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Transcriptomic and proteomic characterization of cell and protein biomarkers of checkpoint inhibitor-induced liver injury [scRNA-seq]

GSE287541 Homo sapiens (human) Single-cell RNA-seq 92 samples Submitted 2025/01/22 Platform GPL30173
At a glance

Single-cell RNA-seq · human · 92 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
92 samples: 92 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
Circulating exhausted CD8+ effector memory cells differentiate immune checkpoint inhibitor-induced liver injury from other acute immune-mediated liver injuriesJournal for immunotherapy of cancer · 2026
Summary (written by the submitters)
Immune checkpoint inhibitors (ICI) targeting CTLA-4 and PD-1 have shown remarkable antitumor efficacy but can also cause immune-related adverse events, including checkpoint inhibitor-induced liver injury (ChILI). This multi-omic study aimed to investigate changes in blood samples from treated cancer patients who developed ChILI. PBMCs were analyzed by transcriptomic and T cell receptor sequencing (bulk and single cell), and extracellular vesicle (EV) enrichment from plasma was analyzed by mass spectroscopy proteomics.
Read the full summary ↓
Data were analyzed by comparing the ChILI patient group to the control group who did not develop ChILI and by comparing the onset of ChILI to pre-ICI treatment baseline. We identified significant changes in T cell clonality, gene expression, and protein levels in peripheral blood mononuclear cells (PBMCs) or plasma at the time of liver injury. Onset of ChILI was accompanied by an increase in T-cell clonality. Pathway analysis highlighted the involvement of innate and cellular immune responses, mitosis, pyroptosis, and oxidative stress. Single-cell RNA sequencing revealed that these changes were primarily found in select T cell subtypes (including CD8+ effector memory cells), while CD16+ monocytes exhibited enrichment in metabolic pathways. Proteomic analysis of plasma extracellular vesicles showed enrichment in liver-associated proteins among differentially expressed proteins. Interestingly, an increase in PBMC PD-L1 gene expression and plasma PD-L1 protein was also found to be associated with onset of ChILI.
Published in
Circulating exhausted CD8+ effector memory cells differentiate immune checkpoint inhibitor-induced liver injury from other acute immune-mediated liver injuries
Astbury S, Atallah E, Grove JI et al. · Journal for immunotherapy of cancer 2026 · PMID 41895713 · doi:10.1136/jitc-2025-014178
This dataset
Open GSE287541 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 GSE287541_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 PRJNA1213711 and SRA study SRP558677. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41895713) ↗
Study design
92 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 92 sample titles: 92 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

+ 52 more — browse all 92 samples with per-sample file links →

Datasets on a similar topic

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