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Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina [bulk_ATAC]

GSE276851 Homo sapiens Genome binding/occupancy profiling by high throughput sequencing 8 samples Submitted 2025/01/15 Platform GPL30173
Summary
Most genetic risk variants linked to ocular diseases are non-protein coding and presumably contribute to disease through dysregulation of gene expression, however, deeper understanding of their mechanisms of action has been impeded by an incomplete annotation of the transcriptional regulatory elements across different retinal cell types. To address this knowledge gap, we carried out single-cell multiomics assays to investigate gene expression, chromatin accessibility, DNA methylome and 3D chromatin architecture in human retina, macula, and retinal pigment epithelium (RPE). We identified 420,824 unique candidate regulatory elements and characterized their chromatin state in 23 sub-classes of retinal cells. Comparative analysis of chromatin landscapes between human and mouse retina cells revealed both evolutionarily conserved and divergent retinal gene-regulatory programs. Leveraging the rapid advancements in deep-learning techniques, we developed sequence-based predictors to interpret non-coding risk variants of retina diseases. Our study establishes retina-wide, single-cell transcriptome, epigenome, and 3D genome atlases, and provides a resource for studying the gene regulatory programs of the human retina, leading to mechanistic insights into a wide-spectrum of eye diseases.
Published in
Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina
Yuan Y, Biswas P, Zemke NR et al. · bioRxiv : the preprint server for biology 2025 · PMID 39764062 · doi:10.1101/2024.12.28.630634
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Direct links to NCBI, no account and no request form: the whole study as GSE276851_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 8 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1159282 and SRA study SRP531879. Searching any of these in the dataset finder brings you back here.

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