Rapid profiling method for the analysis of lipids in human plasma using ion mobility enabled-reversed phase-ultra high performance liquid chromatography/mass spectrometry
File(s)
Supporting information
Accepted version
Author(s)
Type
Journal Article
Abstract
The incorporation of ion mobility (IM) into LC–MS analysis has been demonstrated to result in the generation of superior quality MS and MS/MS spectral data as well as providing enhanced resolution in the IM dimension based on lipid class. Here a sub 4 min microbore LC-ion mobility-accurate mass MS (LC-IM-MS) method has been developed for the rapid, profiling of lipids in biological fluids. The method was scaled directly from a conventional, 12 min, LC-MS analysis maintaining the chromatographic performance and lipid separation observed in the longer methodology giving a 75% saving in mobile phase consumption and analysis time. Because of the additional dimension of separation provided by IM, improvements in mass spectral quality from the increased resolution of co-eluting species were also seen when compared to the same separation without IM, thus aiding the identification of target lipids. When applied to human plasma samples some 5037 (positive ESI) and 2020 (negative ESI) mass/retention time features were detected following adduct deconvolution and, of these, 3727 and 800 of those present in the pooled plasma QC samples had a CV of below 30% for positive and negative ESI modes respectively. The method was applied to the analysis of a pilot set of commercially sourced breast cancer plasma samples enabling the differentiation of samples from healthy controls and patients based on their lipid phenotypes. Analysis of the resulting data showed that phosphatidylcholines, triglycerides and diglycerides exhibited lower expression and phosphatidylserine showed increased expression in the breast cancer samples compared to those of healthy subjects. The coefficients of variation, determined by reference to the QC data, for all of the features identified as potential markers of disease, were 6% or less.
Date Issued
2020-01-25
Date Acceptance
2019-10-03
Citation
Journal of Chromatography A, 2020, 1611, pp.1-10
ISSN
0021-9673
Publisher
Elsevier
Start Page
1
End Page
10
Journal / Book Title
Journal of Chromatography A
Volume
1611
Copyright Statement
© 2019 Elsevier B.V. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000510312700015&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Biochemical Research Methods
Chemistry, Analytical
Biochemistry & Molecular Biology
Chemistry
Cancer
Lipidomics
Liquid chromatography
Mass spectrometry
Ion mobility spectrometry
COLLISION CROSS-SECTION
MASS-SPECTROMETRY
HIGH-THROUGHPUT
LIPIDOMICS
OPTIMIZATION
METABOLOMICS
CHALLENGES
DATABASE
REVEALS
BRAIN
Publication Status
Published
Article Number
ARTN 460597
Date Publish Online
2019-10-04