Abstract
Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC–MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC–MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.
Highlights
Interest in the analysis of the metabolome has increased significantly due to its utility for understanding biological processes and for biomarker discovery [1]
In Liquid chromatography coupled to mass spectrometry (LC–MS) metabolomics, this criterion is often interpreted as an exact match of the peak feature in the measured sample and to the chemical standard by accurate mass (AM) and retention time (RT)
Data analysis in data independent acquisition (DIA) metabolomics metabolomics, this criterion is often interpreted as an exact match of the peak feature in the measured is currently limited to the use of libraries constructed using dependent acquisition (DDA) MS2 spectra without information on sample and to the chemical standard by accurate mass (AM) and retention time (RT)
Summary
Interest in the analysis of the metabolome has increased significantly due to its utility for understanding biological processes and for biomarker discovery [1]. Liquid chromatography coupled to mass spectrometry (LC–MS) is a widespread metabolomics method owing to its sensitivity, and its measurement strategies are broadly classified into targeted and nontargeted approaches [2]. Nontargeted metabolomics enables the discovery of unknown compounds; metabolite identification is a major bottleneck in data interpretation [4]. In LC–MS metabolomics, this criterion is often interpreted as an exact match of the peak feature in the measured sample and to the chemical standard by accurate mass (AM) and retention time (RT). These two properties may not be sufficient to reliably identify compounds due to co- or closely eluting compounds and RT fluctuations of certain chromatography techniques (e.g., HILIC)
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