Development of a Novel System for Microdosimetry and Cellular Radiation Response
Abstract
Purpose
The overarching goal is to develop a novel system for microdosimetry integrated with cellular molecular radiation response using Monte Carlo (MC) simulations, Raman spectroscopy (RS), and radiobiological assays. MC simulations of the system and initial investigation of cellular responses are presented here.
Methods
MC38 murine colon carcinoma cells are irradiated with doses 0-10 Gy and assessed at 24 and 48h post-irradiation. MC simulations replicating the experimental setup are used to quantify energy deposition in cells. Cellular responses are measured using flow cytometry for mitochondrial superoxide generation (MitoSOX), cell membrane integrity (propidium iodide) and proliferation (CFSE). Additionally, RS measurements characterize radiation-induced macromolecular changes within individual cell nuclei. The integrated cells-on-radiochromic-film (cells+RCF) system is studied using MC simulations to evaluate correlations between energy deposition in cells and in RCF to assess feasibility of the system for microdosimetry.
Results
MC simulations show that variability in specific energy deposited within cell nuclei decreases with increasing dose: the relative standard deviation decreases from 6% at 0.25 Gy to 2% at 2 Gy. Flow cytometry reveals minimal cytotoxicity at 24h, limited to the highest dose, while dose-dependent oxidative stress, decreased proliferation and viability emerge at 48h for doses ≥5 Gy. RS measurements also indicate dose and time-dependent biochemical changes in cell nuclei: reductions up to 30% in DNA/RNA-associated bands and increases of up to 20% in protein-associated bands for doses ≥5 Gy. MC simulations of cell+RCF show agreement between mean specific energy deposited in nuclei and in the RCF, with differences <2% across all doses.
Conclusion
Together, the cellular response measurements and MC simulations support the potential of the cells+RCF system to investigate microdosimetry and time-dependent radiation response. Ongoing work focuses on RS measurements from this integrated system and the use of machine learning to quantitatively correlate energy deposition to radiation response in cells.