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Macrophage function in tissue repair and remodeling requires IL-4/IL-13 with apoptotic cells [Run2]

GSE98168 Mus musculus (mouse) RNA-seq (gene expression) 7 samples Submitted 2017/05/11 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 7 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
7 samples: 2 × IL-4 rep# vs 2 × aN+IL-4 rep#Groups 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)
Tissue repair is a subset of a broad repertoire of IL-4/IL-13-dependent host responses during helminth infections. Here, we show that IL-4/IL-13 alone were not sufficient, but IL-4/IL-13 together with apoptotic cells induced the tissue repair program in macrophages. Genetic ablation of sensors of apoptotic cells impaired the proliferation of tissue-resident macrophages and induction of anti-inflammatory/tissue repair genes in the lung following helminth infection or the damage caused by induction of colitis in the gut.
Read the full summary ↓
In contrast, recognition of apoptotic cells was dispensable for cytokine-dependent induction of pattern recognition receptor, cell adhesion or chemotaxis genes in macrophages. Detection of apoptotic cells can therefore spatially compartmentalize or prevent premature or ectopic activity of pleiotropic, soluble cytokines, such as IL-4/IL-13.
This dataset
Open GSE98168 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 GSE98168_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 PRJNA384200 and SRA study SRP105185. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28495875) ↗
Study design
2 × IL-4 rep# vs 2 × aN+IL-4 rep#

Supports a between-group comparison across 4 samples.

How we read this: 2 replicated groups read from 7 sample titles; they account for 4 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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