Rapid and Automatic Annotation of Multiple On-Tissue Chemical Modifications in Mass Spectrometry Imaging with Metaspace
Rapid and Automatic Annotation of Multiple On-Tissue Chemical Modifications in Mass Spectrometry Imaging with Metaspace
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DOI：
10.1021/acs.analchem.2c00979
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发表时间：
2022-06-15
影响因子：
7.4
通讯作者：
Lee, Young Jin
中科院分区：
文献类型：
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作者：
Larson, Evan A.;Forsman, Trevor T.;Lee, Young Jin
组织上化学衍生化是一种有价值的工具，用于在非靶向代谢组学研究中扩大化合物覆盖范围，同时使用基质辅助激光解吸/电离质谱试剂进一步增加代谢物覆盖范围，但会导致分析具有挑战性的大型和更复杂的数据集。在这项工作中，我们提出了一个管道，提供严格的注释上组织衍生MSI数据使用Metaspace。为了测试和验证管道，玉米根被用作模型系统以在用四种不同试剂化学衍生化之后获得MSI数据集，所述四种不同试剂是用于羰基的吉拉德的T和P、用于伯胺的松柏醛和用于羧酸的2-吡啶甲基胺。使用这个管道帮助我们注释了CornCyc/BraChem数据库中的631种独特代谢物，而未衍生化数据集中只有256种，但与此同时，与手动处理相比，缩短了处理时间，并提供了稳健和系统的评分和注释。我们还开发了一种去除错误衍生注释的方法，根据试剂的不同，该方法可以从衍生数据中清除5-25%的错误衍生注释。总之，我们的管道有利于使用多种衍生试剂的广泛靶向空间代谢组学的使用。
On-tissue chemical derivatization is a valuable tool for expanding compound coverage in untargeted metabolomic studies with matrix-assisted laser desorption/ionization mass zation agents in parallel increases metabolite coverage even further but results in large and more complex datasets that can be challenging to analyze. In this work, we present a pipeline to provide rigorous annotations for on-tissue derivatized MSI data using Metaspace. To test and validate the pipeline, maize roots were used as a model system to obtain MSI datasets after chemical derivatization with four different reagents, Girard's T and P for carbonyl groups, coniferyl aldehyde for primary amines, and 2-picolylamine for carboxylic acids. Using this pipeline helped us annotate 631 unique metabolites from the CornCyc/BraChem database compared to 256 in the underivatized dataset, yet, at the same time, shortening the processing time compared to manual processing and providing robust and systematic scoring and annotation. We have also developed a method to remove false derivatized annotations, which can clean 5-25% of false derivatized annotations from the derivatized data, depending on the reagent. Taken together, our pipeline facilitates the use of broadly targeted spatial metabolomics using multiple derivatization reagents.