Critical Transition-based Correlation Analysis for Metabolomics

摘要 代谢组提供了一个独特窗口来监控系统的分子状态，这是由于 和外在事件。利用超高性能液相色谱(UPLC)高分辨质量 光谱(HRMS)，代谢组学的黄金标准技术，可以测量数百个 数千到数百万个细胞中的代谢物，以增强痕量化合物的信号。近期 HRMS技术的进步已经将这些测量扩展到对稀有或 珍贵的样本，取平均值是不可行的，或妨碍结果解释。尽管有可用的 高灵敏度HRMS是代谢组学的一个瓶颈，缺乏能够检测痕量水平的软件工具 由此产生的复杂代谢组学数据中的信号。拟议的工作通过以下方式填补了这一技术空白 开发一个软件套件，用于调查HRMS数据集以获取跟踪电平信号(具体目标1)，并帮助找到 代谢物差异之间的相关性(特定目标2)。该方法源于人工数据处理 已为高灵敏度分析以及关键过渡建立和验证的协议 有效指示网络中过渡点的物理模型。该软件使用HRMS进行了验证 南非早期发育胚胎中为分化细胞而获得的数据集 爪蛙(非洲爪蛙)，在细胞和发育研究以及功能实验中的强大模型 这测试了精选代谢物的发育意义。这项工作包括旨在确保 该软件与来自广泛类型的质谱仪的HRMS数据和 不同类型的代谢组学研究，包括神经科学和药物代谢以及存放在 代谢学工作台，一个公共代谢组学数据库。最终产品是代谢组学软件 这适用于广泛类型的代谢组学研究。除了提供新的软件外，数据 为细胞分化过程中的代谢变化提供了新的信息 在发育中的胚胎中。拟议工作的结果与RFA-RM-15-021的目标相匹配， “代谢组学数据分析(R03)。”所提出的软件可扩展到来自不同仪器的HRMS数据 供应商，帮助识别痕量代谢物信号，并促进代谢物分析- 系统中的代谢物相关性，这反过来又促进了假设驱动的研究的设计，以帮助 更好地理解健康。

Abstract The metabolome provides a unique window to monitor a system's molecular state as a result of both intrinsic and extrinsic events. Using ultrahigh-performance liquid chromatography (UPLC) high-resolution mass spectrometry (HRMS), the gold standard technology of metabolomics, it is possible to measure hundreds of metabolites in thousands-to-millions of cells to enhance the signal for trace-level compounds. Recent advances in HRMS technology have extended these measurements to also trace-level detection for rare or precious samples, where averaging is not feasible or hinders results interpretation. Despite the availability of high-sensitivity HRMS, a bottleneck in metabolomics is a lack of software tools capable of detecting trace-level signals in the resulting complex metabolomics data. The proposed work fills this technological gap by developing a software suite that surveys HRMS data sets for trace-level signals (Specific Aim 1) and helps find correlations between metabolite variances (Specific Aim 2). The approach stems from manual data processing protocols that have been established and validated for high-sensitivity analyses as well as critical transition models in physics that efficiently indicate transition points in a network. The software is validated using HRMS data sets that have been acquired for differentiating cells in the early developing embryo of the South African clawed frog (Xenopus laevis), a powerful model in cell and developmental studies, and functional experiments that test the developmental significance of select metabolites. The work includes tests designed to ensure the compatibility of the software to HRMS data from broad types of mass spectrometry instrumentation and different types of metabolomics studies, including neuroscience and drug metabolism and data deposited in MetabolomicsWorkbench, a public metabolomics data repository. The final product is metabolomics software that is applicable to broad types of metabolomics investigations. Besides providing new software, the data that are obtained during this work provide new information on metabolomic changes underlying cell differentiation in the developing embryo. The outcomes of the proposed work are matched with the goals of RFA-RM-15-021, “Metabolomics Data Analysis (R03).” The proposed software is scalable to HRMS data from diverse instrument vendors, aids the identification of trace-level metabolite signals, and facilitates the analysis of metabolite- metabolite correlations in the system, which in turn facilitates the design of hypothesis-driven studies to help better understand health.