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Device-free isolation of photoreceptor cells from patient iPSC-derived retinal organoids

GSE266526 Homo sapiens (human) Single-cell RNA-seq 19 samples Submitted 2024/09/02 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 19 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
19 samples: 19 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
Device-free isolation of photoreceptor cells from patient iPSC-derived retinal organoidsJCI insight · 2025
Summary (written by the submitters)
Purpose: Autologous cell replacement shows great promise for the treatment of inherited retinal degeneration. While mature differentiation protocols exist to produce retinal organoids from patient-derived stem cells, not all cell types present in these organoids are desirable for transplant. To increase the potency of future cell therapies, methods for isolating photoreceptors from dissociated retinal organoids are needed.
Read the full summary ↓
In this work, we show how partial dissociation can be used to exploit the spatial organization of retinal organoids to produce highly pure photoreceptor populations without the use of specialized sorting devices or reagents such as xenobiotic antibodies. Methods: Retinal organoids were generated as we have described previously. For flow cytometry experiments, organoids were dissociated using papain for 30 minutes or for 90 minutes followed by trituration. For scRNAseq experiments, liberated cells were collected from papain-dissociating organoids at 20-minute intervals. At the conclusion of the experiment, remaining tissue was fully dissociated. A control 60-minute full dissociation was performed in parallel. Photoreceptor purity was assessed via flow cytometry and scRNAseq. Results: Photoreceptors desired for transplant are typically found in the outer layers of retinal organoids, suggesting that partial dissociation could selectively release these cells. Flow cytometry results indicating an increase in purity of CD133+ cells from 21.1% to 91.7% when cells were partially dissociated as compared to full dissociation. A time dependent release of photoreceptors, with a maximum purity of 96% CRX+ cells at 40 minutes as compared to 66% pure for traditional dissociation. Conclusions: By timing the dissociation of retinal organoids, highly pure photoreceptor cell populations suitable for cell replacement therapies can be obtained under cGMP without the use of sorting reagents or equipment.
Published in
Device-free isolation of photoreceptor cells from patient iPSC-derived retinal organoids
Stone NE, Bohrer LR, Mullin NK et al. · JCI insight 2025 · PMID 40504625 · doi:10.1172/jci.insight.186338
This dataset
Open GSE266526 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 GSE266526_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 PRJNA1107509 and SRA study SRP505677. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40504625) ↗
Study design
19 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 19 sample titles: 19 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

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