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Optimisation of saliva volumes for lipidomic analysis by nanoflow ultrahigh performance liquid chromatography-tandem mass spectrometry.
- Source :
-
Analytica chimica acta [Anal Chim Acta] 2022 Feb 08; Vol. 1193, pp. 339318. Date of Electronic Publication: 2021 Nov 23. - Publication Year :
- 2022
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Abstract
- Saliva is a readily accessible and clinically useful biofluid that can be used to develop disease biomarkers because of a variety of biologically active molecules in it that are also found in blood. However, even though saliva sampling is simple and non-invasive, few studies have investigated the use of salivary lipids as biomarkers, and the extraction of lipids from saliva needs to be examined thoroughly. In the present study, methods (i.e., saliva sample volume, 0.1-1.0 mL) for the extraction and analysis of salivary lipids by nanoflow ultrahigh performance liquid chromatography-tandem mass spectrometry (nUHPLC-ESI-MS/MS) were evaluated according to the matrix effect, extraction recovery, and number of quantifiable lipids. A total of 780 lipids were identified in a pooled saliva sample from 20 healthy volunteers, and 372 lipids without differentiating acyl chain structures were quantified, along with comprehensive information on salivary lipid composition and individual lipid levels. Even though extraction recovery was maintained at saliva sample volumes as low as 0.2 mL, the matrix effect and limit of detection (LOD) were relatively large with 1.0 mL. Considering the matrix effect, LOD, and number of quantifiable lipids (>limit of quantitation), the minimum volume of saliva sufficient for lipidomic analysis using nUHPLC-ESI-MS/MS was determined to be 0.5 mL.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2021 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1873-4324
- Volume :
- 1193
- Database :
- MEDLINE
- Journal :
- Analytica chimica acta
- Publication Type :
- Academic Journal
- Accession number :
- 35058012
- Full Text :
- https://doi.org/10.1016/j.aca.2021.339318