Stem cell‐derived motoneurons: Tools for studying motoneuron disease and motoneuron development
Bibliographic record
Abstract
Ten years ago, Tom Jessell and colleagues demonstrated for the first time that mouse embryonic stem (ES) cells can be directed to differentiate into motoneurons by simply treating them in culture with two mitogens, retinoic acid and a sonic hedgehog (Shh) agonist. Since this seminal discovery there has been an explosion of research using motoneurons derived from human and mouse ES cells, as well as induced pluripotent stem (iPS) cells, to study motoneuron disease and development. My presentation will first focus on developmental studies where we exploited the fact that mouse ES cell‐derived motoneurons differentiate into a single subclass of motoneurons that selectively innervate postural muscles lining the vertebral column. We show that ES cell‐derived motoneurons are an ideal model system to examine intracellular signaling cascades regulating motor axon guidance during embryogenesis. Second, I will describe a novel in vitro model system to study synapse formation at the neuromuscular junction using ES cell‐derived motoneurons and chick myotube co‐cultures. Finally, I will explore the use of motoneurons directly derived from fibroblasts as a tool to study motoneuron diseases.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".