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Single-Cell RNA Sequencing Unveils Macrophage Involvement in Aortic Valve Stenosis Progression

GSE291890 Homo sapiens (human) Single-cell RNA-seq 33 samples Submitted 2026/03/01 Platform GPL34281Platform GPL24676
At a glance

Single-cell RNA-seq · human · 33 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
33 samples: 33 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.
Summary (written by the submitters)
Introduction: Calcific aortic stenosis (AS) is an age-related progressive disease with increasing global prevalence. Although chronic inflammation is a key factor in its pathogenesis, the detailed immune cell profiles and their interactions with non-immune cells remain poorly understood. This study aimed to characterize the immune cell landscape in calcific AS at a single-cell level and evaluate the potential of anti-inflammatory therapy.
Read the full summary ↓
Methods: Human aortic valve samples were obtained from four patients with non-calcified aortic regurgitation (AR) and 11 patients with AS undergoing surgical aortic valve replacement. Single-cell 3’ gene expression sequencing (scRNA-seq (3’)) was performed on CD45+ immune cells, while single-cell RNA sequencing Flex (scRNA-seq (Flex)) and bulk RNA sequencing were conducted on unsorted cells. Additionally, an anti-TNFα antibody experiment was performed in a murine AS model induced by aortic valve wire injury. Results: In myeloid cells, LYVE1 resident macrophages were significantly decreased, whereas classical monocytes expressing pro-inflammatory genes and C1Q macrophages were increased in AS valves compared to AR valves. These macrophages exhibited strong interactions with valve interstitial cells, particularly with chondrogenic subtypes expressing COMP and SPARC. Anti-TNFα treatment in the murine model significantly reduced aortic valve calcification without hematopoietic complications. Conclusions: This study is the first to integrate scRNA-seq (3’) and scRNA-seq (Flex) analyses of human aortic valves, revealing single cell-level characteristics of immune cells and their interactions with non-immune cells in calcific AS. These findings suggest that anti-inflammatory therapy could be a therapeutic target for calcific AS.
This dataset
Open GSE291890 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 GSE291890_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 PRJNA1235730 and SRA study SRP570282. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
33 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 33 sample titles: 33 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

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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.