629563. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 14 sample titles: 14 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.
- GSM9267732 Sample1, symptomatic
- GSM9267733 Sample2, symptomatic
- GSM9267734 Sample3, symptomatic
- GSM9267735 Sample4, symptomatic
- GSM9267736 Sample5, symptomatic
- GSM9267737 Sample6, symptomatic
- GSM9267738 Sample7, asymptomatic
- GSM9267739 Sample8, asymptomatic
- GSM9267740 Sample9, asymptomatic
- GSM9267741 Sample10, asymptomatic
- GSM9267742 Sample11, asymptomatic
- GSM9267743 Sample12, asymptomatic
- GSM9267744 Sample13, carotid bifurcation
- GSM9267745 Sample14, common carotid
- GSE287688 Sex-specific KDM6A-HNF4A-CREBH network controls lipoprotein cholesterol metabolism and atherosclerosis via epigenetic reprograming of hepatocytes [RNA-seq] 68 samples shares atherosclerosis
- GSE326269 Hepatic HuR protects against atherosclerosis through bile acid production 38 samples shares atherosclerosis
- GSE287736 Sex-specific KDM6A-HNF4A-CREBH network controls lipoprotein cholesterol metabolism and atherosclerosis via epigenetic reprograming of hepatocytes 138 samples shares atherosclerosis
- GSE246318 Decoding the immune checkpoint signatures in human atherosclerosis: implications of type 2 diabetes and lipid-lowering intervention 49 samples shares atherosclerosis
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.