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A fully automated morphological analysis of yeast mitochondria from wide-field fluorescence images
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SYSNO ASEP 0602859 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název A fully automated morphological analysis of yeast mitochondria from wide-field fluorescence images Tvůrce(i) 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)Číslo článku 30144 Zdroj.dok. Scientific Reports. - : Nature Publishing Group - ISSN 2045-2322
Roč. 14, č. 1 (2024)Poč.str. 13 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova Yeast ; Mitochondria ; Deep learning ; Oxidative stress ; Mmi1 ; tctp Obor OECD Microbiology CEP LUASK22100 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy EH22_008/0004575 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy 8J20AT023 GA MŠMT - Ministerstvo školství, mládeže a tělovýchovy Způsob publikování Open access Institucionální podpora MBU-M - RVO:61388971 ; FGU-C - RVO:67985823 UT WOS 001369754000020 EID SCOPUS 85211364628 DOI https://doi.org/10.1038/s41598-024-81241-0 Anotace 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. Pracoviště Mikrobiologický ústav Kontakt Eliška Spurná, eliska.spurna@biomed.cas.cz, Tel.: 241 062 231 Rok sběru 2025 Elektronická adresa https://www.nature.com/articles/s41598-024-81241-0
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