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ICI-SJS/TEN mediated by macrophage-derived CXCL10 and abated by TNF blockade

GSE273720 Homo sapiens (human) Single-cell RNA-seq 46 samples Submitted 2024/08/26 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 46 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
46 samples: 46 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
Immune checkpoint inhibitor-induced severe epidermal necrolysis mediated by macrophage-derived CXCL10 and abated by TNF blockadeNature communications · 2024
Summary (written by the submitters)
Immune checkpoint inhibitors (ICI) represent new anticancer agents and have been used worldwide. However, ICI can potentially induce life-threatening severe cutaneous adverse reaction (SCAR), such as Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), hindering continuous ICI therapy. We examined 6 cohorts within 25 ICI-induced SJS/TEN patients and conducted single-cell RNA sequencing (scRNA-seq) analysis, which revealed overexpression of macrophage-derived CXCL10 that recruited CXCR3+ cytotoxic T lymphocytes (CTL) in blister cells from ICI-SJS/TEN skin lesions.
Read the full summary ↓
ScRNA expression profiles and ex vivo blocking studies further identified TNF signaling as the key pathway responsible for macrophage-derived CXCL10 and CTL activation. Based on the trajectory analysis, ICI-activated T cells from whole blood are proposed to serve as the initial cells involved in inflammation, that lead to monocytes differentiating into macrophages and increasing their susceptibility to migrate to the lesion sites. Compared with systemic corticosteroids treatment, ICI-induced SJS/TEN patients treated with biologic TNF blockade showed a significantly rapid recovery and no recurrence of SCAR with continuous ICI therapy. Our findings identified that macrophage-eliciting CTL contribute the pathogenesis of ICI-induced epidermal necrolysis and provide the therapeutic targets for the management and prevention of SCAR induced by ICI therapy.
Published in
Immune checkpoint inhibitor-induced severe epidermal necrolysis mediated by macrophage-derived CXCL10 and abated by TNF blockade
Chen CB, Hung SI, Chang JW et al. · Nature communications 2024 · PMID 39737932 · doi:10.1038/s41467-024-54180-7
This dataset
Open GSE273720 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 GSE273720_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 PRJNA1142921 and SRA study SRP523640. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39737932) ↗
Study design
46 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 46 sample titles: 46 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

+ 6 more — browse all 46 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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