Enrichment of Oligodendrocyte Progenitors from Differentiated Neural Precursors by Clonal Sphere Preparations
Bibliographic record
Abstract
Remyelination is the goal of potential cell transplantation therapies for demyelinating diseases and other central nervous system injuries. Transplantation of oligodendrocyte precursor cells (OPCs) can result in remyelination in the central nervous system, and induced pluripotent stem cells (iPSCs) are envisioned to be an autograft cell source of transplantation therapy for many cell types. However, it remains time-consuming and difficult to generate OPCs from iPSCs. Clonal sphere preparations are reliable cell culture methods for purifying select populations of proliferating cells. To make clonal neurospheres from human embryonic stem cell (ESC)/iPSC colonies, we have found that a monolayer differentiation phase helps to increase the numbers of neural precursor cells. Indeed, we have compared a direct isolation of neural stem cells from human ESC/iPSC colonies (protocol 1) with monolayer neural differentiation, followed by clonal neural stem cell sphere preparations (protocol 2). The two-step method combining monolayer neuralization, followed by clonal sphere preparations, is more useful than direct sphere preparations in generating mature human oligodendrocytes. The initial monolayer culture stage appears to bias cells toward the oligodendrocyte lineage. This method of deriving oligodendrocyte lineage spheres from iPSCs represents a novel strategy for generating OPCs.
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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 | 0.001 | 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) | 0.002 | 0.002 |
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".