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Epigenetic attenuation of interferon signaling drives aging-related improvements in systemic lupus

GSE277909 Homo sapiens (human) Single-cell RNA-seq 271 samples Submitted 2025/06/26 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 271 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
271 samples: 271 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
Epigenetic attenuation of interferon signaling is associated with aging-related improvements in systemic lupus erythematosusScience translational medicine · 2025
Summary (written by the submitters)
In the general human population, aging is associated with a rise in systemic inflammation, primarily involving innate immune pathways related to interferon (IFN), toll-like receptor, and cytokine signaling. In systemic lupus erythematosus (SLE), a prototypical systemic autoimmune disease, aging is distinctly associated with improvements in disease activity, suggesting a unique relationship between aging and inflammation in this disease.
Read the full summary ↓
Using a multi-omic approach incorporating transcriptional profiling, single cell RNA sequencing and methylation analysis, we studied age-related changes in the immune profiles of 287 SLE patients between 20 and 83 years old, and compared the results against 928 healthy controls aged between 21 and 89 years old. In contrast to the increase in inflammatory gene expression that occurs with aging in most healthy adults, SLE patients exhibited the opposite. Most notable was a decrease in type I and II IFN signaling that was evident across multiple cell types, with natural killer (NK), CD4+ effector memory T cells, and naïve B cells exhibiting the most significant differences. We found that aging in SLE patients was also associated with differential methylation of the genome. Notably, of the genes both downregulated and hypermethylated with older age, IFN-related genes were disproportionately represented, suggesting that age-related decreases in IFN signaling were driven in part by epigenetic silencing. Both SLE patients and healthy controls demonstrated age-related declines in naïve T cells and lymphoid progenitor cells, but only SLE patients demonstrated age-related increases in CD56-dim NK cells. Taken together, our work provides new insight into the phenomenon of inflammaging and the unique clinical improvement in disease activity that occurs in SLE patients as they age.
Published in
Epigenetic attenuation of interferon signaling is associated with aging-related improvements in systemic lupus erythematosus
Narendra R, Van Phan H, Patterson SL et al. · Science translational medicine 2025 · PMID 40561001 · doi:10.1126/scitranslmed.adt5550
This dataset
Open GSE277909 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 GSE277909_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 PRJNA1164739. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40561001) ↗
Study design
271 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 271 sample titles: 271 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

+ 231 more — browse all 271 samples with per-sample file links →

Datasets on a similar topic

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