Abstract
The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility – mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5–2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.
Highlights
The metabolome includes not just known and unknown metabolites; metabolite annotation remains the bottleneck in untargeted metabolomics
We compared the structural diversity of compounds in experimental AllCCS database with human metabolome database (HMDB) and DrugBank
The results showed that experimental collision cross-section (CCS) values covered 51.3% and 78.4% of chemical spaces of HMDB and DrugBank, respectively (Supplementary Fig. 4)
Summary
The metabolome includes not just known and unknown metabolites; metabolite annotation remains the bottleneck in untargeted metabolomics. We curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Other bioinformatic approaches (e.g., GNPS9, MetDNA10) use MS2 spectra and molecular networking algorithms for metabolite annotations All of these strategies require unique and high quality of experimental. Annotation of unknown metabolites with new chemical structures is still a challenge in untargeted metabolomics[3,11] These issues cause low coverage and high false-positive rate of metabolite annotation, suggesting that other physiochemical properties should be developed for metabolite annotation. Limited studies have integrated multi-dimensional properties in IM–MS towards the large-scale annotation of both known and unknown metabolite in untargeted metabolomics[11]
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