← BioTransfer GEO Dataset Finder
GEO series

Single Cell and Spatial Transcriptomics Identify Novel Immune-Stromal Interactions in Cardiac Allograft Vasculopathy

GSE317680 Homo sapiens (human) Spatial transcriptomics 13 samples Submitted 2026/03/06 Platform GPL24676
At a glance

Spatial transcriptomics · human · 13 samples

What it is
Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism
Homo sapiens (human)
Samples
13 samples: 12 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.
Summary (written by the submitters)
Cardiac allograft vasculopathy (CAV) is the leading cause of mortality in heart transplant recipients. Despite the prevalence of CAV, there are no targeted therapeutic options to prevent or reverse disease progression, and patients ultimately require retransplant. CAV is defined by progressive neointimal hyperplasia in donor heart coronary arteries, leading to luminal obliteration and ultimately allograft failure or sudden cardiac death.
Read the full summary ↓
Although immune and stromal cell interactions are believed to play a key role in CAV pathogenesis, the specific cellular players and molecular signals driving disease remain undefined. In this study, we leverage single-cell RNA sequencing and spatial transcriptomics of human coronary arteries to transcriptionally characterize CAV and define the neointimal microenvironment. We compare arteries with CAV to atherosclerotic coronary artery disease and non-disease controls to identify a unique CAV transcriptional signature.
This dataset
Open GSE317680 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 GSE317680_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: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA1414637 and SRA study SRP666722. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 distinct titles, 1 of them repeated
pt1_RCA ×2

Supports a case/control comparison: 2 samples read as cases, 1 as controls.

How we read this: Read from 13 sample titles: 12 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 →

Share this dataset

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.