prm-PASEF-Based Quantification and Isomeric Model for Extended Coverage of Human Plasma Lipidome in Parkinson’s Disease

This study introduces a clinical lipidomics platform
leveraging
fragment-based quantification on parallel reaction monitoring (PRM)-parallel
accumulation serial fragmentation (PASEF) for lipid quantification.
An isomeric model, termed “SN regression model”, built
on specific PASEF-fragment ion patterns, was developed for the quantification
of coeluting sn positional isomers without prior derivatization. This
PASEF-isomeric lipidomics aids in the resolution and quantification
of 176 lipid isomers coeluting in chromatography and/or ion mobility
dimensions, expanding the lipidome quantitative coverage to 481 plasma
lipids covering 14 lipid subclasses with CV <40% for 32 plasma
replicates. We demonstrated the method’s advantage for clinical
research by detailed quantitative lipidomic phenotyping of patients
with Parkinson’s disease, enabling the delineation of new biochemical
pathways affected by the disorder and stratification of patients.
The method’s amenability for high-throughput deep quantitative
coverage of highly structurally resolved lipidome has implications
for improving the diagnosis and understanding of the distinct metabolic
alterations in Parkinson’s disease subgroups and, generally,
for disorders associated with lipid dysregulation.

本研究构建了一款临床脂质组学（lipidomics）平台，该平台依托基于片段定量的平行反应监测-平行累积连续碎裂（parallel reaction monitoring (PRM)-parallel accumulation serial fragmentation (PASEF)）技术实现脂质定量。研究开发了一种基于特定PASEF片段离子模式的异构体模型，命名为“sn位回归模型”，可在无需预先衍生化的前提下，对共洗脱的sn位置异构体进行定量分析。该PASEF异构体脂质组学方法可实现色谱及/或离子迁移维度中共洗脱的176种脂质异构体的分离与定量，将血浆脂质组的定量覆盖范围拓展至481种血浆脂质，涵盖14个脂质亚类，且32份血浆重复样本的变异系数（coefficient of variation, CV）均小于40%。本研究通过对帕金森病（Parkinson’s disease）患者开展精细的定量脂质组学表型分析，验证了该方法在临床研究中的优势，得以阐明该疾病影响的全新生化通路，并实现患者分层。该方法具备高通量深度定量覆盖高分辨率结构脂质组的能力，有望助力改善帕金森病亚型乃至所有与脂质代谢失调相关疾病的诊断水平与发病机制理解。