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Using plasma cell-free mRNA to profile immune response and myocardial damage in immune checkpoint inhibitor-induced myocarditis

GSE296680 Homo sapiens (human) Single-cell RNA-seq 26 samples Submitted 2025/05/24 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 26 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
26 samples: 14 × Plasma bulk cell-free RNA, group C patient vs 7 × Plasma bulk cell-free RNA, group B patient vs 5 × Plasma bulk cell-free RNA, group A patientGroups 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
Using plasma cell-free mRNA to profile immune response and myocardial damage in immune checkpoint inhibitor-induced myocarditisThe Journal of clinical investigation · 2025
Summary (written by the submitters)
Immune checkpoint inhibitor-induced myocarditis (ICI-m) is a highly fatal cancer treatment complication with challenging diagnosis and invasive diagnostic methods. We developed a plasma cell-free mRNA (cf-mRNA) platform for noninvasive characterization of the immune and myocardial landscape in ICI-m. We sequenced bulk cell-free total RNA from plasma of cancer patients treated with ICIs: without immune-related adverse events (irAE) (group A, n=5), with noncardiac irAE (group B, n=7), and with ICI-m with/without noncardiac irAE (group C, n=10).
Read the full summary ↓
Publicly available healthy control cfRNA (n=30) served as external controls. Cell type-specific gene panels from single cell RNA-sequencing of circulating immune and cardiac cells were used for cellular deconvolution. Cancer and inflammatory pathways were enriched in ICI-treated patients versus controls. Cardiac pathways and conduction system genes were significantly enriched in group C versus A and B. Temra CD8+ T cell and cardiomyocyte signature scores, with CCL5 and MYH6 expression, were elevated in group C. Our custom ICI-m classifier, containing three cardiac- and three immune cell-specific genes, effectively differentiated group C from A (AUC 0.903, 95% CI 0.858-0.903) and B patients (AUC 0.983, 95% CI 0.970-0.983), outperforming unsupervised feature selection. outperforming unsupervised feature selection. This platform captures disease-specific signatures enabling precision diagnostics for inflammatory diseases.
Published in
Using plasma cell-free mRNA to profile immune response and myocardial damage in immune checkpoint inhibitor-induced myocarditis
Raissadati A, Zhou X, Chou H et al. · The Journal of clinical investigation 2025 · PMID 40829177 · doi:10.1172/JCI188817
This dataset
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.

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 GSE296680_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1260525 and SRA study SRP583945. Searching any of these in the dataset finder brings you back here.

Study design
14 × Plasma bulk cell-free RNA, group C patient vs 7 × Plasma bulk cell-free RNA, group B patient vs 5 × Plasma bulk cell-free RNA, group A patient

Supports a between-group comparison across 26 samples.

How we read this: 3 replicated groups read from 26 sample titles; they account for 26 of them. 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
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