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A fully automated morphological analysis of yeast mitochondria from wide-field fluorescence images
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SYSNO ASEP 0602859 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title A fully automated morphological analysis of yeast mitochondria from wide-field fluorescence images Author(s) Vojtová, Jana (MBU-M) ORCID
Čapek, Martin (FGU-C) RID, ORCID
Willeit, S. (AT)
Groušl, Tomáš (MBU-M) RID, ORCID
Chvalová, Věra (MBU-M) ORCID
Kutejová, E. (SK)
Pevala, V. (SK)
Valášek, Leoš Shivaya (MBU-M) RID, ORCID
Rinnerthaler, M. (AT)Article number 30144 Source Title Scientific Reports. - : Nature Publishing Group - ISSN 2045-2322
Roč. 14, č. 1 (2024)Number of pages 13 s. Language eng - English Country US - United States Keywords Yeast ; Mitochondria ; Deep learning ; Oxidative stress ; Mmi1 ; tctp OECD category Microbiology R&D Projects LUASK22100 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) EH22_008/0004575 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) 8J20AT023 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Open access Institutional support MBU-M - RVO:61388971 ; FGU-C - RVO:67985823 UT WOS 001369754000020 EID SCOPUS 85211364628 DOI https://doi.org/10.1038/s41598-024-81241-0 Annotation 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 mmi1 Delta 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. Workplace Institute of Microbiology Contact Eliška Spurná, eliska.spurna@biomed.cas.cz, Tel.: 241 062 231 Year of Publishing 2025 Electronic address https://www.nature.com/articles/s41598-024-81241-0
Number of the records: 1