Oxidative Effects during Irreversible Electroporation of Melanoma Cells-In Vitro Study.
Wojciech SzlasaAleksander KiełbikAnna SzewczykNina RembiałkowskaVitalij NovickijMounir TarekJolanta SaczkoJulita KulbackaPublished in: Molecules (Basel, Switzerland) (2020)
Irreversible electroporation (IRE) is today used as an alternative to surgery for the excision of cancer lesions. This study aimed to investigate the oxidative and cytotoxic effects the cells undergo during irreversible electroporation using IRE protocols. To do so, we used IRE-inducing pulsed electric fields (PEFs) (eight pulses of 0.1 ms duration and 2-4 kV/cm intensity) and compared their effects to those of PEFs of intensities below the electroporation threshold (eight pulses, 0.1 ms, 0.2-0.4 kV/cm) and the PEFs involving elongated pulses (eight pulses, 10 ms, 0.2-0.4 kV/cm). Next, to follow the morphology of the melanoma cell membranes after treatment with the PEFs, we analyzed the permeability and integrity of their membranes and analyzed the radical oxygen species (ROS) bursts and the membrane lipids' oxidation. Our data showed that IRE-induced high cytotoxic effect is associated both with irreversible cell membrane disruption and ROS-associated oxidation, which is occurrent also in the low electric field range. It was shown that the viability of melanoma cells characterized by similar ROS content and lipid membrane oxidation after PEF treatment depends on the integrity of the membrane system. Namely, when the effects of the PEF on the membrane are reversible, aside from the high level of ROS and membrane oxidation, the cell does not undergo cell death.
Keyphrases
- cell death
- endoplasmic reticulum stress
- cell cycle arrest
- mass spectrometry
- dna damage
- multiple sclerosis
- hydrogen peroxide
- induced apoptosis
- reactive oxygen species
- ms ms
- minimally invasive
- cell therapy
- squamous cell carcinoma
- magnetic resonance imaging
- fatty acid
- electronic health record
- big data
- image quality
- coronary artery disease
- drug induced
- data analysis
- childhood cancer