Comparing Viral Vectors and Fate Mapping Approaches for Astrocyte-to-Neuron Reprogramming in the Injured Mouse Cerebral Cortex
Bibliographic record
Abstract
Direct neuronal reprogramming is a promising approach to replace neurons lost due to disease via the conversion of endogenous glia reacting to brain injury into neurons. However, it is essential to demonstrate that the newly generated neurons originate from glial cells and/or show that they are not pre-existing endogenous neurons. Here, we use controls for both requirements while comparing two viral vector systems (Mo-MLVs and AAVs) for the expression of the same neurogenic factor, the phosphorylation-resistant form of Neurogenin2. Our results show that Mo-MLVs targeting proliferating glial cells after traumatic brain injury reliably convert astrocytes into neurons, as assessed by genetic fate mapping of astrocytes. Conversely, expressing the same neurogenic factor in a flexed AAV system results in artefactual labelling of endogenous neurons fatemapped by birthdating in development that are negative for the genetic fate mapping marker induced in astrocytes. These results are further corroborated by chronic live in vivo imaging. Taken together, the phosphorylation-resistant form of Neurogenin2 is more efficient in reprogramming reactive glia into neurons than its wildtype counterpart in vivo using retroviral vectors (Mo-MLVs) targeting proliferating glia. Conversely, AAV-mediated expression generates artefacts and is not sufficient to achieve fate conversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".