eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics

Gas
chromatography coupled to mass spectrometry (GC/MS) has been
a long-standing approach used for identifying small molecules due
to the highly reproducible ionization process of electron impact ionization
(EI). However, the use of GC-EI MS in untargeted metabolomics produces
large and complex data sets characterized by coeluting compounds and
extensive fragmentation of molecular ions caused by the hard electron
ionization. In order to identify and extract quantitative information
on metabolites across multiple biological samples, integrated computational
workflows for data processing are needed. Here we introduce eRah,
a free computational tool written in the open language R composed
of five core functions: (i) noise filtering and baseline removal of
GC/MS chromatograms, (ii) an innovative compound deconvolution process
using multivariate analysis techniques based on compound match by
local covariance (CMLC) and orthogonal signal deconvolution (OSD),
(iii) alignment of mass spectra across samples, (iv) missing compound
recovery, and (v) identification of metabolites by spectral library
matching using publicly available mass spectra. eRah outputs a table
with compound names, matching scores and the integrated area of compounds
for each sample. The automated capabilities of eRah are demonstrated
by the analysis of GC-time-of-flight (TOF) MS data from plasma samples
of adolescents with hyperinsulinaemic androgen excess and healthy
controls. The quantitative results of eRah are compared to centWave,
the peak-picking algorithm implemented in the widely used XCMS package,
MetAlign, and ChromaTOF software. Significantly dysregulated metabolites
are further validated using pure standards and targeted analysis by
GC-triple quadrupole (QqQ) MS, LC-QqQ, and NMR. eRah is freely available
at http://CRAN.R-project.org/package=erah.

气相色谱-质谱联用法（Gas Chromatography-Mass Spectrometry, GC/MS）因电子轰击电离（Electron Impact Ionization, EI）的电离过程重复性极佳，长期以来一直是小分子鉴定的经典方法。然而，在非靶向代谢组学研究中使用GC-EI MS会生成体量庞大且结构复杂的数据集，这类数据集以共流出化合物以及硬电子电离导致的分子离子大量碎裂为核心特征。为实现在多份生物样本中鉴定代谢物并提取其定量信息的目标，亟需集成化的计算数据分析工作流。本文介绍eRah——一款基于开源语言R编写的免费计算工具，其包含五大核心功能模块：（1）GC/MS色谱图的噪声过滤与基线校正；（2）基于局部协方差化合物匹配（Compound Match by Local Covariance, CMLC）与正交信号解卷积（Orthogonal Signal Deconvolution, OSD）的多元分析技术，实现创新性的化合物解卷积流程；（3）跨样本质谱谱图对齐；（4）缺失化合物补全；（5）利用公开质谱谱图进行谱库匹配，完成代谢物鉴定。eRah可输出包含各样本中化合物名称、匹配得分以及化合物积分峰面积的标准化结果表格。本研究通过分析高胰岛素血症性雄激素过多症青少年与健康对照者的血浆样本的气相色谱-飞行时间质谱（Gas Chromatography-Time of Flight Mass Spectrometry, GC-TOF MS）数据，验证了eRah的自动化分析性能。研究将eRah的定量分析结果与三款常用工具进行了对比：广泛使用的XCMS软件包中集成的峰拾取算法centWave、MetAlign软件以及ChromaTOF软件。研究进一步通过纯标准品以及气相色谱-三重四极杆质谱（Gas Chromatography-Triple Quadrupole Mass Spectrometry, GC-QqQ MS）、液相色谱-三重四极杆质谱（Liquid Chromatography-Triple Quadrupole Mass Spectrometry, LC-QqQ）与核磁共振波谱法（Nuclear Magnetic Resonance, NMR）的靶向分析，对显著失调的代谢物进行了实验验证。eRah可于http://CRAN.R-project.org/package=erah免费获取。