Abstract
Stable-isotope resolved metabolomics (SIRM) is a powerful approach for characterizing metabolic states in cells and organisms. By incorporating isotopes, such as 13C, into substrates, researchers can trace reaction rates across specific metabolic pathways. Integrating metabolomics data with gene expression profiles further enriches the analysis, as we demonstrated in our prior study on glioblastoma metabolic symbiosis. However, the bioinformatics tools for analyzing tracer metabolomics data have been limited. In this protocol, we encourage the researchers to use SIRM and transcriptomics data and to perform the downstream analysis using our software tool DIMet. Indeed, DIMet is the first comprehensive tool designed for the differential analysis of tracer metabolomics data, alongside its integration with transcriptomics data. DIMet facilitates the analysis of stable-isotope labeling and metabolic abundances, offering a streamlined approach to infer metabolic changes without requiring complex flux analysis. Its pathway-based "metabologram" visualizations effectively integrate metabolomics and transcriptomics data, offering a versatile platform capable of analyzing corrected tracer datasets across diverse systems, organisms, and isotopes. We provide detailed steps for sample preparation and data analysis using DIMet through its intuitive, web-based Galaxy interface. To showcase DIMet's capabilities, we analyzed LDHA/B knockout glioblastoma cell lines compared to controls. Accessible to all researchers through Galaxy, DIMet is free, user-friendly, and open source, making it a valuable resource for advancing metabolic research. Key features • Glioblastoma tumor spheroids in vitro replicate tumors' three-dimensional structure and natural nutrient, metabolite, and gas gradients, providing a more realistic model of tumor biology. • Joint analysis of tracer metabolomics and transcriptomics datasets provides deeper insights into the metabolic states of cells. • DIMet is a web-based tool for differential analysis and seamless integration of metabolomics and transcriptomics data, making it accessible and user-friendly. • DIMet enables researchers to infer metabolic changes, offering intuitive and visually appealing "metabologram" outputs, surpassing conventional visual representations commonly used in the field.