Combining mitochondrial morphology and cellular metabolism measurements improves colorectal cancer cell classification

Background In many diseases, including cancer, the number, distribution and shape of mitochondria are affected. Under stress conditions and in tumor cells, changes in cellular, nuclear and mitochondrial morphology are frequently observed. Mitochondria are responsible of energy production and it is recognized that metabolic reprogramming is a hallmark of cancer, including colorectal cancer (CRC), the third most common type of cancer and second most deadly cancer in the world. CRC is a heterogeneous disease, each subtype having its own molecular properties and resulting in diverse clinical outcomes. The relationship between mitochondrial morphology, metabolic status and CRC progression has not been formally investigated so far. Here, we sought to determine whether quantitative imaging of mitochondrial shapes, in addition to metabolic measurements, could provide useful information for CRC subtyping.MethodsWe have recently developed a novel wet-and-dry imaging pipeline (MITOMATICS) that enables the quantitative measurement of a wide variety of mitochondrial shapes in their native cellular environment using high-content confocal microscopy screening. This automated pipeline, which includes statistical analysis and visualization tools (Mann Whitney and t-tests, LDA, PCA, SSMD), was applied to monitor mitochondrial morphology in a cellular model of colon cancer progression consisting of various CRC cell lines along with paired non-tumoral cell lines. The metabolic phenotype of the multiple cell subsets was also determined in order to draw inter-assay comparisons.ResultsWe observed that mitochondria of CRC cells were swollen with a fragmented mitochondrial network, whereas in their non-tumor counterparts, mitochondria were found to be elongated and organized in a complex branched network. Statistical analysis confirmed that there was a clear separation between normal and CRC cells and between the various CRC subtypes based on mitochondrial morphology. In addition, our results showed that glycolysis and OXPHOS are both increased as a function of CRC progression with a specific metabolic signature displayed by each tumor cell line. Interestingly, combining both types of mito-signatures improved accuracy of the classification.ConclusionIntegration of mitochondrial shape phenotyping and metabolic profiling improves CRC cancer cell classification and could offer a new type of biomarker for CRC screening and therapeutic decision making.