Neurons derived from NeuroD1-expressing astrocytes transition through transit-amplifying intermediates but lack functional maturity
Bibliographic record
Abstract
In vivo conversion of nonneuronal cells into neurons is a proposed strategy to replace neurons lost to CNS injury or disease. Glia-to-neuron trans-differentiation by viral vector–mediated GFAP mini-promoter–driven NeuroD1 remains hotly debated. Developing inducible, lineage-traceable transgenic mice, we find that astrocyte-to-neuron conversion is restricted to a specific time window within the lesion core of injured spinal cord and brain. Spatiotemporal lineage-mapping combined with single-cell transcriptomics reveals that ectopic NeuroD1 induces astrocyte-to-neuron conversion specifically in lesion cores via transit-amplifying OLIG2 + progenitors during early injury phase, but not in late phases or in nonreactive astrocytes. Neither a loss-of-function NeuroD1 mutant nor stemness-reprogramming factor SOX2 induces astrocyte-to-neuron conversion. However, contrary to previous reports, the neuronal-like cells generated by NeuroD1 lack mature neuroelectrical properties, limiting their functional integration into neural circuits. Together, our findings establish a spatiotemporal framework for NeuroD1-driven glia-to-neuron conversion, revealing a mechanistic shift from direct astrocyte conversion toward transit-amplifying intermediates and highlighting the functional immaturity of NeuroD1-converted neurons.
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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 | 0.000 |
| 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".