Detecting and Grouping In-Source Fragments with Low-Energy Stepped HCD, Together with MS(3), Increases Identification Confidence in Untargeted LC-Orbitrap Metabolomics of Plantago lanceolata Leaves and P. ovata Husk

利用低能量阶梯式HCD检测和分组源内碎片,并结合MS(3),可提高车前草叶片和卵叶车前草果壳非靶向LC-Orbitrap代谢组学的鉴定置信度。

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Abstract

Background: Comprehensive and accurate compound composition characterization in natural sources has high relevance in food and nutrition, health and medicine, environmental and agriculture research areas, though profiling of plant metabolites is a challenging task due to the structural complexity of natural products. This study delves into the identification and characterization of compounds within the Plantago genus, leveraging state-of-the-art analytical techniques. Methods: Utilizing an ultra-high-performance liquid chromatography (UHPLC) system in conjunction with Orbitrap™ IQ-X™ Tribrid™ mass spectrometer (MS), we employed a Phenyl-Hexyl HPLC column alongside optimized extraction protocols to analyze both husk and leaf samples. To maximize compound identification, we implemented data-dependent acquisition (DDA) methods including MS(2) (ddMS2), MS(3) (ddMS3), AcquireX™ deep scan, and real-time library search (RTLS). Results: Our results demonstrate a significant increase in the number of putatively yet confidently assigned compounds, with 472 matches in P. lanceolata leaves and 233 in P. ovata husk identified through combined acquisition methods. The inclusion of an additional fragmentation level (MS(3)) noticeably enhanced the confidence in compound annotation, facilitating the differentiation of isomeric compounds. Furthermore, the application of low-energy fragmentation (10 normalized collision energy (NCE) for higher-energy collisional dissociation (HCD)) improved the detection and grouping of MS(1) fragments by 55% in positive mode and by 16% in negative mode, contributing to a more comprehensive analysis with minimal loss in compound identification. Conclusions: These advancements underscore the potential of our methodologies in expanding the chemical profile of plant materials, offering valuable insights into natural product analysis and dereplication of untargeted data.

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