[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127144-en":3,"doc-seo-127144-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127144,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning","Regional responses to inhaled toxicants are central to understanding lung disease mechanisms. This study evaluates how combined allergen sensitization and ozone exposure shape spatial differences in lipid distribution in mouse lung. Established machine learning enables normalization and segmentation of high-resolution mass spectrometry imaging data, aligning segmented regions with histologically validated lung regions. The analysis identifies abundance shifts across spatially distinct lipids and points to lipid saturation and sex differences after ozone exposure, supporting future relative quantification across replicates and broader sample types.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nResolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning.  \nPermalink  \n[https://escholarship.org/uc/item/4qc4221p](https://escholarship.org/uc/item/4qc4221p)  \nJournal  \nNature Communications, 16(1)  \nAuthors  \nStevens, Nathanial  \nShen, Tong Martinez, Joshua et al.  \nPublication Date  \n2025-03-26  \nDOI  \n10.1038/s41467-025-58135-4  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nArticle [https://doi.org/10.1038/s41467-025-58135-4](https://doi.org/10.1038/s41467-025-58135-4)  \nResolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning  \nReceived: 17 July 2024  \n\n| Accepted: 11 March 2025 |\n| --- |\n|  |\n| Check for updates |\n\nNathanial C. Stevens Morgan C. Domanico  \n1, Tong Shen 1, Joshua Martinez2, Veneese J. B. Evans2, 2, Elizabeth K. Neumann3, Laura S. Van Winkle2,4 &  \nOliver Fiehn 1   \nRegional responses to inhaled toxicants are essential to understand the pathogenesis oflung disease under exposure to air pollution. We evaluate the effect of combined allergen sensitization and ozone exposure on eliciting spatial differences in lipid distribution in the mouse lung that may contribute to ozone-induced exacerbations in asthma. We demonstrate the ability to normalize and segment high resolution mass spectrometry imaging data by applying established machine learning algorithms. Interestingly, our segmented regions overlap with histologically validated lung regions, enabling regional analysis across biological replicates. Our data reveal differences in the abundance of spatially distinct lipids, support the potential role of lipid saturation in healthy lung function, and highlight sex differences in regional lung lipid distribution following ozone exposure. Our study provides a framework for future mass spectrometry imaging experiments capable ofrelative quantification across biological replicates and expansion to multiple sample types, including human tissue.  \nMore than 137 million people in the United States live in areas with unhealthy levels of air pollution1. Exposure to major components of air pollution, including particulate matter and oxidant gases, are well characterized for their ability to worsen existing lung disease and to potentially cause new-onset respiratory disease2–4. Despite extensive evaluation of the acute and chronic adverse health outcomes of inhaled toxicants, the molecular mechanisms underlying these effects are still not well understood. Importantly, previous studies have demonstrated that particulate matter and oxidant gases such as ozone (O3) elicit site-specific toxicity, which is dependent upon the physiochemical properties of a toxicant and its inhaled concentration5–7. Notably, the region-specific effects of O3 exposure on the conducting airways are well studied, which acutely induces airway hyperreactivity, airway inflammation, and damages lung surfactant. O3 exposure is also a well-known risk factor for exacerbating pre-existing asthma in  \nhumans, although potential mechanisms that may explain this association are not well understood1,4,5. The use of combined exposure models incorporating O3 and common human allergens such as house dust mite (HDM) may elucidate mechanisms of O3-induced exacerbations in asthma.  \nIn addition to the region-specific effects of an inhaled toxicant, the wide array of cell types within the lung, differences in xenobiotic metabolism, and unequal distribution of cell populations along the respiratory tract all lead to effects that are often confined to individual cell types or lung regions8,9. Elucidating site-specific responses is therefore necessary for implicating individual types of cells or regions in promoting lung disease and to develop targeted therapeutic approaches to mitigate the outcomes of inhaled toxicant exposure. Prior studies evaluating regional differences w","cbCaiuhy3PuN3IJr","https://ap.wps.com/l/cbCaiuhy3PuN3IJr","pdf",3747923,1,14,"English","en",105,"# Introduction\n## Air pollution and inhaled toxicants in lung disease\n## Need for site-specific mechanistic insight\n## Limitations of microdissection and emerging omics approaches\n# Methods and Analytical Framework\n## Machine learning for MSI normalization and segmentation\n## Linking segmented regions to histological validation\n# Results and Implications\n## Spatial lipid differences after ozone exposure\n## Role of lipid saturation and sex-specific regional patterns\n## Framework for future spatial metabolomics experiments","[{\"question\":\"What question does the study address about inhaled toxicants?\",\"answer\":\"How combined allergen sensitization and ozone exposure produce spatial differences in lipid distribution in mouse lung that may contribute to ozone-related asthma exacerbations.\"},{\"question\":\"How do the authors process high-resolution mass spectrometry imaging data?\",\"answer\":\"They apply established machine learning algorithms to normalize and segment the MSI data into regions suitable for regional lipid analysis.\"},{\"question\":\"Why is segmentation important for the biological interpretation of results?\",\"answer\":\"Segmented regions overlap with histologically validated lung regions, allowing regional analysis across biological replicates and enhancing confidence in site-specific findings.\"}]","Resolving multi-image spatial lipidomic responses to inhaled toxicants by machine learning | 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question does the study address about inhaled toxicants?","Question",{"text":76,"@type":77},"How combined allergen sensitization and ozone exposure produce spatial differences in lipid distribution in mouse lung that may contribute to ozone-related asthma exacerbations.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do the authors process high-resolution mass spectrometry imaging data?",{"text":81,"@type":77},"They apply established machine learning algorithms to normalize and segment the MSI data into regions suitable for regional lipid analysis.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is segmentation important for the biological interpretation of results?",{"text":85,"@type":77},"Segmented regions overlap with histologically validated lung regions, allowing regional analysis across biological replicates and enhancing confidence in site-specific 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