Oligodendrocyte Precursor Cell Transplantation into Organotypic Cerebellar Shiverer Slices: A Model to Study Myelination and Myelin Maintenance
Bibliographic record
Abstract
Current in vitro models to investigate the consequence of oligodendrocyte-specific loss-of-function mutations on myelination are primarily limited to co-culture experiments, which do not accurately recapitulate the complex in vivo environment. Here, we describe the development of an in vitro model of myelination and myelin maintenance in which oligodendrocyte precursor cells are transplanted into organotypic cerebellar slice cultures derived from dysmyelinated shiverer mice. Compared to neuron-oligodendrocyte co-cultures, organotypic slices more closely mimic the environment in vivo, while utilizing a genetic background that allows for straight-forward identification of myelin generated by transplanted cells. We show at the ultrastructural level that the myelin generated by wild-type transplanted oligodendrocytes is compact and terminates in cytoplasmic loops that form paranodal junctions with the axon. This myelination results in the appropriate sequestering of axonal proteins into specialized domains surrounding the nodes of Ranvier. We also demonstrate the applicability of this approach for xenograft transplantation of oligodendrocyte precursor cells derived from rat or human sources. This method provides a time-efficient and cost-effective adjunct to conditional knockout mouse lines or in vivo transplantation models to study oligodendrocyte-specific loss-of-function mutations. Furthermore, the approach can be readily used to assess the effect of pharmacological manipulations on myelin, providing a tool to better understand myelination and develop effective therapeutic strategies to treat myelin-related 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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".