Direct Reprogramming of Spiral Ganglion Non-neuronal Cells into Neurons: Toward Ameliorating Sensorineural Hearing Loss by Gene Therapy
Bibliographic record
Abstract
Primary auditory neurons (PANs) play a critical role in hearing by transmitting sound information from the inner ear to the brain. Their progressive degeneration is associated with excessive noise, disease and aging. The loss of PANs leads to permanent hearing impairment since they are incapable of regenerating. Spiral ganglion non-neuronal cells (SGNNCs), comprised mainly of glia, are resident within the modiolus and continue to survive after PAN loss. These attributes make SGNNCs an excellent target for replacing damaged PANs through cellular reprogramming. We used the neurogenic pioneer transcription factor Ascl1 and the auditory neuron differentiation factor NeuroD1 to reprogram SGNNCs into induced neurons (iNs). The overexpression of both Ascl1 and NeuroD1 in vitro generated iNs at high efficiency and these iNs expressed several key markers of neuron identity. Transcriptome analyses revealed that iNs displayed a transcriptome profile resembling that of endogenous PANs. Furthermore, iNs extended projections towards cochlear hair cells and cochlear nucleus neurons when cultured with each respective tissue. Taken together, our study demonstrates that PAN-like neurons can be generated from endogenous SGNNCs. This work suggests that gene therapy can be a viable strategy to treat sensorineural hearing loss caused by degeneration of PANs.
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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".