Saito Kosuke, Ohno Yasuo, Saito Yoshiro
Division of Medical Safety Science, National Institute of Health Sciences, Setagaya, Tokyo, Japan.
Kihara Memorial Yokohama Foundation for the Advancement of Life Sciences, Yokohama, Kanagawa, Japan.
J Chromatogr B Analyt Technol Biomed Life Sci. 2017 Jun 15;1055-1056:20-28. doi: 10.1016/j.jchromb.2017.04.019. Epub 2017 Apr 13.
In this study, we delineated the importance of MS resolving power on the ion-peak quantification of lipids using an Orbitrap Fusion instrument and established a liquid chromatography-based, high-performance lipidomics platform. The ion-peak recognition of several lipids in human plasma, such as LPC(15:0), LPE(22:5), and PC(35:0), was clearly improved by increasing the MS resolving power. In addition, we evaluated the impact of resolving power on the quantitative detection of lipids by automatic ion-peak recognition with calculation of the coefficient of variance (CV). The extracted ions obtained from human plasma were automatically annotated by Compound Discoverer software with manual confirmation of standards or MS/MS fragments (class- and acyl side chain-specific ions and neutral losses). Quantitative evaluation of 499 lipids in human plasma in terms of their CV values clearly demonstrated an improvement in the quantitative performance by enriching the resolving power. Moreover, we evaluated our new lipidomics platform with enriched MS resolving power (setting of 240,000, full width at half maximum at m/z 200). Because automatic annotation by TraceFinder software overlooks several lipid ions, we further manually annotated additional lipid ions, which were confirmed by standards or MS/MS fragments. Eventually, our platform detected 967 lipids encompassing 34 lipid classes, which were confirmed with standards or MS/MS fragments. Of these lipids, 922 scored <20% of the CV values. Taken together, enriching the resolving power improved ion-peak quantification on our novel lipidomics platform, which enabled us to detect broad-spectrum lipids from human plasma.
在本研究中,我们阐述了使用Orbitrap Fusion仪器时质谱分辨率对脂质离子峰定量的重要性,并建立了基于液相色谱的高性能脂质组学平台。通过提高质谱分辨率,人血浆中几种脂质(如LPC(15:0)、LPE(22:5)和PC(35:0))的离子峰识别得到了明显改善。此外,我们通过计算变异系数(CV),评估了分辨率对脂质自动离子峰识别定量检测的影响。从人血浆中提取的离子由Compound Discoverer软件自动注释,并通过标准品或MS/MS碎片(类别和酰基侧链特异性离子及中性丢失)进行人工确认。根据CV值对人血浆中499种脂质进行定量评估,结果清楚地表明,提高分辨率可改善定量性能。此外,我们用提高了质谱分辨率(设定为240,000,m/z 200处半高宽)的新脂质组学平台进行了评估。由于TraceFinder软件的自动注释会忽略一些脂质离子,我们进一步人工注释了其他脂质离子,这些离子通过标准品或MS/MS碎片得到了确认。最终,我们的平台检测到967种脂质,涵盖34个脂质类别,这些脂质通过标准品或MS/MS碎片得到了确认。在这些脂质中,922种的CV值小于20%。综上所述,提高分辨率改善了我们新型脂质组学平台上的离子峰定量,使我们能够从人血浆中检测到广谱脂质。