Abstract
Metabolic plasticity, i.e., the capability of cells to modify their metabolic state, is a hallmark of cancer and an important factor in the formation of metastases as well as a major contributor to chemoresistance and tumor recurrence. Furthermore, the tumor microenvironment, namely the collection of environmental properties (pH, nutrient availability, fatty acids) and cell types that surround the main tumor mass, is regarded to as the main driver of the metabolic state of tumor cells. Unfortunately, no technique can non-invasively assess the metabolic state of single cells in living samples, limiting our understanding of the heterogeneity and dynamics of metabolic state transitions. Here, we apply a technique, named ESPRESSO (Environmental Sensors Profiling Relayed by Subcellular Structures and Organelles) that combines organelle-specific environment-sensitive probes (ESPs) with hyperspectral imaging and quantitative bioimage analysis. We use a mixture of lipid droplet-, mitochondria- and lysosome-specific ESPs to quantify of morphological (e.g., number, size, organization) and functional (e.g., membrane potential, pH, polarity) characteristics to identify the metabolic state in single, living cells. To understand the differences in metabolic plasticity of non-tumorigenic breast epithelial (MCF10a) and triple negative breast cancer (MDA-MB-231) cells, we determined their metabolic state with ESPRESSO under a variety of stress and environmental conditions, identifying the characteristic metabolic signature of the cell lines under those conditions. We further investigated the response to diverse chemotherapy drugs (doxorubicin, paclitaxel, curcumin) in an effort to understand their effect on cancer and healthy cells, highlighting the environmental conditions that would favor the resistance to a specific drug treatment. Taken together, we provide a framework to identify the metabolic response of cancer and non-tumorigenic cells under different drugs, stressors and environmental conditions, providing in-depth characterization of their metabolic response at the single cell level.
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