Low-efficiency conversion of proliferative glia into induced neurons by Ascl1 in the postnatal mouse cerebral cortex <i>in vivo</i>
Bibliographic record
Abstract
ABSTRACT The proneural transcription factor Achaete-scute complex-like 1 (Ascl1) is a major regulator of neural progenitor fate, implicated both in neurogenesis and oligodendrogliogenesis. Ascl1 has been widely used to reprogram non-neuronal cells into induced neurons. In vitro , Ascl1 induces efficient reprogramming of proliferative astroglia from the early postnatal cerebral cortex into interneuron-like cells. Here, we examined whether Ascl1 can similarly induce neuronal reprogramming of glia undergoing proliferation in the postnatal mouse cerebral cortex in vivo . Toward this, we targeted cortical glia at the peak of proliferative expansion (i.e., postnatal day 5) by injecting a retrovirus encoding for Ascl1 into the mouse cerebral cortex. In sharp contrast to the very efficient reprogramming in vitro , Ascl1-transduced glial cells were converted into doublecortin-immunoreactive neurons only with low efficiency in vivo . Interfering with the phosphorylation of Ascl1 by mutation of six conserved proline-directed serine/threonine phosphorylation sites (Ascl1SA6) has been previously shown to increase its neurogenic activity in the early embryonic cerebral cortex. We therefore tested whether transduction of proliferative glia with a retrovirus encoding Ascl1SA6 improved their conversion into neurons. While in vitro glia-to-neuron conversion was markedly enhanced, in vivo reprogramming efficiency remained low. However, both wild-type and mutant Ascl1 reduced the relative number of cells expressing the astrocytic marker glial fibrillary acidic protein (GFAP) and increased the relative number of cells expressing the oligodendroglial marker Sox10 in vivo . Together, our results indicate that the enhanced neurogenic response of proliferative postnatal glia to Ascl1SA6 versus Ascl1 observed in vitro is not recapitulated in vivo .
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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) | 0.001 | 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 | 0.001 |
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".