Functional Genomics Center Zurich, University of Zurich/ETH Zurich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland.
SIB Swiss Institute of Bioinformatics, Quartier Sorge-Batiment Amphipole, 1015 Lausanne, Switzerland.
Molecules. 2020 Sep 12;25(18):4189. doi: 10.3390/molecules25184189.
Non-target screening (NTS) based on the combination of liquid chromatography coupled to high-resolution mass spectrometry has become the key method to identify organic micro-pollutants (OMPs) in water samples. However, a large number of compounds remains unidentified with current NTS approaches due to poor quality fragmentation spectra generated by suboptimal fragmentation methods. Here, the potential of the alternative fragmentation technique ultraviolet photodissociation (UVPD) to improve identification of OMPs in water samples was investigated. A diverse set of water-relevant OMPs was selected based on k-means clustering and unsupervised artificial neural networks. The selected OMPs were analyzed using an Orbitrap Fusion Lumos equipped with UVPD. Therewith, information-rich MS2 fragmentation spectra of compounds that fragment poorly with higher-energy collisional dissociation (HCD) could be attained. Development of an R-based data analysis workflow and user interface facilitated the characterization and comparison of HCD and UVPD fragmentation patterns. UVPD and HCD generated both unique and common fragments, demonstrating that some fragmentation pathways are specific to the respective fragmentation method, while others seem more generic. Application of UVPD fragmentation to the analysis of surface water enabled OMP identification using existing HCD spectral libraries. However, high-throughput applications still require optimization of informatics workflows and spectral libraries tailored to UVPD.
基于液相色谱与高分辨质谱联用的非靶向筛选（NTS）已成为鉴定水样中有机微量污染物（OMPs）的关键方法。然而，由于不理想的碎片化方法产生的碎片化谱质量较差，当前的 NTS 方法仍有大量化合物无法识别。在此，研究了替代碎片化技术——紫外光解离（UVPD）提高水样中 OMPs 鉴定能力的潜力。基于 K-均值聚类和无监督人工神经网络，选择了一组基于水的多样化 OMPs。使用配备有 UVPD 的 Orbitrap Fusion Lumos 对所选 OMPs 进行分析。由此，可以获得与高能碰撞解离（HCD）碎片化效果差的化合物具有丰富信息的 MS2 碎片化谱。基于 R 的数据分析工作流程和用户界面的开发促进了 HCD 和 UVPD 碎片化模式的特征描述和比较。UVPD 和 HCD 产生了独特和共同的片段，这表明一些碎片化途径是特定于各自的碎片化方法的，而另一些则似乎更为通用。将 UVPD 碎片化应用于地表水分析，可以使用现有的 HCD 光谱库来识别 OMPs。然而，高通量应用仍需要对信息学工作流程和针对 UVPD 的光谱库进行优化。
