Department of Anesthesiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Division of Anesthetics, Pain Medicine and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Chelsea and Westminster Hospital, Imperial College London, United Kingdom.
Biosci Rep. 2020 Apr 30;40(4). doi: 10.1042/BSR20194027.
Cancer cell lines are often used for cancer research. However, continuous genetic instability-induced heterogeneity of cell lines can hinder the reproducibility of cancer research. Molecular profiling approaches including transcriptomics, chromatin modification profiling, and proteomics are used to evaluate the phenotypic characteristics of cell lines. However, these do not reflect the metabolic function at the molecular level. Metabolic phenotyping is a powerful tool to profile the biochemical composition of cell lines. In the present study, 1H-NMR spectroscopy-based metabolic phenotyping was used to detect metabolic differences among five cancer cell lines, namely, lung (A549), colonic (Caco2), brain (H4), renal (RCC), and ovarian (SKOV3) cancer cells. The concentrations of choline, creatine, lactate, alanine, fumarate and succinate varied remarkably among different cell types. The significantly higher intracellular concentrations of glutathione, myo-inositol, and phosphocholine were found in the SKOV3 cell line relative to other cell lines. The concentration of glutamate was higher in both SKOV3 and RCC cells compared with other cell lines. For cell culture media analysis, isopropanol was found to be the highest in RCC media, followed by A549 and SKOV3 media, while acetone was the highest in A549, followed by RCC and SKOV3. These results demonstrated that 1H-NMR-based metabolic phenotyping approach allows us to characterize specific metabolic signatures of cancer cell lines and provides phenotypical information of cellular metabolism.
癌细胞系常用于癌症研究。然而，细胞系不断的遗传不稳定性诱导的异质性会阻碍癌症研究的可重复性。包括转录组学、染色质修饰谱分析和蛋白质组学在内的分子分析方法用于评估细胞系的表型特征。然而，这些方法并不能反映细胞系在分子水平上的代谢功能。代谢表型分析是一种用于分析细胞系生化组成的强大工具。在本研究中，我们使用基于 1H-NMR 光谱的代谢表型分析来检测五种癌细胞系（肺（A549）、结肠（Caco2）、脑（H4）、肾（RCC）和卵巢（SKOV3））之间的代谢差异。不同细胞类型之间的胆碱、肌酸、乳酸、丙氨酸、富马酸和琥珀酸的浓度差异显著。与其他细胞系相比，SKOV3 细胞系的细胞内谷胱甘肽、肌醇和磷酸胆碱浓度显著升高。与其他细胞系相比，SKOV3 和 RCC 细胞中的谷氨酸浓度更高。对于细胞培养培养基分析，发现 RCC 培养基中的异丙醇浓度最高，其次是 A549 和 SKOV3 培养基，而 A549 培养基中的丙酮浓度最高，其次是 RCC 和 SKOV3 培养基。这些结果表明，基于 1H-NMR 的代谢表型分析方法使我们能够表征癌细胞系的特定代谢特征，并提供细胞代谢的表型信息。
