Creating a Reliable Mass Spectral–Retention Time Library for All Ion Fragmentation-Based Metabolomics

Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC–MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC–MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.

精准的代谢物鉴定始终是代谢组学研究的核心挑战之一。可靠的化学光谱库可提升代谢物注释的置信度，而公共数据库中原始与已注释数据的公开共享，为液相色谱-质谱联用法（Liquid chromatography coupled to mass spectrometry，LC-MS）在不同实验室间的方法迁移提供了便利。本文阐述了如何通过结合MS2质谱图、精准质荷比与保留时间，提升代谢物注释的置信度，并提供了一套针对全离子碎裂（all ion fragmentation，AIF）数据构建可靠光谱库的技术方案，重点聚焦保留时间的特征表征。所构建的光谱库纳入了AIF数据中的加合物与源内碎裂信息，并通过多重去卷积流程有效抑制噪声峰。此外，本文还报道了质谱库管理工具（Mass Spectral LIbrary MAnager，MS-LIMA）的开发，以加速光谱库在不同实验室间的共享与迁移。该库构建策略可提升基于LC-MS的代谢组学研究中AIF数据的注释置信度，并将推动代谢组学领域内保留时间与质谱数据的共享流通。