Transcriptome-Wide Analysis Reveals a Role for Extracellular Matrix and Integrin Receptor Genes in Otic Neurosensory Differentiation from Human iPSCs
Bibliographic record
Abstract
We analyzed transcriptomic data from otic sensory cells differentiated from human induced pluripotent stem cells (hiPSCs) by a previously described method to gain new insights into the early human otic neurosensory lineage. We identified genes and biological networks not previously described to occur in the human otic sensory developmental cell lineage. These analyses identified and ranked genes known to be part of the otic sensory lineage program (SIX1, EYA1, GATA3, etc.), in addition to a number of novel genes encoding extracellular matrix (ECM) (COL3A1, COL5A2, DCN, etc.) and integrin (ITG) receptors (ITGAV, ITGA4, ITGA) for ECM molecules. The results were confirmed by quantitative PCR analysis of a comprehensive panel of genes differentially expressed during the time course of hiPSC differentiation in vitro. Immunocytochemistry validated results for select otic and ECM/ITG gene markers in the in vivo human fetal inner ear. Our screen shows ECM and ITG gene expression changes coincident with hiPSC differentiation towards human otic neurosensory cells. Our findings suggest a critical role of ECM-ITG interactions with otic neurosensory lineage genes in early neurosensory development and cell fate determination in the human fetal inner ear.
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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 | 0.001 | 0.001 |
| 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".