DeGennaro Christine M, Savir Yonatan, Springer Michael
Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, 02115, United States of America.
Department of Physiology, Biophysics and Systems Biology, Faculty of Medicine, Technion, Haifa, 31096, Israel.
PLoS One. 2016 Mar 17;11(3):e0151659. doi: 10.1371/journal.pone.0151659. eCollection 2016.
Metabolism underlies many important cellular decisions, such as the decisions to proliferate and differentiate, and defects in metabolic signaling can lead to disease and aging. In addition, metabolic heterogeneity can have biological consequences, such as differences in outcomes and drug susceptibilities in cancer and antibiotic treatments. Many approaches exist for characterizing the metabolic state of a population of cells, but technologies for measuring metabolism at the single cell level are in the preliminary stages and are limited. Here, we describe novel analysis methodologies that can be applied to established experimental methods to measure metabolic variability within a population. We use mass spectrometry to analyze amino acid composition in cells grown in a mixture of (12)C- and (13)C-labeled sugars; these measurements allow us to quantify the variability in sugar usage and thereby infer information about the behavior of cells within the population. The methodologies described here can be applied to a large range of metabolites and macromolecules and therefore have the potential for broad applications.
新陈代谢是许多重要细胞决策的基础，比如增殖和分化的决策，代谢信号传导缺陷会导致疾病和衰老。此外，代谢异质性会产生生物学后果，例如癌症和抗生素治疗中结果和药物敏感性的差异。存在许多用于表征细胞群体代谢状态的方法，但单细胞水平代谢测量技术尚处于初步阶段且存在局限性。在此，我们描述了可应用于既定实验方法以测量群体内代谢变异性的新型分析方法。我们使用质谱分析法来分析在含有（12）C和（13）C标记糖的混合物中生长的细胞的氨基酸组成；这些测量使我们能够量化糖使用的变异性，从而推断群体内细胞行为的信息。这里描述的方法可应用于多种代谢物和大分子，因此具有广泛应用的潜力。
