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Depletion of endothelial KLF4 drives age-related neurovascular dysfunction and neuropsychiatric impairment [ATAC-seq]

GSE333395 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 9 samples Submitted 2026/05/28 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 9 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
9 samples: 3 × Young Brain ECK4KO vs 2 × Young Brain CdH5Cre vs 2 × Old Brain ECk4KO vs 2 × Old Brain CdH5 CREGroups 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 KLF4 depletion drives age-related neurovascular dysfunction and neuropsychiatric impairmentProceedings of the National Academy of Sciences of the United States of America · 2026
Summary (written by the submitters)
Omni-ATAC sequencing was performed on purified brain endothelial nuclei from young and aged endothelial-specific Klf4 knockout (EC-K4KO) and WT Cre mice to investigate chromatin accessibility changes associated with endothelial aging and neurovascular dysfunction. Endothelial KLF4 loss induced chromatin remodeling enriched for inflammatory, stress-response, and senescence-associated pathways.
Published in
Endothelial KLF4 depletion drives age-related neurovascular dysfunction and neuropsychiatric impairment
Dhar M, Vázquez-Rosa E, Chaubey K et al. · Proceedings of the National Academy of Sciences of the United States of America 2026 · PMID 42313933 · doi:10.1073/pnas.2426990123
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 GSE333395_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 PRJNA1471023 and SRA study SRP704030. Searching any of these in the dataset finder brings you back here.

Study design
3 × Young Brain ECK4KO vs 2 × Young Brain CdH5Cre vs 2 × Old Brain ECk4KO vs 2 × Old Brain CdH5 CRE

Supports a between-group comparison across 9 samples.

How we read this: 4 replicated groups read from 9 sample titles; they account for 9 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
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