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RNA-Seq analysis of mouse group 2 innate lymphoid cells (ILC2s) cultured with IL-33, IFN-g, and IL-27

GSE73272 Mus musculus (mouse) RNA-seq (gene expression) 9 samples Submitted 2015/10/30 Platform GPL18480
At a glance

RNA-seq (gene expression) · mouse · 9 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
9 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)
Purpose: We found that IFN-g and IL-27 had suppressive effects on ILC2s cultured with IL-33. The goal of this study is to clarify the expressions of RNA induced by IFN-g and IL-27 in ILC2s. Methods: ILC2s were isolated from fat-asociated lymphid clusters (FALC) of wild-type mice. They were cultured with IL-33 (10ng/ml), IL-33 + IFN-g (10ng/ml), or IL-33 + IL-27 (10ng/ml) for 48hrs.
Read the full summary ↓
RNA was isolated by Allprep DNA/RNA Micro Kit (QIAGEN), and cDNA libraries were prepared by TruSeq RNA Sample Preparation kits v2 (Illumina) according to the manufacturer’s low sample protocol. A HiSeq 1500 system (Illumina) was used for 50 single-end bases (50SE) sequencing. Results: Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to the reference genome (mm9) using Bowtie2 v2.1.0 and TopHat2 v2.0.8. The transcript abundances were estimated as FPKM (fragments per kilobase of exon million fragments mapped) value using Cufflinks v2.1.1. We found that both IL-27 and IFN-g upregulated the expression of STAT1 and IRF1 which are regulated downstream of IFN-g receptor signaling, but there was no difference in the expression of GATA3, a critical transcription factor for ILC2 functions. Conclusions: Our study represents the detailed differences of RNA expressions by RNA-seq technology.
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 GSE73272_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 PRJNA296499 and SRA study SRP063941. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 9 more — browse all 9 samples with per-sample file links →

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