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CLAW-MRM:使用大型语言模型进行多次反应监控的全面的LIPIDOMICS工作流程

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  • 1Department of Chemistry, Purdue University, 560 Oval Drive, West Lafayette, Indiana 47907, United States.

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概括

本研究介绍了CLAW-MRM,一种用于脂管学分析的自动化工作流程. 它简化了脂质识别和统计分析,为阿尔茨海默病的脂质代谢提供了洞察力.

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科学领域:

  • 生物化学
  • 生物信息学
  • 神经科学

背景情况:

  • 脂质分析生成复杂的数据,阻碍了手动注释和解释.
  • 现有的工具缺乏自动化工作流程和与统计/生物信息分析的整合.
  • 脂质结构和化学多样性使脂质组分析复杂化.

研究的目的:

  • 引入多重反应监测 (CLAW-MRM) 的综合性脂组学自动化工作流程.
  • 自动化脂质注释,统计分析和高吞吐量脂质学数据解析.
  • 通过将脂质表达与基因模式联系起来,并使人工智能驱动的相互作用能够增强生物相关性.

主要方法:

  • 使用定制多重反应监测 (MRM) 产品前体离子过渡进行脂质分析.
  • 采用修剪的m值平均值 (TMM) 规范化来进行可靠的交叉样本比较.
  • 集成的LIGER (脂质体基因丰富反应) 用于将脂质体与基因表达联系起来,并利用由大型语言模型驱动的自然语言接口.

主要成果:

  • 在老鼠阿尔茨海默病 (AD) 肝脏组织和脑脂液中分析脂质特征.
  • 评估了正常化策略对TMM正常化的脂质学结果的影响.
  • 通过使用基于CLAW-MRM的LIGER在AD中富含差异表达的代谢途径.

结论:

  • CLAW-MRM简化了端到端的脂管学数据采集和解释.
  • 该平台自动化了脂质结构识别,并集成了人工智能辅助的生物信息学.
  • 在阿尔茨海默氏症中,CLAW-MRM提供了关于脂质代谢变化的有价值的见解.