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Targeting JAK1/3-STAT1 signaling attenuates cytotoxic T lymphocytes activation for the treatment in Stevens-Johnson syndrome and toxic epidermal necrolysis

GSE333617 Homo sapiens (human) Single-cell RNA-seq 26 samples Submitted 2026/06/25 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: 26 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.
Summary (written by the submitters)
Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are life-threatening severe cutaneous adverse reactions (SCARs). We integrate single-cell and spatial transcriptomic analyses on blood, blisters, and lesional skin from SJS/TEN patients, revealing IFN-γ and JAK/STAT signaling as the key pathways responsible for driving epidermal necrolysis.
Read the full summary ↓
We identify JAK1/3–STAT1 signaling as a primary driver of the activation of CD8+ cytotoxic T lymphocytes (CTLs), natural killer (NK)/NKT cells, macrophages and conventional dendritic cells in skin lesions. The high proportion of cytotoxic protein (granulysin and granzyme B)–expressing CD8+ CTLs in SJS/TEN blisters is associated with STAT1 phosphorylation. Immunostaining and ex vivo blocking assays further confirm the potential for the JAK1/3 inhibitor to attenuate CD8+ CTL activation in SJS/TEN. We conduct a proof-of-concept clinical trial in 20 patients with SJS/TEN (ClinicalTrials.gov: NCT06474078), and find that the JAK1/3 inhibitor tofacitinib improves clinical outcomes by reducing skin-healing time with no observed mortality. Our study provides an effective and alternative mechanism-based therapeutic approach for SJS/TEN.
This dataset
Open GSE333617 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 GSE333617_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 PRJNA1471675 and SRA study SRP704506. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
26 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 26 sample titles: 26 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
Datasets on a similar topic

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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.