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Dendritic cells regulate the innate-adaptive balance in lymph nodes for optimal host defense

GSE288378 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/02/03 Platform GPL13112
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 8 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.
Summary (written by the submitters)
Lymph nodes (LNs) enable innate defense to limit pathogen dissemination while also driving adaptive immunity. Yet, certain innate responses can restrict adaptive processes, suggesting that these must be tightly regulated. Here, we report that after infection or immunization, LN architecture is rapidly altered, with large-scale, polarized recruitment of neutrophils and monocytes from inflamed blood vessels, and intranodal repositioning of NK cells.
Read the full summary ↓
Mechanistically, dendritic cells (DCs) promote this process through expression of inflammatory chemokines and integrin ligands. While necessary for efficient pathogen containment, DC-driven innate cell responses paradoxically limit early adaptive immunity, with infiltrating neutrophils displacing lymphocytes and reducing the LN area available for T cell priming. Upon threat secession however, DCs and DC-recruited monocytes phagocytose the neutrophils, restoring tissue architecture and generating polarized domains for downstream adaptive immune cell activation. Thus, DCs orchestrate innate cell organization during inflammation, serving as rheostats of innate versus adaptive functions of the LN.
This dataset
Open GSE288378 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 GSE288378_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 PRJNA1217051 and SRA study SRP560649. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
Naive_IVneg_res_cDC1 ×3 Naive_IVneg_res_cDC2 ×3 Naive_IVpos_res_cDC1 ×3 Naive_IVpos_res_cDC2 ×3 CpG6hrs_IVneg_res_cDC1 ×3 CpG6hrs_IVneg_res_cDC2 ×3 CpG6hrs_IVpos_res_cDC1 ×3 CpG6hrs_IVpos_res_cDC2 ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 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
Datasets on a similar topic

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