Evidence map›Paper›PMID 28348510›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2016

Comprehensive Optimization of LC-MS Metabolomics Methods Using Design of Experiments (COLMeD).

Seth D Rhoades, Aalim M Weljie

Abstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  11. Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview.Advances in experimental medicine and biology · 2021
    Article
  12. Circadian and Sleep Metabolomics Across Species.Journal of molecular biology · 2020
    Review
  13. Article
  14. Article
  15. Article
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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

2 authors.

Seth D RhoadesDepartment of Systems Pharmacology and Translational Therapeutics, Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.
Aalim M WeljieDepartment of Systems Pharmacology and Translational Therapeutics, Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.

Funding

INSTITUTIONAL CLINICAL AND TRANSLATIONAL SCIENCE AWARDUL1RR024134 · NCRR · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2006 to 2011
$70.4M
PREDOCTORAL TRAINING PROGRAM IN PHARMACOLOGYT32GM008076 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI BLENDY, JULIE A · 1985 to 2023
$10.8M
NCRR NIH HHS UL1 RR024134NIGMS NIH HHS T32 GM008076
6 · The paper itself

Abstract

introductionBoth reverse-phase and HILIC chemistries are deployed for liquid-chromatography mass spectrometry (LC-MS) metabolomics analyses, however HILIC methods lag behind reverse-phase methods in reproducibility and versatility. Comprehensive metabolomics analysis is additionally complicated by the physiochemical diversity of metabolites and array of tunable analytical parameters.

objectiveOur aim was to rationally and efficiently design complementary HILIC-based polar metabolomics methods on multiple instruments using Design of Experiments (DoE).

methodsWe iteratively tuned LC and MS conditions on ion-switching triple quadrupole (QqQ) and quadrupole-time-of-flight (qTOF) mass spectrometers through multiple rounds of a workflow we term COLMeD (Comprehensive optimization of LC-MS metabolomics methods using design of experiments). Multivariate statistical analysis guided our decision process in the method optimizations.

resultsLC-MS/MS tuning for the QqQ method on serum metabolites yielded a median response increase of 161.5% (p<0.0001) over initial conditions with a 13.3% increase in metabolite coverage. The COLMeD output was benchmarked against two widely used polar metabolomics methods, demonstrating total ion current increases of 105.8% and 57.3%, with median metabolite response increases of 106.1% and 10.3% (p<0.0001 and p<0.05 respectively). For our optimized qTOF method, 22 solvent systems were compared on a standard mix of physiochemically diverse metabolites, followed by COLMeD optimization, yielding a median 29.8% response increase (p<0.0001) over initial conditions.

conclusionsThe COLMeD process elucidated response tradeoffs, facilitating improved chromatography and MS response without compromising separation of isobars. COLMeD is efficient, requiring no more than 20 injections in a given DoE round, and flexible, capable of class-specific optimization as demonstrated through acylcarnitine optimization within the QqQ method.

Indexed as

Design of ExperimentsHILIC LC-MSMethod DevelopmentMultivariate Statistical Analysis

Identifiers

PMID28348510
PMCPMC5364801

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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.