MicroRNA-mediated neuronal detargeting alters astrocyte cell fate conversion trajectories in vivo
Bibliographic record
Abstract
Neuronal reprogramming using adeno-associated viruses with a GFAP mini-promoter offers a promising strategy for astrocyte-to-neuron conversion; however, specificity remains a challenge due to off-target transgene expression in endogenous neurons. To address this issue, here we incorporated microRNA-124 target sequences (124T) into a transcriptional cassette containing the GFAP mini-promoter and Ascl1SA6, a potent reprogramming transcription factor. Lineage tracing via Slc1a3-CreERT and Aldh1l1-CreERT2, used to pre-label astrocytes prior to conversion, confirmed the glial derivation of reprogrammed neuron-like cells, even with 124T. Single-cell transcriptomics identified a transitional cluster emerging from a proliferative astrocyte population with low GSK3 signaling, which branched towards hybrid neuronal and oligodendrocyte fates. Pseudotime trajectory analysis revealed that Ascl1SA6 drives rapid neuronal transitions, whereas 124T delays conversion and introduces lineage bifurcation. Ascl1SA6 favors a GABAergic interneuron-like identity, while Ascl1SA6 -124T biases fate transitions towards an oligodendrocyte-like fate, and to a lesser extent, glutamatergic neuronal-like cells. SeeSawPred linked these distinct trajectories to transcription factor shifts, including Foxo1 in neuron-like cell fates and Stat1 in oligodendrocyte lineages. 124 T thus effectively detargets endogenous neurons, refining target cell specificity, while further guiding reprogramming outcomes. This approach establishes a foundation for precision reprogramming platforms aimed at restoring specific neural cell types. In vivo astrocyte-to-neuron conversion is driven by Ascl1SA6 expression restricted to astrocytes via miR-124-target sites, with astrocyte pre-labeling marking reprogrammed neurons, confirmed by single-cell transcriptomics and pseudotime analysis.
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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".