Chemical Exchange Saturation Transfer (CEST) Signal at -1.6 ppm and Its Application for Imaging a C6 Glioma Model.
Qi-Xuan WuHong-Qing LiuYi-Jiun WangTsai-Chen ChenZi-Ying WeiJung-Hsuan ChangTing-Hao ChenJaya SeemaEugene C LinPublished in: Biomedicines (2022)
The chemical exchange saturation transfer (CEST) signal at -1.6 ppm is attributed to the choline methyl on phosphatidylcholines and results from the relayed nuclear Overhauser effect (rNOE), that is, rNOE(-1.6). The formation of rNOE(-1.6) involving the cholesterol hydroxyl is shown in liposome models. We aimed to confirm the correlation between cholesterol content and rNOE(-1.6) in cell cultures, tissues, and animals. C57BL/6 mice (N = 9) bearing the C6 glioma tumor were imaged in a 7 T MRI scanner, and their rNOE(-1.6) images were cross-validated through cholesterol staining with filipin. Cholesterol quantification was obtained using an 18.8-T NMR spectrometer from the lipid extracts of the brain tissues from another group of mice (N = 3). The cholesterol content in the cultured cells was manipulated using methyl-β-cyclodextrin and a complex of cholesterol and methyl-β-cyclodextrin. The rNOE(-1.6) of the cell homogenates and their cholesterol levels were measured using a 9.4-T NMR spectrometer. The rNOE(-1.6) signal is hypointense in the C6 tumors of mice, which matches the filipin staining results, suggesting that their tumor region is cholesterol deficient. The tissue extracts also indicate less cholesterol and phosphatidylcholine contents in tumors than in normal brain tissues. The amplitude of rNOE(-1.6) is positively correlated with the cholesterol concentration in the cholesterol-manipulated cell cultures. Our results indicate that the cholesterol dependence of rNOE(-1.6) occurs in cell cultures and solid tumors of C6 glioma. Furthermore, when the concentration of phosphatidylcholine is carefully considered, rNOE(-1.6) can be developed as a cholesterol-weighted imaging technique.
Keyphrases
- low density lipoprotein
- high resolution
- single cell
- magnetic resonance
- gene expression
- cell therapy
- stem cells
- computed tomography
- deep learning
- type diabetes
- oxidative stress
- white matter
- machine learning
- adipose tissue
- optical coherence tomography
- mass spectrometry
- photodynamic therapy
- cell death
- flow cytometry
- endoplasmic reticulum stress
- cerebral ischemia