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GEO series

Long COVID involves activation of proinflammatory and immune exhaustion pathways

GSE226260 Homo sapiens (human) RNA-seq (gene expression) 331 samples Submitted 2025/10/21 Platform GPL34284Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 331 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
331 samples: 331 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
Long COVID involves activation of proinflammatory and immune exhaustion pathwaysNature immunology · 2026
Summary (written by the submitters)
Long COVID (LC) involves a spectrum of chronic symptoms after acute severe acute respiratory syndrome coronavirus 2 infection. Current hypotheses for the pathogenesis of LC include persistent virus, tissue damage, autoimmunity, endocrine insufficiency, immune dysfunction and complement activation. We performed immunological, virological, transcriptomic and proteomic analyses from a cohort of 142 individuals between 2020 and 2021, including uninfected controls (n = 35), acutely infected individuals (n = 54), convalescent controls (n = 24) and patients with LC (n = 28).
Read the full summary ↓
The LC group was characterized by persistent immune activation and proinflammatory responses for more than 180 days after initial infection compared with convalescent controls, including upregulation of JAK-STAT, interleukin-6, complement, metabolism and T cell exhaustion pathways. Similar findings were observed in a second cohort enrolled between 2023 and 2024, including convalescent controls (n = 20) and patients with LC (n = 18). These data suggest that LC is characterized by persistent activation of chronic inflammatory pathways, suggesting new therapeutic targets and potential biomarkers of disease.
Published in
Long COVID involves activation of proinflammatory and immune exhaustion pathways
Aid M, Boero-Teyssier V, McMahan K et al. · Nature immunology 2026 · PMID 41388153 · doi:10.1038/s41590-025-02353-x
This dataset
Open GSE226260 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 GSE226260_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA939253 and SRA study SRP424803. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41388153) ↗
Study design
331 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 331 sample titles: 331 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

+ 291 more — browse all 331 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.