Sub-retinal transplantation of human iPSC-derived retinal sheets: A promising approach for the treatment of macular degeneration
Bibliographic record
Abstract
ABSTRACT Retinal degenerative diseases affect millions of people worldwide, and legal blindness is generally associated with the loss of cone photoreceptors located in the retina’s central region called the macula. Currently, there is no treatment to replace the macula. Addressing this unmet need, we employed control isogenic and hypoimmunogenic induced pluripotent stem cell (iPSC) lines to generate spontaneously polarized retinal sheets (RSs). They presented the advantage of facile customization and large-scale production, exhibiting a polarized 3D architecture within a 2D cell culture environment, thus readily adaptable for clinical applications. RSs were enriched in retinal progenitor and cone precursor cells, which could differentiate into mature S- and M/L-cones in long-term cultures. Single-cell RNA-seq analysis showed that RSs recapitulate the ontogeny of the developing human retina and are devoid of pluripotent stem cells. Isolation of neural rosettes for sub-retinal transplantation effectively eliminated unwanted cells such as RPE cells. In a porcine model of chemically induced retinal degeneration, grafts integrated the host retina and formed a new, yet immature, photoreceptor layer, exhibiting viability for up to 2 months. In one transplanted animal that met all criteria, including correct apicobasal orientation and seamless graft integration, functional and immunohistochemical assays suggest that grafts exhibited responsiveness to light stimuli and established putative synaptic connections with host bipolar neurons. This study underscores the potential and challenges of iPSC-derived RS for clinical applications while establishing the optimal methodology and conditions for RS transplantation, thus paving the path for future long-term functional investigations using RS grafts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".