PiTMaP: A new analytical platform for high-throughput direct Metabolome analysis by probe electrospray ionization/tandem massspectrometry using an R software-based data pipeline
PiTMaP: A new analytical platform for high-throughput direct Metabolome analysis by probe electrospray ionization/tandem massspectrometry using an R software-based data pipeline
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PiTMaP：一种新的分析平台，使用基于 R 软件的数据管道，通过探针电喷雾电离/串联质谱进行高通量直接代谢组分析
DOI：
10.1021/acs.analchem.0c01271
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发表时间：
2020
影响因子：
7.4
通讯作者：
Akira Iguchi
中科院分区：
文献类型：
--
作者：
Kei Zaitsu;Seiichiro Eguchi;Tomomi Ohara;Kenta Kondo;Akira Ishii;Hitoshi Tsuchihashi;Takakazu Kawamata;Akira Iguchi
开发了一种新的分析平台PiTMaP，用于使用基于R软件的数据管道通过探针电喷雾电离/串联质谱（PESI/MS/MS）进行高通量直接代谢组分析。采用PESI/MS/MS作为数据采集技术，应用程序选择反应监测方法来扩展目标代谢物。选取了72种主要与中枢能量代谢相关的代谢物，并利用小鼠肝脏和脑组织样本对数据采集时间进行了优化，结果表明2.4 min数据采集方法的重复性高于1.2 min和4.8 min方法。使用R软件构建数据管道，并且证明其可以（i）自动生成所有代谢物的盒须图，（ii）进行多变量分析，例如主成分分析（PCA）和投影到潜在结构-判别分析（PLS-DA），（iii）生成PCA和PLS-DA的得分和加载图，（iv）计算投影的变量重要性（VIP）值，（v）通过VIP值标准确定统计学家族，（vi）用错误发现率（FDR）校正方法进行显著性检验，以及（vii）仅对显著变化的代谢物绘制盒须图。这些任务可以在短时间内完成。最后，将PiTMaP应用于两种情况：（1）对乙酰氨基酚诱导的急性肝损伤模型和对照小鼠，以及（2）具有不同等级（G1-G3）的人脑膜瘤样品，证明PiTMaP的可行性。PiTMaP被发现可以进行数据采集，而无需繁琐的样品制备和事后数据分析。因此，这将是一个通用的平台，进行快速代谢分析的生物样品。
A new analytical platform called PiTMaP was developed for high-throughput direct metabolome analysis by probe electrospray ionization/tandem mass spectrometry (PESI/MS/MS) using an R software-based data pipeline. PESI/MS/MS was used as the data acquisition technique, applying a scheduled-selected reaction monitoring method to expand the targeted metabolites. Seventy-two metabolites mainly related to the central energy metabolism were selected; data acquisition time was optimized using mouse liver and brain samples, indicating that the 2.4 min data acquisition method had a higher repeatability than the 1.2 and 4.8 min methods. A data pipeline was constructed using the R software, and it was proven that it can (i) automatically generate box-and-whisker plots for all metabolites, (ii) perform multivariate analyses such as principal component analysis (PCA) and projection to latent structures-discriminant analysis (PLS-DA), (iii) generate score and loading plots of PCA and PLS-DA, (iv) calculate variable importance of projection (VIP) values, (v) determine a statistical family by VIP value criterion, (vi) perform tests of significance with the false discovery rate (FDR) correction method, and (vii) draw box-and-whisker plots only for significantly changed metabolites. These tasks could be completed within ca. 1 min. Finally, PiTMaP was applied to two cases: (1) an acetaminophen-induced acute liver injury model and control mice and (2) human meningioma samples with different grades (G1–G3), demonstrating the feasibility of PiTMaP. PiTMaP was found to perform data acquisition without tedious sample preparation and a posthoc data analysis within ca. 1 min. Thus, it would be a universal platform to perform rapid metabolic profiling of biological samples.