The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death
Jazyk angličtina Země Velká Británie, Anglie Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
32005874
PubMed Central
PMC6994697
DOI
10.1038/s41598-020-58474-w
PII: 10.1038/s41598-020-58474-w
Knihovny.cz E-zdroje
- MeSH
- algoritmy MeSH
- apoptóza * účinky léků MeSH
- buněčná smrt * účinky léků MeSH
- buňky ultrastruktura MeSH
- časosběrné zobrazování metody MeSH
- časové faktory MeSH
- kultivované buňky MeSH
- lidé MeSH
- nádorové buněčné linie MeSH
- optické zobrazování metody MeSH
- počet buněk MeSH
- statistické modely MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Cell viability and cytotoxicity assays are highly important for drug screening and cytotoxicity tests of antineoplastic or other therapeutic drugs. Even though biochemical-based tests are very helpful to obtain preliminary preview, their results should be confirmed by methods based on direct cell death assessment. In this study, time-dependent changes in quantitative phase-based parameters during cell death were determined and methodology useable for rapid and label-free assessment of direct cell death was introduced. The goal of our study was distinction between apoptosis and primary lytic cell death based on morphologic features. We have distinguished the lytic and non-lytic type of cell death according to their end-point features (Dance of Death typical for apoptosis versus swelling and membrane rupture typical for all kinds of necrosis common for necroptosis, pyroptosis, ferroptosis and accidental cell death). Our method utilizes Quantitative Phase Imaging (QPI) which enables the time-lapse observation of subtle changes in cell mass distribution. According to our results, morphological and dynamical features extracted from QPI micrographs are suitable for cell death detection (76% accuracy in comparison with manual annotation). Furthermore, based on QPI data alone and machine learning, we were able to classify typical dynamical changes of cell morphology during both caspase 3,7-dependent and -independent cell death subroutines. The main parameters used for label-free detection of these cell death modalities were cell density (pg/pixel) and average intensity change of cell pixels further designated as Cell Dynamic Score (CDS). To the best of our knowledge, this is the first study introducing CDS and cell density as a parameter typical for individual cell death subroutines with prediction accuracy 75.4% for caspase 3,7-dependent and -independent cell death.
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