Evidence map›Paper›PMID 28654323›Full record

ReviewAnnual review of biochemistry2017

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow.

Wenyun Lu, Xiaoyang Su, Matthias S Klein, Ian A Lewis, Oliver Fiehn, Joshua D Rabinowitz

Abstract readReview
In one paragraph

Review in Annual review of biochemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 220 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
220citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

220 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  19. SIMBA-GNN: mechanistic graph learning for microbiome prediction.NPJ systems biology and applications · 2025
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160 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Wenyun LuLewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544; email: joshr@princeton.edu.
Xiaoyang SuLewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544; email: joshr@princeton.edu.
Matthias S KleinDepartment of Biological Science, University of Calgary, Calgary, Alberta T2N 1N4, Canada.
Ian A LewisDepartment of Biological Science, University of Calgary, Calgary, Alberta T2N 1N4, Canada.
Oliver FiehnNational Institutes of Health West Coast Metabolomics Center, University of California, Davis, California 95616.
Joshua D RabinowitzLewis Sigler Institute for Integrative Genomics and Department of Chemistry, Princeton University, Princeton, New Jersey 08544; email: joshr@princeton.edu.

Funding

VIRAL VECTOR COREP30DK019525 · NIDDK · UNIVERSITY OF PENNSYLVANIA · PI DOUGLAS J EPSTEIN · 1986 to 2026
$48.3M
West Coast Central Comprehensive Metabolomics Resource Core (WC3MRC)U24DK097154 · NIDDK · UNIVERSITY OF CALIFORNIA AT DAVIS · PI FIEHN, OLIVER · 2012 to 2017
$9.9M
Tumor cell metabolism in actionR01CA163591 · NCI · RUTGERS, THE STATE UNIV OF N.J. · PI JOSHUA D RABINOWITZ, Eileen P. White · 2012 to 2026
$7.0M
Metabolism in Action: Quantitative Fluxes in MammalsDP1DK113643 · NIDDK · PRINCETON UNIVERSITY · PI RABINOWITZ, JOSHUA D · 2016 to 2020
$5.7M
Revealing cancer metabolism via mass spectrometry and isotope tracersR50CA211437 · NCI · PRINCETON UNIVERSITY · PI Wenyun Lu · 2016 to 2026
$1.3M
NCI NIH HHS R01 CA163591NCI NIH HHS R50 CA211437NIDDK NIH HHS DP1 DK113643NIDDK NIH HHS P30 DK019525NIDDK NIH HHS U24 DK097154
6 · The paper itself

Abstract

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Indexed as

Adenosine TriphosphateAnimalsChromatography, LiquidGas Chromatography-Mass SpectrometryGlutathioneGuidelines as TopicHumansLiquid Phase MicroextractionMagnetic Resonance SpectroscopyMass SpectrometryMetabolomicsMiceNADPSolventsAdenosine TriphosphateGlutathioneNADPSolventsaccuracymass spectroscopymetabolite extractionmetabolomicsmetabonomicsstability

Identifiers

PMID28654323
PMCPMC5734093

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.