Studying the Metabolism of Epithelial-Mesenchymal Plasticity Using the Seahorse XFe96 Extracellular Flux Analyzer.

ABSTRACT

The critical role of metabolism in facilitating cancer cell growth and survival has been demonstrated by a combination of methods including, but not limited to, genomic sequencing, transcriptomic and proteomic analyses, measurements of radio-labelled substrate flux and the high throughput measurement of oxidative metabolism in unlabelled live cells using the Seahorse Extracellular Flux (XF) technology. These studies have revealed that tumour cells exhibit a dynamic metabolic plasticity, using numerous pathways including both glycolysis and mitochondrial oxidative phosphorylation (OXPHOS) to support cell proliferation, energy production and the synthesis of biomass. These advanced technologies have also demonstrated metabolic differences between cancer cell types, between molecular subtypes within cancers and between cell states. This has been exemplified by examining the transitions of cancer cells between epithelial and mesenchymal phenotypes, referred to as epithelial-mesenchymal plasticity (EMP). A growing number of studies are demonstrating significant metabolic alterations associated with these transitions, such as increased use of glycolysis by triple negative breast cancers (TNBC) or glutamine addiction in lung cancer. Models of EMP, including invasive cell lines and xenografts, isolated circulating tumour cells and metastatic tissue have been used to examine EMP metabolism. Understanding the metabolism supporting molecular and cellular plasticity and increased metastatic capacity may reveal metabolic vulnerabilities that can be therapeutically exploited. This chapter describes protocols for using the Seahorse Extracellular Flux Analyzer (XFe96), which simultaneously performs real-time monitoring of oxidative phosphorylation and glycolysis in living cells. As an example, we compare the metabolic profiles generated from two breast cancer sublines that reflect epithelial and mesenchymal phenotypes, respectively. We use this example to show how the methodology described can generate bioenergetic results that in turn can be correlated to EMP phenotypes. Normalisation of bioenergetic studies should be considered with respect to cell number, and to potential differences in mitochondrial mass, itself being an important bioenergetics endpoint.