Nijssen Rosalie, Blokland Marco H, Wegh Robin S, de Lange Erik, van Leeuwen Stefan P J, Berendsen Bjorn J A, van de Schans Milou G M
Wageningen Food Safety Research, Part of Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands.
Metabolites. 2023 Jun 21;13(7):777. doi: 10.3390/metabo13070777.
Liquid chromatography combined with high-resolution mass spectrometry (LC-HRMS) is a frequently applied technique for suspect screening (SS) and non-target screening (NTS) in metabolomics and environmental toxicology. However, correctly identifying compounds based on SS or NTS approaches remains challenging, especially when using data-independent acquisition (DIA). This study assessed the performance of four HRMS-spectra identification tools to annotate in-house generated data-dependent acquisition (DDA) and DIA HRMS spectra of 32 pesticides, veterinary drugs, and their metabolites. The identification tools were challenged with a diversity of compounds, including isomeric compounds. The identification power was evaluated in solvent standards and spiked feed extract. In DDA spectra, the mass spectral library mzCloud provided the highest success rate, with 84% and 88% of the compounds correctly identified in the top three in solvent standard and spiked feed extract, respectively. The in silico tools MSfinder, CFM-ID, and Chemdistiller also performed well in DDA data, with identification success rates above 75% for both solvent standard and spiked feed extract. MSfinder provided the highest identification success rates using DIA spectra with 72% and 75% (solvent standard and spiked feed extract, respectively), and CFM-ID performed almost similarly in solvent standard and slightly less in spiked feed extract (72% and 63%). The identification success rates for Chemdistiller (66% and 38%) and mzCloud (66% and 31%) were lower, especially in spiked feed extract. The difference in success rates between DDA and DIA is most likely caused by the higher complexity of the DIA spectra, making direct spectral matching more complex. However, this study demonstrates that DIA spectra can be used for compound annotation in certain software tools, although the success rate is lower than for DDA spectra.
液相色谱与高分辨率质谱联用（LC-HRMS）是代谢组学和环境毒理学中常用于可疑物筛查（SS）和非目标物筛查（NTS）的技术。然而，基于SS或NTS方法正确鉴定化合物仍然具有挑战性，尤其是在使用数据非依赖采集（DIA）时。本研究评估了四种高分辨率质谱光谱鉴定工具对32种农药、兽药及其代谢物的内部生成的依赖数据采集（DDA）和DIA高分辨率质谱光谱进行注释的性能。这些鉴定工具面临着多种化合物的挑战，包括同分异构体化合物。在溶剂标准品和加标饲料提取物中评估了鉴定能力。在DDA光谱中，质谱图库mzCloud的成功率最高，在溶剂标准品和加标饲料提取物中，分别有84%和88%的化合物在前三位中被正确鉴定。计算机工具MSfinder、CFM-ID和Chemdistiller在DDA数据中也表现良好，溶剂标准品和加标饲料提取物的鉴定成功率均高于75%。MSfinder使用DIA光谱时的鉴定成功率最高，分别为72%和75%（溶剂标准品和加标饲料提取物），CFM-ID在溶剂标准品中的表现几乎相同，在加标饲料提取物中的表现略低（72%和63%）。Chemdistiller（66%和38%）和mzCloud（66%和31%）的鉴定成功率较低，尤其是在加标饲料提取物中。DDA和DIA之间成功率的差异很可能是由于DIA光谱的复杂性较高，使得直接光谱匹配更加复杂。然而，本研究表明，尽管成功率低于DDA光谱，但DIA光谱可用于某些软件工具中的化合物注释。
