Toward Generating Subtype-Specific Mesencephalic Dopaminergic Neurons in vitro
Bibliographic record
Abstract
Mesencephalic dopaminergic (mDA) neurons derived from pluripotent stem cells (PSCs) have proven to be pivotal for disease modeling studies and as a source of transplantable tissue for regenerative therapies in Parkinson's disease (PD). Current differentiation protocols can generate standardized and reproducible cell products of dopaminergic neurons that elicit the characteristic transcriptional profile of ventral midbrain. Nonetheless, dopamine neurons residing in the mesencephalon comprise distinct groups of cells within diffusely defined anatomical boundaries and with distinct functional, electrophysiological, and molecular properties. Here we review recent single cell sequencing studies that are shedding light on the neuronal heterogeneity within the mesencephalon and discuss how resolving the complex molecular profile of distinct sub-populations within this region could help refine patterning and quality control assessment of PSC-derived mDA neurons to subtype-specificity in vitro. In turn, such advances would have important impact in improving cell replacement therapy, disease mechanistic studies and drug screening in PD.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".