Cited by 453Metabolomics and Mass Spectrometry StudiesAdvanced Proteomics Techniques and ApplicationsMicrobial Metabolic Engineering and Bioproduction

Metabolomics: A Primer

Open access
Liu, Xiaojing, Locasale, Jason W.·2017·Trends in Biochemical Sciences

Abstract

Metabolomics generates a profile of small molecules that are derived from cellular metabolism and can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology. Technological advances have enabled rapid and increasingly expansive data acquisition with samples as small as single cells; however, substantial challenges in the field remain. In this primer we provide an overview of metabolomics, especially mass spectrometry (MS)-based metabolomics, which uses liquid chromatography (LC) for separation, and discuss its utilities and limitations. We identify and discuss several areas at the frontier of metabolomics. Our goal is to give the reader a sense of what might be accomplished when conducting a metabolomics experiment, now and in the near future.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.tibs.2017.01.004

Cites

117 of 117 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Non-targeted metabolomics methods have gained significant improvement over the last decade with regards to metabolite coverage, annotation rates, and standardization of data acquisition, analysis and FAIR data sharing.

    “Non-targeted metabolomics methods have gained significant improvement over the last decade with regards to metabolite coverage, annotation rates as well as standardization of data acquisition, analysis and FAIR data sharing.171,172”

    From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [171]

    The 'Challenges and future directions' section reports substantial progress in metabolomics, efforts to cover more metabolites, and work toward better data standardization. The 'From mass spectrometry data to metabolite profiling' section also describes advances in untargeted feature identification. Our copy does not establish improved annotation rates or standardization of acquisition and analysis, and it does not discuss FAIR data sharing; it presents some standardization as work still needed. Page numbers are unavailable in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Liu, X., & Locasale, J. W.
Affiliations
Duke University (US)
Corresponding author
Locasale, Jason W.
Venue
Trends in Biochemical Sciences
Publisher
Elsevier BV
Published
2017-04
Volume
42
Issue
4
Pages
274-284
ISSN
0968-0004 (ISSN-L)
OpenAlex
W2587034866
Cited by
453
References
117
Citations by year
2026: 41 · 2025: 69 · 2024: 75 · 2023: 60 · 2022: 48 · 2021: 50 · 2020: 50 · 2019: 32 · 2018: 15 · 2017: 13
FWCI
2.81
Type
journal article
Language
en
Open access
Locations
  • Trends in Biochemical Sciences (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Cancer Institute (R00CA168997, R01CA193256)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
Keywords
metabolomics, mass spectrometry-based metabolomics, liquid chromatography-mass spectrometry, small molecules, cellular metabolism, single-cell metabolomics
MeSH terms
Animals, Chromatography, Liquid, Humans, Mass Spectrometry, Metabolomics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review