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Integrated multi-omics single cell atlas of the human retina

GSE265774 Homo sapiens (human) Single-cell RNA-seq 63 samples Submitted 2025/10/14 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 63 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
63 samples: 52 distinct titles, 7 of them repeatedGroups 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 atlas of the transcriptome and chromatin accessibility in the human retinaNature genetics · 2026
Summary (written by the submitters)
Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here, we present an integrated multimodal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 2.4 million cells from 55 donors, including 1.4 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) of transcriptome and chromatin accessibility, unveiling over 110 types.
Read the full summary ↓
Engaging the retina community, we annotated each cluster, refined the Cell Ontology for the retina, identified distinct marker genes, and characterized cis-regulatory elements and gene regulatory networks (GRNs) for these cell types. Our analysis uncovered intriguing differences in transcriptome, chromatin, and GRNs across cell types. In addition, we modeled changes in gene expression and chromatin openness across gender and age. This integrated atlas also enabled the fine-mapping of GWAS and eQTL variants. Accessible through interactive browsers, this multimodal cross-donor and cross-lab HRCA, can facilitate a better understanding of retinal function and pathology.
Published in
Single-cell atlas of the transcriptome and chromatin accessibility in the human retina
Li J, Wang J, Ibarra IL et al. · Nature genetics 2026 · PMID 41578023 · doi:10.1038/s41588-025-02454-1
This dataset
Open GSE265774 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 GSE265774_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 PRJNA1093457 and SRA study SRP499089. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41578023) ↗
Study design
52 distinct titles, 7 of them repeated
10xATAC_19_D19_Mac_NeuN ×3 10xATAC_19_D3_Mac_NeuN ×3 10xATAC_19_D6_Mac_NeuN ×3 10xATAC_19_D010_Mac_NeuN ×3 10xATAC_D028_13_Mac_NeuN ×2 19_D011_NeuN ×2 19_D011_Nu ×2

How we read this: Read from 63 sample titles: 52 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

+ 23 more — browse all 63 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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