Ross Dylan H, Lee Joon-Yong, Bilbao Aivett, Orton Daniel J, Eder Josie G, Burnet Meagan C, Deatherage Kaiser Brooke L, Kyle Jennifer E, Zheng Xueyun
Pacific Northwest National Laboratory, Richland, WA, 99354, USA.
PrognomiQ, Inc, San Mateo, CA, 94403, USA.
Commun Chem. 2023 Apr 19;6(1):74. doi: 10.1038/s42004-023-00867-9.
Lipids play essential roles in many biological processes and disease pathology, but unambiguous identification of lipids is complicated by the presence of multiple isomeric species differing by fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds. Conventional liquid chromatography-mass spectrometry (LC-MS/MS) analyses enable the determination of fatty acyl chain lengths (and in some cases sn position) and number of double bonds, but not carbon-carbon double bond positions. Ozone-induced dissociation (OzID) is a gas-phase oxidation reaction that produces characteristic fragments from lipids containing double bonds. OzID can be incorporated into ion mobility spectrometry (IMS)-MS instruments for the structural characterization of lipids, including additional isomer separation and confident assignment of double bond positions. The complexity and repetitive nature of OzID data analysis and lack of software tool support have limited the application of OzID for routine lipidomics studies. Here, we present an open-source Python tool, LipidOz, for the automated determination of lipid double bond positions from OzID-IMS-MS data, which employs a combination of traditional automation and deep learning approaches. Our results demonstrate the ability of LipidOz to robustly assign double bond positions for lipid standard mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics.
脂质在许多生物过程和疾病病理学中发挥着重要作用，但由于存在多种因脂肪酰链长度、立体专一编号（sn）位置以及双键位置/立体化学不同的同分异构体，脂质的明确鉴定变得复杂。传统的液相色谱 - 质谱联用（LC - MS/MS）分析能够确定脂肪酰链长度（在某些情况下还能确定sn位置）和双键数量，但无法确定碳 - 碳双键的位置。臭氧诱导解离（OzID）是一种气相氧化反应，能从含有双键的脂质中产生特征性碎片。OzID可整合到离子淌度谱（IMS） - MS仪器中，用于脂质的结构表征，包括额外的异构体分离和双键位置的确切归属。OzID数据分析的复杂性和重复性以及缺乏软件工具支持，限制了OzID在常规脂质组学研究中的应用。在此，我们展示了一种开源的Python工具LipidOz，用于从OzID - IMS - MS数据中自动确定脂质双键位置，该工具采用了传统自动化和深度学习方法相结合的方式。我们的结果表明，LipidOz能够为脂质标准混合物和复杂脂质提取物可靠地确定双键位置，从而使OzID在未来脂质组学中得到实际应用。
