Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics
Autonomous METLIN-Guided In-source Fragment Annotation for Untargeted Metabolomics
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DOI：
10.1021/acs.analchem.8b03126
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发表时间：
2019-03-05
影响因子：
7.4
通讯作者：
Siuzdak, Gary
中科院分区：
文献类型：
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作者：
Domingo-Almenara, Xavier;Montenegro-Burke, J. Rafael;Siuzdak, Gary
非靶向分析中的计算代谢物注释旨在揭示潜在代谢物的中性分子质量并分配那些具有假定身份的分子。现有的注释策略依赖于加合物的观察和注释来确定代谢物中性质量。然而，通常在非靶向实验中检测到的特征的很大一部分仍未注释，这限制了我们确定中性分子质量的能力。尽管有可用的注释工具，但相对较少的工具受益于液相色谱-电喷雾电离-质谱中源片段的固有存在。在本研究中，我们引入了一种策略，使用 METLIN 库中的低能串联质谱 (MS) 谱来注释非目标数据中的源内片段。我们的算法 MISA（METLIN 引导的源内注释）将检测到的特征与 MS/MS 谱图中的低能片段进行比较，从而实现基于低能谱匹配的代谢特征的稳健注释和推定识别。该算法通过对采用液相色谱-质谱分析的三组不同生物样品中总共 140 种代谢物进行注释分析来评估。结果表明，在未形成或检测到加合物的情况下，MISA 能够通过源内片段匹配发现中性分子质量。 MISA 还能够通过两个注释分数提供推定的代谢物身份。这些分数考虑了匹配的源内片段数量以及实验数据和参考低能 MS/MS 谱图之间的相对强度相似性。总体而言，结果表明，源内碎片是一种频繁出现的现象，应考虑进行综合特征注释。因此，与加合物注释相结合，该策略添加了补充注释层，使得能够对源内片段进行注释并增加假定的识别置信度。该算法已集成到 XCMS Online 平台中，可在 http://xcmsonline.scripps.edu 免费获取。
Computational metabolite annotation in untargeted profiling aims at uncovering neutral molecular masses of underlying metabolites and assign those with putative identities. Existing annotation strategies rely on the observation and annotation of adducts to determine metabolite neutral masses. However, a significant fraction of features usually detected in untargeted experiments remains unannotated, which limits our ability to determine neutral molecular masses. Despite the availability of tools to annotate, relatively few of them benefit from the inherent presence of in source fragments in liquid chromatography-electrospray ionization-mass spectrometry. In this study, we introduce a strategy to annotate in-source fragments in untargeted data using low-energy tandem mass spectrometry (MS) spectra from the METLIN library. Our algorithm, MISA (METLIN-guided in-source annotation), compares detected features against low-energy fragments from MS/MS spectra, enabling robust annotation and putative identification of metabolic features based on low-energy spectral matching. The algorithm was evaluated through an annotation analysis of a total of 140 metabolites across three different sets of biological samples analyzed with liquid chromatography-mass spectrometry. Results showed that, in cases where adducts were not formed or detected, MISA was able to uncover neutral molecular masses by in-source fragment matching. MISA was also able to provide putative metabolite identities via two annotation scores. These scores take into account the number of in-source fragments matched and the relative intensity similarity between the experimental data and the reference low-energy MS/MS spectra. Overall, results showed that in-source fragmentation is a highly frequent phenomena that should be considered for comprehensive feature annotation. Thus, combined with adduct annotation, this strategy adds a complementary annotation layer, enabling in-source fragments to be annotated and increasing putative identification confidence. The algorithm is integrated into the XCMS Online platform and is freely available at http://xcmsonline.scripps.edu.