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To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid and similar, but a robust platform for deep tissue characterization of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi\u2010omics platform to investigate COVID\u201019\u2010infected lung tissues. Five tissue\u2010profiling technologies were combined by a novel computational mapping methodology to comprehensively characterize and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi\u2010omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS\u2010CoV\u20102 on the lungs.<\/jats:p>","DOI":"10.1111\/imm.13679","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T06:19:38Z","timestamp":1692685178000},"page":"401-418","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A robust platform for integrative spatial multi\u2010omics analysis to map immune responses to\n                    <scp>SARS\u2010CoV<\/scp>\n                    \u20102 infection in lung tissues"],"prefix":"10.1111","volume":"170","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-7993","authenticated-orcid":false,"given":"Xiao","family":"Tan","sequence":"first","affiliation":[{"name":"Institute for 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