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iDIA-QC:基于人工智能的数据独立采集,基于质谱的质量控制

Huanhuan Gao^1,2,3, Yi Zhu^4,5,6, Dongxue Wang^7,8

¹Affiliated Hangzhou First People's Hospital, State Key Laboratory of Medical Proteomics, School of Medicine, Westlake University, Hangzhou, Zhejiang Province, China.

Nature communications

|January 21, 2025

概括

数据独立采集 (DIA) 质量控制 (QC) 度量比质谱 (MS) 的数据依赖采集 (DDA) 度量更为敏感. 一个AI模型和iDIA-QC软件提高了基于DIA的QC性能.

科学领域:

蛋白质组学是指蛋白质组学.
质谱测量质量谱测量
分析化学分析化学

背景情况:

基于质谱 (MS) 的蛋白质组学中的质量控制 (QC) 通常依赖于数据依赖采集 (DDA).
需要更敏感的质量控制指标,特别是随着数据独立采集 (DIA) 的日益使用.

研究的目的:

为了比较基于DIA和基于DDA的QC指标的灵敏度,以评估液体染色学-并联质谱 (LC-MS/MS) 状态.
开发和验证基于 DIA 的 QC 的人工智能 (AI) 模型.
创建用户友好的软件来实现开发的质量控制方法.

主要方法:

收集和分析了2754个DIA和2638个DDA文件,来自21个质谱仪的小鼠肝消化.
优先考虑15个共识质量控制指标用于DIA分析.
使用2110 DIA培训文件开发了一个AI模型,并在独立的数据集上验证了它.
创建了一个离线软件iDIA-QC.

主要成果:

与基于DDA的指标相比,基于DIA的共识QC指标在检测LC-MS状态的变化方面表现出更高的灵敏度.
人工智能模型在第一个验证数据集中实现了LC (0.91) 和MS (0.97) 的高AUC,在独立数据集中达到0.78 (LC) 和0.94 (MS).
对DIA文件的手动专家评估是根据优先级指标进行的.

结论:

基于DIA的QC指标为监测蛋白质组学中的LC-MS/MS性能提供了卓越的灵敏度.
开发的AI模型和iDIA-QC软件为基于DIA的强大质量控制提供了有效和可访问的工具.

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