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（GC/MS）凭借电子轰击电离（electron impact ionization，EI）过程高度可重复的电离特性，长期以来一直是小分子鉴定的经典手段。然而，在非靶向代谢组学中应用GC-EI质谱时，会生成庞大且复杂的数据集，这类数据集存在共流出化合物，且硬电子轰击电离会引发分子离子的剧烈碎裂。为了在多份生物样本中鉴定代谢物并提取其定量信息，亟需集成化的计算数据分析流程。本研究介绍一款基于开源语言R开发的免费计算工具eRah，其包含五大核心功能：（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）与正交信号去卷积（orthogonal signal deconvolution，OSD）的多元分析技术，实现创新性的化合物去卷积流程；（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面积的结果表格。本研究通过分析患有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GC-飞行时间（time-of-flight，TOF）质谱数据，验证了eRah的自动化分析能力。将eRah的定量分析结果与三款工具进行对比：广泛使用的XCMS软件包中的峰拾取算法centWave、MetAlign以及ChromaTOF软件。对于筛选得到的差异显著代谢物，本研究进一步采用纯标准品以及GC-三重四极杆（triple quadrupole，QqQ）质谱、液相色谱-三重四极杆质谱（LC-QqQ）与核磁共振波谱法（NMR）开展靶向分析验证。eRah可在http://CRAN.R-project.org/package=erah免费获取。