Abstract
Stable-isotope labeled metabolic analysis is an essential methodology to characterize metabolic regulation during biological processes. However, the method using stable-isotope-labeled tracer (e.g., 13C-glucose) in live animal is only beginning to be developed. Here, we contribute a qualitative metabolic labeling experiment protocol in Drosophila melanogaster using stable-isotope-labeled 13C-glucose tracer followed by liquid chromatography-mass spectrometry (LC-MS) analysis. Detailed experimental setup, data acquisition and analysis are provided to facilitate the application of in vivo metabolic labeling analysis that might be applied in a wide range of biological studies.
Keywords: Stable-isotope labeling, 13C-glucose tracer, Metabolic analysis, Qualitative analysis, Liquid chromatography-mass spectrometry, Drosophila melanogaster
Background
Metabolomics is a newly emergent omic-level study aiming to profile small molecule metabolites in a complex biological system. It has been applied in diverse research areas pertaining to human health and disease, such as biomarker discovery, disease pathogenesis, and assessment of drug toxicity. Measurement of metabolites is important to determine alterations in metabolic pathway in response to endogenous and exogenous changes. To accurately characterize metabolic pathway activity, isotope-labeled tracers (e.g., 13C and 15N) have been used (Park et al., 2016; Jang et al., 2018). There are many such studies (both quantitatively and qualitatively) in cultured cells (Buescher et al., 2015; Liu et al., 2018), however, stable-isotope based metabolic labeling experiment in live animal remain largely unexplored. In the current protocol, we describe a qualitative metabolic labeling analysis by using the labeled 13C-glucose as a tracer, and we have successfully applied this protocol to comparatively analyze the activity of glycolysis pathway in Drosophila melanogaster, during aging and between wild-type and mutant animals.
Materials and Reagents
Equipment
Software
Procedure
Data analysis
Recipes
Acknowledgments
We thank the financial support provided by the startup funding from Interdisciplinary Research Center on Biology and Chemistry (IRCBC), and Agilent Technologies Thought Leader Award. N.L. and Z.-J. Z. are also supported by Thousand Youth Talents Program. This protocol is also a part of our previous work by Ma et al., 2018.
Competing interests
The authors declare no competing financial interest.
References
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