Radiation-Induced Mitotic Catastrophe Enhanced by Gold Nanoparticles: Assessment with a Specific Automated Image Processing Workflow.

In recent years, the use of gold-based nanoparticles in radiotherapy has been extensively studied, and the associated radiosensitization mechanism has been evaluated in a variety of in vitro studies. Given that mitotic catastrophe is widely involved in radiation-induced cell death, we evaluated the effect of gold nanoparticles on this key event. Most of the methods currently used to visualize and quantify morphological changes and multinucleation are manual. To circumvent this time-consuming step, we developed and optimized an image processing workflow (based on freely accessible software and plugins) for the automated quantification of mitotic catastrophes. We validated this approach in three cell lines by comparing the number of radiation-induced mitotic catastrophes detected using the automated and manual methods in the presence and absence of nanoparticles. With the Bland-Altman analysis, the automated and manual counting methods were found to be fully interchangeable. The ultimate goal of this work was to determine whether mitotic catastrophe was critically involved in radiationinduced cell death after prior exposure to gold nanoparticles. In the radioresistant U87 cell line, exposure to gold nanoparticles was associated with a shorter time course for the events related to mitotic catastrophe, which peaked at 96 h postirradiation. Mitotic catastrophe was dose-dependent in both the presence and absence of gold nanoparticles. These results demonstrate that cell exposure to gold nanoparticles led to an increase in mitotic catastrophe events, and confirm the marked radiosensitizing effect observed in clonogenic assays.

Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles.

This article addresses the in silico-in vitro prediction issue of organometallic nanoparticles (NPs)-based radiosensitization enhancement. The goal was to carry out computational experiments to quickly identify efficient nanostructures and then to preferentially select the most promising ones for the subsequent in vivo studies. To this aim, this interdisciplinary article introduces a new theoretical Monte Carlo computational ranking method and tests it using 3 different organometallic NPs in terms of size and composition. While the ranking predicted in a classical theoretical scenario did not fit the reference results at all, in contrast, we showed for the first time how our accelerated in silico virtual screening method, based on basic in vitro experimental data (which takes into account the NPs cell biodistribution), was able to predict a relevant ranking in accordance with in vitro clonogenic efficiency. This corroborates the pertinence of such a prior ranking method that could speed up the preclinical development of NPs in radiation therapy.