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Truncating RELA variants drive autoinflammation and autoimmunity by impairing the negative feedback control of NF-k

GSE339599 Homo sapiens (human) Single-cell RNA-seq 16 samples Submitted 2026/07/30 Platform GPL30882
At a glance

Single-cell RNA-seq · human · 16 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
16 samples: 16 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)
The NF-κB signaling pathway coordinates inflammation, cell survival, and proliferation, while restraining excessive cell death to maintain immune homeostasis. Truncating mutations in RELA, encoding the NF-κB subunit p65, have been linked to autoinflammation and autoimmunity, but the underlying mechanisms remain incompletely defined. We investigated six patients from five unrelated families carrying novel heterozygous truncating RELA variants.
Read the full summary ↓
Despite reduced p65 expression, patients exhibited a broad spectrum of inflammatory manifestations alongside elevated baseline and stimulus-induced pro-inflammatory cytokines. Functional analyses in patient-derived cells and mutant RELA knock-in models showed that upstream NF-κB signaling was intact, but induction of inhibitory regulators such as IκBα and A20 was impaired. This defective feedback control shifted immune homeostasis toward amplified inflammatory responses that depended on the residual activity of the remaining functional RELA allele. Single-cell transcriptomics revealed distinct cell type-specific consequences: monocytes displayed constitutive type I interferon and NF-κB activation, B cells retained partial compensatory signaling, whereas T and NK cells exhibited transcriptional signatures of cell death pathways. Patient fibroblasts and mutant RELA knock-in cells further confirmed enhanced TNF-induced inflammatory gene expression and hypersensitivity to apoptosis and necroptosis. These findings establish RELA haploinsufficiency as a cause of systemic immune dysregulation, and link defective NF-κB feedback control to unchecked inflammation and inflammatory cell death.
This dataset
Open GSE339599 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 GSE339599_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 PRJNA1499264. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 6 samples read as cases, 10 as controls.

How we read this: Read from 16 sample titles: 16 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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