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Multi-dimensional Characterization and Identification of Sterols in Untargeted LC-MS Analysis Using All Ion Fragmentation Technology
Analytica Chimica Acta ( IF 5.7 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.aca.2020.10.058
Jiaqian Qiu , Tongzhou Li , Zheng-Jiang Zhu

Sterols are an important type of lipids, and play many important roles in physiological and pathological processes. However, comprehensive analysis of sterols especially identification of unknown sterols is challenging. In this work, LC-MS with all ion fragmentation (AIF) technology was developed for untargeted analysis of sterols in biological samples. AIF technology provided holistic and multi-dimensional characterization for both knowns and unknowns sterols, including accurate m/z, isotope pattern, retention time (RT), and co-eluted peak profiles between MS1 and MS2 ions in one analysis. We further developed an analysis strategy by integrating the multi-dimensional properties to support unambiguous identification of sterols, including distinguishing sterol isomers. The developed strategy enabled to identify a total of 23 sterols in mouse samples, and quantified 19 sterols in mouse liver tissues. More importantly, we demonstrated that AIF based multi-dimensional analysis provided a possibility to identify sterols without chemical standards and facilitated to discover novel compounds with sterol-like structures in biological samples. In summary, we employed the LC-MS based AIF technology to develop multi-dimensional characterization and identification of both known and unknown sterols in complex biological samples. The comprehensive analysis of sterols facilitates to provide molecular insights to many physiological and pathological activities in biology.

中文翻译:

使用全离子裂解技术的非靶向 LC-MS 分析中甾醇的多维表征和鉴定

甾醇是一类重要的脂质,在生理和病理过程中起着许多重要的作用。然而,甾醇的综合分析尤其是未知甾醇的鉴定具有挑战性。在这项工作中,采用全离子碎裂 (AIF) 技术的 LC-MS 被开发用于生物样品中甾醇的非靶向分析。AIF 技术为已知和未知甾醇提供了整体和多维表征,包括准确的 m/z、同位素模式、保留时间 (RT) 以及一次分析中 MS1 和 MS2 离子之间的共洗脱峰图。我们通过整合多维特性进一步开发了一种分析策略,以支持甾醇的明确鉴定,包括区分甾醇异构体。所开发的策略能够在小鼠样本中鉴定总共 23 种甾醇,并量化小鼠肝组织中的 19 种甾醇。更重要的是,我们证明基于 AIF 的多维分析提供了无需化学标准即可识别甾醇的可能性,并有助于在生物样品中发现具有甾醇样结构的新化合物。总之,我们采用基于 LC-MS 的 AIF 技术开发复杂生物样品中已知和未知甾醇的多维表征和鉴定。甾醇的综合分析有助于为生物学中的许多生理和病理活动提供分子见解。我们证明了基于 AIF 的多维分析提供了在没有化学标准的情况下识别甾醇的可能性,并有助于在生物样品中发现具有甾醇样结构的新化合物。总之,我们采用基于 LC-MS 的 AIF 技术开发复杂生物样品中已知和未知甾醇的多维表征和鉴定。甾醇的综合分析有助于为生物学中的许多生理和病理活动提供分子见解。我们证明了基于 AIF 的多维分析提供了在没有化学标准的情况下识别甾醇的可能性,并有助于在生物样品中发现具有甾醇样结构的新化合物。总之,我们采用基于 LC-MS 的 AIF 技术开发复杂生物样品中已知和未知甾醇的多维表征和鉴定。甾醇的综合分析有助于为生物学中的许多生理和病理活动提供分子见解。我们采用基于 LC-MS 的 AIF 技术开发复杂生物样品中已知和未知甾醇的多维表征和鉴定。甾醇的综合分析有助于为生物学中的许多生理和病理活动提供分子见解。我们采用基于 LC-MS 的 AIF 技术开发复杂生物样品中已知和未知甾醇的多维表征和鉴定。甾醇的综合分析有助于为生物学中的许多生理和病理活动提供分子见解。
更新日期:2021-01-01
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