Direct conversion of human pluripotent stem cells into cranial motor neurons using a piggyBac vector
Bibliographic record
Abstract
Human pluripotent stem cells (PSCs) are widely used for in vitro disease modeling. One of the challenges in the field is represented by the ability of converting human PSCs into specific disease-relevant cell types. The nervous system is composed of a wide variety of neuronal types with selective vulnerability in neurodegenerative diseases. This is particularly relevant for motor neuron diseases, in which different motor neurons populations show a different susceptibility to degeneration. Here we developed a fast and efficient method to convert human induced Pluripotent Stem Cells into cranial motor neurons of the branchiomotor and visceral motor subtype. These populations represent the motor neuron subgroup that is primarily affected by a severe form of amyotrophic lateral sclerosis with bulbar onset and worst prognosis. This goal was achieved by stable integration of an inducible vector, based on the piggyBac transposon, allowing controlled activation of Ngn2, Isl1 and Phox2a (NIP). The NIP module effectively produced electrophysiologically active cranial motor neurons. Our method can be easily extended to PSCs carrying disease-associated mutations, thus providing a useful tool to shed light on the cellular and molecular bases of selective motor neuron vulnerability in pathological conditions.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".