Koelmel Jeremy P, Li Xiangdong, Stow Sarah M, Sartain Mark J, Murali Adithya, Kemperman Robin, Tsugawa Hiroshi, Takahashi Mikiko, Vasiliou Vasilis, Bowden John A, Yost Richard A, Garrett Timothy J, Kitagawa Norton
Department of Pathology, Immunology and Laboratory Medicine, University of Florida, 32610 Gainesville, FL, USA.
Department of Environmental Health Sciences, Yale School of Public Health, 06520 New Haven, CT, USA.
Metabolites. 2020 Mar 12;10(3):101. doi: 10.3390/metabo10030101.
Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.
脂质组学在各种应用中具有巨大潜力；然而，脂质组学的一个主要瓶颈是对高分辨率串联质谱数据进行准确且全面的注释。尽管在过去五年中，可用的脂质组学软件数量急剧增加，但减少假阳性并实现获得结构准确的注释仍然是一项重大挑战。我们推出了脂质注释器（Lipid Annotator），这是一款用户友好型软件，用于对通过液相色谱高分辨率串联质谱（LC-HRMS/MS）收集的数据进行脂质组学分析。我们对照脂质标准品和其他脂质组学软件验证了注释准确性。脂质注释器被整合到一个工作流程中，该流程采用迭代排除MS/MS采集策略，使用反相LC-HRMS/MS对美国国家标准与技术研究院（NIST）的SRM 1950人冷冻血浆中的代谢物进行分析。脂质注释器、LipidMatch和MS-DIAL分别在正模式和负模式下检测到的98%和96%的特征的脂质类别水平上产生了一致的注释。脂质注释器提供了脂肪酰基组成物种的百分比，并采用基于概率论的评分算法，这比现有软件常用的容差和加权匹配分数主观性更低。与之前的脂质组学软件相比，脂质注释器能够分析大量样本队列并提高数据处理通量。
