Automated segmentation and tracking of mitochondria in live-cell time-lapse images
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 tim...
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| Veröffentlicht in: | Nature methods 2021-09, Vol.18 (9), p.1091-1102 |
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| creator | Lefebvre, Austin E. Y. T. Ma, Dennis Kessenbrock, Kai Lawson, Devon A. Digman, Michelle A. |
| description | 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.
Mitometer enables efficient, rapid, and accurate automated segmentation and tracking of mitochondria from time-lapse images. Mitometer performs well on diverse input images and can be used to monitor dynamic fission and fusion events. |
| doi_str_mv | 10.1038/s41592-021-01234-z |
| format | Article |
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Mitometer enables efficient, rapid, and accurate automated segmentation and tracking of mitochondria from time-lapse images. 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Y. T.</au><au>Ma, Dennis</au><au>Kessenbrock, Kai</au><au>Lawson, Devon A.</au><au>Digman, Michelle A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Automated segmentation and tracking of mitochondria in live-cell time-lapse images</atitle><jtitle>Nature methods</jtitle><stitle>Nat Methods</stitle><addtitle>Nat Methods</addtitle><date>2021-09-01</date><risdate>2021</risdate><volume>18</volume><issue>9</issue><spage>1091</spage><epage>1102</epage><pages>1091-1102</pages><issn>1548-7091</issn><eissn>1548-7105</eissn><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.
Mitometer enables efficient, rapid, and accurate automated segmentation and tracking of mitochondria from time-lapse images. Mitometer performs well on diverse input images and can be used to monitor dynamic fission and fusion events.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>34413523</pmid><doi>10.1038/s41592-021-01234-z</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-1692-0517</orcidid><orcidid>https://orcid.org/0000-0003-4611-7100</orcidid></addata></record> |
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| subjects | 631/1647/245/2225 631/1647/328 631/1647/794 631/80/2373 631/80/642 Algorithms Applications software Automation Bioinformatics Biological Microscopy Biological Techniques Biomedical and Life Sciences Biomedical Engineering/Biotechnology Breast cancer Breast Neoplasms - metabolism Breast Neoplasms - pathology Cell interaction Cell research Cells, Cultured Cytology Female Fission Fluorescence microscopy Humans Image processing Image segmentation Imaging, Three-Dimensional - methods Life Sciences Mammary Glands, Human - cytology Mechanical properties Methods Mitochondria Mitochondria - metabolism Molecular dynamics Morphology NAD - metabolism Physiological aspects Proteomics Reproducibility of Results Software Time-Lapse Imaging - methods Tracking Triple Negative Breast Neoplasms - pathology |
| title | Automated segmentation and tracking of mitochondria in live-cell time-lapse images |
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