Ion mobility based informatics and visualization strategies in support of metabolomics

项目摘要 代谢物是细胞功能的基石，因此了解其基础机制 不同的生理条件和过程将提供对疾病或异常状态的洞察。创新型 高通量分析技术和数据分析的发展使系统级 要进行代谢组学分析，这些技术中的许多都以质谱学为中心。 人类代谢体中的不同化学结构显示出广泛的浓度，溶解度， 具有高度多样化的结构形式和物理化学性质的极性和挥发性以及高 同分异构体的数量，因此需要尽可能多的正交分离 代谢组学实验(即多维数据集)。 质谱学方法结合 离子迁移质谱仪(LC-IM-MS/MS)分析已显示出对全球 非靶向代谢图谱实验。由于离子迁移率-质谱仪(IM-MS)是一种 相对较新的商业可用技术，结合了离子迁移率测量(通过碰撞 横截面，CCS)纳入当前代谢组学数据分析识别策略的可能性很小。这个 典型 离子迁移率的分析用途是快速的化学分离(这允许降低噪音，增加 峰值容量等)，然而IM也可以用来增加识别和 这是因为CCS提供了关于单个代谢物的特定结构信息。《长河》 这项提案的任期目标都围绕着将IM测量纳入代谢组学 基于化学信息学和生物信息学的管道。这些措施包括：(1)确定在何种程度上 离子迁移率维度可以解决异构体代谢物的分子专一性，(2)开发一种 基于IM的 使用CCS值作为描述符来筛选和分配未知代谢物的文库，最后，(3) 整合用于导航多维数据集的可视化工具，这将使科学家能够更好地 揭示代谢物与人类健康之间的关系。IM维度的分子特异性将是 通过分析先前生成的IM-MS数据来解决 从市场上可买到的代谢物库 (&gt；600主要代谢物，其中&gt；20%是同分异构体)。代谢物库中的单个代谢物将 也被询问以进行CCS文库的管理，并且这些值将用于分子鉴定 之前生成的全球非靶向代谢物研究的流水线。最后，多维自组织 地图将用于可视化和导航各种维度的数据(LC、CCS、m/z等)。最终 允许用户对指示疾病状态的代谢物进行优先排序和高置信度识别。 实现本提案中概述的目标将被视为克服了以下几个关键障碍 到目前为止，都阻碍了离子迁移率在基于MS的代谢组学工作流程中的常规和广泛使用。

Project Summary Metabolites are building blocks of cellular function, thus understanding the mechanisms that underlie various physiological conditions and processes will provide insight into disease or aberrant states. Innovative developments in high-throughput analytical technologies and data analysis have allowed for systems-level metabolomics analyses to be performed, many of these technologies have centered around mass spectrometry. The diverse chemical structures in the human metabolome exhibit a wide range of concentration, solubility, polarity and volatility with highly diverse structural forms and physiochemical properties as well as a high number of isomers, therefore the need for as many orthogonal separations as possible is necessary for metabolomics experiments (i.e., multidimensional data sets). Mass spectrometry approaches incorporating liquid chromatography ion mobility mass spectrometry (LC-IM-MS/MS) analyses have shown utility for global untargeted metabolic profiling experiments. Since ion mobility coupled to mass spectrometry (IM-MS) is a relatively new commercially available technology, the incorporation of the ion mobility measurement (via collision cross section, CCS) into current metabolomics data analysis identification strategies is minimal. The typical analytical use of ion mobility is as a quick chemical separation (which allows for noise reduction, increase in peak capacity, etc.), however IM can also be used to increase the confidence in identification and characterization because CCS provides structure specific information about individual metabolites. The long term objectives of this proposal are all centered around incorporating IM measurements into metabolomics based chemoinformatic and bioinformatics pipelines. These include: (1) determining the extent at which the ion mobility dimension can address molecular specificity of isomeric metabolites, (2) developing an IM-based library using CCS values as a descriptor to screen and assign identities to unknown metabolites and lastly, (3) incorporating visualization tools for navigating multidimensional datasets which will allow scientists to better uncover relationships between metabolites and human health. Molecular specificity of the IM dimension will be addressed by analyzing previously generated IM-MS data from a commercially available metabolite library (>600 primary metabolites, of which >20% are isomeric). Individual metabolites in the metabolite library will also be interrogated for curation of the CCS library and these values will be used in the molecular identification pipeline for global untargeted metabolite studies generated previously. Lastly, multidimensional self-organizing maps will be utilized to visualize and navigate various dimensions of data (LC, CCS, m/z, etc.) and ultimately allow the user to prioritize and identify with high confidence metabolites indicative of disease state. Accomplishing the aims outlined in this proposal will be seen as overcoming several critical barriers which have so far hindered the routine and widespread use of ion mobility in MS-based metabolomics workflows.