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Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-¹³C]Pyruvate.

ABSTRACT: 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.

SUBMITTER: Hsieh CY

PROVIDER: S-EPMC9332172 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The detection and treatment monitoring of inflammatory states remain challenging in part due to the multifactorial mechanisms of immune activation and spectrum of clinical manifestations. Currently, diagnostic strategies tend to be subjective and limited quantitative tools exist to monitor optimal treatment strategies. Pro-inflammatory M1 polarized macrophages exhibit a distinct metabolic glycolytic phenotype compared to the continuum of M2 polarization states. In the present study, the distinct metabolic phenotypes of resting and activated macrophages were successfully characterized and quantified using hyperpolarized carbon-13 (13C) labeled pyruvate and its metabolic products, i.e. lactate, as a biomarker of resting, disease and treated states. Methods: Mouse macrophage J774A.1 cells were used as a model system in an NMR compatible bioreactor to facilitate dynamic hyperpolarized 13C measurements. The glycolytic metabolism of the cells in the quiescent or resting state were compared with macrophages stimulated by lipopolysaccharide, a classical M1 activator using hyperpolarized 13C labeled pyruvate. Additionally, the activated macrophages were also treated with a non-steroidal anti-inflammatory drug to assess the changes in hyperpolarized lactate signal. The hyperpolarized lactate signals were then correlated using biochemical and molecular assays. Results: We first validated our model system of inflammatory cells by the hallmarks of M1 polarization using steady state metabolic profiling with high resolution NMR in conjunction with nitric oxide Greiss assay, enzyme activity, and mRNA expression. Subsequently, we clearly showed that the cutting edge technology of hyperpolarized 13C NMR can be used to detect elevated lactate levels in M1 polarized macrophages in comparison to control and non-steroidal anti-inflammatory drug treated M2 states. Conclusion: Hyperpolarized 13C lactate has the potential to serve as a biomarker to non-invasively detect and quantify pro-inflammatory state of immune regulatory cells and its response to therapy.

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Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-¹³C]Pyruvate.

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Dataset Information

Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-13C]Pyruvate.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Developing a Method to Estimate the Downstream Metabolite Signals from Hyperpolarized [1-¹³C]Pyruvate.