Astrocytes in a dish: Using pluripotent stem cells to model neurodegenerative and neurodevelopmental disorders
Bibliographic record
Abstract
Neuroscience and Neurobiology have historically been neuron biased, yet up to 40% of the cells in the brain are astrocytes. These cells are heterogeneous and regionally diverse but universally essential for brain homeostasis. Astrocytes regulate synaptic transmission as part of the tripartite synapse, provide metabolic and neurotrophic support, recycle neurotransmitters, modulate blood flow and brain blood barrier permeability and are implicated in the mechanisms of neurodegeneration. Using pluripotent stem cells (PSC), it is now possible to study regionalised human astrocytes in a dish and to model their contribution to neurodevelopmental and neurodegenerative disorders. The evidence challenging the traditional neuron-centric view of degeneration within the CNS is reviewed here, with focus on recent findings and disease phenotypes from human PSC-derived astrocytes. In addition we compare current protocols for the generation of regionalised astrocytes and how these can be further refined by our growing knowledge of neurodevelopment. We conclude by proposing a functional and phenotypical characterisation of PSC-derived astrocytic cultures that is critical for reproducible and robust disease modelling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".