GEO series

Single-cell RNA-sequencing identifies unique cell-specific gene expression profiles in high-grade cardiac allograft vasculopathy

GSE271408 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2024/12/21 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 16 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: 16 samples: 16 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: Single-cell RNA-sequencing identifies unique cell-specific gene expression profiles in high-grade cardiac allograft vasculopathyThe Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation · 2025

Summary (written by the submitters)

Background: Cardiac allograft vasculopathy (CAV), a diffuse thickening of the intima of the coronary arteries and microvasculature, is the leading cause of late graft failure and mortality after heart transplantation (HT). Diagnosis involves invasive coronary angiography, which carries substantial risk, and minimally-invasive approaches to CAV diagnosis are urgently needed.

Read the full summary ↓

Using single-cell RNA-sequencing in peripheral blood mononuclear cells (PBMCs), we sought to identify cell-specific gene expression profiles in CAV. Methods: Whole blood was collected from 22 HT recipients with angiographically-confirmed CAV and 18 HT recipients without CAV. PBMCs were isolated and subjected to single-cell RNA-sequencing using a 10X Genomics microfluidic platform. Downstream analyses focused on differential expression of genes, cell compositional changes, and T cell receptor repertoire analyses. Results: Across 40 PBMC samples, we isolated 134,984 cells spanning 8 major clusters and 31 subclusters of cell types. Compositional analyses showed subtle, but significant increases in CD4+ T central memory cells, and CD14+ and CD16+ monocytes in high-grade CAV (CAV-2 and CAV-3) as compared to low-grade or absent CAV. After adjusting for age, gender, and prednisone use, 745 genes were differentially expressed in a cell-specific manner in high-grade CAV. Weighted gene co-expression network analyses showed enrichment for putative pathways involved in inflammation and angiogenesis. There were no significant differences in T cell clonality or diversity with increasing CAV severity. Conclusions: Unbiased whole transcriptomic analyses at single-cell resolution identify unique, cell-specific gene expression patterns in CAV, suggesting the potential utility of peripheral gene expression biomarkers in diagnosing CAV.

Published in

Single-cell RNA-sequencing identifies unique cell-specific gene expression profiles in high-grade cardiac allograft vasculopathy

Amancherla K, Schlendorf KH, Chow N et al. · The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation 2025 · PMID 39580136 · doi:10.1016/j.healun.2024.11.017

This dataset

Open GSE271408 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 GSE271408_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 PRJNA1131346 and SRA study SRP517829. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39580136) ↗

Study design

16 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 16 sample titles: 16 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

GSM8376397 PBMCs, Batch 1, GEX
GSM8376398 PBMCs, Batch 2, GEX
GSM8376399 PBMCs, Batch 3, GEX
GSM8376400 PBMCs, Batch 4, GEX
GSM8376401 PBMCs, Batch 5, GEX
GSM8376402 PBMCs, Batch 6, GEX
GSM8376403 PBMCs, Batch 7, GEX
GSM8376404 PBMCs, Batch 8, GEX
GSM8376405 PBMCs, Batch 1, Feature barcodes
GSM8376406 PBMCs, Batch 2, Feature barcodes
GSM8376407 PBMCs, Batch 3, Feature barcodes
GSM8376408 PBMCs, Batch 4, Feature barcodes
GSM8376409 PBMCs, Batch 5, Feature barcodes
GSM8376410 PBMCs, Batch 6, Feature barcodes
GSM8376411 PBMCs, Batch 7, Feature barcodes
GSM8376412 PBMCs, Batch 8, Feature barcodes

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