Literature DB >> 25748542

Isotopologue ratio normalization for non-targeted metabolomics.

Daniel Weindl1, André Wegner1, Christian Jäger1, Karsten Hiller2.   

Abstract

Robust quantification of analytes is a prerequisite for meaningful metabolomics experiments. In non-targeted metabolomics it is still hard to compare measurements across multiple batches or instruments. For targeted analyses isotope dilution mass spectrometry is used to provide a robust normalization reference. Here, we present an approach that allows for the automated semi-quantification of metabolites relative to a fully stable isotope-labeled metabolite extract. Unlike many previous approaches, we include both identified and unidentified compounds in the data analysis. The internal standards are detected in an automated manner using the non-targeted tracer fate detection algorithm. The ratios of the light and heavy form of these compounds serve as a robust measure to compare metabolite levels across different mass spectrometric platforms. As opposed to other methods which require high resolution mass spectrometers, our methodology works with low resolution mass spectrometers as commonly used in gas chromatography electron impact mass spectrometry (GC-EI-MS)-based metabolomics. We demonstrate the validity of our method by analyzing compound levels in different samples and show that it outperforms conventional normalization approaches in terms of intra- and inter-instrument reproducibility. We show that a labeled yeast metabolite extract can also serve as a reference for mammalian metabolite extracts where complete stable isotope labeling is hard to achieve.
Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  IDMS; NTFD; Non-targeted metabolomics; Normalization; Quantification

Mesh:

Substances:

Year:  2015        PMID: 25748542     DOI: 10.1016/j.chroma.2015.02.025

Source DB:  PubMed          Journal:  J Chromatogr A        ISSN: 0021-9673            Impact factor:   4.759


  9 in total

1.  Reference Standardization for Quantification and Harmonization of Large-Scale Metabolomics.

Authors:  Ken H Liu; Mary Nellis; Karan Uppal; Chunyu Ma; ViLinh Tran; Yongliang Liang; Douglas I Walker; Dean P Jones
Journal:  Anal Chem       Date:  2020-06-15       Impact factor: 6.986

Review 2.  Towards quantitative mass spectrometry-based metabolomics in microbial and mammalian systems.

Authors:  Rahul Vijay Kapoore; Seetharaman Vaidyanathan
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2016-10-28       Impact factor: 4.226

3.  Analysis of stable isotope assisted metabolomics data acquired by GC-MS.

Authors:  Xiaoli Wei; Biyun Shi; Imhoi Koo; Xinmin Yin; Pawel Lorkiewicz; Hamid Suhail; Ramandeep Rattan; Shailendra Giri; Craig J McClain; Xiang Zhang
Journal:  Anal Chim Acta       Date:  2017-05-13       Impact factor: 6.558

4.  Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data.

Authors:  Chanisa Thonusin; Heidi B IglayReger; Tanu Soni; Amy E Rothberg; Charles F Burant; Charles R Evans
Journal:  J Chromatogr A       Date:  2017-09-09       Impact factor: 4.759

5.  Isotopic Ratio Outlier Analysis of the S. cerevisiae Metabolome Using Accurate Mass Gas Chromatography/Time-of-Flight Mass Spectrometry: A New Method for Discovery.

Authors:  Yunping Qiu; Robyn Moir; Ian Willis; Chris Beecher; Yu-Hsuan Tsai; Timothy J Garrett; Richard A Yost; Irwin J Kurland
Journal:  Anal Chem       Date:  2016-02-17       Impact factor: 6.986

Review 6.  How close are we to complete annotation of metabolomes?

Authors:  Mark R Viant; Irwin J Kurland; Martin R Jones; Warwick B Dunn
Journal:  Curr Opin Chem Biol       Date:  2017-01-21       Impact factor: 8.822

7.  Bridging the gap between non-targeted stable isotope labeling and metabolic flux analysis.

Authors:  Daniel Weindl; Thekla Cordes; Nadia Battello; Sean C Sapcariu; Xiangyi Dong; Andre Wegner; Karsten Hiller
Journal:  Cancer Metab       Date:  2016-04-23

8.  Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes.

Authors:  Yunping Qiu; Robyn D Moir; Ian M Willis; Suresh Seethapathy; Robert C Biniakewitz; Irwin J Kurland
Journal:  Metabolites       Date:  2018-01-18

Review 9.  Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review.

Authors:  Emma Graham; Jessica Lee; Magda Price; Maja Tarailo-Graovac; Allison Matthews; Udo Engelke; Jeffrey Tang; Leo A J Kluijtmans; Ron A Wevers; Wyeth W Wasserman; Clara D M van Karnebeek; Sara Mostafavi
Journal:  J Inherit Metab Dis       Date:  2018-05-02       Impact factor: 4.982

  9 in total

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