Advances in analytical tools and current statistical methods used in ultra-high-performance liquid chromatography-mass spectrometry of glycero-, glycerophospho- and sphingolipids

• Published in: International journal of mass spectrometry Vol. 457; p. 116408
• Main Authors: Avela, Henri F., Sirén, Heli
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2020
• Subjects: Chemometrics; Lipidomics; Mass spectrometry; Multicomponent analysis; Nano-liquid chromatography; Statistical methods; Ultrahigh performance liquid chromatography; Multicomponent analysis; Lipidomics; Statistical methods; Nano-liquid chromatography; Ultrahigh performance liquid chromatography; Mass spectrometry; Chemometrics
• ISSN: 1387-3806; 1873-2798
• DOI: 10.1016/j.ijms.2020.116408

The review concentrates on the properties of analytical and statistical ultrahigh-performance liquid chromatographic (UHPLC) – mass spectrometric (MS) methods suitable for glycero-, glycerophospho- and sphingolipids in lipidomics published between the years 2017–2019. Trends and fluctuations of conventional and nano-UHPLC methods with MS and tandem MS detection were observed in context of analysis conditions and tools used for data-analysis. Whereas general workflow characteristics are agreed upon, more details related to the chromatographic methodology (i.e. stationary and mobile phase conditions) need evidently agreements. Lipid quantitation relies upon isotope-labelled standards in targeted analyses and fully standardless algorithm-based untargeted analyses. Furthermore, a wide spectrum of setups have shown potential for the elucidation of complex and large datasets by minimizing the risks of systematic misinterpretation like false positives. This kind of evaluation was shown to have increased importance and usage for cross-validation and data-analysis. [Display omitted] •Method development and application enhancements in lipidomics.•The review sums up chemometric and statistical methods for current lipidomics.•State of the art data collection and evaluation is discussed.•Identification/quantitation of biological lipids.•Tandem MS data-independent and data-dependent analysis.