Evidence map›Paper›PMID 31289941›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2019

A large-scale analysis of targeted metabolomics data from heterogeneous biological samples provides insights into metabolite dynamics.

Ho-Joon Lee, Daniel M Kremer, Peter Sajjakulnukit, Li Zhang, Costas A Lyssiotis

Abstract read
In one paragraph

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 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 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

51 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Metabolomic analyses of amyotrophic lateral sclerosis, muscle cramps, and TJ-68 treatment.Metabolomics : Official journal of the Metabolomic Society · 2026
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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

5 authors.

Ho-Joon LeeDepartment of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. esavant@med.umich.edu.ORCID 0000-0003-3616-5387
Daniel M KremerDepartment of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Peter SajjakulnukitDepartment of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Li ZhangDepartment of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Costas A LyssiotisDepartment of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. clyssiot@med.umich.edu.ORCID 0000-0001-9309-6141

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI THEODORE S LAWRENCE · 1988 to 2026
$178.2M
Statistics and Bioinformatics Core - WangU24DK097153 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BURANT, CHARLES F · 2012 to 2017
$10.1M
Training Program in Tumor MicroenvironmentT32CA009676 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Maria G Castro, Analisa Virginia DiFeo · 1992 to 2026
$7.3M
NCI NIH HHS P30 CA046592NCI NIH HHS T32 CA009676NIDDK NIH HHS U24 DK097153
6 · The paper itself

Abstract

introductionWe previously developed a tandem mass spectrometry-based label-free targeted metabolomics analysis framework coupled to two distinct chromatographic methods, reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC), with dynamic multiple reaction monitoring (dMRM) for simultaneous detection of over 200 metabolites to study core metabolic pathways.

objectivesWe aim to analyze a large-scale heterogeneous data compendium generated from our LC-MS/MS platform with both RPLC and HILIC methods to systematically assess measurement quality in biological replicate groups and to investigate metabolite abundance changes and patterns across different biological conditions.

methodsOur metabolomics framework was applied in a wide range of experimental systems including cancer cell lines, tumors, extracellular media, primary cells, immune cells, organoids, organs (e.g. pancreata), tissues, and sera from human and mice. We also developed computational and statistical analysis pipelines, which include hierarchical clustering, replicate-group CV analysis, correlation analysis, and case-control paired analysis.

resultsWe generated a compendium of 42 heterogeneous deidentified datasets with 635 samples using both RPLC and HILIC methods. There exist metabolite signatures that correspond to various phenotypes of the heterogeneous datasets, involved in several metabolic pathways. The RPLC method shows overall better reproducibility than the HILIC method for most metabolites including polar amino acids. Correlation analysis reveals high confidence metabolites irrespective of experimental systems such as methionine, phenylalanine, and taurine. We also identify homocystine, reduced glutathione, and phosphoenolpyruvic acid as highly dynamic metabolites across all case-control paired samples.

conclusionsOur study is expected to serve as a resource and a reference point for a systematic analysis of label-free LC-MS/MS targeted metabolomics data in both RPLC and HILIC methods with dMRM.

Indexed as

MetabolomeAmino AcidsAnimalsCase-Control StudiesCell LineChromatography, High Pressure LiquidChromatography, Reverse-PhaseCluster AnalysisHomocystineHumansHydrophobic and Hydrophilic InteractionsMetabolic Networks and PathwaysMetabolomicsTandem Mass SpectrometryAmino AcidsHomocystineAmino acidsHILICLC–MS/MSMeasurement reliabilityMetabolite dynamicsRPLCTargeted metabolomics

Identifiers

PMID31289941
PMCPMC6616221

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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.