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In the aftermath of stroke or myocardial infarction, neutrophil extracellular traps disrupt systemic immunoglobulin production

GSE254410 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2024/03/17 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples: 12 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)
Post-injury dysfunction of humoral immunity accounts for infections and poor outcome in cardiovascular diseases. Immunoglobulin A (IgA), the most abundant mucosal antibody is produced by plasma B cells in intestinal Peyer’s patches (PP) and lamina propria. Here, we show that stroke and myocardial ischemia (MI) patients had strongly reduced IgA blood levels.
Read the full summary ↓
This was phenocopied in experimental mouse models where decreased plasma and fecal IgA were accompanied by rapid and macroscopic shrinkage of PP caused by substantial B cell loss. Stroke/MI triggered neutrophils to release neutrophil extracellular traps (NETs). Depletion of neutrophils, NET-degradation, or blockade of NET-release inhibited PP shrinkage and loss of B cells and IgA in stroke mice. Also, stroke and MI patients had higher amounts of circulating NETs, correlating with reduced IgA levels. Strikingly, stroke patients treated with DNase-I had reduced circulating NETs and stable IgA levels. Our results unveil how tissue-injury-triggered systemic NET release disrupts physiological IgA secretion and how this can be inhibited in patients.
This dataset
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.

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 GSE254410_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1070340 and SRA study SRP486645. Searching any of these in the dataset finder brings you back here.

Study design
12 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 12 sample titles: 12 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
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