Combining NGN2 programming and dopaminergic patterning for a rapid and efficient generation of hiPSC-derived midbrain neurons
Bibliographic record
Abstract
The use of human derived induced pluripotent stem cells (hiPSCs) differentiated to dopaminergic (DA) neurons offers a valuable experimental model to decorticate the cellular and molecular mechanisms of Parkinson's disease (PD) pathogenesis. However, the existing approaches present with several limitations, notably the lengthy time course of the protocols and the high variability in the yield of DA neurons. Here we report on the development of an improved approach that combines neurogenin-2 programming with the use of commercially available midbrain differentiation kits for a rapid, efficient, and reproducible directed differentiation of hiPSCs to mature and functional induced DA (iDA) neurons, with minimum contamination by other brain cell types. Gene expression analysis, associated with functional characterization examining neurotransmitter release and electrical recordings, support the functional identity of the iDA neurons to A9 midbrain neurons. iDA neurons showed selective vulnerability when exposed to 6-hydroxydopamine, thus providing a viable in vitro approach for modeling PD and for the screening of small molecules with neuroprotective proprieties.
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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) | 0.000 | 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".