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Noninvasive measurements of glycogen in perfused mouse livers using chemical exchange saturation transfer NMR and comparison to (13)C NMR spectroscopy.

Miller CO, Cao J, Chekmenev EY, Damon BM, Cherrington AD, Gore JC - Anal. Chem. (2015)

Bottom Line: Glycogen measurements from serially acquired CEST Z-spectra of livers were compared with measurements from interleaved natural abundance (13)C NMR spectra.We also observed that the CEST signal from glycogen in liver was significantly less than that observed from identical amounts in solution.Our results demonstrate that CEST provides an accurate, precise, and readily accessible method to noninvasively measure liver glycogen levels and their changes.

View Article: PubMed Central - PubMed

Affiliation: †Merck Research Laboratories, 2000 Galloping Hill Rd., Kenilworth, New Jersey 07033, United States.

ABSTRACT
Liver glycogen represents an important physiological form of energy storage. It plays a key role in the regulation of blood glucose concentrations, and dysregulations in hepatic glycogen metabolism are linked to many diseases including diabetes and insulin resistance. In this work, we develop, optimize, and validate a noninvasive protocol to measure glycogen levels in isolated perfused mouse livers using chemical exchange saturation transfer (CEST) NMR spectroscopy. Model glycogen solutions were used to determine optimal saturation pulse parameters which were then applied to intact perfused mouse livers of varying glycogen content. Glycogen measurements from serially acquired CEST Z-spectra of livers were compared with measurements from interleaved natural abundance (13)C NMR spectra. Experimental data revealed that CEST-based glycogen measurements were highly correlated with (13)C NMR glycogen spectra. Monte Carlo simulations were then used to investigate the inherent (i.e., signal-to-noise-based) errors in the quantification of glycogen with each technique. This revealed that CEST was intrinsically more precise than (13)C NMR, although in practice may be prone to other errors induced by variations in experimental conditions. We also observed that the CEST signal from glycogen in liver was significantly less than that observed from identical amounts in solution. Our results demonstrate that CEST provides an accurate, precise, and readily accessible method to noninvasively measure liver glycogen levels and their changes. Furthermore, this technique can be used to map glycogen distributions via conventional proton magnetic resonance imaging, a capability universally available on clinical and preclinical magnetic resonance imaging (MRI) scanners vs (13)C detection, which is limited to a small fraction of clinical-scale MRI scanners.

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R2 value for the 13C NMR–CESTcorrelation as a function of which region of the MTRasym curve was used for integration. The x-axis representsthe beginning of the integration region, and a 2 ppm interval wasused. Note that the maximum R2 value occursat approximately 0.5 ppm (arrow) suggesting that the optimal rangeof the MTRasym curve to use for integration is 0.5–2.5ppm. Data shown is for the group of measurements made after glucagonwas administered to the perfused livers; however, the data from thepreglucagon measurement is similar.
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fig6: R2 value for the 13C NMR–CESTcorrelation as a function of which region of the MTRasym curve was used for integration. The x-axis representsthe beginning of the integration region, and a 2 ppm interval wasused. Note that the maximum R2 value occursat approximately 0.5 ppm (arrow) suggesting that the optimal rangeof the MTRasym curve to use for integration is 0.5–2.5ppm. Data shown is for the group of measurements made after glucagonwas administered to the perfused livers; however, the data from thepreglucagon measurement is similar.

Mentions: We also investigated which regionof the MTRasym curvewould be optimal to use for correlation with the 13C NMRdata by comparing the R2 value for thecorrelation between 13C NMR determined glycogen and CESTMTRasym AUC as different regions of the MTRasym curve were incorporated into the AUC calculation. Figure 6 shows the dependence of this R2 value on which region of the MTRasym curvewas integrated (for fixed integration range of +2 ppm). Here, we seethat the region that produced the maximum R2 value is 0.5–2.5 ppm which is consistent with the glycogen−OHs being reported to resonate at approximately 1.2 ppm downfieldfrom water.11


Noninvasive measurements of glycogen in perfused mouse livers using chemical exchange saturation transfer NMR and comparison to (13)C NMR spectroscopy.

Miller CO, Cao J, Chekmenev EY, Damon BM, Cherrington AD, Gore JC - Anal. Chem. (2015)

R2 value for the 13C NMR–CESTcorrelation as a function of which region of the MTRasym curve was used for integration. The x-axis representsthe beginning of the integration region, and a 2 ppm interval wasused. Note that the maximum R2 value occursat approximately 0.5 ppm (arrow) suggesting that the optimal rangeof the MTRasym curve to use for integration is 0.5–2.5ppm. Data shown is for the group of measurements made after glucagonwas administered to the perfused livers; however, the data from thepreglucagon measurement is similar.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920106&req=5

fig6: R2 value for the 13C NMR–CESTcorrelation as a function of which region of the MTRasym curve was used for integration. The x-axis representsthe beginning of the integration region, and a 2 ppm interval wasused. Note that the maximum R2 value occursat approximately 0.5 ppm (arrow) suggesting that the optimal rangeof the MTRasym curve to use for integration is 0.5–2.5ppm. Data shown is for the group of measurements made after glucagonwas administered to the perfused livers; however, the data from thepreglucagon measurement is similar.
Mentions: We also investigated which regionof the MTRasym curvewould be optimal to use for correlation with the 13C NMRdata by comparing the R2 value for thecorrelation between 13C NMR determined glycogen and CESTMTRasym AUC as different regions of the MTRasym curve were incorporated into the AUC calculation. Figure 6 shows the dependence of this R2 value on which region of the MTRasym curvewas integrated (for fixed integration range of +2 ppm). Here, we seethat the region that produced the maximum R2 value is 0.5–2.5 ppm which is consistent with the glycogen−OHs being reported to resonate at approximately 1.2 ppm downfieldfrom water.11

Bottom Line: Glycogen measurements from serially acquired CEST Z-spectra of livers were compared with measurements from interleaved natural abundance (13)C NMR spectra.We also observed that the CEST signal from glycogen in liver was significantly less than that observed from identical amounts in solution.Our results demonstrate that CEST provides an accurate, precise, and readily accessible method to noninvasively measure liver glycogen levels and their changes.

View Article: PubMed Central - PubMed

Affiliation: †Merck Research Laboratories, 2000 Galloping Hill Rd., Kenilworth, New Jersey 07033, United States.

ABSTRACT
Liver glycogen represents an important physiological form of energy storage. It plays a key role in the regulation of blood glucose concentrations, and dysregulations in hepatic glycogen metabolism are linked to many diseases including diabetes and insulin resistance. In this work, we develop, optimize, and validate a noninvasive protocol to measure glycogen levels in isolated perfused mouse livers using chemical exchange saturation transfer (CEST) NMR spectroscopy. Model glycogen solutions were used to determine optimal saturation pulse parameters which were then applied to intact perfused mouse livers of varying glycogen content. Glycogen measurements from serially acquired CEST Z-spectra of livers were compared with measurements from interleaved natural abundance (13)C NMR spectra. Experimental data revealed that CEST-based glycogen measurements were highly correlated with (13)C NMR glycogen spectra. Monte Carlo simulations were then used to investigate the inherent (i.e., signal-to-noise-based) errors in the quantification of glycogen with each technique. This revealed that CEST was intrinsically more precise than (13)C NMR, although in practice may be prone to other errors induced by variations in experimental conditions. We also observed that the CEST signal from glycogen in liver was significantly less than that observed from identical amounts in solution. Our results demonstrate that CEST provides an accurate, precise, and readily accessible method to noninvasively measure liver glycogen levels and their changes. Furthermore, this technique can be used to map glycogen distributions via conventional proton magnetic resonance imaging, a capability universally available on clinical and preclinical magnetic resonance imaging (MRI) scanners vs (13)C detection, which is limited to a small fraction of clinical-scale MRI scanners.

Show MeSH
Related in: MedlinePlus