Abstract
Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.
Highlights
Liquid chromatography mass spectrometry (LC/MS)-based metabolomics has become an important tool in clinical and translational research
Separate sample type specific databases (STSDBs) can be developed that comprehensive LC/MS analysis of tens to hundreds of samples representing a single biofluid, tissue, or cell type; (2) perform untargeted data extraction/mass and time alignment, including collapsing of like features, to generate a list of compounds; (3) evaluate the reproducibility of the compound and generate a composite chemical characteristic quality score; (4) calculate the frequency with the compound appears in the dataset and use this “Detection Frequency”
STSDBs should provide an improved compound annotation and ID workflow through an overall quality score (Ov-quality scores scores (QS)- Figures 3 and 4), which includes a composite compound characteristic quality score (CCC-QS)
Summary
Liquid chromatography mass spectrometry (LC/MS)-based metabolomics has become an important tool in clinical and translational research. While many tools have been developed for plants, natural products, and industrial applications, these are not always applicable for human or translational studies This perspective article focuses on LC/MS-based clinical metabolomics and proposes a strategy to improve identification of compounds in these studies. Spectral libraries can contain empirical or in silico derived (i.e., computer generated) spectra Another strategy entails the use of customized databases that may include some MS/MS spectra, including those that focus on specific sample types [10,11]. For the purpose of this perspective article, a traditional compound ID workflow is compared to a proposed STSDB workflow, followed by a discussion of challenges related to traditional compound ID workflows This is followed by a proposed framework for developing sample type specific databases (STSDBs) aimed at addressing these challenges. MS database ororMS/MS on the Typically, MS/MSlibrary librarysearch searchisisconducted conductedfor for initial initial annotation annotation of compounds
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