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Record W3048101218 · doi:10.1002/sctm.20-0201

Coculture techniques for modeling retinal development and disease, and enabling regenerative medicine

2020· review· en· W3048101218 on OpenAlexfundno aff
Ali Ghareeb, Majlinda Lako, David Steel

Bibliographic record

VenueStem Cells Translational Medicine · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
FundersBiotechnology and Biological Sciences Research CouncilMedical Research CouncilDirectorate for Biological SciencesMedical Research Council CanadaNational Centre for the Replacement, Refinement and Reduction of Animals in ResearchNational Institute for Health and Care ResearchMacular SocietyRetina UKEuropean Research CouncilFight for Sight
KeywordsRegenerative medicineRetinalComputational biologyMedicineBiologyOphthalmologyStem cellCell biology

Abstract

fetched live from OpenAlex

Abstract Stem cell-derived retinal organoids offer the opportunity to cure retinal degeneration of wide-ranging etiology either through the study of in vitro models or the generation of tissue for transplantation. However, despite much work in animals and several human pilot studies, satisfactory therapies have not been developed. Two major challenges for retinal regenerative medicine are (a) physical cell-cell interactions, which are critical to graft function, are not formed and (b) the host environment does not provide suitable queues for development. Several strategies offer to improve the delivery, integration, maturation, and functionality of cell transplantation. These include minimally invasive delivery, biocompatible material vehicles, retinal cell sheets, and optogenetics. Optimizing several variables in animal models is practically difficult, limited by anatomical and disease pathology which is often different to humans, and faces regulatory and ethical challenges. High-throughput methods are needed to experimentally optimize these variables. Retinal organoids will be important to the success of these models. In their current state, they do not incorporate a representative retinal pigment epithelium (RPE)-photoreceptor interface nor vascular elements, which influence the neural retina phenotype directly and are known to be dysfunctional in common retinal diseases such as age-related macular degeneration. Advanced coculture techniques, which emulate the RPE-photoreceptor and RPE-Bruch’s-choriocapillaris interactions, can incorporate disease-specific, human retinal organoids and overcome these drawbacks. Herein, we review retinal coculture models of the neural retina, RPE, and choriocapillaris. We delineate the scientific need for such systems in the study of retinal organogenesis, disease modeling, and the optimization of regenerative cell therapies for retinal degeneration. Significance statement The light-sensitive neural retina is nourished by the retinal pigment epithelium (RPE), while the choriocapillaris, a dense capillary network, supplies oxygen and metabolites. Coculture of these tissues is therefore required to understand normal retinal development and disease. Transplanted retinal precursors fail to fully integrate within host tissues and form the normal RPE-photoreceptor and RPE-choriocapillaris interactions which sustain vision. Coculture techniques will enable in vitro optimization of regenerative cell therapies for degenerative retinal diseases, forming a step to successful in vivo transplant experiments. Furthermore, coculture of neural retina, RPE, and choriocapillaris will facilitate the development of transplantable multitissue sheets.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.305
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations24
Published2020
Admission routes1
Has abstractyes

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