Evidence map›Paper›PMID 31584114›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2019

Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics.

Casey A Chamberlain, Vanessa Y Rubio, Timothy J Garrett

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Article in Metabolomics : Official journal of the Metabolomic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

  1. Review
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  12. Multiplatform untargeted metabolomics.Magnetic resonance in chemistry : MRC · 2023
    Review
  13. Article
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  19. A Novel Propidium Monoazide-Based PCR Assay Can Measure Viable UropathogenicFrontiers in cellular and infection microbiology · 2022
    Article
  20. Article
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

3 authors.

Casey A ChamberlainDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, 32610, USA.
Vanessa Y RubioDepartment of Chemistry, University of Florida, Gainesville, FL, 32611, USA.
Timothy J GarrettDepartment of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, 32610, USA. tgarrett@ufl.edu.ORCID 0000-0003-1623-009X

Funding

Probiotic-Induced Elimination of Oxalate to Treat Hyperoxaluria Associated with PR01DK088892 · NIDDK · UNIVERSITY OF FLORIDA · PI HATCH, MARGUERITE · 2010 to 2019
$3.5M
NIDDK NIH HHS R01 DK088892
6 · The paper itself

Abstract

introductionLC-MS-based untargeted metabolomics has become increasingly popular due to the vast amount of information gained in a single analysis. Many studies utilize metabolomics to profile metabolic changes in various representative biofluids, tissues, or other sample types. Most analyses are performed measuring changes in the metabolic pool of a single biological matrix due to an altered phenotype, such as disease versus normal. Measurements in such experiments are typically highly reproducible with little variation due to analytical and technological advancements in mass spectrometry. With the expanded application of metabolomics into various non-analytical scientific disciplines, the emergence of studies comparing the signal intensities of specific analytes across different biological matrices (e.g. plasma vs. urine) is becoming more common, but the matrix effect between sample types is often neglected. Additionally, the practice of comparing the signal intensities of different analytes and correlating to relative abundance is also increasingly prevalent, but the response ratio between analytes due to differences in ionization efficiency is not always accounted for in data analysis. This report serves to communicate and raise awareness of these two well-recognized issues to prevent improper data interpretation in the field of metabolomics.

objectivesWe demonstrate the impact of matrix effects and ionization efficiency with labeled analytical standards in human plasma, serum, and urine and describe how the direct comparison of non-quantitative signal intensities between biofluids, as well as between different analytes in the same biofluid, in untargeted metabolomics is inaccurate without proper response corrections.

methodsHuman plasma, serum, and urine (n = 4 technical replicates per biofluid) were spiked with a panel of labeled internal standards all at identical concentrations, simultaneously extracted, and analyzed by UHPLC-HRMS. Signal intensities were compared for demonstration of the impact of matrix effects in untargeted metabolomics. A neat mixture of two co-eluting, structurally-similar labeled standards at the same concentration was also analyzed to demonstrate the effect of ionization efficiency on signal intensity.

resultsDespite being spiked at identical concentrations, labeled standards we examined in this study showed significant differences in their signal intensities between biofluids, as well as from each other in the same biofluid, due to matrix effects. Co-eluting standards were also found to yield significantly different signal intensities at identical concentrations due to differences in ionization efficiency.

conclusionsDue to the presence of matrix effects in untargeted, non-quantitative metabolomics, the signal intensity of any single analyte cannot be directly compared to the signal intensity of that same analyte (or any other analyte) between any two different matrices. Due to differences in ionization efficiency, the signal intensity of any single analyte cannot be directly compared to the signal intensity of any other analyte, even in the same matrix.

Indexed as

MetabolomicsChromatography, High Pressure LiquidHumansMass SpectrometryProteinsProteinsIonization efficiencyLC–MSMass spectrometryMatrix effectMetabolomics

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Registered trials

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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.