Insights into the Biology of Hearing and Deafness Revealed by Single-Cell RNA Sequencing
Bibliographic record
Abstract
Single-cell RNA sequencing is a powerful tool by which to characterize the transcriptional profile of low-abundance cell types, but its application to the inner ear has been hampered by the bony labyrinth, tissue sparsity, and difficulty dissociating the ultra-rare cells of the membranous cochlea. Herein, we present a method to isolate individual inner hair cells (IHCs), outer hair cells (OHCs), and Deiters' cells (DCs) from the murine cochlea at any post-natal time point. We harvested more than 200 murine IHCs, OHCs, and DCs from post-natal days 15 (p15) to 228 (p228) and leveraged both short- and long-read single-cell RNA sequencing to profile transcript abundance and structure. Our results provide insights into the expression profiles of these cells and document an unappreciated complexity in isoform variety in deafness-associated genes. This refined view of transcription in the organ of Corti improves our understanding of the biology of hearing and deafness.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".