{"doi":"10.1016/j.xpro.2025.103984","title":"Protocol for developing a mouse model of post-primary pulmonary tuberculosis after hematogenous spread in native lungs and lung implants","abstract":"Here, we present a protocol for a mouse model for studying mechanisms of post-primary pulmonary tuberculosis (PTB) caused by virulent Mycobacterium tuberculosis (Mtb) using subcutaneous hock infection and lung tissue implantation. We describe steps for collagen instillation of lungs, lung and spleen implantation, preparation of Mtb for infection, and hock infection of mice. We then detail procedures for the perfusion of the lung and collection of organs, tissue processing, and histopathologic interpretation. For complete details on the use and execution of this protocol, please refer to Yabaji et al. 1 , 2 • Selection of an Mtb-susceptible mouse genetic background • Guidance on dose and route of infection using virulent Mtb to induce lung TB lesions • Procedures for preparation of lung and spleen implants to study pulmonary TB lesions Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. Here, we present a protocol for a mouse model for studying mechanisms of post-primary pulmonary tuberculosis (PTB) caused by virulent Mycobacterium tuberculosis (Mtb) using subcutaneous hock infection and lung tissue implantation. We describe steps for collagen instillation of lungs, lung and spleen implantation, preparation of Mtb for infection, and hock infection of mice. We then detail procedures for the perfusion of the lung and collection of organs, tissue processing, and histopathologic interpretation.","journal":"STAR Protocols","year":2025,"id":539789,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9325,"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":1330561,"name":"Suruchi Lata","orcid":"0000-0001-5548-9315","position":1,"is_corresponding":false},{"id":942027,"name":"Igor Gavrish","orcid":"0000-0001-9810-4091","position":2,"is_corresponding":false},{"id":1047695,"name":"Ming Lo","orcid":"0000-0002-4280-3560","position":3,"is_corresponding":false},{"id":817251,"name":"Aoife K. O’Connell","orcid":"0000-0001-7027-4267","position":4,"is_corresponding":false},{"id":817632,"name":"Hans P. Gertje","orcid":null,"position":5,"is_corresponding":false},{"id":768459,"name":"Colleen E Thurman","orcid":null,"position":6,"is_corresponding":false},{"id":559047,"name":"Nicholas A. Crossland","orcid":"0000-0003-3873-9188","position":7,"is_corresponding":false},{"id":351321,"name":"Lester Kobzik","orcid":"0000-0003-4328-937X","position":8,"is_corresponding":false},{"id":351323,"name":"Igor Kramnik","orcid":"0000-0001-6511-9246","position":9,"is_corresponding":false},{"id":559044,"name":"Shivraj M. Yabaji","orcid":"0000-0002-5793-1661","position":0,"is_corresponding":true}],"reference_count":14,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:52:34.520788Z","pmid":"40714561","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":[]}