A fully automated morphological analysis of yeast mitochondria from wide-field fluorescence images
Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články
Grantová podpora
LUASK22100
Ministry of Education, Youth, and Sports (Czech Republic)
VEGA 2/0069/23
the Slovak Grant Agency
ITMS: 305011X666
Interreg V-A Slovakia-Austria StruBioMol
SK-CZ-RD-21-0104
the Slovak Research and Development Agency
CZ.02.01.01/00/22_008/0004575
ERDF and MEYS
P33511
the Austrian Science Fund FWF
PubMed
39627480
PubMed Central
PMC11615301
DOI
10.1038/s41598-024-81241-0
PII: 10.1038/s41598-024-81241-0
Knihovny.cz E-zdroje
- Klíčová slova
- Deep learning, Mitochondria, Mmi1, Oxidative stress, TCTP, Yeast,
- MeSH
- deep learning MeSH
- fluorescenční mikroskopie metody MeSH
- mitochondrie * metabolismus MeSH
- oxidační stres MeSH
- počítačové zpracování obrazu * metody MeSH
- Saccharomyces cerevisiae - proteiny metabolismus genetika MeSH
- Saccharomyces cerevisiae * genetika metabolismus MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- Saccharomyces cerevisiae - proteiny MeSH
Mitochondrial morphology is an important parameter of cellular fitness. Although many approaches are available for assessing mitochondrial morphology in mammalian cells, only a few technically demanding and laborious methods are available for yeast cells. A robust, fully automated and user-friendly approach that would allow (1) segmentation of tubular and spherical mitochondria in the yeast Saccharomyces cerevisiae from conventional wide-field fluorescence images and (2) quantitative assessment of mitochondrial morphology is lacking. To address this, we compared Global thresholding segmentation with deep learning MitoSegNet segmentation, which we retrained on yeast cells. The deep learning model outperformed the Global thresholding segmentation. We applied it to segment mitochondria in strain lacking the MMI1/TMA19 gene encoding an ortholog of the human TCTP protein. Next, we performed a quantitative evaluation of segmented mitochondria by analyses available in ImageJ/Fiji and by MitoA analysis available in the MitoSegNet toolbox. By monitoring a wide range of morphological parameters, we described a novel mitochondrial phenotype of the mmi1Δ strain after its exposure to oxidative stress compared to that of the wild-type strain. The retrained deep learning model, all macros applied to run the analyses, as well as the detailed procedure are now available at https://github.com/LMCF-IMG/Morphology_Yeast_Mitochondria .
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