Molecular MedicineNeurosciencePeer-reviewed journal article
Single-cell analysis reveals cellular heterogeneity and limits of marker-based assessment in retinal ganglion cell-enriched organoid cultures
Jessica Yuen Wuen MaiD, Dulce B. Vargas-LandíniD, Janya Grainok, Alice PébayiD
Abstract
Human pluripotent stem cell (hPSC)-derived retinal organoids provide an in vitro system for generating retinal ganglion cells (RGCs), yet the cellular composition and developmental fidelity of RGC-enriched cultures remain insufficiently characterised. Here, we tested an RGC-enriched approach involving dissociation of hPSC-derived retinal organoids at day 40, corresponding to peak expression of RGC markers, followed by two-dimensional culture conditions intended to enrich for RGC survival. Flow cytometry was used to assess the expression of RGC markers, including POU4F, ISL1, SNCG, and THY1. Across four samples, POU4F expression ranged from 79-95%, ISL1 from 18-58%, SNCG from 22%-91% and THY1 from 3%-29%, indicating substantial variability between markers and samples. Single-cell RNA sequencing analysis of 73,642 cells identified multiple retinal lineages, including retinal progenitors, RGCs, photoreceptor-committed cells, amacrine and horizontal cells, and retinal pigment epithelium (RPE), as well as off-target populations comprising HOX-enriched posterior neural cells and other cell types. Cellular composition varied across samples. Transcriptomically defined RGCs accounted for 19-45% of cells across samples, with different subtypes identified. These findings indicate that marker-based assessments alone may overestimate RGC identity and provide a detailed single-cell characterisation of cellular heterogeneity in RGC-enriched retinal organoid cultures.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 22th percentile
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