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Single Cell Multiome Atlas of the Human Fetal Retina

GSE268630 Homo sapiens (human) Single-cell RNA-seq 50 samples Submitted 2024/05/30 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 50 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
50 samples: 50 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 dual-omic atlas of the human developing retinaNature communications · 2024
Summary (written by the submitters)
As the light-sensing part of the visual system, the retina is comprised of five classes of neurons, including photoreceptors, horizontal, amacrine, bipolar, and retinal ganglion cells, along with several non-neuronal cell types such as Muller glia. These major cell classes can be further classified into hundreds of distinct cell subtypes.
Read the full summary ↓
The development of the retina is under tight temporal control where multipotent progenitor cells differentiate into specific mature cell types in a sequential, but overlapping, order. Additionally, the developmental process is under tight spatial control, with cells at the central retina developing earlier than cells at the periphery. To provide a comprehensive view of the human fetal retina at the molecular level and investigate transcriptional regulatory mechanisms controlling the differentiation process, we profiled more than 300,000 single nuclei of the human fetal retina from 12 donors spanning post conception week 10 and 23 with Multiome RNA-seq and ATAC-seq.
Published in
Single cell dual-omic atlas of the human developing retina
Zuo Z, Cheng X, Ferdous S et al. · Nature communications 2024 · PMID 39117640 · doi:10.1038/s41467-024-50853-5
This dataset
Open GSE268630 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 GSE268630_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 PRJNA1117936 and SRA study SRP510712. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39117640) ↗
Study design
50 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 50 sample titles: 50 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

+ 10 more — browse all 50 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.