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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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".