Cellular blueprint of healthy and diseased human epiglottis and subglottis—a study of the Canadian Airways Research (CARE) group
Bibliographic record
Abstract
BACKGROUND: The larynx consists of the supraglottis, glottis, and subglottis and each differ in tissue composition, lymphatic drainage, ability to counter infections, and response to injuries. However, the cellular mechanisms driving laryngeal homoeostasis remain largely unexplored. As a result, understanding disease pathogenesis within the larynx including idiopathic subglottic stenosis (iSGS) and intubation-related traumatic stenosis has been challenging. Here, we sought to characterise the cellular processes governing laryngeal health and disease. METHODS: As part of the prospective Canadian Airways Research (CARE) iSGS study, we characterised 122,004 high-quality transcriptomes using single nucleus RNA-sequencing to profile 11 human epiglottis and 17 human subglottis biopsies across three different conditions: control, iSGS, and intubation-related traumatic stenosis to define cell populations and pathways associated with disease. We validated our results using cohort-level bulk transcriptomics using 114 human epiglottis and 121 human subglottis. FINDINGS: We defined the single-cell taxonomy of the human subglottis and epiglottis using single-nucleus sequencing in both healthy and disease states. Mechanistically, we discovered the presence of unique epithelial and fibroblast progenitor subsets within the control subglottis but not within the anatomically adjacent epiglottis. The uncontrolled proliferation of these cellular subsets exhibited skewed sex hormone signalling and orchestrated a fibro-inflammatory cascade. We leveraged cohort-level bulk transcriptomics to define hallmarks of iSGS associated with disease covariates and introduced the first biomarker associated with recurrent relapse. Longitudinal sampling demonstrated that the subglottic microenvironment in patients with iSGS is changing dynamically with and without therapeutic intervention. INTERPRETATION: Together, our data refines our understanding of laryngeal biology, nominates candidate compounds for iSGS treatment, and serves as a transformative platform for future clinical investigations to further precision laryngology. FUNDING: This study was funded by a grant from the American Laryngology Association (#1082), an Academic Medical Organisation of Southwestern Ontario innovation fund grant (INN21-016), grant support from the Departments of Otolaryngology-Head and Neck Surgery at University of Toronto, Canada and Western University, Canada. ACN was supported by the Wolfe Surgical Research Professorship in the Biology of Head and Neck Cancers Fund. PYFZ was supported by a Vanier Canada Graduate Scholarship and PSI Foundation fellowship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".