![]() ![]() 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. ![]() Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.doi: 10.Ching-Hsiang Chu †, Wen-Wei Tseng †, Chan-Min Hsu and An-Chi Wei* Improved structure, function and compatibility for CellProfiler: modular high-throughput image analysis software. Kamentsky L, Jones TR, Fraser A, Bray M-A, Logan DJ, Madden KL, et al. ![]() CellProfiler: image analysis software for identifying and quantifying cell phenotypes. doi: 10.1016/j.jbiotec.2017.07.028Ĭarpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, et al. KNIME for reproducible cross-domain analysis of life science data. ImageJ2: ImageJ for the next generation of scientific image data. Rueden CT, Schindelin J, Hiner MC, DeZonia BE, Walter AE, Arena ET, et al. 3D, three-dimensional FISH, fluorescent in situ hybridization GAPDH, glyceraldehyde 3-phosphate dehydrogenase WGA, wheat germ agglutinin.Įliceiri KW, Berthold MR, Goldberg IG, Ibáñez L, Manjunath BS, Martone ME, et al. Images were provided by Javier Frias Aldeguer and Nicolas Rivron from Hubrecht Institute, Netherlands, and are available from the Broad Bioimage Benchmark Collection ( ). The underlying measurements may be downloaded as S1 File. (F) Examples of analysis that can be done by CellProfiler: (top) cell volume relative nucleus volume, (middle) GAPDH transcript quantity in each cell using CellProfiler’s “RelateObjects” module, (bottom) number of GAPDH transcripts in Z-plane (bin size = 2.5 μm). (E) Segmentation of GAPDH transcript foci using CellProfiler, as viewed in Fiji. (D) Segmentation of cells after setting nuclei as seeds by CellProfiler, as viewed in Fiji. (C) Nuclei after segmentation by CellProfiler, as viewed in Fiji. Figure labels: RH (“RemoveHoles”), Close (“Closing”), Erode (“Erosion”), Mask (“MaskImage”), Math (“ImageMath”), EorS Features (“EnhanceOrSuppressFeatures”). (B) CellProfiler 3.0 image processing modules used for membrane image processing. (A) Original 3D image of blastocyst cell membrane prior to analysis. Images were captured of a mouse embryo blastocyst cell membrane stained with WGA and FISH for GAPDH transcripts. ![]()
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