Ion mobility based informatics and visualization strategies in support of metabolomics

项目摘要 代谢酶是细胞功能的基石，因此了解其背后的机制 各种生理条件和过程将提供对疾病或异常状态的洞察。创新 高通量分析技术和数据分析的发展允许系统级 尽管许多技术需要进行代谢组学分析，但这些技术中的许多技术都以质谱法为中心。 人体代谢物组中的不同化学结构表现出广泛的浓度、溶解度 极性和挥发性，具有高度多样的结构形式和理化性质， 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）。 质谱法结合 液相色谱-离子迁移质谱（LC-IM-MS/MS）分析显示出对全球的实用性。 非靶向代谢分析实验。由于离子迁移率耦合质谱法（IM-MS）是一种有效的方法， 相对较新的商业上可获得的技术，离子迁移率测量（经由碰撞）的结合， 横截面，CCS）到目前的代谢组学数据分析识别策略是最小的。的 典型 离子迁移率的分析用途是作为快速化学分离（其允许降低噪音、增加 峰值容量等），然而，IM也可以用于增加识别的置信度， 因为CCS提供了关于单个代谢物的结构特异性信息。长 该提案的长期目标都围绕着将IM测量纳入代谢组学 基于化学信息学和生物信息学管道。（1）确定在何种程度上 离子迁移率维度可以解决异构代谢物的分子特异性，（2）开发 基于IM 使用CCS值作为描述符的库来筛选未知代谢物并为其分配身份，最后，（3） 整合用于导航多维数据集的可视化工具，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 来自市售代谢物库 （>600种初级代谢物，其中>20%是异构体）。代谢物库中的单个代谢物将 也可以询问CCS文库的管理，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管道。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）并最终 允许用户优先化并以高置信度识别指示疾病状态的代谢物。 实现本建议中概述的目标将被视为克服了几个关键障碍， 到目前为止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.