Literature DB >> 28391692

Customized Consensus Spectral Library Building for Untargeted Quantitative Metabolomics Analysis with Data Independent Acquisition Mass Spectrometry and MetaboDIA Workflow.

Gengbo Chen1, Scott Walmsley2,3, Gemmy C M Cheung4,5, Liyan Chen6, Ching-Yu Cheng5,6,7, Roger W Beuerman5,6,7,8, Tien Yin Wong4,5,6,7, Lei Zhou5,6,7, Hyungwon Choi1,9.   

Abstract

Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis.

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Year:  2017        PMID: 28391692     DOI: 10.1021/acs.analchem.6b05006

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


  11 in total

1.  High-resolution MS/MS metabolomics by data-independent acquisition reveals urinary metabolic alteration in experimental colitis.

Authors:  Zhixiang Yan; Ting Li; Bin Wei; Panpan Wang; Jianbo Wan; Yitao Wang; Ru Yan
Journal:  Metabolomics       Date:  2019-04-30       Impact factor: 4.290

2.  MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data.

Authors:  Konstantinos Tzanakis; Tim W Nattkemper; Karsten Niehaus; Stefan P Albaum
Journal:  BMC Bioinformatics       Date:  2022-07-08       Impact factor: 3.307

3.  Applications of Chromatography-Ultra High-Resolution MS for Stable Isotope-Resolved Metabolomics (SIRM) Reconstruction of Metabolic Networks.

Authors:  Qiushi Sun; Teresa W-M Fan; Andrew N Lane; Richard M Higashi
Journal:  Trends Analyt Chem       Date:  2019-10-01       Impact factor: 12.296

4.  Comparative Evaluation of Data Dependent and Data Independent Acquisition Workflows Implemented on an Orbitrap Fusion for Untargeted Metabolomics.

Authors:  Pierre Barbier Saint Hilaire; Kathleen Rousseau; Alexandre Seyer; Sylvain Dechaumet; Annelaure Damont; Christophe Junot; François Fenaille
Journal:  Metabolites       Date:  2020-04-18

5.  A Perspective and Framework for Developing Sample Type Specific Databases for LC/MS-Based Clinical Metabolomics.

Authors:  Nichole A Reisdorph; Scott Walmsley; Rick Reisdorph
Journal:  Metabolites       Date:  2019-12-21

6.  A serum metabolomics study of patients with nAMD in response to anti-VEGF therapy.

Authors:  Yan Gao; Yi Chong Kelvin Teo; Roger W Beuerman; Tien Yin Wong; Lei Zhou; Chui Ming Gemmy Cheung
Journal:  Sci Rep       Date:  2020-01-28       Impact factor: 4.379

7.  Plasma Metabolome and Lipidome Associations with Type 2 Diabetes and Diabetic Nephropathy.

Authors:  Yan Ming Tan; Yan Gao; Guoshou Teo; Hiromi W L Koh; E Shyong Tai; Chin Meng Khoo; Kwok Pui Choi; Lei Zhou; Hyungwon Choi
Journal:  Metabolites       Date:  2021-04-08

8.  Plasma Metabolomics of Intermediate and Neovascular Age-Related Macular Degeneration Patients.

Authors:  Sabrina L Mitchell; Chunyu Ma; William K Scott; Anita Agarwal; Margaret A Pericak-Vance; Jonathan L Haines; Dean P Jones; Karan Uppal; Milam A Brantley
Journal:  Cells       Date:  2021-11-12       Impact factor: 6.600

Review 9.  Recent advances in analytical strategies for mass spectrometry-based lipidomics.

Authors:  Tianrun Xu; Chunxiu Hu; Qiuhui Xuan; Guowang Xu
Journal:  Anal Chim Acta       Date:  2020-09-30       Impact factor: 6.558

10.  Association of Human Plasma Metabolomics with Delayed Dark Adaptation in Age-Related Macular Degeneration.

Authors:  Kevin M Mendez; Janice Kim; Inês Laíns; Archana Nigalye; Raviv Katz; Shrinivas Pundik; Ivana K Kim; Liming Liang; Demetrios G Vavvas; John B Miller; Joan W Miller; Jessica A Lasky-Su; Deeba Husain
Journal:  Metabolites       Date:  2021-03-21
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