Evidence map›Paper›PMID 37155916›Full record

ArticleAnalytical chemistry2023

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis.

Zhikun Jia, Qiongju Qiu, Ruiping He, Tianyu Zhou, Li Chen

Abstract read
In one paragraph

Article in Analytical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–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

14 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Evaluation of the detuning ratio as a tool to detect potential interference in LC-MSMS analysis.Journal of mass spectrometry and advances in the clinical lab · 2025
    Article
  5. Comprehensive profiling of folates across polyglutamylation and one-carbon states.Metabolomics : Official journal of the Metabolomic Society · 2025
    Article
  6. Review
  7. Article
  8. Review
  9. Analysis types and quantification methods applied in UHPLC-MS metabolomics research: a tutorial.Metabolomics : Official journal of the Metabolomic Society · 2024
    Review
  10. Understanding isotopes, isomers, and isobars in mass spectrometry.Journal of mass spectrometry and advances in the clinical lab · 2024
    Review
  11. Article
  12. Integrating the analysis of human biopsies using post-translational modifications proteomics.Protein science : a publication of the Protein Society · 2024
    Review
  13. Article
  14. Review
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

5 authors.

Zhikun JiaShanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism & Integrative Biology, Fudan University, Shanghai 200433, China.
Qiongju QiuShanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism & Integrative Biology, Fudan University, Shanghai 200433, China.
Ruiping HeShanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism & Integrative Biology, Fudan University, Shanghai 200433, China.
Tianyu ZhouShanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism & Integrative Biology, Fudan University, Shanghai 200433, China.
Li ChenShanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism & Integrative Biology, Fudan University, Shanghai 200433, China.ORCID 0000-0001-8685-3466

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

Indexed as

MetabolomicsTandem Mass SpectrometryChromatography, LiquidIonsReference StandardsIons

Identifiers

PMID37155916
PMCPMC10209981

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.