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大型临床队列的神经鞘脂组学分析。第 1 部分:技术说明和实际考虑。

Sphingolipidomics analysis of large clinical cohorts. Part 1: Technical notes and practical considerations.

机构信息

Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore.

Singapore Lipidomics Incubator (SLING), Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

出版信息

Biochem Biophys Res Commun. 2018 Oct 7;504(3):596-601. doi: 10.1016/j.bbrc.2018.04.076. Epub 2018 Apr 19.

Abstract

Lipids comprise an exceptionally diverse class of bioactive macromolecules. While quantitatively abundant lipid species serve fundamental roles in cell structure and energy metabolism, thousands of structurally-distinct, quantitatively minor species may serve as important regulators of cellular processes. Historically, a complete understanding of the biological roles of these lipids has been limited by a lack of sensitive, discriminating analytical techniques. The class of sphingolipids alone, for example, is known to consist of over 600 different confirmed species, but is likely to include tens of thousands of metabolites with potential biological significance. Advances in mass spectrometry (MS) have improved the throughput and discrimination of lipid analysis, allowing for the determination of detailed lipid profiles in large cohorts of clinical samples. Databases emerging from these studies will provide a rich resource for the identification of novel biomarkers and for the discovery of potential drug targets, analogous to that of existing genomics databases. In this review, we will provide an overview of the field of sphingolipidomics, and will discuss some of the challenges and considerations facing the generation of robust lipidomics databases.

摘要

脂质是一类具有特殊多样性的生物活性大分子。虽然数量丰富的脂质种类对细胞结构和能量代谢起着基本作用,但数以千计的结构不同、数量较少的种类可能是细胞过程的重要调节剂。从历史上看,由于缺乏敏感、有区别的分析技术,这些脂质的生物学作用的全面理解一直受到限制。例如,仅鞘脂类就已知由 600 多种不同的确认物种组成,但可能还包括数以万计具有潜在生物学意义的代谢物。质谱 (MS) 的进步提高了脂质分析的通量和辨别力,使得能够在大量临床样本中确定详细的脂质图谱。这些研究中出现的数据库将为鉴定新型生物标志物和发现潜在的药物靶点提供丰富的资源,类似于现有的基因组学数据库。在这篇综述中,我们将概述鞘脂组学领域,并讨论生成稳健的脂质组学数据库所面临的一些挑战和考虑因素。

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