Extracellular vesicles (EVs) are ubiquitous nanoscale particles released from many different types of cells. They have been shown to contain proteins, DNA, RNA, miRNA, and, most recently, metabolites. These particles can travel through the intercellular space and bloodstream to have regulatory effects on distant recipients. When an EV reaches a target cell, it is taken up and degraded to release its contents for utilization within the cell. In addition to regulatory effects, EVs have been shown to supplement the high metabolic demands of recipient cells in a nutrient-deprived tumor microenvironment. We developed an integrated empirical and computational platform to quantify metabolic contribution of source cell-derived EVs to recipient cells. The versatile Exo-MFA software tool utilizes 13C stable-isotope tracing data to quantify the metabolic contributions of EVs from a source cell type on a recipient cell type. This is accomplished by creating EV-depleted culture medium, producing isotope-labeled EVs from the source cells, isolating the labeled EVs from the culture supernatant, culturing the recipient cells in the presence of the labeled EVs, and measuring the resulting metabolite levels across several time points.
Yu X, Odenthal M, Fries JWU (2016) Exosomes as miRNA carriers: formation-function-future. Int J Mol Sci 17(12):2028CrossRefGoogle Scholar
Achreja A et al (2017) Exo-MFA – a 13C metabolic flux analysis framework to dissect tumor microenvironment-secreted exosome contributions towards cancer cell metabolism. Metab Eng 43(Pt B):156–172CrossRefGoogle Scholar
Dey P et al (2017) Genomic deletion of malic enzyme 2 confers collateral lethality in pancreatic cancer. Nature 542:119CrossRefGoogle Scholar
Yang L et al (2016) Targeting stromal glutamine synthetase in tumors disrupts tumor microenvironment-regulated cancer cell growth. Cell Metab 24(5):685–700CrossRefGoogle Scholar
Zupke C, Stephanopoulos G (1994) Modeling of isotope distributions and intracellular fluxes in metabolic networks using atom mapping matrices. Biotechnol Prog 10(5):489–498CrossRefGoogle Scholar
Schmidt K et al (1997) Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices. Biotechnol Bioeng 55(6):831–840CrossRefGoogle Scholar
Thery C et al (2018) Minimal information for studies of extracellular vesicles 2018 (MISEV2018): a position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J Extracell Vesicles 7(1):1535750CrossRefGoogle Scholar