Department of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, 43210, USA.
Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, 46556, USA.
Angew Chem Int Ed Engl. 2020 Feb 24;59(9):3439-3443. doi: 10.1002/anie.201912387. Epub 2020 Jan 27.
Metabolomics is a powerful systems biology approach that monitors changes in biomolecule concentrations to diagnose and monitor health and disease. However, leading metabolomics technologies, such as NMR and mass spectrometry (MS), access only a small portion of the metabolome. Now an approach is presented that uses the high sensitivity and chemical specificity of surface-enhanced Raman scattering (SERS) for online detection of metabolites from tumor lysates following liquid chromatography (LC). The results demonstrate that this LC-SERS approach has metabolite detection capabilities comparable to the state-of-art LC-MS but suggest a selectivity for the detection of a different subset of metabolites. Analysis of replicate LC-SERS experiments exhibit reproducible metabolite patterns that can be converted into barcodes, which can differentiate different tumor models. Our work demonstrates the potential of LC-SERS technology for metabolomics-based diagnosis and treatment of cancer.
代谢组学是一种强大的系统生物学方法，可监测生物分子浓度的变化，用于诊断和监测健康和疾病。然而，主要的代谢组学技术，如 NMR 和质谱 (MS)，仅能检测代谢组的一小部分。现在提出了一种方法，该方法利用表面增强拉曼散射 (SERS) 的高灵敏度和化学特异性，用于在液相色谱 (LC) 后在线检测肿瘤裂解物中的代谢物。结果表明，这种 LC-SERS 方法具有与最先进的 LC-MS 相当的代谢物检测能力，但表明对检测不同亚组代谢物具有选择性。对重复的 LC-SERS 实验的分析显示出可转化为条形码的可重复的代谢物模式，这些条形码可区分不同的肿瘤模型。我们的工作证明了 LC-SERS 技术在基于代谢组学的癌症诊断和治疗中的潜力。
