Semiautomated Alignment of High-Throughput Metabolite Profiles with Chemometric Tools.

Ze-Ying Wu,Yi-Zeng Liang,Hoi-Yan Chan,Zi-Dan Xiao,Zhong-Da Zeng,Daniel Kam-Wah Mok,Foo-Tim Chau

doi:10.1155/2017/9402045

Abstract

The rapid increase in the use of metabolite profiling/fingerprinting techniques to resolve complicated issues in metabolomics has stimulated demand for data processing techniques, such as alignment, to extract detailed information. In this study, a new and automated method was developed to correct the retention time shift of high-dimensional and high-throughput data sets. Information from the target chromatographic profiles was used to determine the standard profile as a reference for alignment. A novel, piecewise data partition strategy was applied for the determination of the target components in the standard profile as markers for alignment. An automated target search (ATS) method was proposed to find the exact retention times of the selected targets in other profiles for alignment. The linear interpolation technique (LIT) was employed to align the profiles prior to pattern recognition, comprehensive comparison analysis, and other data processing steps. In total, 94 metabolite profiles of ginseng were studied, including the most volatile secondary metabolites. The method used in this article could be an essential step in the extraction of information from high-throughput data acquired in the study of systems biology, metabolomics, and biomarker discovery.

Highlights

Hyphenated chromatographic instruments, coupled to mass spectrometers, have been preferred tools and play a substantial role in the study of complicated problems in systems biology, such as functional genomics, proteomics, and metabolomics, all of which have a large number of targets being detected [1,2,3]
The information content (IC) (φ) of all 94 total ion chromatograms (TIC) profiles obtained from the hyphenated GC-MS were calculated using (1) to determine the standard profile
The automated or semiautomated alignment of the retention time shift is a primary focus in biomarker discovery, metabolomics, and systems biology research

Summary

Introduction

Hyphenated chromatographic instruments, coupled to mass spectrometers, have been preferred tools and play a substantial role in the study of complicated problems in systems biology, such as functional genomics, proteomics, and metabolomics, all of which have a large number of targets (genes, proteins, and small molecules) being detected [1,2,3]. Gas or liquid chromatography coupled with mass spectroscopy (GC-MS or LC-MSn) is widely used to analyze biosamples, such as serum, urine, stool, and cerebrospinal and synovial fluids [3,4,5,6]. These analyses are a necessary part of investigating complex systems and help us understand the mechanisms of important life processes. The retention time shift strongly contributes to obtaining accurate qualitative and quantitative information on components that are hidden in complicated chromatographic peak clusters by deconvolution methods, which assumes trilinearity in tensor data sets [12]. Data treatment, such as correction of the retention time shift, is needed to improve data quality, and it is helpful in obtaining fruitful conclusions in most metabolomics studies

Methods

Results

Discussion

Conclusion