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在质谱学中对依赖数据的获取参数进行优化,用于识别寡核酸

Audriy Jebet^1,2, Xipeng Ma^1,2, Liqing He^1,2

¹Department of Chemistry, University of Louisville, Louisville, Kentucky 40292, United States.

Journal of the American Society for Mass Spectrometry

|October 28, 2025

概括

优化质谱参数可以提高RNA寡核化物 (OGN) 在表谱学中的识别. 这项研究为准确的OGN表征和RNA修饰分析提供了一个强大的工作流.

科学领域:

生物化学生物化学
分子生物学分子生物学
分析化学分析化学

背景情况:

对RNA修饰的准确表征对于理解RNA生物学至关重要.
质谱学,特别是数据依赖采集 (DDA),对于在表谱学中识别寡核酸 (OGN) 是必不可少的.
优化DDA参数对于增强OGN识别至关重要.

研究的目的:

为了优化 Orbitrap Fusion Lumos 质谱仪上的关键 DDA 参数.
为改进OGN识别开发一次性大规模排除MS/MS获取方法.
建立一个强大的工作流程,以在基于MS的表谱学中进行可靠的OGN识别.

主要方法:

优化DDA参数,包括完整的MS和MS/MS分辨率,前15个MS/MS扫描和正常化的HCD碰撞能量.
开发一个代的质量排除MS/MS获取方法.
使用优化的DDA设置对RNase T1消化的*E. coli*rRNA进行分析.

主要成果:

通过12万个完整的MS分辨率,15,000个MS/MS分辨率,前15个MS/MS扫描,以及30%的正常化HCD碰撞能量,实现了最佳的DDA性能.
从*大肠杆菌*rRNA中平均识别了358个独特的OGN.
实现了58%的rRNA序列覆盖率,证明了增强的识别能力.

结论:

定制的DDA参数优化显著改善了基于质谱学的OGN识别.
开发的代大规模排除方法为自信的OGN识别提供了强大的工作流.
这项研究通过提供精细的RNA特征表征方法来推进表观转录学领域.

关键词:

这就是LC-MS.轨道飞行器融合灯光质谱仪标志性转录组学标志性转录组学类核酸类分子.优化的优化优化优化.

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