GEO series

Modeling retinal ganglion cell axonal pathology associated with glaucoma using human pluripotent stem cell-based microfluidic platforms

GSE286236 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2025/01/13 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 16 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 16 samples: 4 × Control Axon vs 4 × OPTN Axon vs 4 × Soma_OPTN RGCs vs 3 × Soma_RGCsGroups 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: Modeling human retinal ganglion cell axonal outgrowth, development, and pathology using pluripotent stem cell-based microfluidic platformsProceedings of the National Academy of Sciences of the United States of America · 2025

Summary (written by the submitters)

Retinal ganglion cells (RGCs) are highly compartmentalized cells, and previous studies have demonstrated that RGC degeneration in glaucoma occurs in a compartmentalized manner, with distinct injury responses in axonal and somatodendritic compartments. Thus, the goal of this study was to establish a novel microfluidic-based platform for the analysis of RGC compartmentalization in health and disease states.

Read the full summary ↓

Human pluripotent stem cell (hPSC)-derived RGCs were seeded into microfluidics, enabling the recruitment and isolation of axons apart from the somatodendritic compartment. Initial studies explored axonal outgrowth and compartmentalization of axons and dendrites. We then compared the differential response of RGCs differentiated from hPSCs carrying the OPTN(E50K) glaucoma mutation with isogenic control RGCs in their respective axonal and somatodendritic compartments, followed by analysis of axonal transport. Further, we explored the axonal transcriptome via RNA-seq, focusing on disease-related axonal differences. Finally, we established models to uniquely orient astrocytes along the axonal compartment combined with modulation of astrocyte reactivity as a pathological feature of neurodegeneration. Overall, RGC culture within microfluidic chips allowed enhanced cell growth and maturation, including long-distance axonal projections and proper compartmentalization. OPTN(E50K) RGCs exhibited axonal outgrowth deficits as well as decreased rate of axonal transport, compared to isogenic controls. Finally, upon introduction of astrocytes into the proximal axonal compartment, the induction of astrocyte reactivity led to the onset of neurodegenerative phenotypes in RGCs. These results represent the first study to effectively recapitulate the highly compartmentalized properties of hPSC-derived RGCs in healthy and disease states, providing a more physiologically relevant in vitro model for neuronal development and degeneration.

Published in

Modeling human retinal ganglion cell axonal outgrowth, development, and pathology using pluripotent stem cell-based microfluidic platforms

Gomes C, Huang KC, Lavekar SS et al. · Proceedings of the National Academy of Sciences of the United States of America 2025 · PMID 40924455 · doi:10.1073/pnas.2423682122

This dataset

Open GSE286236 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 GSE286236_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1208464 and SRA study SRP556267. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40924455) ↗

Study design

4 × Control Axon vs 4 × OPTN Axon vs 4 × Soma_OPTN RGCs vs 3 × Soma_RGCs

Supports a between-group comparison across 15 samples.

How we read this: 4 replicated groups read from 16 sample titles; they account for 15 of them. 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

GSM8721444 Soma_RGCs_1 Soma_RGCs
GSM8721445 Soma_RGCs_2 Soma_RGCs
GSM8721446 Soma_RGC_3
GSM8721447 Soma_RGCs_4 Soma_RGCs
GSM8721448 Control Axon_1 Control Axon
GSM8721449 Control Axon_2 Control Axon
GSM8721450 Control Axon_3 Control Axon
GSM8721451 Control Axon_4 Control Axon
GSM8721452 OPTN Axon_1 OPTN Axon
GSM8721453 OPTN Axon_2 OPTN Axon
GSM8721454 OPTN Axon_3 OPTN Axon
GSM8721455 OPTN Axon_4 OPTN Axon
GSM9038075 Soma_OPTN RGCs_1 Soma_OPTN RGCs
GSM9038076 Soma_OPTN RGCs_2 Soma_OPTN RGCs
GSM9038077 Soma_OPTN RGCs_3 Soma_OPTN RGCs
GSM9038078 Soma_OPTN RGCs_4 Soma_OPTN RGCs

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