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The spectrum and regulatory landscapes of intestinal innate lymphoid cells are shaped by the microbiome (ChIP-Seq)

GSE85156 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2016/08/18 Platform GPL19057
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 24 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
24 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)
Innate lymphoid cells (ILCs) are critical modulators of mucosal immunity, inflammation, and tissue homeostasis, but their full spectrum of cellular states and regulatory landscapes remain elusive. Here, we use a combination of genome-wide RNA-seq, ChIP-seq and ATAC-seq to compare the transcriptional and epigenetic identity of small intestinal ILCs, identifying thousands of distinct gene profiles and regulatory elements.
Read the full summary ↓
Single-cell RNA-seq, cytometry, and imaging analyses reveal functional compartmentalization of cytokine expression and metabolic activity within the three classical ILC subtypes, and highlight transcriptional states beyond the current canonical classification. In addition, using antibiotic intervention and germ-free mice, we characterize the effect of the microbiome on the ILC regulatory landscape, and determine the response of ILCs to microbial colonization at the single-cell level. Together, our work characterizes the spectrum of transcriptional identities of small intestinal ILCs and describes how ILCs differentially integrate signals from the microbial microenvironment to generate phenotypic and functional plasticity.
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 GSE85156_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA336450 and SRA study SRP080959. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 24 more — browse all 24 samples with per-sample file links →

Datasets on a similar topic

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