{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE327nnn/GSE327129/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Other"],"species":["Mus musculus"],"gds_type":["Other"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE327129"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"Subcellular mRNA localization patterns across tissues resolved with spatial transcriptomics","description":"Subcellular RNA localization, including nuclear retention and apical-basal compartmentalization in polarized epithelia plays a central role in post-transcriptional regulation. However, methods for high-throughput mapping of mRNA localization within intact tissue sections remain limited. Here, we apply high-resolution spatial transcriptomics to systematically resolve intracellular mRNA localization across diverse mammalian tissues. We introduce a computational approach that leverages image-derived features to extract subcellular information from spatial data and quantifies transcript localization patterns. Using this framework, we map apical-basal mRNA localization and nuclear retention in gastrointestinal epithelia and in liver hepatocytes. Our analyses reveal conserved and tissue-specific localization signatures that can be readily obtained from standard high-definition spatial transcriptomics experiments. This approach broadens the scope of spatial transcriptomics by enabling routine investigation of intracellular RNA distributions in both healthy and diseased tissues.","dates":{"publication":"2026/04/06"},"accession":"GSE327129","cross_references":{"GSM":["GSM9648894","GSM9648895"],"GPL":["34328"],"GSE":["327129"],"taxon":["Mus musculus"]}}