Increasing the Coverage, Sensitivity and Specificity of Rapid Lipidomic Measurements

项目摘要/摘要 脂质是一类重要的分子，在生物过程中扮演着无数重要的和不同的角色。完全 然而，理解脂质的作用是困难的，因为脂质种类的数量和多样性是巨大的 表达数万种不同脂类的细胞。虽然在层析和高密度脂蛋白方面的最新进展 分辨率质谱仪极大地提高了我们对潜在的脂类物种的了解 许多不同的样本类型，有效地分离大量的脂类仍然是一个问题，因为 同分异构体类脂。同分异构体的脂类，如由亚类异构体、不同的酰基链产生的 连接性(sn-1、sn-2或sn-3)、不同的双键位置和取向(顺式或反式)以及唯一性 官能团立体化学(R与S)由于许多原因使得脂类表征特别困难 具有完全相同的质量。为了应对这一挑战，离子迁移率光谱分离，离子-分子 反应和碎裂技术越来越多地被添加到脂质分析工作流中，以允许 物种分离和改进的特性。然而，目前这些分析仍然不能完全 评估复杂的脂类混合物中存在的脂类种类的数量或提供深入的分子分析 基于它们在组织和器官中的空间位置的差异。此外，当几个分析 技术分开使用，大大延长了实验和数据分析的时间，使之规模化 评估是困难的或不可能的。本研究的总体目标是开发一种新的分析平台 以及相应的数据分析和可视化方法，以提高覆盖范围、吞吐量和空间 脂肪组学分析的评估。使用分析方法的组合方法，包括 传统的色谱方法，手性分离，自动固相萃取(SPE)，气相 化学衍生化、多重离子迁移率光谱-质谱仪(IMS-MS)分离和 自动化的数据分析将为众多的类脂异构体和物种提供前所未有的覆盖面 存在于复杂的样品中。然后，这个高度具体和敏感的自动化平台将应用于 在大规模组织筛查分析中对各种脂类进行有针对性的量化，以评估1000多种 每天的脂肪样本。

PROJECT SUMMARY/ABSTRACT Lipids are a vital class of molecules that play countless important and varied roles in biological processes. Fully understanding lipid roles, however, is difficult since the number and diversity of lipid species is immense with cells expressing tens of thousands of diverse lipid species. While recent advances in chromatography and high- resolution mass spectrometry have greatly improved our understanding of the potential lipid species present in many different sample types, effectively separating the numerous lipids still remains problematic due to the many isomeric lipids. Isomeric lipid species such as those resulting from subclass isomers, distinct acyl chains connectivity (sn-1, sn-2, or sn-3), different double bond positions and orientations (cis or trans), and unique functional group stereochemistry (R versus S) have made lipid characterization especially difficult due to many having the exact same mass. To address this challenge, ion mobility spectrometry separations, ion-molecule reactions and fragmentation techniques have increasingly been added to lipid analysis workflows to allow both species separation and improved characterization. However, currently these analyses are still not able to fully assess the number of lipid species present in complex lipid mixtures or provide an in-depth analysis of molecular differences based on their spatial position in tissues and organs. Furthermore, when several analytical techniques are utilized separately, experimental and data analysis times are greatly extended, making largescale evaluations difficult or impossible. The overall objective of this research is to develop a new analytical platform and corresponding data analysis and visualization methods to increase the coverage, throughput and spatial assessment of lipidomic analyses. The use of a combinatorial approach of analytical methods including traditional chromatographic methods, chiral separations, automated solid phase extractions (SPE), gas phase chemical derivatizations, multiplexed ion mobility spectrometry-mass spectrometry (IMS-MS) separations and automated data analysis will provide unprecedented coverage for the numerous lipid isomers and species present in complex samples. This highly specific and sensitive, automated platform will then be applied to the targeted quantification of various lipid species in largescale tissue screening analyses to assess over a 1000 lipidomic samples per day.