Online Raman Diagnostics of Oncometabolites

项目摘要 癌细胞利用正常的代谢过程来促进肿瘤存活。比如说， 奥托瓦尔堡和其他人发现，肿瘤增加了葡萄糖的摄取，糖酵解和乳酸 生产，往往减少柠檬酸循环。虽然“有氧糖酵解”乍一看是积极的 对肿瘤细胞来说是昂贵的，因为它避免了柠檬酸循环产生高ATP， 癌细胞在低营养或低氧条件下生存，并使用糖酵解中间体 用于合成基本的细胞构建单元，而无需进一步的能量投资。的这种变化 代谢物调节是监测和诊断癌症的有效方法。 本项目旨在开发表面增强拉曼散射（Sers）作为在线检测方法， 来自乳腺癌肿瘤模型的代谢物的表征。使用肿瘤的Sers结果 裂解物，诊断算法将被构建，以改善癌症的治疗。结果表明，流体 动力学可用于增加流动液体中Sers检测的再现性和灵敏度。我们 我建议开发一种方法，使这种创新能够用于研究癌细胞中的代谢物 使用与Sers流动检测器偶联的毛细管电泳测定裂解物。我们将研究已知的代谢物 与癌症有关的代谢物，以及检查与致癌基因相关的关键代谢物。Sers 收集的数据将用于制定诊断算法，该算法可以提供癌症的是/否指标。 该项目的具体目标如下： · AIM 1。证明新型流量检测器在评估关键代谢物变化方面的实用性， 肿瘤细胞裂解物。将肿瘤细胞裂解物与非癌细胞裂解物进行比较， 确定这些代谢物与乳腺癌相关的趋势。 · AIM 2。将鉴别和定量能力与当前的金标准LC- 这一目标将评估如何Sers表征既与现有技术相比， 也增加了代谢组的覆盖范围。 · AIM 3。我们将使用代谢物开发统计机器学习算法来预测 样本标签（癌症或非癌症）。所获得的预测因子将被用作癌症的诊断工具。 提供独特的化学品特定信息的新技术的开发将能够改善化学品的质量。 用于治疗癌症的诊断测定。

Project Abstract Cancer cells utilize normal metabolic processes out of context to promote tumor survival. For example, Otto Warburg and others discovered that tumors have increased glucose uptake, glycolysis, and lactate production, often with a reduction in citric acid cycle. While “aerobic glycolysis” at first glance is energetically expensive for tumor cells because it circumvents high ATP production from the citric acid cycle, it allows cancer cells to survive under low nutrient or low oxygen conditions and to instead use glycolytic intermediates for the synthesis of essential cellular building blocks without further energy investment. This change in metabolite regulation suggests a powerful method for monitoring and diagnosing cancer. This project seeks to develop surface enhanced Raman scattering (SERS) as online detection method for the characterization of metabolites from breast cancer tumor models. Using the SERS results from tumor lysates, diagnostic algorithms will be constructed to improve treatment for cancer. Results show that fluid dynamics can be used to increase the reproducibility and sensitivity of SERS detection in flowing liquids. We propose to develop methodology to enable the use this innovation to investigate metabolites in cancer cell lysates using capillary electrophoresis coupled to a SERS flow detector. We will investigate known metabolites that have been linked to cancer, as well as examine key metabolites associated with oncogenes. The SERS data collected will be used to formulate diagnostic algorithms that can provide a yes/no indicator of cancer. The specific aims of this project are as follows: · AIM 1. Demonstrate the utility of the novel flow detector to assess changes in key metabolites from tumor cell lysates. The tumor cell lysates will be compared with non-cancerous cell lysates to identify trends in these metabolites relevant to breast cancer. · AIM 2. Compare the identification and quantification capabilities with the current gold standard, LC- MS. This aim will assess how SERS characterization both compares with existing technology but also increases coverage of the metabolome. · AIM 3. We will use the metabolites to develop statistical machine learning algorithms to predict the sample label (cancer or not). The predictor obtained will be used as a diagnostic tool of cancer. The development of new technologies that provide unique chemical specific information will enable improved diagnostic assays for the treatment of cancer.