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Widespread gene-environment interactions shape the immune response to SARS-CoV-2 infection in hospitalized COVID-19 patients

GSE281237 Homo sapiens (human) Single-cell RNA-seq 29 samples Submitted 2026/04/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 29 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
29 samples: 5 conditions, mostly in duplicateGroups 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
Widespread gene-environment interactions shape the immune response to SARS-CoV-2 infection in hospitalized COVID-19 patientsNature communications · 2026
Summary (written by the submitters)
Genome-wide association studies performed in COVID-19 patients have uncovered various loci significantly associated with susceptibility to SARS-CoV-2 infection and disease severity. However, the underlying cis-regulatory genetic factors contributing to heterogeneity in the response to SARS-CoV-2 infection and their impact on clinical phenotypes remain enigmatic.
Read the full summary ↓
Here, we use single-cell RNA-sequencing to quantify genetic contributions to cis-regulatory variation in 361,119 peripheral blood mononuclear cells of 63 acute COVID-19 patients, 39 convalescent samples, and 106 healthy controls. Expression quantitative trait loci mapping across cell types within each disease state group reveals thousands of cis-associated variants, of which hundreds are detected exclusively in immune cells derived from acute patients. Patient-specific genetic effects dissipate as infection resolves, suggesting that distinct gene regulatory networks are at play in the active infection state. Further, 20.3% of tested loci demonstrate significant cell state interactions with genotype, with pathways related to interferon responses and oxidative phosphorylation showing pronounced cell state-dependent variation, predominantly in CD14+ monocytes. Overall, we estimate that 16.8% of tested genes exhibit gene-environment interaction effects, highlighting the importance of environmental modifiers in the transcriptional regulation of the immune response to SARS-CoV-2. Our findings argue for the existence of extensive gene-environment effects among patients responding to an infection.
Published in
Widespread gene-environment interactions shape the immune response to SARS-CoV-2 infection in hospitalized COVID-19 patients
Randolph HE, Aguirre-Gamboa R, Brunet-Ratnasingham E et al. · Nature communications 2026 · PMID 42049757 · doi:10.1038/s41467-026-72393-w
This dataset
Open GSE281237 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 GSE281237_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 PRJNA1182788 and SRA study SRP543634. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42049757) ↗
Study design
5 conditions, mostly in duplicate
pl ×21 COVID-1 ×2 COVID-2 ×2 COVID-3 ×2 COVID-4 ×2

Supports a between-group comparison across 29 samples.

How we read this: 5 replicated groups read from 29 sample titles; they account for 29 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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