Related Experiment Video
Updated: Oct 23, 2025
Specific Labeling of Mitochondrial Nucleoids for Time-lapse Structured Illumination Microscopy
Published on: June 4, 2020
Automated segmentation and tracking of mitochondria in live-cell time-lapse images
Austin E Y T Lefebvre1,2, Dennis Ma3, Kai Kessenbrock3
1Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA.
Abstract:
Mitochondria display complex morphology and movements, which complicates their segmentation and tracking in time-lapse images. Here, we introduce Mitometer, an algorithm for fast, unbiased, and automated segmentation and tracking of mitochondria in live-cell two-dimensional and three-dimensional time-lapse images. Mitometer requires only the pixel size and the time between frames to identify mitochondrial motion and morphology, including fusion and fission events. The segmentation algorithm isolates individual mitochondria via a shape- and size-preserving background removal process. The tracking algorithm links mitochondria via differences in morphological features and displacement, followed by a gap-closing scheme. Using Mitometer, we show that mitochondria of triple-negative breast cancer cells are faster, more directional, and more elongated than those in their receptor-positive counterparts. Furthermore, we show that mitochondrial motility and morphology in breast cancer, but not in normal breast epithelia, correlate with metabolic activity. Mitometer is an unbiased and user-friendly tool that will help resolve fundamental questions regarding mitochondrial form and function.
Insights
Mitometer is a new algorithm for automated segmentation and tracking of mitochondria in live-cell imaging. This tool reveals differences in mitochondrial dynamics and morphology between breast cancer subtypes and links motility to metabolic activity.
Area of Science:
- Cell Biology
- Mitochondrial Dynamics
- Cancer Research
Background:
- Mitochondria exhibit complex morphology and movement, posing challenges for segmentation and tracking in live-cell imaging.
- Accurate analysis of mitochondrial dynamics is crucial for understanding cellular function and disease states, particularly in cancer.
Purpose of the Study:
- To introduce Mitometer, a novel algorithm for automated, unbiased segmentation and tracking of mitochondria in 2D and 3D time-lapse images.
- To investigate mitochondrial morphology and motility in different breast cancer subtypes and their correlation with metabolic activity.
Main Methods:
- Developed Mitometer, an algorithm utilizing pixel size and frame interval for mitochondrial identification, including fusion and fission events.
- Employed shape- and size-preserving background removal for individual mitochondrion segmentation.
- Implemented a tracking algorithm based on morphological features, displacement, and a gap-closing scheme.
Main Results:
- Mitometer successfully segmented and tracked mitochondria in live-cell images, identifying motion, morphology, fusion, and fission.
- Mitochondria in triple-negative breast cancer cells were found to be faster, more directional, and more elongated than in receptor-positive cells.
- Mitochondrial motility and morphology correlated with metabolic activity in breast cancer cells, but not in normal breast epithelia.
Conclusions:
- Mitometer provides a fast, unbiased, and user-friendly tool for analyzing mitochondrial dynamics and morphology.
- The study highlights distinct mitochondrial characteristics in different breast cancer subtypes and their functional relevance.
- Mitometer facilitates fundamental research into the relationship between mitochondrial form, function, and cancer progression.
