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Conserved Transcriptional Circuits Regulate Cardiac Fibroblast-Mediated Fibrosis

GSE268853 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2026/05/15 Platform GPL24676Platform GPL24247
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: 12 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)
Cardiac fibrosis is a major cause of cardiac dysfunction. Recently, single-cell genomic approaches have revealed in unprecedented resolution the orchestrated cellular responses driving cardiac fibrosis. Yet, the fibrosis-causing phenotypes that emerge in the heart following non-ischemic cardiac stress, and the transcriptional circuits that govern cell identity and drive fibrosis, are not well understood.
Read the full summary ↓
Applying a paired multiomic approach, we reveal key transcriptional circuits, in mouse and human hearts, which are associated with fibrosis development following non-ischemic cardiac insults, independent of disease model, species or biological sex. Strikingly, we find the key regulatory events driving fibrosis are reversible at the single-cell transcriptional and epigenomic level, further pointing to key factors regulating fibrosis development and resolution. The transcriptional regulators identified in this study represent promising targets to ameliorate the development of fibrosis in the context of chronic stressors such as aging and hypertension.
This dataset
Open GSE268853 in finder → Find more 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 GSE268853_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 PRJNA1119184 and SRA study SRP511217. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
12 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 12 sample titles: 12 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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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.