scholarly journals Live-cell high resolution magic angle spinning magnetic resonance spectroscopy for in vivo analysis of Pseudomonas aeruginosa metabolomics

2013 ◽  
Vol 1 (5) ◽  
pp. 707-712 ◽  
Author(s):  
VALERIA RIGHI ◽  
CATERINA CONSTANTINOU ◽  
MEENU KESARWANI ◽  
LAURENCE G. RAHME ◽  
ARIA A. TZIKA
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Magnetic Resonance Spectroscopy ◽  
Magic Angle Spinning ◽  
Resonance Spectroscopy ◽  
Magic Angle ◽  
In Vivo Analysis ◽  
Angle Spinning

Using high-resolution magic angle spinning magnetic resonance spectroscopy to characterize the metabolomic profile of renal cell carcinoma.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 710-710
Author(s):  
Melissa Jessica Huynh ◽  
Andrew Gusev ◽  
Francesco Palmas ◽  
Lindsey Vandergrift ◽  
Chin-Lee Wu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Magnetic Resonance Spectroscopy ◽  
Renal Cell ◽  
Magic Angle Spinning ◽  
Resonance Spectroscopy ◽  
Magic Angle ◽  
Metabolomic Profile ◽  
Angle Spinning

710 Background: Renal cell carcinoma (RCC) is a metabolic disease, with the various subtypes exhibiting aberrations in different metabolic pathways. Metabolomics may offer greater sensitivity for revealing disease biology than evaluation of tissue morphology. In this study, we investigate the metabolomic profile of RCC using high resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS). Methods: Tissue samples were obtained from radical or partial nephrectomy specimens that were fresh frozen & stored at -80ºC. Tissue HRMAS MRS was performed by a Bruker AVANCE spectrometer. Metabolomic profiles of RCC & adjacent benign renal tissue were compared, and false discovery rates (FDR) were used to account for multiple testing. Regions of interest (ROI) with FDR < 0.05 were selected as potential predictors of malignancy. The Wilcoxon rank sum test was used to compare median MRS relative intensities for the candidate predictors. Logistic regression was used to determine odds ratios for risk of malignancy based on abundance of each metabolite. Results: There were 38 RCC (16 clear cell, 11 papillary, 11 chromophobe) & 13 adjacent normal tissue specimens (matched pairs). Metabolic predictors of malignancy based on FDR include histidine, phenylalanine, phosphocholine, serine, phosphocreatine, creatine, glycerophosphocholine, valine, glycine, myo-inositol, scylla-inositol, taurine, glutamine, spermine, acetoacetate & lactate. Higher levels of spermine, histidine & phenylalanine at 3.15-3.13 ppm were associated with a decreased risk of RCC (OR 4x10−5, 95% CI 7.42x10−8, 0.02), while 2.84-2.82 ppm increased the risk of malignant pathology (OR 7158.67, 95% CI 6.3, 8.3x106), and the specific metabolites characterizing this region remain to be identified. Tumor stage did not appear to affect the metabolomics of malignant tumors, suggesting that metabolites are more dependent on histologic subtype. Conclusions: HRMAS-MRS identified many metabolites that may predict RCC. We demonstrated that those in the 3.14-3.13 ppm ROI were present in lower levels in RCC, while higher levels of metabolites in the 2.84-2.82 ppm ROI substantially increased the risk of RCC.

Using high-resolution magic angle spinning magnetic resonance spectroscopy to characterize the metabolomic profile of fat-poor angiomyolipoma and renal cell carcinoma.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 711-711
Author(s):  
Melissa Jessica Huynh ◽  
Andrew Gusev ◽  
Francesco Palmas ◽  
Lindsey Vandergrift ◽  
Chin-Lee Wu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Magnetic Resonance Spectroscopy ◽  
Magic Angle Spinning ◽  
Resonance Spectroscopy ◽  
Magic Angle ◽  
Metabolomic Profile ◽  
Risk Of Malignancy ◽  
Angle Spinning

711 Background: Fat-poor angiomyolipoma (AML) can be difficult to differentiate from renal cell carcinoma (RCC) radiographically and may lead to biopsy or unnecessary intervention. In vivoplatforms with the ability to identify tumor histology based on metabolic profiles may avoid unnecessary procedures & their complications. The metabolomics of AML have not been characterized, & research into this area may provide targetable molecules for large AMLs. In this study, we investigate the metabolomic profile of AMLs compared to clear cell RCC (ccRCC) using high resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS). Methods: Tissue samples were obtained from radical or partial nephrectomy specimens that were fresh frozen & stored at -80ºC. Tissue HRMAS MRS was performed by a Bruker AVANCE spectrometer. Metabolomic profiles of RCC & adjacent benign renal tissue were compared, and false discovery rates (FDR) accounted for multiple testing. Regions of interest (ROI) with FDR <0.05 were considered potential predictors of ccRCC rather than AML. The Wilcoxon rank sum test was used to compare median MRS relative intensities for candidate predictors. Logistic regression was used to determine odds ratios for risk of malignancy based on abundance of each metabolite. Results: There were 16 ccRCC samples & 7 AML specimens. Candidate predictors of malignancy rather than AML based on FDR p-values include histidine, phenylalanine, phosphocholine, serine, alanine, glutamate, glutathione, glycerophosphocholine, & glutamine. While an abundance of these metabolites is associated with higher risk of malignancy, the odds ratio was particularly high in the 3.5-3.49 ppm spectral region (OR 2.99x106, 95% CI 3.27, 2.73x1012, p=0.033)in ccRCC samples. Conclusions: HRMAS MRS identified metabolites that may help differentiate fat-poor AML from ccRCC. In particular, metabolites in the 3.5-3.49 ppm spectral region increased the risk of harboring RCC. Our findings may contribute to future in vivostudies to help identify which patients require intervention for malignancy & which may be observed for benign AML without requiring biopsy.

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