Increasing the Coverage, Sensitivity and Specificity of Rapid Lipidomic Measurements

提高快速脂质组学测量的覆盖范围、灵敏度和特异性

• 批准号: 10709875
• 负责人: Erin S Baker
• 金额: $ 30.05万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-24 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10709875
• 关键词: Address; Area; Biological; Biological Markers; Biological Models; Biological Process; Cancerous; Categories; Cells; Characteristics; Chemicals; Chromatography; Clinical assessments; Code; Complex; Computer software; Coupled; Data; Data Analyses; Derivation procedure; Detection; Development; Disease; Disease Progression; Dissociation; Evaluation; Gases; Genomics; Geometry; Goals; Head; Histology; Ions; Isomerism; Length; Libraries; Lipids; Location; Marker Discovery; Mass Spectrum Analysis; Measurement; Methods; Mission; Molecular; Molecular Analysis; National Institute of General Medical Sciences; Neoplasm Metastasis; Organ; Organelles; Outcome; Ozone; Phase; Play; Positioning Attribute; Protein Isoforms; Proteomics; Protocols documentation; Radial; Reaction; Research; Resolution; Role; Sampling; Sensitivity and Specificity; Shotguns; Solid; Specificity; Specimen; Spectrometry; System; Systems Biology; Techniques; Technology; Time; Tissues; Translating; Variant; Visualization; Work; analytical method; biological systems; cheminformatics; combinatorial; data analysis pipeline; data visualization; disease diagnosis; disorder prevention; experimental analysis; functional group; improved; innovation; ion mobility; lipid structure; lipidomics; millisecond; novel; open data; pi bond; programs; screening; stereochemistry; user friendly software; web app

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.

项目总结/摘要 脂质是一类重要的分子，在生物过程中发挥着无数重要和不同的作用。充分 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， 表达成千上万种不同脂质种类的细胞。虽然最近的进展，色谱和高- 分辨率质谱分析大大提高了我们对存在于 由于许多不同的样品类型，有效地分离许多脂质仍然存在问题， 异构脂质。异构体脂质物质，例如由亚类异构体、不同的酰基链 连接性（sn-1、sn-2或sn-3）、不同的双键位置和方向（顺式或反式）以及独特的 官能团立体化学（R对S）使得脂质表征特别困难， 质量完全相同为了应对这一挑战，离子迁移谱分离，离子分子 反应和片段化技术已经越来越多地添加到脂质分析工作流程中， 物种分离和改进的表征。然而，目前这些分析还不能完全 评估复杂脂质混合物中存在的脂质种类的数量，或提供分子的深入分析， 基于它们在组织和器官中的空间位置的差异。此外，当一些分析 技术单独使用，实验和数据分析时间大大延长，使大规模 评估困难或不可能。本研究的总体目标是开发一个新的分析平台 以及相应的数据分析和可视化方法，以增加覆盖率、吞吐量和空间 脂质组学分析的评估。使用分析方法的组合方法，包括 传统色谱方法、手性分离、自动固相萃取（SPE）、气相色谱 化学衍生、多路离子迁移谱-质谱（IMS-MS）分离， 自动化数据分析将为众多脂质异构体和物质提供前所未有的覆盖范围 存在于复杂的样品中。这种高度特异性和敏感性的自动化平台将应用于 在大规模组织筛选分析中对各种脂质种类进行靶向定量，以评估超过1000种 脂质组学样本。