{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE325nnn/GSE325755/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Other"],"species":["Homo sapiens"],"gds_type":["Other"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE325755"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"Spatial membrane lipid remodeling encodes epithelial lineage and state in human colon cancer","description":"Cancer progression is driven by profound intratumoral heterogeneity that is spatially organized within tissues, yet remains difficult to resolve at the molecular level. While transcriptomic spatial approaches have advanced rapidly, the spatial organization and functional relevance of tumor lipid metabolism remain poorly understood. Here, we present an integrative spatial multi-omic strategy that anchors high-resolution lipid imaging to transcriptome-defined cellular states. Applying this approach to human primary and metastatic colon cancer specimens, we jointly profiled spatial lipid distributions and gene expression across tumor epithelial compartments. This approach revealed specific, spatially restricted epithelial lipid phenotypes defined by membrane phospholipid composition. These lipid phenotypes were tightly associated with transcriptional programs reflecting epithelial lineage, differentiation state, and proliferative activity. In addition, we observed that secretory-associated epithelial states were enriched in metastatic lesions and were associated with increased levels of DHA-containing phospholipids and transcriptional activation of peroxisomal lipid metabolic pathways. Together, these findings demonstrate that membrane phospholipid composition encodes biologically meaningful epithelial cell states within human tumors. More broadly, this work establishes a generalizable strategy for interpreting spatial lipidomic data through biologically informed integration with spatial transcriptomics, enabling cell-state–resolved metabolic phenotyping in cancer.","dates":{"publication":"2026/08/27"},"accession":"GSE325755","cross_references":{"GSM":["GSM9612569","GSM9612568","GSM9612570","GSM9612563","GSM9612573","GSM9612572","GSM9612571","GSM9612567","GSM9612566","GSM9612565","GSM9612564"],"GPL":["24676"],"GSE":["325755"],"taxon":["Homo sapiens"]}}