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Side- and disease-dependent changes in human aortic valve cell population and transcriptomic heterogeneity determined by single-cell RNA sequencing

GSE220774 Homo sapiens (human) Single-cell RNA-seq 15 samples Submitted 2024/12/01 Platform GPL24676
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.
Organism
Homo sapiens (human)
Samples
15 samples: 13 distinct titles, 1 of them repeatedGroups 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
Side- and Disease-Dependent Changes in Human Aortic Valve Cell Population and Transcriptomic Heterogeneity Determined by Single-Cell RNA SequencingGenes · 2024
Summary (written by the submitters)
Calcific aortic valve disease develops preferentially on the fibrosa side while the ventricularis side remains relatively spared with unknown mechanisms. We hypothesized that the fibrosa is prone to the disease due to side-dependent differences in transcriptomic patterns and cell phenotypes. To test this hypothesis, we performed single-cell RNA-sequencing using a new method to collect endothelial-enriched samples from either the fibrosa or ventricularis of freshly-obtained human AV leaflets from 5 donors ranging from non-diseased to fibrocalcific stages.
Published in
Side- and Disease-Dependent Changes in Human Aortic Valve Cell Population and Transcriptomic Heterogeneity Determined by Single-Cell RNA Sequencing
Villa-Roel N, Park C, Andueza A et al. · Genes 2024 · PMID 39766890 · doi:10.3390/genes15121623
This dataset
Open GSE220774 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 GSE220774_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 PRJNA911302 and SRA study SRP412622. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39766890) ↗
Study design
13 distinct titles, 1 of them repeated
Donor #4 Leftove ×3

How we read this: Read from 15 sample titles: 13 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.

Samples in this study
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.