GEO series

Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina [10x multiome]

GSE277326 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2025/01/15 Platform GPL34284

At a glance

Single-cell RNA-seq · human · 18 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 18 samples: 18 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: Single-cell analysis of the epigenome and 3D chromatin architecture in the human retinabioRxiv : the preprint server for biology · 2025

Summary (written by the submitters)

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.

Read the full summary ↓

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

This dataset

Open GSE277326 in finder → Find more human 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 GSE277326_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 PRJNA1161713 and SRA study SRP533003. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39764062) ↗

Study design

18 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 18 sample titles: 18 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

GSM8519431 Macula, donor 1, multiome RNA
GSM8519432 Macula, donor 2, multiome RNA
GSM8519433 Retina, donor 1, multiome RNA
GSM8519434 Retina, donor 2, multiome RNA
GSM8519435 RPE, donor 1, multiome RNA
GSM8519436 RPE, donor 2, multiome RNA
GSM8519437 Macula, donor 3, multiome RNA
GSM8519438 Retina, donor 3, multiome RNA
GSM8519439 RPE, donor 3, multiome RNA
GSM8519440 Macula, donor 1, multiome ATAC
GSM8519441 Macula, donor 2, multiome ATAC
GSM8519442 Retina, donor 1, multiome ATAC
GSM8519443 Retina, donor 2, multiome ATAC
GSM8519444 RPE, donor 1, multiome ATAC
GSM8519445 RPE, donor 2, multiome ATAC
GSM8519446 Macula, donor 3, multiome ATAC
GSM8519447 Retina, donor 3, multiome ATAC
GSM8519448 RPE, donor 3, multiome ATAC

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