GEO series

Tonic type I interferon signaling optimizes the antiviral function of plasmacytoid dendritic cells [Bulk RNA-seq]

GSE252188 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2025/08/05 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 15 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: 15 samples: 5 conditions, mostly in triplicateGroups 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: Tonic type I interferon signaling optimizes the antiviral function of plasmacytoid dendritic cellsNature immunology · 2025

Summary (written by the submitters)

Plasmacytoid dendritic cells (pDCs) mount powerful type I interferon (IFN-I) responses against viruses and virus-derived nucleic acids. Only a small fraction of pDCs produces high levels of IFN-I, yet neither the molecular basis nor the purpose of this functional heterogeneity are known. We report that naive murine pDCs comprise three transcriptionally (but not epigenetically) distinct subsets.

Read the full summary ↓

This heterogeneity is generated by tonic IFN-I signaling elicited in part by the cGAS/STING and TLR9 pathways. A small "IFN-I-naive" subset (pDC-A) was expanded in STING deficiency or after transient IFN-I receptor blockade, but was abolished by exogenous IFN-I. pDC-A showed strong cytokine responses yet were susceptible to infection with vesicular stomatitis virus (VSV). Conversely, the majority of pDCs comprised the "IFN-I-primed" subsets (pDC-B/C) that showed lower IFN-I responses but were resistant to VSV. Thus, tonic IFN-I signaling decreases the cytokine-producing capacity of pDCs but increases their virus resistance, achieving the optimal functional balance for antiviral responses.

Published in

Tonic type I interferon signaling optimizes the antiviral function of plasmacytoid dendritic cells

Pucella JN, Maqueda-Alfaro RA, Ni H et al. · Nature immunology 2025 · PMID 41087726 · doi:10.1038/s41590-025-02279-4

This dataset

Open GSE252188 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 GSE252188_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 PRJNA1058465 and SRA study SRP480486. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41087726) ↗

Study design

5 conditions, mostly in triplicate

pDC-A, WT, ×3 pDC-B, WT, ×3 pDC-C, WT, ×3 pDC-A, Ifnar1-KO, ×3 pDC-C, WT, CpG-A, ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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

GSM7996375 pDC-A, WT, rep1 pDC-A, WT,
GSM7996376 pDC-A, WT, rep2 pDC-A, WT,
GSM7996377 pDC-A, WT, rep3 pDC-A, WT,
GSM7996378 pDC-B, WT, rep1 pDC-B, WT,
GSM7996379 pDC-B, WT, rep2 pDC-B, WT,
GSM7996380 pDC-B, WT, rep3 pDC-B, WT,
GSM7996381 pDC-C, WT, rep1 pDC-C, WT,
GSM7996382 pDC-C, WT, rep2 pDC-C, WT,
GSM7996383 pDC-C, WT, rep3 pDC-C, WT,
GSM7996384 pDC-A, Ifnar1-KO, rep1 pDC-A, Ifnar1-KO,
GSM7996385 pDC-A, Ifnar1-KO, rep2 pDC-A, Ifnar1-KO,
GSM7996386 pDC-A, Ifnar1-KO, rep3 pDC-A, Ifnar1-KO,
GSM7996387 pDC-C, WT, CpG-A, rep1 pDC-C, WT, CpG-A,
GSM7996388 pDC-C, WT, CpG-A, rep2 pDC-C, WT, CpG-A,
GSM7996389 pDC-C, WT, CpG-A, rep3 pDC-C, WT, CpG-A,

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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.