Published January 2021 | Version v1
Journal article

Multi-dimensional characterization and identification of sterols in untargeted LC-MS analysis using all ion fragmentation technology

  • 1. University of Chinese Academy of Sciences, Beijing, 100049 (China)
  • 2. Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032 (China)

Description

Highlights: • A LC-MS based all ion fragmentation (AIF) technology was developed for untargeted analysis of sterols in biological samples. • The integration of multi-dimensional properties supported unambiguous identification of sterols, including distinguishing sterol isomers. • AIF based multi-dimensional analysis provided a possibility to identify sterols without chemical standards and facilitated to discover novel compounds with sterol-like structures. Sterols are an important type of lipids, and play many important roles in physiological and pathological processes. However, comprehensive analysis of sterols especially identification of unknown sterols is challenging. In this work, LC-MS with all ion fragmentation (AIF) technology was developed for untargeted analysis of sterols in biological samples. AIF technology provided holistic and multi-dimensional characterization for both knowns and unknowns sterols, including accurate m/z, isotope pattern, retention time (RT), and co-eluted peak profiles between MS1 and MS2 ions in one analysis. We further developed an analysis strategy by integrating the multi-dimensional properties to support unambiguous identification of sterols, including distinguishing sterol isomers. The developed strategy enabled to identify a total of 23 sterols in mouse samples, and quantified 19 sterols in mouse liver tissues. More importantly, we demonstrated that AIF based multi-dimensional analysis provided a possibility to identify sterols without chemical standards and facilitated to discover novel compounds with sterol-like structures in biological samples. In summary, we employed the LC-MS based AIF technology to develop multi-dimensional characterization and identification of both known and unknown sterols in complex biological samples. The comprehensive analysis of sterols facilitates to provide molecular insights to many physiological and pathological activities in biology.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.aca.2020.10.058

Additional details

Identifiers

DOI
10.1016/j.aca.2020.10.058;
PII
S0003267020310904;

Publishing Information

Journal Title
Analytica Chimica Acta
Journal Volume
1142
Journal Page Range
p. 108-117
ISSN
0003-2670
CODEN
ACACAM

INIS

Country of Publication
Netherlands
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
53101258
Subject category
S60: APPLIED LIFE SCIENCES; S37: INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL CHEMISTRY;
Descriptors DEI
ANIMAL TISSUES; BIOLOGICAL MATERIALS; ISOMERS; ISOTOPES; LIPIDS; LIVER; MANY-DIMENSIONAL CALCULATIONS; MICE; STEROLS
Descriptors DEC
ANIMALS; BODY; DIGESTIVE SYSTEM; GLANDS; HYDROXY COMPOUNDS; MAMMALS; MATERIALS; ORGANIC COMPOUNDS; ORGANS; RODENTS; STEROIDS; VERTEBRATES

Optional Information

Copyright
Copyright (c) 2020 Elsevier B.V. All rights reserved.