Development of a Liquid Chromatography–High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition

High-resolution
mass spectrometry (HRMS)-based metabolomics approaches
have made significant advances. However, metabolite identification
is still a major challenge with significant bottleneck in translating
metabolomics data into biological context. In the current study, a
liquid chromatography (LC)–HRMS metabolomics method was developed
using an all ion fragmentation (AIF) acquisition approach. To increase
the specificity in metabolite annotation, four criteria were considered:
(i) accurate mass (AM), (ii) retention time (RT), (iii) MS/MS spectrum,
and (iv) product/precursor ion intensity ratios. We constructed an
in-house mass spectral library of 408 metabolites containing AMRT
and MS/MS spectra information at four collision energies. The percent
relative standard deviations between ion ratios of a metabolite in
an analytical standard vs sample matrix were used as an additional
metric for establishing metabolite identity. A data processing method
for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information
for each of the 413 metabolites. In the data processing method, the
precursor ion and product ion were considered as the quantifier and
qualifier ion, respectively. We also included a scheme to distinguish
coeluting isobaric compounds by selecting a specific product ion as
the quantifier ion instead of the precursor ion. An advantage of the
current AIF approach is the concurrent collection of full scan data,
enabling identification of metabolites not included in the database.
Our data acquisition strategy enables a simultaneous mixture of database-dependent
targeted and nontargeted metabolomics in combination with improved
accuracy in metabolite identification, increasing the quality of the
biological information acquired in a metabolomics experiment.

基于高分辨质谱（HRMS）的代谢组学方法已取得显著进展。然而，代谢物鉴定仍是一项核心挑战，在将代谢组学数据转化为生物学背景信息的过程中仍存在显著瓶颈。本研究采用全离子碎裂（AIF）采集策略，构建了一套液相色谱-高分辨质谱（LC-HRMS）代谢组学分析方法。为提升代谢物注释的特异性，本研究考量了四项判定标准：（i）精确质量（AM）、（ii）保留时间（RT）、（iii）串联质谱（MS/MS）谱图，以及（iv）产物/前体离子强度比。我们搭建了包含408种代谢物的自建质谱库，该库收录了四种碰撞能量下的精确质量、保留时间及串联质谱谱图信息。以分析标准品与样品基质中代谢物离子强度比的相对标准偏差百分比，作为代谢物鉴定的额外判定指标。随后我们开发了一款靶向代谢物筛选的数据处理方法，针对413种代谢物分别整合了质荷比（m/z）、保留时间、串联质谱谱图及离子比例信息。在该数据处理方法中，前体离子与产物离子分别被用作定量离子与定性离子。我们还新增了一套区分共流出同重质化合物的方案：通过选择特定产物离子而非前体离子作为定量离子，实现对这类化合物的分辨。本研究采用的全离子碎裂方法的一大优势在于可同步采集全扫描数据，从而能够鉴定数据库中未收录的代谢物。我们的数据采集策略可同时实现依赖数据库的靶向代谢组学与非靶向代谢组学分析，同时提升代谢物鉴定的准确度，进而优化代谢组学实验中所获生物学信息的质量。