GEO series

Multiple layers of innate immune response antagonism of SARS-CoV-2

GSE255483 Mus musculus (mouse) Single-cell RNA-seq 16 samples Submitted 2026/06/03 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 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: Mus musculus (mouse)
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.
Paper: Redundant and distinct mechanisms suppress innate immune activation during SARS-CoV-2 infectionPLoS biology · 2026

Summary (written by the submitters)

Analysis of immune cells in lungs from infected mice with either WT SARS-CoV-2, the NSP1 or the NSP15 mutant or mock-infection by single-cell RNA-seq identified 15 populations of major myeloid and lymphoid cells with changes in the pattern of their activation associated with viral infection. The effects of mutations in NSP1 or NSP15 on these responses suggested different immunosuppressive mechanisms of the two viral proteins.

Published in

Redundant and distinct mechanisms suppress innate immune activation during SARS-CoV-2 infection

Zhou F, Periasamy S, Jackson ND et al. · PLoS biology 2026 · PMID 42160380 · doi:10.1371/journal.pbio.3003808

This dataset

Open GSE255483 in finder → Find more mouse 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 GSE255483_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 PRJNA1075097 and SRA study SRP489157. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42160380) ↗

Study design

16 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 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

GSM8073195 Mock-A1, mock infected
GSM8073196 Mock-A2, mock infected
GSM8073197 Mock-A3, mock infected
GSM8073198 Mock-A4, mock infected
GSM8073199 NSP1-C1, NSP1 mutant SARS-CoV-2 infected
GSM8073200 NSP1-C2, NSP1 mutant SARS-CoV-2 infected
GSM8073201 NSP1-C3, NSP1 mutant SARS-CoV-2 infected
GSM8073202 NSP1-C4, NSP1 mutant SARS-CoV-2 infected
GSM8073203 NSP15-D1, NSP15 mutant SARS-CoV-2 infected
GSM8073204 NSP15-D2, NSP15 mutant SARS-CoV-2 infected
GSM8073205 NSP15-D3, NSP15 mutant SARS-CoV-2 infected
GSM8073206 NSP15-D4, NSP15 mutant SARS-CoV-2 infected
GSM8073207 WT-B1, WT SARS-CoV-2 infected
GSM8073208 WT-B2, WT SARS-CoV-2 infected
GSM8073209 WT-B3, WT SARS-CoV-2 infected
GSM8073210 WT-B4, WT SARS-CoV-2 infected

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