GEO series

CXCR3 ameliorates neutrophils-dependent disease severity in SARS-CoV-2 infection by regulating CD4+ T cell recruitment

GSE309229 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2026/06/08 Platform GPL19057

At a glance

RNA-seq (gene expression) · mouse · 10 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: Mus musculus (mouse)
Samples: 10 samples: 10 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)

Understanding the host immune response to SARS-CoV-2 infection is critical for developing effective immunotherapeutic interventions. Using bulk RNA sequencing of lung tissue from mock-infected and mouse-adapted SARS-CoV-2 strain MA-10-infected mice, we identified CXCL9, CXCL10, and CXCL11 as among the most upregulated transcripts. Notably, their shared receptor, CXCR3, was also upregulated, suggesting activation of the CXCL9/10/11–CXCR3 axis in the lungs.

Read the full summary ↓

Using spectral flow cytometry, we observed that the increased recruitment of CXCR3⁺ immune cells, particularly T cells, innate lymphoid cells (ILCs), and macrophages, correlated with milder disease outcome. Blocking CXCR3 signaling using monoclonal antibodies resulted in worsened disease, which was accompanied by reduced recruitment of T cells, ILCs, and macrophages, and a marked increase in neutrophil infiltration. Depletion of neutrophils using αLy6G antibodies in CXCR3-blocked mice alleviated disease severity, indicating that CXCR3 signaling mitigated neutrophil-driven pathology. CXCR3 blockade failed to exacerbate disease in RAG2⁻/⁻ mice, suggesting that CXCR3-mediated protection requires adaptive immune cells. Adoptive transfer of CD4⁺ T cells from wild type (WT), but not CXCR3⁻/⁻, mice conferred protection in RAG2⁻/⁻ mice. Together, our findings establish a protective role for CXCR3-recruited T cells blocking neutrophil infiltration in the lung, highlighting the mechanistic importance of the CXCL9/10/11–CXCR3 axis in protecting the lung from SARS-CoV-2 infection.

This dataset

Open GSE309229 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 GSE309229_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 PRJNA1334375 and SRA study SRP627212. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

10 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 10 sample titles: 10 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

GSM9263434 WT Control mouse 1; Day 6
GSM9263435 WT Control mouse 2; Day 6
GSM9263436 WT Control mouse 3; Day 6
GSM9263437 WT Control mouse 4; Day 6
GSM9263438 WT Control mouse 5; Day 6
GSM9263439 WT MA10-infected mouse 1; Day 6
GSM9263440 WT MA10-infected mouse 2; Day 6
GSM9263441 WT MA10-infected mouse 3; Day 6
GSM9263442 WT MA10-infected mouse 4; Day 6
GSM9263443 WT MA10-infected mouse 5; Day 6

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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.