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IFN-γ activates an immune-like regulatory network in the cardiac vascular endothelium

GSE263611 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2024/07/12 Platform GPL20301
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 16 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
Homo sapiens (human)
Samples
16 samples: 16 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)
The regulatory mechanisms underlying the response to pro-inflammatory cytokines during myocarditis are poorly understood. Here, we use iPSC-derived cardiovascular progenitor cells (CVPCs) to model the response to interferon gamma (IFN-γ) during myocarditis. We generate RNA-seq and ATAC-seq for four CVPCs that were treated with IFN-γ and compare them with paired untreated controls.
Read the full summary ↓
Transcriptional differences after treatment show that IFN-γ initiates an innate immune cell-like response in the vascular cardiac endothelium. IFN-γ treatment also shifts the CVPC transcriptome towards the adult coronary artery and aorta-like profile and expands the relative endothelial cell population in all four CVPC lines. Analysis of the accessible chromatin shows that IFN-γ is a potent chromatin remodeler and establishes an IRF-STAT immune-cell like regulatory network. Our findings reveal insights into the endothelial-specific protective mechanisms during myocarditis.
This dataset
Open GSE263611 in finder → Find more human 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 GSE263611_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1098322 and SRA study SRP500612. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 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 16 sample titles: 16 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
Datasets on a similar topic

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