Studying metabolic activities in living cells is crucial for understanding human metabolism, but facile methods for profiling metabolic activities in an unbiased, hypothesis-free manner are still lacking. To address this need, we here introduce the deep-labeling method, which combines a custom13C medium with high-resolution mass spectrometry. A proof-of-principle study on human cancer cells demonstrates that deep labeling can identify hundreds of endogenous metabolites as well as active and inactive pathways. For example, protein and nucleic acids were almost exclusivelyde novosynthesized, while lipids were partly derived from serum; synthesis of cysteine, carnitine, and creatine was absent, suggesting metabolic dependencies; and branched-chain keto acids (BCKAs) were formed and metabolized to short-chain acylcarnitines, but did not enter the tricarboxylic acid cycle. Remarkably, BCKAs could substitute for essential amino acids to support growth. The deep-labeling method may prove useful to map metabolic phenotypes across a range of cell types and conditions.

中文翻译：

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通过深13C标记对人体细胞的代谢进行分析

研究活细胞中的代谢活性对于理解人类代谢至关重要，但是仍然缺乏以无偏见，无假设的方式分析代谢活性的简便方法。为了满足这一需求，我们在这里介绍了深度标记方法，该方法将custom13C介质与高分辨率质谱法相结合。对人类癌细胞的一项原理验证研究表明，深度标记可以识别数百种内源性代谢产物以及活性和非活性途径。例如，蛋白质和核酸几乎完全是新合成的，而脂质则部分来自血清。缺少半胱氨酸，肉碱和肌酸的合成，表明代谢依赖性；并形成支链酮酸（BCKA）并代谢为短链酰基肉碱，但没有进入三羧酸循环。值得注意的是，BCKA可以替代必需氨基酸来支持生长。深度标记方法可能被证明可用于绘制一系列细胞类型和条件下的代谢表型。