Stable isotope tracing to assess tumor metabolism in vivo.
Stable isotope tracing to assess tumor metabolism in vivo.
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DOI:
10.1038/s41596-021-00605-2
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发表时间:
2021-11
期刊:
影响因子:
14.8
通讯作者:
DeBerardinis, Ralph J.
中科院分区:
文献类型:
--
作者:
Faubert, Brandon;Tasdogan, Alpaslan;Morrison, Sean J.;Mathews, Thomas P.;DeBerardinis, Ralph J.
Cancer cells undergo diverse metabolic adaptations to meet the energetic demands imposed by dysregulated growth and proliferation. Assessing metabolism in intact tumors allows the investigator to observe the combined metabolic effects of numerous cancer cell-intrinsic and -extrinsic factors that cannot be fully captured in culture models. We have developed methods to use stable isotope-labeled nutrients (e.g., [13C]glucose) to probe metabolic activity within intact tumors in vivo, in mice and humans. In these methods, the labeled nutrient is introduced to the circulation through an intravenous catheter prior to surgical resection of the tumor and adjacent nonmalignant tissue. Metabolism within these tissues during the infusion transfers the isotope label into metabolic intermediates from pathways supplied by the infused nutrient. Extracting metabolites from surgical specimens and analyzing their isotope labeling patterns provides information about metabolism in the tissue. We provide detailed information about this technique, from introduction of the labeled tracer through data analysis and interpretation, including streamlined approaches to quantify isotope labeling in informative metabolites extracted from tissue samples. We focus on infusions with [13C]glucose and the application of mass spectrometry to assess isotope labeling in intermediates from central metabolic pathways, including glycolysis, the tricarboxylic acid cycle and nonessential amino acid synthesis. We outline practical considerations to apply these methods to human subjects undergoing surgical resections of solid tumors. We also discuss the method’s versatility and consider the relative advantages and limitations of alternative approaches to introduce the tracer, harvest the tissue and analyze the data.
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影响因子:
29
作者:
Marin-Valencia I;Yang C;Mashimo T;Cho S;Baek H;Yang XL;Rajagopalan KN;Maddie M;Vemireddy V;Zhao Z;Cai L;Good L;Tu BP;Hatanpaa KJ;Mickey BE;Matés JM;Pascual JM;Maher EA;Malloy CR;Deberardinis RJ;Bachoo RM
通讯作者:
Bachoo RM
影响因子:
3.3
作者:
Romijn, JA;Coyle, EF;Wolfe, RR
通讯作者:
Wolfe, RR
影响因子:
50.3
作者:
Momcilovic M;Bailey ST;Lee JT;Fishbein MC;Braas D;Go J;Graeber TG;Parlati F;Demo S;Li R;Walser TC;Gricowski M;Shuman R;Ibarra J;Fridman D;Phelps ME;Badran K;St John M;Bernthal NM;Federman N;Yanagawa J;Dubinett SM;Sadeghi S;Christofk HR;Shackelford DB
通讯作者:
Shackelford DB
影响因子:
64.5
作者:
Faubert B;Li KY;Cai L;Hensley CT;Kim J;Zacharias LG;Yang C;Do QN;Doucette S;Burguete D;Li H;Huet G;Yuan Q;Wigal T;Butt Y;Ni M;Torrealba J;Oliver D;Lenkinski RE;Malloy CR;Wachsmann JW;Young JD;Kernstine K;DeBerardinis RJ
通讯作者:
DeBerardinis RJ
影响因子:
64.5
作者:
Jang C;Chen L;Rabinowitz JD
通讯作者:
Rabinowitz JD