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Human CRX Regulates Photoreceptor Cells Development via Bidirectional Transcriptional Control in Retinal Organoids

GSE290304 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 11 samples Submitted 2025/11/20 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 11 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
Homo sapiens (human)
Samples
11 samples: 11 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.
Paper
Human CRX regulates photoreceptor cells development via bidirectional transcriptional control in retinal organoidsStem cell reports · 2026
Summary (written by the submitters)
CRX (cone-rod homeobox) is a key regulator of retinal photoreceptor development, yet its human-specific functions remain poorly understood due to scarce human retinal tissues and significant species differences. Here, we established a human CRX-mCherry fluorescent reporter retinal organoid (RO) model to dissect CRX-mediated gene regulation.
Read the full summary ↓
Using FACS, RNA-seq, and CUT&Tag-seq, we identified CRX target genes and revealed its dual regulatory role: it activates photoreceptor-specific genes (e.g., RP1L1, linked to inherited retinal degeneration) in a dose-dependent manner, while suppressing non-photoreceptor genes (e.g., PCDH8 and PROX1). Notably, we first generated the human CRX CUT&Tag dataset, providing direct insights into CRX’s genome-wide regulatory landscape in photoreceptor cell development. These findings demonstrate that CRX functions as both a transcriptional activator and repressor, ensuring photoreceptor-specific gene expression and preventing aberrant cell fate transitions. Our study provides critical insights into the role of human CRX in retinal development and implications for retinal degenerative diseases.
Published in
Human CRX regulates photoreceptor cells development via bidirectional transcriptional control in retinal organoids
Wang Y, Xie B, Song X et al. · Stem cell reports 2026 · PMID 41418782 · doi:10.1016/j.stemcr.2025.102747
This dataset
Open GSE290304 in finder → Find more human 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 GSE290304_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 PRJNA1227441 and SRA study SRP565891. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41418782) ↗
Study design
11 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 11 sample titles: 11 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

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

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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.