Literature DB >> 21862024

A study on retention "projection" as a supplementary means for compound identification by liquid chromatography-mass spectrometry capable of predicting retention with different gradients, flow rates, and instruments.

Paul G Boswell1, Jonathan R Schellenberg, Peter W Carr, Jerry D Cohen, Adrian D Hegeman.   

Abstract

Using current data analysis techniques, even the most advanced LC-MS instrumentation can identify only a small fraction of compounds found in typical biological extracts. Augmenting MS information with HPLC retention information allows many more to be identified. In fact, our calculations indicate that a quadrupole MS is able to identify more compounds than an FTICR-MS when the quadrupole spectrum is augmented with retention information. Unfortunately, retention information is extremely difficult to harness for compound identification. Here, we demonstrate the first use of isocratic data measured on one LC-MS to "project" gradient retention on to different LC-MS systems. Using 35 chemically diverse solutes chosen to encompass the full range of reversed-phase alkylsilica interactions, and using experimental conditions typical of metabolomics experiments, gradient retention was projected from one instrument to another with only 1.2-2.6% error-enough accuracy to considerably improve compound identification. Besides accounting for nonlinear relationships of retention versus solvent composition as well as dead time versus solvent composition, accounting for the precise shape of the gradient profile (not just the dwell volume) improved projection accuracy on one instrument by up to 4 fold whereas flow rate non-idealities likely caused considerable error on the other instrument. Thus, these two factors must be taken into account to accurately project retention on diverse instrumentation.
Copyright © 2011 Elsevier B.V. All rights reserved.

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Year:  2011        PMID: 21862024     DOI: 10.1016/j.chroma.2011.07.105

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


  11 in total

1.  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

2.  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

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

Authors:  Daniel Abate-Pella; Dana M Freund; Yan Ma; Yamil Simón-Manso; Juliane Hollender; Corey D Broeckling; David V Huhman; Oleg V Krokhin; Dwight R Stoll; Adrian D Hegeman; Tobias Kind; Oliver Fiehn; Emma L Schymanski; Jessica E Prenni; Lloyd W Sumner; Paul G Boswell
Journal:  J Chromatogr A       Date:  2015-08-03       Impact factor: 4.759

4.  "Measure Your Gradient": a new way to measure gradients in high performance liquid chromatography by mass spectrometric or absorbance detection.

Authors:  Megan H Magee; Joseph C Manulik; Brian B Barnes; Daniel Abate-Pella; Joshua T Hewitt; Paul G Boswell
Journal:  J Chromatogr A       Date:  2014-10-08       Impact factor: 4.759

5.  Statistical analysis of isocratic chromatographic data using Bayesian modeling.

Authors:  Agnieszka Kamedulska; Łukasz Kubik; Paweł Wiczling
Journal:  Anal Bioanal Chem       Date:  2022-03-28       Impact factor: 4.478

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.  Easy and accurate calculation of programmed temperature gas chromatographic retention times by back-calculation of temperature and hold-up time profiles.

Authors:  Paul G Boswell; Peter W Carr; Jerry D Cohen; Adrian D Hegeman
Journal:  J Chromatogr A       Date:  2012-09-23       Impact factor: 4.759

8.  Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics.

Authors:  Mingshu Cao; Karl Fraser; Jan Huege; Tom Featonby; Susanne Rasmussen; Chris Jones
Journal:  Metabolomics       Date:  2014-09-07       Impact factor: 4.290

Review 9.  Bioinformatics: the next frontier of metabolomics.

Authors:  Caroline H Johnson; Julijana Ivanisevic; H Paul Benton; Gary Siuzdak
Journal:  Anal Chem       Date:  2014-11-20       Impact factor: 6.986

Review 10.  From Ocean to Medicine: Pharmaceutical Applications of Metabolites from Marine Bacteria.

Authors:  José Diogo Santos; Inês Vitorino; Fernando Reyes; Francisca Vicente; Olga Maria Lage
Journal:  Antibiotics (Basel)       Date:  2020-07-28
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