Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.
Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.
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DOI：
10.1021/acs.analchem.1c02149
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发表时间：
2021-12-07
影响因子：
7.4
通讯作者：
Evans, Charles R.
中科院分区：
文献类型：
--
作者：
Anderson, Brady G.;Raskind, Alexander;Habra, Hani;Kennedy, Robert T.;Evans, Charles R.
非靶向代谢组学是系统生物学研究的一个重要组成部分，但它受到高比例的可检测特征的困扰，这些特征与化学结构无关。液-质联用(LC-MS/MS)实验产生的光谱可以在数据库中搜索，以帮助识别或分类这些未知数，但许多特征不能产生足够高质量的光谱，从而能够成功地进行注释。在这里，我们探索了反相(RPLC)和亲水相互作用液相色谱(HILIC)的梯度长度、质量载量和滚动前体离子排除参数的变化，以改善对人类血浆样本的化合物识别性能。来自HILIC数据集的光谱匹配的人工审查被用来确定搜索分数和其他指标的合理阈值，以实现半自动的MS/MS数据分析。与典型的LC-MS/MS条件相比，适用于化合物鉴定的方法将可与光谱数据库可靠匹配的独特代谢物总数从170增加到1,829。在数据比对之后，使用常规的20分钟LC-MS运行可以检测和定量来自改进条件的近70%的新识别特征。最后，开发了一个局部化的机器学习模型来对剩余的未知数进行分类，并选择与成功识别的特征具有相同光谱特征的子集。该模型将HILIC数据集中的511个未识别特征归类为高质量未知数，或用于后续分析的更高优先级目标。总体而言，我们的研究提出了一个简单的策略，以更深入地注释非靶向代谢组学数据，并适度增加时间和样本投入。
Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. Manual review of spectral matches from the HILIC dataset was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be confidently matched to a spectral database from 170 to 1,829. Following data alignment, nearly 70% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-minute LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. 511 unidentified features in the HILIC dataset were classified by the model as high-quality unknowns, or higher-priority targets for follow up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.
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影响因子：
7.4
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通讯作者：
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影响因子：
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通讯作者：
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