Measurements of neurite extension and nucleokinesis in an iPSC-derived model system following microtubule perturbation
Bibliographic record
Abstract
In neurons, patterns of different microtubule types are essential for neurite extension and nucleokinesis. Cellular model systems such as rodent primary cultures and induced pluripotent stem cells (iPSC)-derived neurons have provided key insights into how these patterns are created and maintained through the action of microtubule-associated proteins, motor proteins, and regulatory enzymes. iPSC-derived models show tremendous promise but lack benchmarking and validation relative to rodent primary cultures. Here we have characterized a recent iPSC-derived model, in which doxycycline-induced expression of Neurogenin-2 drives consistent transdifferentiation into the neuronal state (EBiSC-NEUR1 neurons, referred to as NGN2 neurons below). We developed a suite of open-access, semiautomated methods to measure neurite extension and nucleokinesis of NGN2 neurons, which compare favorably to published data from other models. Then, we challenged NGN2 neurons with a panel of drugs that perturb microtubule physiology. NGN2 neurons extension and nucleokinesis were significantly perturbed by two microtubule-targeting drugs, namely a taxane (paclitaxel) and a vinca alkaloid (DZ-2384). In contrast, inhibition of microtubule severing (spastazoline) or of deacetylation (trichostatin A) had a limited effect on nucleokinesis only. Our results support the primary importance of microtubule dynamics in neuronal development and demonstrate the power of NGN2 neurons as a model system.
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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 | 0.000 | 0.001 |
| 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".