Deregulation of neuro‐developmental genes and primary cilium cytoskeleton anomalies in iPSc‐derived retinal sheets from human syndromic ciliopathies
Bibliographic record
Abstract
Modeling human developmental and degenerative diseases has been always logistically challenging in particular in the case of rare diseases where no exhaustive animal models are available. Generation of sustainable human disease models that allow in‐depth analysis of the molecular mechanism is one of the big challenges nowadays. Stem cell technology holds great potential in disease modeling and represents a new powerful tool for generating scalable and animal‐free models that can more accurately illustrate clinical phenotypes of complex human diseases. Ciliopathies are a group of heterogeneous genetic diseases affecting proteins involved in primary cilium structure and function. Syndromic ciliopathies have a broad spectrum of symptoms ranging from retinal degeneration to skeletal and neuro‐development anomalies, including polydactylism and mental retardation. Herein we describe the generation and molecular characterization of iPSC‐derived photoreceptor sheets from patients affected by ciliopathies. Photoreceptor sheets are characterized by a polarized, multi‐layered tissue expressing outer segment, connecting cilium, and nuclear photoreceptor markers. Ciliopathic photoreceptors displayed significant common alterations in the expression of hundreds of developmental genes. Moreover, they showed several anomalies in the formation and maintenance of cilia, the positioning of the mother centriole and the activation of a stress response to misfolded proteins. Furthermore, we observed genomic instabilities and accumulation of DNA damage in the photoreceptors progenitors of one of the patients. This study reveals how combining cell reprogramming and organogenesis technologies with nextgeneration sequencing enable the elucidation of molecular and cellular mechanisms involved in human ciliopathies. The same approach, combining photoreceptor sheet differentiation and wide genome expression profile could be applied to model many genetic, developmental and degenerative diseases affecting photoreceptors. These patient‐derived retinal sheets may be useful for elucidating the molecular mechanisms underlining these diseases, for drug screening of compounds with potential therapeutic effect and predicting drugs side effects. Support or Funding Information Stem Cell Network (SCN), Fondation de l’Hôpital Maisonneuve‐Rosemont (FHMR), Réseau de recherche en santé de la vision (RRSV), Université de Montréal (UdeM)
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".