Witting Michael, Salzer Liesa, Meyer Sven W, Barsch Aiko
Metabolomics and Proteomics Core, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 2, 85354, Freising, Germany.
Metabolomics. 2025 Feb 20;21(2):29. doi: 10.1007/s11306-024-02216-w.
The identification of lipids is a cornerstone of lipidomics, and due to the specific characteristics of lipids, it requires dedicated analysis workflows. Identifying novel lipids and lipid species for which no reference spectra are available is tedious and often involves a lot of manual work. Integrating high-resolution mass spectrometry with enhancements from chromatographic and ion mobility separation enables the in-depth investigation of intact lipids.
We investigated phosphorylated glycosphingolipids from the nematode Caenorhabditis elegans, a biomedical model organism, and aimed to identify different species from this class of lipids, which have been described in one particular publication only. We checked if these lipids can be detected in lipid extracts of C. elegans.
We used UHPLC-UHR-TOF-MS and UHPLC-TIMS-TOF-MS in combination with dedicated data analysis to check for the presence of phosphorylated glycosphingolipids. Specifically, candidate features were identified in two datasets using Mass Spec Query Language (MassQL) to search fragmentation data. The additional use of retention time (RT) and collisional cross section (CCS) information allowed to filter false positive annotations.
As a result, we detected all previously described phosphorylated glycosphingolipids and novel species as well as their biosynthetic precursors in two different lipidomics datasets. MassQL significantly speeds up the process by saving time that would otherwise be spent on manual data investigations. In total over 20 sphingolipids could be described.
MassQL allowed us to search for phosphorylated glycosphingolipids and their potential biosynthetic precursors systematically. Using orthogonal information such as RT and CCS helped filter false positive results. With the detection in two different datasets, we demonstrate that these sphingolipids are a general part of the C. elegans lipidome.
脂质鉴定是脂质组学的基石，由于脂质具有特定特性，因此需要专门的分析流程。鉴定没有参考光谱的新型脂质和脂质种类既繁琐又常常涉及大量人工操作。将高分辨率质谱与色谱和离子淌度分离增强技术相结合，能够对完整脂质进行深入研究。
我们研究了生物医学模式生物秀丽隐杆线虫中的磷酸化糖鞘脂，旨在鉴定这类脂质中的不同种类，此前仅有一篇特定文献对此进行过描述。我们检查了这些脂质是否能在秀丽隐杆线虫的脂质提取物中被检测到。
我们使用超高效液相色谱 - 超高分辨飞行时间质谱（UHPLC - UHR - TOF - MS）和超高效液相色谱 - 捕集离子淌度飞行时间质谱（UHPLC - TIMS - TOF - MS）并结合专用数据分析来检查磷酸化糖鞘脂的存在情况。具体而言，使用质谱查询语言（MassQL）在两个数据集中识别候选特征以搜索碎片数据。保留时间（RT）和碰撞截面（CCS）信息的额外使用有助于过滤假阳性注释。
结果，我们在两个不同的脂质组学数据集中检测到了所有先前描述的磷酸化糖鞘脂、新种类及其生物合成前体。MassQL通过节省原本会花在人工数据研究上的时间，显著加快了这一过程。总共可以描述20多种鞘脂。
MassQL使我们能够系统地搜索磷酸化糖鞘脂及其潜在的生物合成前体。使用诸如RT和CCS等正交信息有助于过滤假阳性结果。通过在两个不同数据集中的检测，我们证明这些鞘脂是秀丽隐杆线虫脂质组的常见组成部分。
