{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Vannan A"],"funding":["BLRD VA","United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs","NHLBI NIH HHS","United States Department of Defense | United States Army | Army Medical Command | Congressionally Directed Medical Research Programs (CDMRP)","U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)","U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute","U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute","Department of Health | National Health and Medical Research Council (NHMRC)","Department of Health | National Health and Medical Research Council","U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)"],"pagination":["647-658"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11906353"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["57(3)"],"pubmed_abstract":["Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecula"],"journal":["Nature genetics"],"pubmed_title":["Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis."],"pmcid":["PMC11906353"],"funding_grant_id":["W81XWH1910415","U01HL175444","R01 HL175555","R01HL160551","P01 HL172729","R01 HL145372","GNT1162829","GNT1195595","R01HL145372","I01 BX002378","U01 HL175444","R01HG011886","P01HL092870","HL126176","HL158906"],"pubmed_authors":["Williams AL","Hadad N","McCall AS","Vannan A","Shim H","Gokey JJ","Mee ED","Hirsh S","Taylor CJ","Shaver CM","Hirsh J","Bacchetta MJ","Banovich NE","Ware LB","Blackwell TS","Kropski JA","Nichols DS","Negretti NM","McCarthy DJ","Walia R","Calvi CL","Sucre JMS","Polosukhin VV","Serezani APM","Lyu R"],"additional_accession":[]},"is_claimable":false,"name":"Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis.","description":"Large-scale changes in the structure and cellular makeup of the distal lung are a hallmark of pulmonary fibrosis (PF), but the spatial contexts that contribute to disease pathogenesis have remained uncertain. Using image-based spatial transcriptomics, we analyzed the gene expression of 1.6 million cells from 35 unique lungs. Through complementary cell-based and innovative cell-agnostic analyses, we characterized the localization of PF-emergent cell types, established the cellular and molecular basis of classical PF histopathologic features and identified a diversity of distinct molecularly defined spatial niches in control and PF lungs. Using machine learning and trajectory analysis to segment and rank airspaces on a gradient of remodeling severity, we identified compositional and molecula","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2026-04-08T19:54:29.121Z","creation":"2025-04-22T07:23:17.129Z"},"accession":"S-EPMC11906353","cross_references":{"pubmed":["39901013"],"doi":["10.1038/s41588-025-02080-x"]}}