Current Challenges and Recent Developments in Mass Spectrometry-Based Metabolomics

High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification...

Full description

Saved in:
Bibliographic Details
Published in:Annual review of analytical chemistry (Palo Alto, Calif.) Calif.), 2021-07, Vol.14 (1), p.467-487
Main Authors: Collins, Stephanie L, Koo, Imhoi, Peters, Jeffrey M, Smith, Philip B, Patterson, Andrew D
Format: Article
Language:English
Subjects:
chromatography
Computer applications
Contamination
Data retrieval
Ions
Isomers
isotope tracing
Liquid chromatography
Mass spectra
Mass spectrometry
Mass spectroscopy
Metabolism
Metabolites
Metabolomics
multi-stage mass spectrometry
retention indices
Scientific imaging
Software
Spectroscopy
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification strategies, feature reduction software, and data sharing have not kept up with the influx of MS data. Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this review focuses on the challenges of liquid chromatography-mass spectrometry-based methods. We then highlight important instrumentational, experimental, and computational tools that have been created to address these challenges and how they have enabled the advancement of metabolomics research.
ISSN:1936-1327
1936-1335
DOI:10.1146/annurev-anchem-091620-015205