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Identification of epigenetic regulators of fibrotic transformation in cardiac fibroblasts through bulk and single-cell CRISPR screens (ATAC-Seq)

GSE261740 Mus musculus (mouse) Single-cell RNA-seq 35 samples Submitted 2025/09/12 Platform GPL30172
At a glance

Single-cell RNA-seq · mouse · 35 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
35 samples: 17 conditions, mostly in duplicateGroups 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 mediated by the persistent activity of myofibroblasts, which differentiate from resident cardiac fibroblasts in response to tissue damage and stress signals. The signaling pathways and transcription factors regulating fibrotic transformation have been thoroughly studied. By contrast, the roles of chromatin factors in fibroblast differentiation and their contribution to pathogenic cardiac fibrosis remain poorly understood.
Read the full summary ↓
Here, we have combined bulk and single-cell CRISPR screens to characterize the roles of chromatin factors inex vivofibroblast differentiation. We uncover strong regulators of fibrotic states including Srcap and Tip60 chromatin remodelers, the NSL complex and the co-factors Hcfc1 and Wdr82. We confirm these factors are required for cellular processes underlying fibrosis including collagen synthesis and cell contractility. We use epigenetic profiling to demonstrate these chromatin factors facilitate the activity of pro-fibrotic transcription factors. Finally, using chemical perturbation in primary human cardiac fibroblasts we suggest Tip60 inhibition as a potential therapeutic approach for cardiac fibrosis.
This dataset
Open GSE261740 in finder → Find more mouse ChIP / ATAC / CUT&Tag 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 GSE261740_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1088524 and SRA study SRP495517. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
17 conditions, mostly in duplicate
Activated-Kat5_ATAC4 ×2 Activated-Kat5_ATAC7 ×2 Activated-Kat8_ATAC3 ×2 Activated-Mock_ATAC7 ×2 Activated-NtC5_ATAC3 ×2 Activated-NtC5_ATAC4 ×2 Activated-NtC5_ATAC5 ×2 Activated-NtC5_ATAC ×2 +10 more

Supports a between-group comparison across 34 samples.

How we read this: 17 replicated groups read from 35 sample titles; they account for 34 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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