An in vitro model for studying CNS white matter: functional properties and experimental approaches
Bibliographic record
Abstract
The normal development and maintenance of CNS white matter, and its responses to disease and injury, are defined by synergies between axons, oligodendrocytes, astrocytes and microglia, and further influenced by peripheral components such as the gut microbiome and the endocrine and immune systems. Consequently, mechanistic insights, therapeutic approaches and safety tests rely ultimately on in vivo models and clinical trials. However, in vitro models that replicate the cellular complexity of the CNS can inform these approaches, reducing costs and minimising the use of human material or experimental animals; in line with the principles of the 3Rs. Using electrophysiology, pharmacology, time-lapse imaging, and immunological assays, we demonstrate that murine spinal cord-derived myelinating cell cultures recapitulate spinal-like electrical activity and innate CNS immune functions, including responses to disease-relevant myelin debris and pathogen associated molecular patterns (PAMPs). Further, we show they are (i) amenable to siRNA making them suitable for testing gene-silencing strategies; (ii) can be established on microelectrode arrays (MEAs) for electrophysiological studies; and (iii) are compatible with multi-well microplate formats for semi-high throughput screens, maximising information output whilst further reducing animal use. We provide protocols for each of these. Together, these advances increase the utility of this in vitro tool for studying normal and pathological development and function of white matter, and for screening therapeutic molecules or gene targets for diseases such as multiple sclerosis, motor neuron disease or spinal cord injury, whilst avoiding in vivo approaches on experimental animals.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".