9/4/2023 0 Comments CellprofilerMitochondrial fusion is associated with a well-developed mitochondrial network and increased OXPHOS capacity. Processes called mitochondrial dynamics are crucial for the mitochondrial network, DNA (mtDNA) integrity, ATP synthesis, ROS generation, and cell cycle progression. Mitochondria are also motile organelles that constantly undergo fission, fusion, mitophagy, and biogenesis cycles. Īs the hub for bioenergetics, metabolism, signal transduction, and cellular survival, mitochondria play crucial roles in cancer cell proliferation, progression, and response to treatment. Cancer cells might reconfigure their metabolic pathways to facilitate epithelial-mesenchymal transition and metastasis. In addition to aerobic glycolysis, previous studies have revealed that cancer cells could adopt a variety of metabolic phenotypes, such as high OXPHOS, intermediate type (high glycolysis and OXPHOS), and idling type (low glycolysis and low OXPHOS), and gene expression profiles predicted via systems biology simulations. However, cancer cells exhibit metabolic plasticity to survive and thrive in various tumor microenvironments. Aerobic glycolysis provides metabolic intermediates for tumor biomass expansion, is an alternative pathway for generating energy (ATP), reduces the oxygen requirement of the hypoxic tumor interior, evades mitochondria-mediated apoptosis, and exports lactate to create an acidic extracellular environment that favors cancer cell survival. In addition to ATP generation via mitochondrial oxidative phosphorylation (OXPHOS), glycolytic flux is often enhanced despite the presence of oxygen. Aerobic glycolysis, which is known as the Warburg effect, is a hallmark of cancer cell metabolism. Quantification of the mitochondrial morphology provides potential indicators for identifying metabolic changes and drug responses in cancer cells.Ĭancer cells have different metabolic profiles from healthy cells to ensure their unregulated proliferation and survival in tumor microenvironments. Furthermore, we discussed the potential of automatic mitochondrial segmentation, classification and prediction of mitochondrial abnormalities using machine learning techniques. We listed and applied pipelines and packages available in ImageJ/Fiji, CellProfiler, MATLAB, Java, and Python for the analysis of fluorescently labeled mitochondria in microscopy images and compared their performance, usability and applications. We reviewed the current image-based analytical tools and machine-learning techniques for phenotyping mitochondrial morphology in different cancer cell lines from confocal microscopy images. The mitochondrial dynamics is related to the initiation, migration, and invasion of diverse human cancers and thus affects cancer metastasis, metabolism, drug resistance, and cancer stem cell survival. Depending on the environmental conditions, the mitochondrial morphology dynamically changes to match the energy demands. Mitochondria are dynamic organelles that integrate bioenergetics, biosynthesis, and signaling in cells and regulate redox homeostasis, apoptotic pathways, and cell proliferation and differentiation.
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