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IFN-γ induction of GBP1 drives aberrant macrophage activation in human skin granulomas

GSE290458 Homo sapiens (human) Single-cell RNA-seq 47 samples Submitted 2026/04/18 Platform GPL21290
At a glance

Single-cell RNA-seq · human · 47 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
47 samples: 8 conditions, mostly with about 6 replicates eachGroups 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
A STAT1/ETC/GBP1 axis represents a potential therapeutic target for noncommunicable granulomatous skin diseaseScience advances · 2026
Summary (written by the submitters)
Granuloma annulare (GA) and cutaneous sarcoidosis (cSAR), non-communicable skin conditions, possess an overlapping immunopathology, in which the aberrant activation of macrophages by IFN-γ constitutes a central driver of disease. Nevertheless, our understanding of the molecular events occurring in pathologically activated GA and cSAR macrophages remains limited.
Read the full summary ↓
Here, we analyzed published single-cell RNA sequencing data of GA and cSAR and performed in-vitro experiments with primary human cells, allowing us to show that oxidative phosphorylation (OXPHOS) is a dominant metabolic pathway in IFN-γ-activated macrophages. Furthermore, we identify an IFN-γ-induced, OXPHOS-sensitive response network in human GA and cSAR macrophages. Within this network, GBP1 plays a central role in controlling IFN-γ-mediated macrophage activation. Meanwhile, inhibition of OXPHOS and GBP1 both promoted resolution of granulomas and prevented granuloma induction in a human in-vitro granuloma model. Taken together, our study suggests that OXPHOS and GBP1 represent therapeutic strategies for treating cutaneous granulomatous diseases.
Published in
A STAT1/ETC/GBP1 axis represents a potential therapeutic target for noncommunicable granulomatous skin disease
Huerta Arana M, Klapproth H, Stanczak MA et al. · Science advances 2026 · PMID 42213834 · doi:10.1126/sciadv.aea0773
This dataset
Open GSE290458 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 GSE290458_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 PRJNA1228071 and SRA study SRP566244. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42213834) ↗
Study design
8 conditions, mostly with about 6 replicates each
Gal_Control ×6 Gal_IFNg ×6 Gal_LPS ×6 Gal_LPS_IFNg ×6 Glc_Control ×6 Glc_LPS ×6 Glc_LPS_IFNg ×6 Glc_IFNg ×5

Supports a between-group comparison across 47 samples.

How we read this: 8 replicated groups read from 47 sample titles; they account for 47 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

+ 7 more — browse all 47 samples with per-sample file links →

Datasets on a similar topic

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