Protocol for generating and characterizing a nasal epithelial model using imaging with application for respiratory viruses
Bibliographic record
Abstract
Air-liquid interface (ALI) culture can differentiate airway epithelial cells to recapitulate the respiratory tract in vitro . Here, we present a protocol for isolating and culturing nasal epithelial cells from turbinate tissues for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We describe steps to overcome challenges of imaging fragile cultures, detect the production of mucus, and quantify intracellular virus post-SARS-CoV-2 infection. We present data on the optimal duration of ALI maturation prior to experimentation and describe which steps can be altered to optimize testing of specific hypotheses. • Isolation of primary nasal epithelial cells from donor tissue turbinate • Generation and characterization of an in vitro nasal model by air-liquid interface • Double-embedding and antigen retrieval for histology and immunohistochemistry staining • Application of a matured 42-day nasal epithelial model on SARS-CoV-2 infection Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Air-liquid interface (ALI) culture can differentiate airway epithelial cells to recapitulate the respiratory tract in vitro . Here, we present a protocol for isolating and culturing nasal epithelial cells from turbinate tissues for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We describe steps to overcome challenges of imaging fragile cultures, detect the production of mucus, and quantify intracellular virus post-SARS-CoV-2 infection. We present data on the optimal duration of ALI maturation prior to experimentation and describe which steps can be altered to optimize testing of specific hypotheses.
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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 | 0.000 |
| 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) | 0.000 | 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".