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Rare ADAR1 loss-of-function variants alter RNA editing and drive interferon-dependent inflammation in psoriasis

GSE301804 Homo sapiens (human) Single-cell RNA-seq 19 samples Submitted 2026/04/20 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 19 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
19 samples: 7 × 9260-JF vs 4 × 8911-JF vs 4 × 10082-JF vs 4 × 8128-JFGroups 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)
In 4 unrelated pedigrees, we report rare heterozygous loss-of-function ADAR1 mutations cosegregating with autosomal dominant, early onset psoriasis and increased type I interferon (IFN)-stimulated gene (ISG) transcripts in skin lesions and/or blood. We identified 6 additional variants in a psoriasis cohort. Skin single cell transcriptomics identified keratinocytes and melanocytes as key IFN sources.
Read the full summary ↓
ADAR1 knockdown in keratinocytes and melanocytes, and transduction of ADAR1G1119R and ADAR1P3A alleles in keratinocytes, led to reduced A-to-I RNA editing and upregulation of ISG expression, reversed by JAK1 and TYK2 inhibition, mirroring their clinical benefit in these patients. This set of data defines a new IFN-dependent psoriasis subtype resulting from inborn errors of ADAR1-dependent RNA editing, leading to IFN constitutive dysregulation, and opens new avenues in precision medicine.
This dataset
Open GSE301804 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 GSE301804_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 PRJNA1287583 and SRA study SRP599157. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
7 × 9260-JF vs 4 × 8911-JF vs 4 × 10082-JF vs 4 × 8128-JF

Supports a between-group comparison across 19 samples.

How we read this: 4 replicated groups read from 19 sample titles; they account for 19 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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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.