Tools for Leveraging High-Resolution MS Detection of Stable Isotope Enrichments to Upgrade the Information Content of Metabolomics Datasets

项目总结/摘要 高分辨率质谱（HRMS）仪器的最新进展尚未得到充分利用 更新从稳定同位素标记研究中获得的代谢组学数据集的信息内容。这 主要是由于缺乏有效的软件工具，用于从HRMS中提取和解释同位素富集 数据集。本申请的总体目标是开发能够实现代谢组学的工具。 社区充分利用稳定同位素来分析代谢网络动态。两个新工具将 在开源OpenMS软件库中实现，该软件库提供了快速 开发和传播质谱分析软件。第一个工具将自动执行 从HRMS数据集中提取同位素富集信息，第二个工具将使用此信息， 基于同位素标记的相似模式将离子峰分组为相互作用网络。将对工具进行验证 使用来自动植物系统代谢通量研究的内部数据集，以及通过 来自代谢组学社区的反馈。这项研究的基本原理是，软件工具将使 代谢组学研究人员解决有关途径动力学和调控的重要问题， 不需要使用稳定同位素就能回答。第一个目标是开发一个软件工具来自动化数据 从HRMS数据集提取和定量同位素体分布。该软件将提供多个 目前可用的代谢组学软件中不包括的关键特征：i）图形交互式用户界面 适合非专业用户，ii）支持本地仪器文件格式，iii）支持 用多种稳定同位素标记，iv）支持串联质谱，和v）支持多基团或 时间序列比较第二个目标是开发一个应用机器学习的配套软件， 基于相关性的算法，根据代谢物的相似性将未知代谢物分组为模块和途径， 同位素标记第三个目的是通过比较分析稳定同位素标记在 从动物和植物组织中提取的测试标准品和样品，包括时间序列和双示踪剂实验。一 各种合作者和专业工作组将参与测试和验证软件， 将根据他们的反馈意见完善这些工具。这项研究非常具有创新性，因为它 将提供所需的先进软件功能，用于同位素的靶向和非靶向分析。 标记的代谢物，但在一个灵活和用户友好的环境。这项研究意义重大，因为它将 贡献软件工具，自动化和标准化所需的数据处理步骤，提取和利用 大规模代谢组学数据集的同位素富集信息。这项工作将具有重要的 对代谢组学研究人员利用稳定同位素信息的能力产生积极影响， 识别未知的代谢相互作用和量化代谢网络内的通量。此外，它将使 研究生物系统内代谢动力学的全新方法。

PROJECT SUMMARY/ABSTRACT Recent advances in high-resolution mass spectrometry (HRMS) instrumentation have not been fully leveraged to upgrade the information content of metabolomics datasets obtained from stable isotope labeling studies. This is primarily due to lack of validated software tools for extracting and interpreting isotope enrichments from HRMS datasets. The overall objective of the current application is to develop tools that enable the metabolomics community to fully leverage stable isotopes to profile metabolic network dynamics. Two new tools will be implemented within the open-source OpenMS software library, which provides an infrastructure for rapid development and dissemination of mass spectrometry software. The first tool will automate tasks required for extracting isotope enrichment information from HRMS datasets, and the second tool will use this information to group ion peaks into interaction networks based on similar patterns of isotope labeling. The tools will be validated using in-house datasets derived from metabolic flux studies of animal and plant systems, as well as through feedback from the metabolomics community. The rationale for the research is that the software tools will enable metabolomics investigators to address important questions about pathway dynamics and regulation that cannot be answered without the use of stable isotopes. The first aim is to develop a software tool to automate data extraction and quantification of isotopologue distributions from HRMS datasets. The software will provide several key features not included in currently available metabolomics software: i) a graphical, interactive user interface that is appropriate for non-expert users, ii) support for native instrument file formats, iii) support for samples that are labeled with multiple stable isotopes, iv) support for tandem mass spectra, and v) support for multi-group or time-series comparisons. The second aim is to develop a companion software that applies machine learning and correlation-based algorithms to group unknown metabolites into modules and pathways based on similarities in isotope labeling. The third aim is to validate the tools through comparative analysis of stable isotope labeling in test standards and samples from animal and plant tissues, including time-series and dual-tracer experiments. A variety of collaborators and professional working groups will be engaged to test and validate the software, and the tools will be refined based on their feedback. The proposed research is exceptionally innovative because it will provide the advanced software capabilities required for both targeted and untargeted analysis of isotopically labeled metabolites, but in a flexible and user-friendly environment. The research is significant because it will contribute software tools that automate and standardize the data processing steps required to extract and utilize isotope enrichment information from large-scale metabolomics datasets. This work will have an important positive impact on the ability of metabolomics investigators to leverage information from stable isotopes to identify unknown metabolic interactions and quantify flux within metabolic networks. In addition, it will enable entirely new approaches to study metabolic dynamics within biological systems.