Invited Paper PB+BI+PS-TuM1
Spatial Distribution of Biological Effects Induced by Plasma Reactive Species

Tuesday, October 31, 2017, 8:00 am, Room 12

Several in vitro and in vivo studies have been conducted in a variety of cancer cell lines that demonstrate the efficacy of cold plasmas in causing cell death since the advent of this new research area in the plasma physics community in 2010. Due to the complexity of both the plasma and biological systems, many questions must be answered to sharply improve our understanding of the physical, chemical, and biological processes underlying their interactions. However, since cold plasmas produce a cocktail of reactive oxygen species (ROS) and reactive nitrogen species (RNS), these species are believed to be key agents that can induce a number of biological effects, including impairment of cell substructures and even cell death. Moreover, cancer cells have proven to be more susceptible to damage by these reactive species than normal cells subjected to plasma exposure. The outcome of cell responses to plasma treatment has inspired the potential application of plasma as an effective and safe tool for novel cancer therapy. Our research focuses on investigations of nucleus DNA damage in cancer cells and bacterial inactivation caused by exposure to plasma reactive species. Initially, to detect ROS and RNS that reached the targeted biological systems we used semi-quantitative test strips, while to investigate biological effects in cells we used digital imaging or immunofluorescence microscopy. Recently, to obtain the high-resolved spatial distribution of DNA strand breaks we developed a workflow with algorithms for image analysis using CellProfiler and MATLAB, including background correction, cell segmentation, feature extraction, cell classification, and data visualization. This method well preserves the essential spatial information about cell distribution, which is critical because of the localized nature of the plasma jet treatment. By applying both supervised and unsupervised machine learning techniques to the images, we were also able to classify the cells according to different cell cycle phases, and thus obtain spatial information regarding plasma jet effects on cell cycle progression.