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Neuromedin U receptor-1 amplifies inflammatory lung innate lymphoid cells

GSE102299 Mus musculus (mouse) Single-cell RNA-seq 770 samples Submitted 2017/09/17 Platform GPL19057
At a glance

Single-cell RNA-seq · mouse · 770 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Mus musculus (mouse)
Samples
770 samples
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)
Type 2 innate lymphoid cells (ILC2s) promote mucosal homeostasis, yet also contribute to pathologic type 2 inflammation in allergic asthma. Alarmin cytokines produced by damaged and stressed epithelial cells, such as IL-25, activate ILC2s, but it remains unclear if these cytokines are unique in switching homeostatic ILC2s into pro-inflammatory cells that drive tissue inflammation.
Read the full summary ↓
To identify molecular cues that modulate ILC responses to alarmins, we collected single-cell RNA-seq profiles of lung-resident ILCs at steady state and after in vivo stimulation. Computational and functional analysis identified that ILC2s express the neuropeptide receptor Nmur1. Neuromedin U (NMU), the ligand of Nmur1, activates ILCs in vitro, and when co-administered with IL-25 in vivo, NMU dramatically amplifies allergic inflammation and induces ILC2 expansion. Finally, loss of NMU/Nmur1-signaling reduces ILC2 frequency and effector function following allergen challenge in vivo. Our results demonstrate that Nmur1 signaling modifies alarmin-mediated ILC2 responses to promote pro-inflammatory ILCs, and highlights the importance of neuro-immune crosstalk in allergic inflammatory responses at mucosal surfaces.
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 GSE102299_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA397374 and SRA study SRP114926. Searching any of these in the dataset finder brings you back here.

Samples in this study

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