Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics

Abstract

Liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics is constantly challenged by large-scale and unambiguous metabolite annotation in complex biological matrices, due to the enormous chemical and compositional diversity of metabolome. While standard tandem mass spectral databases have readily supported metabolite identification, the number of identified metabolites in untargeted metabolomics has remained limited. Over the past years, several phenomenal informatic and analytical approaches have been developed to strengthen metabolite annotation by improving coverage, accuracy, and unknown elucidation. Here, we review the major advancements of metabolite annotation strategies in LC–MS-based untargeted metabolomics, which include tandem mass spectral match and scoring algorithms, in-silico MS/MS spectral prediction, and the network-based approaches. Further, we review the expansion of analytical dimensions to support multidimensional metabolite annotation including the liquid chromatographic separation derived retention time (RT) and ion mobility separation derived collision cross-section (CCS). In addition, we highlight the strengths of stable-isotope labeling in aiding structural verification of metabolites. Finally, we discuss and outline emerging directions in this fast-paced field, with the ultimate goal of revealing novel and functional metabolites in biological investigations. Together, this review summarizes the state-of-the-art approaches in annotating metabolites for LC–MS-based untargeted metabolomics, wherein a tremendous number of true unknown metabolites are awaiting to be discovered towards functional metabolomics.