Single cell approaches define neural stem cell niches and identify microglial ligands that can enhance precursor-mediated oligodendrogenesis
Bibliographic record
Abstract
Here, we used single cell RNA sequencing and single cell spatial transcriptomics to characterize the forebrain neural stem cell (NSC) niche under homeostatic and injury conditions. We defined the dorsal and lateral ventricular-subventricular zones (V-SVZs) as two distinct neighborhoods and showed that, after white matter injury, NSCs are activated to make oligodendrocytes dorsally for remyelination. This activation is coincident with an increase in transcriptionally distinct microglia in the dorsal V-SVZ niche. We modeled ligand-receptor interactions within this changing niche and identified two remyelination-associated microglial ligands, insulin growth factor 1 and oncostatin M, that promote precursor proliferation and oligodendrogenesis in culture. Infusion of either ligand into the lateral ventricles also enhanced oligodendrogenesis, even in the lateral V-SVZ, where NSCs normally make neuroblasts. These data support a model where gliogenesis versus neurogenesis is determined by the local NSC neighborhood and where injury-induced niche alterations promote NSC activation, local oligodendrogenesis, and likely contribute to myelin repair.
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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 | 0.001 |
| 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".