Literature DB >> 29064229

Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms.

Tomas Cajka1, Jennifer T Smilowitz2,3, Oliver Fiehn1,4.   

Abstract

Liquid chromatography-mass spectrometry (LC-MS) methods are most often used for untargeted metabolomics and lipidomics. However, methods have not been standardized as accepted "best practice" documents, and reports lack harmonization with respect to quantitative data that enable interstudy comparisons. Researchers use a wide variety of high-resolution mass spectrometers under different operating conditions, and it is unclear if results would yield different biological conclusions depending on the instrument performance. To this end, we used 126 identical human plasma samples and 29 quality control samples from a nutritional intervention study. We investigated lipidomic data acquisitions across nine different MS instruments (1 single TOF, 1 Q/orbital ion trap, and 7 QTOF instruments). Sample preparations, chromatography conditions, and data processing methods were kept identical. Single-point internal standard calibrations were used to estimate absolute concentrations for 307 unique lipids identified by accurate mass, MS/MS spectral match, and retention times. Quantitative results were highly comparable between the LC-MS platforms tested. Using partial least-squares discriminant analysis (PLS-DA) to compare results between platforms, a 92% overlap for the most discriminating lipids based on variable importance in projection (VIP) scores was achieved for all lipids that were detected by at least two instrument platforms. Importantly, even the relative positions of individual samples on the PLS-DA projections were identical. The key for success in harmonizing results was to avoid ion saturation by carefully evaluating linear dynamic ranges using serial dilutions and adjusting the resuspension volume and/or injection volume before running actual study samples.

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Year:  2017        PMID: 29064229     DOI: 10.1021/acs.analchem.7b03404

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  63 in total

1.  A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

Authors:  Nguyen Phuoc Long; Sang Jun Yoon; Nguyen Hoang Anh; Tran Diem Nghi; Dong Kyu Lim; Yu Jin Hong; Soon-Sun Hong; Sung Won Kwon
Journal:  Metabolomics       Date:  2018-08-10       Impact factor: 4.290

2.  Metabolomics-related nutrient patterns at seroconversion and risk of progression to type 1 diabetes.

Authors:  Randi K Johnson; Lauren A Vanderlinden; Brian C DeFelice; Ulla Uusitalo; Jennifer Seifert; Sili Fan; Tessa Crume; Oliver Fiehn; Marian Rewers; Katerina Kechris; Jill M Norris
Journal:  Pediatr Diabetes       Date:  2020-08-09       Impact factor: 4.866

3.  Lipokine 5-PAHSA Is Regulated by Adipose Triglyceride Lipase and Primes Adipocytes for De Novo Lipogenesis in Mice.

Authors:  Veronika Paluchova; Marina Oseeva; Marie Brezinova; Tomas Cajka; Kristina Bardova; Katerina Adamcova; Petr Zacek; Kristyna Brejchova; Laurence Balas; Hana Chodounska; Eva Kudova; Renate Schreiber; Rudolf Zechner; Thierry Durand; Martin Rossmeisl; Nada A Abumrad; Jan Kopecky; Ondrej Kuda
Journal:  Diabetes       Date:  2019-12-05       Impact factor: 9.461

4.  Cloud-based archived metabolomics data: A resource for in-source fragmentation/annotation, meta-analysis and systems biology.

Authors:  Amelia Palermo; Tao Huan; Duane Rinehart; Markus M Rinschen; Shuzhao Li; Valerie B O'Donnell; Eoin Fahy; Jingchuan Xue; Shankar Subramaniam; H Paul Benton; Gary Siuzdak
Journal:  Anal Sci Adv       Date:  2020-06-13

5.  Non-targeted Lipidomics Using a Robust and Reproducible Lipid Separation Using UPLC with Charged Surface Hybrid Technology and High-Resolution Mass Spectrometry.

Authors:  Giorgis Isaac; Vladimir Shulaev; Robert S Plumb
Journal:  Methods Mol Biol       Date:  2022

6.  Longitudinal Plasma Lipidome and Risk of Type 2 Diabetes in a Large Sample of American Indians With Normal Fasting Glucose: The Strong Heart Family Study.

Authors:  Guanhong Miao; Ying Zhang; Zhiguang Huo; Wenjie Zeng; Jianhui Zhu; Jason G Umans; Gert Wohlgemuth; Diego Pedrosa; Brian DeFelice; Shelley A Cole; Amanda M Fretts; Elisa T Lee; Barbara V Howard; Oliver Fiehn; Jinying Zhao
Journal:  Diabetes Care       Date:  2021-10-26       Impact factor: 19.112

7.  Lipidomics profiling of biological aging in American Indians: the Strong Heart Family Study.

Authors:  Pooja Subedi; Helena Palma-Gudiel; Oliver Fiehn; Lyle G Best; Elisa T Lee; Barbara V Howard; Jinying Zhao
Journal:  Geroscience       Date:  2022-08-11       Impact factor: 7.581

Review 8.  Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets.

Authors:  Dinesh Kumar Barupal; Sili Fan; Oliver Fiehn
Journal:  Curr Opin Biotechnol       Date:  2018-02-06       Impact factor: 9.740

9.  Ultrahigh-Resolution Mass Spectrometry-Based Platform for Plasma Metabolomics Applied to Type 2 Diabetes Research.

Authors:  Yanlong Zhu; Benjamin Wancewicz; Michael Schaid; Timothy N Tiambeng; Kent Wenger; Yutong Jin; Heino Heyman; Christopher J Thompson; Aiko Barsch; Elizabeth D Cox; Dawn B Davis; Allan R Brasier; Michelle E Kimple; Ying Ge
Journal:  J Proteome Res       Date:  2020-10-15       Impact factor: 4.466

10.  A metabolomics pipeline for the mechanistic interrogation of the gut microbiome.

Authors:  Shuo Han; Will Van Treuren; Curt R Fischer; Bryan D Merrill; Brian C DeFelice; Juan M Sanchez; Steven K Higginbottom; Leah Guthrie; Lalla A Fall; Dylan Dodd; Michael A Fischbach; Justin L Sonnenburg
Journal:  Nature       Date:  2021-07-14       Impact factor: 49.962

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