Metabolomics

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Known Metabolite Annotation in Different Biological Samples using Ion Mobility Collision Cross-Section Atlas (AllCCS)


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 curated an ion mobility CCS atlas, namely AllCCS, and developed 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 demonstrated 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 facilitated multi-dimensional match and substantially improved 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.


Known Metabolite Annotation in Different Biological Samples assays are reported in the current study MTBLS1693.

Unknown Metabolite Annotation in Mouse Aging assay is reported in MTBLS1622.

INSTRUMENT(S): Liquid Chromatography MS - positive - hilic, Liquid Chromatography MS - negative - hilic

SUBMITTER: Zhiwei Zhou  

PROVIDER: MTBLS1693 | MetaboLights | 2020-07-28

REPOSITORIES: MetaboLights

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Publications

Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics.

Shen Xiaotao X   Wang Ruohong R   Xiong Xin X   Yin Yandong Y   Cai Yuping Y   Ma Zaijun Z   Liu Nan N   Zhu Zheng-Jiang ZJ  

Nature communications 20190403 1


Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes  ...[more]

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