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Metabolomics. 2019 Jul 9;15(7):103. doi: 10.1007/s11306-019-1564-8.

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

Author information

1
Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. esavant@med.umich.edu.
2
Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. esavant@med.umich.edu.
3
Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, 48109, USA. esavant@med.umich.edu.
4
Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA. esavant@med.umich.edu.
5
Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
6
Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI, 48109, USA.
7
Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. clyssiot@med.umich.edu.
8
Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA. clyssiot@med.umich.edu.
9
Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA. clyssiot@med.umich.edu.

Abstract

INTRODUCTION:

We 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.

OBJECTIVES:

We 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.

METHODS:

Our 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.

RESULTS:

We 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.

CONCLUSIONS:

Our 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.

KEYWORDS:

Amino acids; HILIC; LC–MS/MS; Measurement reliability; Metabolite dynamics; RPLC; Targeted metabolomics

PMID:
31289941
DOI:
10.1007/s11306-019-1564-8

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