Alterations in metabolism following radiotherapy affect therapeutic efficacy, although the mechanism underlying such alterations is unclear. A new imaging technique—named dynamic nuclear polarization (DNP) carbon-13 magnetic resonance imaging (MRI)—probes the glycolytic flux in a real-time, dynamic manner. The [1-13C]pyruvate is transported by the monocarboxylate transporter (MCT) into cells and converted into [1-13C]lactate by lactate dehydrogenase (LDH). To capture the early glycolytic alterations in the irradiated cancer and immune cells, we designed a preliminary DNP 13C-MRI study by using hyperpolarized [1-13C]pyruvate to study human FaDu squamous carcinoma cells, HMC3 microglial cells, and THP-1 monocytes before and after irradiation. The pyruvate-to-lactate conversion rate (kPL [Pyr.]) calculated by kinetic modeling was used to evaluate the metabolic alterations. Western blotting was performed to assess the expressions of LDHA, LDHB, MCT1, and MCT4 proteins. Following irradiation, the pyruvate-to-lactate conversion rates on DNP 13C-MRI were significantly decreased in the FaDu and the HMC3 cells but increased in the THP-1 cells. Western blot analysis confirmed the similar trends in LDHA and LDHB expression levels. In conclusion, DNP 13C-MRI non-invasively captured the different glycolytic alterations among cancer and immune systems in response to irradiation, implying its potential for clinical use in the future.

Hyperpolarized carbon-13 MRI has the advantage of allowing the study of glycolytic flow in vivo or in vitro dynamically in real-time. The apparent exchange rate constant of a metabolite dynamic signal reflects the metabolite changes of a disease. Downstream metabolites can have a low signal-to-noise ratio (SNR), causing apparent exchange rate constant inconsistencies. Thus, we developed a method that estimates a more accurate metabolite signal. This method utilizes a kinetic model and background noise to estimate metabolite signals. Simulations and in vitro studies with photon-irradiated and control groups were used to evaluate the procedure. Simulated and in vitro exchange rate constants estimated using our method were compared with the raw signal values. In vitro data were also compared to the Area-Under-Curve (AUC) of the cell medium in 13C Nuclear Magnetic Resonance (NMR). In the simulations and in vitro experiments, our technique minimized metabolite signal fluctuations and maintained reliable apparent exchange rate constants. In addition, the apparent exchange rate constants of the metabolites showed differences between the irradiation and control groups after using our method. Comparing the in vitro results obtained using our method and NMR, both solutions showed consistency when uncertainty was considered, demonstrating that our method can accurately measure metabolite signals and show how glycolytic flow changes. The method enhanced the signals of the metabolites and clarified the metabolic phenotyping of tumor cells, which could benefit personalized health care and patient stratification in the future.