Progress towards in vivo brain 13C-MRS in mice: Metabolic flux analysis in small tissue volumes
Progress towards in vivo brain 13C-MRS in mice: Metabolic flux analysis in small tissue volumes
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DOI：
10.1016/j.ab.2017.01.019
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发表时间：
2017-07-15
影响因子：
2.9
通讯作者：
Lanz, Bernard
中科院分区：
文献类型：
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作者：
Lai, Marta;Gruetter, Rolf;Lanz, Bernard
动态C-13 MRS数据的组合下输液的C-13标记的底物和房室模型的脑代谢，使在体内测量的代谢通量与定量和明确的测定细胞的具体活动。在这些示踪剂实验中获得的C-13动态数据的非侵入性性质和化学特异性使其成为一种有吸引力的方法，提供了对脑代谢的独特见解。基因工程小鼠提供了丰富的疾病模型，特别是神经科学界感兴趣的。然而，小鼠脑的体内C-13 NMR研究最近才出现在该领域，这是由于与小小鼠脑体积相关的众多挑战以及在输注实验期间难以遵循NMR系统内的小鼠生理参数。本文综述了近年来在提高体内C-13信噪比方面的研究进展，并对目前的技术水平进行了展望，以期能更好地评价小鼠脑不同区域的葡萄糖代谢。我们描述了如何将实验结果整合到合适的房室模型中，以及如何深入了解大脑代谢取决于不同分子和碳位置中C-13的可靠检测。(C)2017爱思唯尔公司All rights reserved.
The combination of dynamic C-13 MRS data under infusion of C-13-labelled substrates and compartmental models of cerebral metabolism enabled in vivo measurement of metabolic fluxes with a quantitative and distinct determination of cellular-specific activities. The non-invasive nature and the chemical specificity of the C-13 dynamic data obtained in those tracer experiments makes it an attractive approach offering unique insights into cerebral metabolism. Genetically engineered mice present a wealth of disease models particularly interesting for the neuroscience community. Nevertheless, in vivo C-13 NMR studies of the mouse brain are only recently appearing in the field due to the numerous challenges linked to the small mouse brain volume and the difficulty to follow the mouse physiological parameters within the NMR system during the infusion experiment. This review will present the progresses in the quest for a higher in vivo C-13 signal-to-noise ratio up to the present state of the art techniques, which made it feasible to assess glucose metabolism in different regions of the mouse brain. We describe how experimental results were integrated into suitable compartmental models and how a deep understanding of cerebral metabolism depends on the reliable detection of C-13 in the different molecules and carbon positions. (C) 2017 Elsevier Inc. All rights reserved.