Literature DB >> 26292625

Retention projection enables accurate calculation of liquid chromatographic retention times across labs and methods.

Daniel Abate-Pella1, Dana M Freund2, Yan Ma3, Yamil Simón-Manso4, Juliane Hollender5, Corey D Broeckling6, David V Huhman7, Oleg V Krokhin8, Dwight R Stoll9, Adrian D Hegeman10, Tobias Kind11, Oliver Fiehn12, Emma L Schymanski13, Jessica E Prenni14, Lloyd W Sumner15, Paul G Boswell16.   

Abstract

Identification of small molecules by liquid chromatography-mass spectrometry (LC-MS) can be greatly improved if the chromatographic retention information is used along with mass spectral information to narrow down the lists of candidates. Linear retention indexing remains the standard for sharing retention data across labs, but it is unreliable because it cannot properly account for differences in the experimental conditions used by various labs, even when the differences are relatively small and unintentional. On the other hand, an approach called "retention projection" properly accounts for many intentional differences in experimental conditions, and when combined with a "back-calculation" methodology described recently, it also accounts for unintentional differences. In this study, the accuracy of this methodology is compared with linear retention indexing across eight different labs. When each lab ran a test mixture under a range of multi-segment gradients and flow rates they selected independently, retention projections averaged 22-fold more accurate for uncharged compounds because they properly accounted for these intentional differences, which were more pronounced in steep gradients. When each lab ran the test mixture under nominally the same conditions, which is the ideal situation to reproduce linear retention indices, retention projections still averaged 2-fold more accurate because they properly accounted for many unintentional differences between the LC systems. To the best of our knowledge, this is the most successful study to date aiming to calculate (or even just to reproduce) LC gradient retention across labs, and it is the only study in which retention was reliably calculated under various multi-segment gradients and flow rates chosen independently by labs.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Liquid chromatography–mass spectrometry; Multi-laboratory study; Retention database; Retention library; Retention prediction; Retention projection

Mesh:

Year:  2015        PMID: 26292625      PMCID: PMC4556278          DOI: 10.1016/j.chroma.2015.07.108

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


  26 in total

Review 1.  Metabolomics--the link between genotypes and phenotypes.

Authors:  Oliver Fiehn
Journal:  Plant Mol Biol       Date:  2002-01       Impact factor: 4.076

Review 2.  The hydrophobic-subtraction model of reversed-phase column selectivity.

Authors:  L R Snyder; J W Dolan; P W Carr
Journal:  J Chromatogr A       Date:  2004-12-10       Impact factor: 4.759

3.  Practical assessment of frictional heating effects and thermostat design on the performance of conventional (3 microm and 5 microm) columns in reversed-phase high-performance liquid chromatography.

Authors:  Morgane M Fallas; Mark R Hadley; David V McCalley
Journal:  J Chromatogr A       Date:  2009-03-13       Impact factor: 4.759

Review 4.  Strategies for the determination of the volume and composition of the stationary phase in reversed-phase liquid chromatography.

Authors:  Mei Wang; Jennifer Mallette; Jon F Parcher
Journal:  J Chromatogr A       Date:  2008-03-06       Impact factor: 4.759

5.  jmzML, an open-source Java API for mzML, the PSI standard for MS data.

Authors:  Richard G Côté; Florian Reisinger; Lennart Martens
Journal:  Proteomics       Date:  2010-04       Impact factor: 3.984

6.  Retention time prediction for dereplication of natural products (CxHyOz) in LC-MS metabolite profiling.

Authors:  Philippe J Eugster; Julien Boccard; Benjamin Debrus; Lise Bréant; Jean-Luc Wolfender; Sophie Martel; Pierre-Alain Carrupt
Journal:  Phytochemistry       Date:  2014-12       Impact factor: 4.072

7.  Calculation of retention time tolerance windows with absolute confidence from shared liquid chromatographic retention data.

Authors:  Paul G Boswell; Daniel Abate-Pella; Joshua T Hewitt
Journal:  J Chromatogr A       Date:  2015-08-01       Impact factor: 4.759

8.  Standardized high-performance liquid chromatography of 182 mycotoxins and other fungal metabolites based on alkylphenone retention indices and UV-VIS spectra (diode array detection).

Authors:  J C Frisvad; U Thrane
Journal:  J Chromatogr       Date:  1987-08-28

9.  Corrected retention indices in HPLC: their use for the identification of acidic and neutral drugs.

Authors:  M Bogusz; R Aderjan
Journal:  J Anal Toxicol       Date:  1988 Mar-Apr       Impact factor: 3.367

10.  Reversed-phase high-performance liquid chromatographic determination of taxol in mouse plasma.

Authors:  A Sharma; W D Conway; R M Straubinger
Journal:  J Chromatogr B Biomed Appl       Date:  1994-05-13
View more
  15 in total

1.  Warpgroup: increased precision of metabolomic data processing by consensus integration bound analysis.

Authors:  Nathaniel G Mahieu; Jonathan L Spalding; Gary J Patti
Journal:  Bioinformatics       Date:  2015-09-30       Impact factor: 6.937

2.  Mass Spectrometry Fingerprints of Small-Molecule Metabolites in Biofluids: Building a Spectral Library of Recurrent Spectra for Urine Analysis.

Authors:  Yamil Simón-Manso; Ramesh Marupaka; Xinjian Yan; Yuxue Liang; Kelly H Telu; Yuri Mirokhin; Stephen E Stein
Journal:  Anal Chem       Date:  2019-08-30       Impact factor: 6.986

3.  Calculation of retention time tolerance windows with absolute confidence from shared liquid chromatographic retention data.

Authors:  Paul G Boswell; Daniel Abate-Pella; Joshua T Hewitt
Journal:  J Chromatogr A       Date:  2015-08-01       Impact factor: 4.759

4.  A comparison of three liquid chromatography (LC) retention time prediction models.

Authors:  Andrew D McEachran; Kamel Mansouri; Seth R Newton; Brandiese E J Beverly; Jon R Sobus; Antony J Williams
Journal:  Talanta       Date:  2018-01-11       Impact factor: 6.057

5.  Integrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures.

Authors:  Leah Chibwe; Ivan A Titaley; Eunha Hoh; Staci L Massey Simonich
Journal:  Environ Sci Technol Lett       Date:  2017-01-04

6.  Accurate prediction of retention in hydrophilic interaction chromatography by back calculation of high pressure liquid chromatography gradient profiles.

Authors:  Nu Wang; Paul G Boswell
Journal:  J Chromatogr A       Date:  2017-08-26       Impact factor: 4.759

7.  High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra.

Authors:  Erandika Karunaratne; Dennis W Hill; Philipp Pracht; José A Gascón; Stefan Grimme; David F Grant
Journal:  Anal Chem       Date:  2021-07-21       Impact factor: 8.008

Review 8.  Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems.

Authors:  Daniel A Dias; Oliver A H Jones; David J Beale; Berin A Boughton; Devin Benheim; Konstantinos A Kouremenos; Jean-Luc Wolfender; David S Wishart
Journal:  Metabolites       Date:  2016-12-15

Review 9.  The metabolome 18 years on: a concept comes of age.

Authors:  Douglas B Kell; Stephen G Oliver
Journal:  Metabolomics       Date:  2016-09-02       Impact factor: 4.290

10.  The WEIZMASS spectral library for high-confidence metabolite identification.

Authors:  Nir Shahaf; Ilana Rogachev; Uwe Heinig; Sagit Meir; Sergey Malitsky; Maor Battat; Hilary Wyner; Shuning Zheng; Ron Wehrens; Asaph Aharoni
Journal:  Nat Commun       Date:  2016-08-30       Impact factor: 14.919

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.