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Endothelial Cells retain inflammatory memory through chromatin remodeling [ATAC]

GSE301784 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/11/06 Platform GPL34475
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 12 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
12 samples: 3 × Clp_Sp vs 3 × Clp_Saline vs 3 × Sham_Saline vs 3 × Sham_SpGroups 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.
Paper
Endothelial cells retain inflammatory memory through chromatin remodeling in a two-hit model of infection-induced inflammationAmerican journal of physiology. Cell physiology · 2025
Summary (written by the submitters)
Sepsis survivors exhibit long-term endothelial dysfunction, increasing susceptibility to secondary infections such as pneumonia. To investigate the molecular basis of endothelial inflammatory memory, we performed transcriptomic profiling of lung and kidney endothelial cells isolated from a murine two-hit sepsis model. This model incorporates cecal ligation and puncture (CLP) as the primary inflammatory event, followed by a secondary intranasal challenge with Streptococcus pneumoniae.
Published in
Endothelial cells retain inflammatory memory through chromatin remodeling in a two-hit model of infection-induced inflammation
Gonsales Spindola D, Bahr A, Clark S et al. · American journal of physiology. Cell physiology 2025 · PMID 41217002 · doi:10.1152/ajpcell.00567.2025
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 GSE301784_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 PRJNA1287566 and SRA study SRP599054. Searching any of these in the dataset finder brings you back here.

Study design
3 × Clp_Sp vs 3 × Clp_Saline vs 3 × Sham_Saline vs 3 × Sham_Sp

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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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