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Estrogen Receptor 1 Signaling in Hepatic Stellate Cells Designates Resistance to Liver Fibrosis [ChIP-seq]

GSE289421 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 14 samples Submitted 2025/08/21 Platform GPL28330
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 14 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
14 samples: 14 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 prevalence and severity of liver fibrosis appear higher in men than in premenopausal women, while postmenopausal women exhibit the worsened disease. However, the pathophysiological mechanism underlying such clinical observations remains incompletely understood. Here, we show that sex hormone depletion in adult female mice exaggerates the model of liver fibrosis, while estradiol replacement in castrated male mice is sufficient to mitigate the disease severity.
Read the full summary ↓
Transcriptomic analyses and immunohistochemistry then demonstrate that both human and mouse hepatic stellate cells (HSCs), the primary cell type responsible for extracellular fibrous depositions, predominantly express the estrogen receptor 1 (ESR1). Of importance, genetic deletion of ESR1 in mouse HSCs markedly promotes liver fibrosis. Moreover, chromatin immunoprecipitation followed by sequencing (ChIP-seq) and in vitro manipulations reveal that ESR1 can directly target the expression of fibrosis-related genes in HSCs. Together, this study has elucidated a critical aspect of ESR1 signaling in the sexual dimorphism of liver fibrosis.
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 GSE289421_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 PRJNA1222884 and SRA study SRP563490. Searching any of these in the dataset finder brings you back here.

Study design
14 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 14 sample titles: 14 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.