{"doi":"10.1016/j.xpro.2024.103520","title":"Protocol for generating and characterizing a nasal epithelial model using imaging with application for respiratory viruses","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.","journal":"STAR Protocols","year":2025,"id":542698,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9543,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1432565,"name":"Aleena Ghafoor","orcid":null,"position":1,"is_corresponding":false},{"id":1432566,"name":"Yazan N. Khan","orcid":null,"position":2,"is_corresponding":false},{"id":1066273,"name":"Shirley Constable","orcid":null,"position":3,"is_corresponding":false},{"id":660322,"name":"Lane B Buchanan","orcid":"0009-0009-4506-890X","position":4,"is_corresponding":false},{"id":1405112,"name":"David Zuanazzi","orcid":"0000-0003-3490-8319","position":5,"is_corresponding":false},{"id":1405628,"name":"Reeya Parmar","orcid":null,"position":6,"is_corresponding":false},{"id":1432253,"name":"Zeynep Güneş Tepe","orcid":"0000-0002-0575-7737","position":7,"is_corresponding":false},{"id":1432254,"name":"Leigh J. Sowerby","orcid":"0000-0002-5825-2759","position":8,"is_corresponding":false},{"id":520984,"name":"Cindy M. Liu","orcid":"0000-0003-4537-173X","position":9,"is_corresponding":false},{"id":1432255,"name":"Ryan M. Troyer","orcid":"0000-0001-8908-5853","position":10,"is_corresponding":false},{"id":520986,"name":"Jessica L. Prodger","orcid":"0000-0003-0805-4196","position":11,"is_corresponding":false},{"id":1432564,"name":"Valery Lam","orcid":null,"position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T02:53:00.171379Z","pmid":"39772385","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}