{"database":"bioimages","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":[null],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-BIAD1426"],"repository":["bioimages"],"figure_sub":["Imaging method","Specimen","Keywords","Study Component","organisation","Biosample","Associations","Image acquisition"],"pubmed_authors":["Jurgen Kriel","Vinod K. Narayana","Saskia Freytag","Adam Valkovic","James R. Whittle","Tianyao Lu","Oluwaseun E. Fatunla","Malcolm J. McConville","Ana Maluenda","Ellen Tsui","Joel J.D. Moffet","Sarah A. Best"],"additional_accession":[]},"is_claimable":false,"name":"An integrative spatial multi-omic workflow for unified analysis of tumor tissue","description":"Combining molecular profiling with imaging techniques has advanced the field of spatial biology offering new insights into complex biological processes. Focusing on diffuse IDH-mutated low-grade glioma this study presents a workflow for Spatial Multi-omics Integration SMINT specifically combining spatial transcriptomics and spatial metabolomics. Our workflow incorporates both existing and custom-developed computational tools to enable cell segmentation and registration of spatial coordinates from both modalities to a common coordinate framework. During our investigation of cell segmentation strategies we found that nuclei-only segmentation while containing only 40% of segmented cell transcripts enables accurate cell type annotation but does not account for multinucleated cells. Our integra","dates":{"release":"2024-12-28T00:00:00Z","modification":"2026-06-01T10:50:17.489Z","creation":"2024-10-24T03:36:31.271Z"},"accession":"S-BIAD1426","cross_references":{}}