In Vivo NMR Spectroscopy, 2nd Edition

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Finally, a number of exercises are designed as a hands-on experience, detailing the considerations and assumptions of a particular experiment. These exercises are crucial to the deeper understanding of in vivo NMR spectroscopy. This is especially true for users on many commercial MR systems, where a large part of the basic operations have been automated. The reader is encouraged to solve all exercises and provide the author with feed-back in case of erroneous or incomplete solutions at invivonmr hotmail.

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Copyright Information Interested in separation science? All Rights Reserved. To increase precision and reduce parameter correlation, which is especially important for the metabolites with multiple exchanging sites, BME were fitted simultaneously to metabolic Z-spectra for all pH levels obtained at multiple B1 saturation levels of the CEST prepulse. In this work, we used metabolic CEST signatures to study metabolic contributions to brain Z-spectra over a variety of main field strengths, i.

Out of the five hydroxyl proton resonances identified for Glc 29 , three resonances from four protons from 0. The relatively low in vivo concentration of Glc, however, necessitates its exogenous administration for a detectable CEST effect, which has been exploited for imaging of the blood brain barrier disruption in brain tumors 30 , MI is one of the osmolytes in the central nervous system 32 and MI mapping may help in diagnosis and treatment monitoring of many neuropsychiatric conditions. However, the endogenous concentration of MI is much higher than for Glc.

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Rather than quantifying the MI CEST effect at a single frequency, an increase in sensitivity can be achieved if an area from ca. The results of our measurements of resonance frequency and exchange rate for the guanidinium protons of Cr and PCr are close to the literature values Cr is converted by the enzyme creatine kinase CK to PCr, which acts as a temporal energy buffer in brain and muscles, according to the reaction 34 :.

From our estimates, CEST effect in the brain at 2. Glu is the major excitatory neurotransmitter in the brain, the abnormal variation of which is associated with many diseases of the central nervous system The findings of this study can be exploited in the design of novel CEST sequences and advanced Z-spectra processing algorithms, which is the initial step towards quantitative metabolic CEST imaging. Even though, the focus of this work was the brain tissue, the results can be reanalyzed for other tissues as well.

However, it is important to realize that even these saturation schemes never generate a pure contrast for any particular metabolite. Therefore, we analyzed metabolic contributions to Z-spectra as a function of frequency offset. Neither magnetization transfer MT nor upfield nuclear Overhauser enhancement NOE phenomena were included in the BME model, as there are multiple approaches to remove those effects from the Z-spectra 10 , 12 , However, mobile proteins have other types of exchangeable protons, although in a lower concentration compared to the abundant amide protons 26 , 37 , As such, mobile proteins in vivo may have other downfield CEST effects in the range 0—4 ppm.

For instance, the CEST effect at 2 ppm in mouse brain at To make matters worse, protein CEST effects in vivo may be dependent on protein conformational changes 42 , which is difficult to take into account in simulations.

Bibliography

Therefore, all metabolic ratios presented in this work should be considered with the above information in mind. The exchange rate constant of labile protons depends, among other factors, on their chemical environment, which can be highly catalytic Ideally, the composition of in vitro metabolic solutions should reflect the relevant in vivo chemical environment. Unfortunately, that is difficult to achieve as the precise cellular composition is unknown and is highly variable. Free phosphate and its derivatives with similar acid dissociation constant, i. All metabolites were purchased from Sigma-Aldrich and used as received without further purification.

The exchange rate measurements for the labile protons of the major brain metabolites, i. B1 saturation levels of the prepulse were optimized for a particular metabolite. Multiple B1 levels for the CEST prepulse yield a varying level of labeling efficiency for exchangeable protons, which in turn increases precision of the BME fitting algorithm.

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The fixed parameters in BME were proton concentration a product of metabolic concentration and number of exchangeable protons and water T1 measured by IR , whereas lower and upper bounds were set on the other parameters, i. To reduce the number of degrees of freedom, the resonance frequency and T2 of labile protons for a particular metabolite were assumed to be the same across all pH values. Also, for the metabolites with multiple exchanging sites, all labile protons were assumed to have the same T2 relaxation times.

The exchange rate between exchangeable protons and water is defined as follows 43 :. We did not fit separately for the catalytic term k c , and so k 0 may contain a contribution from the k c term. The optimization of CEST-prepulse parameters, i. The water relaxation times at Normalized CEST contribution for a particular metabolite in stacked bar plots was calculated as follows:. Cai, K. Magnetic resonance imaging of glutamate. Chan, K. Natural D-glucose as a biodegradable MRI contrast agent for detecting cancer. Rivlin, M. Haris, M. A technique for in vivo mapping of myocardial creatine kinase metabolism.

In vivo mapping of brain myo-inositol. Neuroimage 54 , — Ling, W. Assessment of glycosaminoglycan concentration in vivo by chemical exchange-dependent saturation transfer gagCEST. Zhou, J. Jones, C. Neuroimage 77 , — Desmond, K. Khlebnikov, V. NMR Biomed.

McConnell, H. Reaction rates by nuclear magnetic resonance. Vorstrup, S. Blood Flow Metab. Tao, Z. Kinetic studies on enzyme-catalyzed reactions: Oxidation of glucose, decomposition of hydrogen peroxide and their combination. Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI.

Application of magnetic resonance technologies in aquatic biology and seafood science

Comparison of pulsed three-dimensional CEST acquisition schemes at 7 tesla: steady state versus pseudosteady state. Mori, S. Application to the consensus zinc finger peptide CP B , 96— Dixon, W. McMahon, M. Quantifying exchange rates in chemical exchange saturation transfer agents using the saturation time and saturation power dependencies of the magnetization transfer effect on the magnetic resonance imaging signal QUEST and QUESP : pH calibration for poly.

Randtke, E.


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Lee, J. Contrast Media Mol. Imaging 11 , 4—14 Features and analysis of the field-dependent saturation spectrum. Neuroimage , — Bociek, S.

In Vivo NMR Spectroscopy (2nd ed.)

Proton exchange in aqueous solutions of glucose. Hydration of carbohydrates. Faraday Trans. Phases 75 , — Xu, X. Dynamic glucose enhanced DGE MRI for combined imaging of blood-brain barrier break down and increased blood volume in brain cancer. Law, R. Regulation of mammalian brain cell volume. Exchange rates of creatine kinase metabolites: Feasibility of imaging creatine by chemical exchange saturation transfer MRI. Clark, J. Creatine and phosphocreatine: A review of their use in exercise and sport.

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Greenhaff, P. Effect of oral creatine supplementation on skeletal muscle phosphocreatine resynthesis. Harris, R. Elevation of creatine in resting and exercised muscle of normal subjects by creatine supplementation. Chen, L. Investigation of the contribution of total creatine to the CEST Z-spectrum of brain using a knockout mouse model.


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Kan, H. Platt, S. The role of glutamate in central nervous system health and disease—a review. Zhang, X. Assignment of the molecular origins of CEST signals at 2 ppm in rat brain. Goerke, S. Signature of protein unfolding in chemical exchange saturation transfer imaging. Liepinsh, E. Proton exchange rates from amino acid side chains—implications for image contrast. Woessner, D. Polders, D. Uncertainty estimations for quantitative in vivo MRI T1 mapping. Stanisz, G. Bojorquez, J. Magnetic Resonance Imaging 35 , 69—80