Capture of Neuroepithelial-Like Stem Cells from Pluripotent Stem Cells Provides a Versatile System for In Vitro Production of Human Neurons
Bibliographic record
Abstract
Human embryonic stem cells (hESC) and induced pluripotent stem cells (iPSC) provide new prospects for studying human neurodevelopment and modeling neurological disease. In particular, iPSC-derived neural cells permit a direct comparison of disease-relevant molecular pathways in neurons and glia derived from patients and healthy individuals. A prerequisite for such comparative studies are robust protocols that efficiently yield standardized populations of neural cell types. Here we show that long-term self-renewing neuroepithelial-like stem cells (lt-NES cells) derived from 3 hESC and 6 iPSC lines in two independent laboratories exhibit consistent characteristics including i) continuous expandability in the presence of FGF2 and EGF; ii) stable neuronal and glial differentiation competence; iii) characteristic transcription factor profile; iv) hindbrain specification amenable to regional patterning; v) capacity to generate functionally mature human neurons. We further show that lt-NES cells are developmentally distinct from fetal tissue-derived radial glia-like stem cells. We propose that lt-NES cells provide an interesting tool for studying human neurodevelopment and may serve as a standard system to facilitate comparative analyses of hESC and hiPSC-derived neural cells from control and diseased genetic backgrounds.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".