← BioTransfer GEO Dataset Finder
GEO series

Immunologic drivers of immune checkpoint inhibitor therapy myocarditis, fatality and anti-tumor response in cancer patients

GSE228597 Homo sapiens (human) Single-cell RNA-seq 75 samples Submitted 2024/06/22 Platform GPL18573Platform GPL24676
At a glance

Single-cell RNA-seq · human · 75 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
75 samples: 70 distinct titles, 3 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
Immune responses in checkpoint myocarditis across heart, blood and tumourNature · 2024
Summary (written by the submitters)
Immune checkpoint inhibitors (ICIs) can offer remarkable benefits to patients with cancer, but these agents can cause morbid and potentially fatal immune-related adverse events (irAEs), the most lethal of which is ICI-related myocarditis (irMyocarditis). The pathogenesis of irMyocarditis and its relationship to anti-tumor immunity remain poorly understood.
Read the full summary ↓
We sought to define how immune cell trafficking between heart, tumor and blood contributes to irMyocarditis pathogenesis and fatality by leveraging single-cell (sc)RNA-seq coupled with T cell receptor (TCR) sequencing, microscopy, and proteomics analysis of 28 irMyocarditis patients and 23 controls. Our analysis of ~284,360 cells from the heart and blood specimens identified enrichment of cytotoxic T-cell subsets, inflammatory macrophage subsets, conventional dendritic cells (cDCs), and fibroblasts in the heart that accompany the upregulation of potentially targetable, pro-inflammatory transcriptional programs. Expanded TCR clones enriched in irMyocarditis are largely distinct from those in paired tumors, suggesting distinct mechanisms between anti-tumor immune response and irMyocarditis. We also identified circulating biomarkers of fatality, including decreased circulating CD4+ T cells, decreased cDCs, and the presence of cardiac-expanded TCRs in a circulating, cycling CD8+ T cell population. Collectively, these findings highlight critical biology driving disease pathogenesis and nominate putative biomarkers and tailored therapeutic interventions for patients with irMyocarditis.
Published in
Immune responses in checkpoint myocarditis across heart, blood and tumour
Blum SM, Zlotoff DA, Smith NP et al. · Nature 2024 · PMID 39506125 · doi:10.1038/s41586-024-08105-5
This dataset
Open GSE228597 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 GSE228597_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 PRJNA950416. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39506125) ↗
Study design
70 distinct titles, 3 of them repeated
A17-341_A ×3 A18-122_A ×3 A19-230_A ×2

Supports a case/control comparison: 1 samples read as cases, 5 as controls.

How we read this: Read from 75 sample titles: 70 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

+ 35 more — browse all 75 samples with per-sample file links →

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.