Sequential Application of Discrete Topographical Patterns Enhances Derivation of Functional Mesencephalic Dopaminergic Neurons from Human Induced Pluripotent Stem Cells
Bibliographic record
Abstract
Parkinson's Disease is a progressive neurodegenerative disorder attributed to death of mesencephalic dopaminergic (DA) neurons. Pluripotent stem cells have great potential in the study for this late-onset disease, but acquirement of cells that are robust in quantity and quality is still technically demanding. Biophysical cues have been shown to direct stem cell fate, but the effect of different topographies in the lineage commitment and subsequent maturation stages of cells have been less examined. Using human induced pluripotent stem cells (iPSCs), we applied topographical patterns sequentially during differentiation stages and examined their ability to influence derivation yield and functionality of regionalized subtype-specific DA neurons. Gratings showed higher yield of DA neurons and may be beneficial for initial lineage commitment. Cells derived on pillars in the terminal differentiation stage have increased neuronal complexity, and were more capable of firing repetitive action potentials, showing that pillars yielded better network formation and functionality. Our topography platform can be applied to patient-derived iPSCs as well, and that cells harbouring LRRK2 mutation were more functionally mature when optimal topographies were applied sequentially. This will hopefully accelerate development of robust cell models that will provide novel insights into discovering new therapeutic approaches for Parkinson's Disease.
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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".