Generation of Neural Stem Cells from Embryonic Stem Cells Using the Default Mechanism: In Vitro and In Vivo Characterization
Bibliographic record
Abstract
Neural stem cell-based approaches to repair damaged white matter in the central nervous system have shown great promise; however, the optimal cell population to employ in these therapies remains undetermined. A default mechanism of neural induction may function during development, and in embryonic stem cells (ESCs) neural differentiation is elicited in the absence of any extrinsic signaling in minimal, serum-free culture conditions. The default mechanism can be used to derive clonal neurosphere-forming populations of neural stem cells that have been termed leukemia inhibitory factor-dependent primitive neural stem cells (pNSCs), which subsequently give rise to fibroblast growth factor 2-dependent definitive NSCs (dNSCs). Here we characterized the neural differentiation pattern of these two cell types in vitro and in vivo when transplanted into the dysmyelinated spinal cords of shiverer mice. We compared the differentiation pattern to that observed for neural stem/progenitor cells derived from the adult forebrain subependymal zone [adult neural precursor cells (aNPCs)]. dNSCs produced a differentiation pattern similar to that of aNPCs in vitro and in the shiverer model in vivo, where both cell types produced terminally differentiated oligodendrocytes that associated with host axons and expressed myelin basic protein. This is the first demonstration of the in vivo differentiation of NSCs, derived from ESCs through the default mechanism, into the oligodendrocyte lineage. We conclude that dNSCs derived through the default pathway of neural induction are a similar cell population to aNPCs and that the default mechanism is a promising approach to generate NSCs from pluripotent cell populations for use in cell therapy or other research applications.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".