Image Analysis of the Mitochondrial Network Morphology With Applications in Cancer Research

Abstract

Mitochondria are dynamic organelles that integrate bioenergetics, biosynthesis, and signaling in cells and regulate redox homeostasis, apoptotic pathways, and cell proliferation and differentiation. Depending on the environmental conditions, the mitochondrial morphology dynamically changes to match the energy demands. 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. 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. 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. Furthermore, we discussed the potential of automatic mitochondrial segmentation, classification and prediction of mitochondrial abnormalities using machine learning techniques. Quantification of the mitochondrial morphology provides potential indicators for identifying metabolic changes and drug responses in cancer cells.