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一个空间代谢学注释工作流利用周期性离子移动性和机器学习预测的碰撞截面

Dmitry Leontyev¹, Eric C Gier¹, Viraj A Master^2,3

¹School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

Journal of the American Society for Mass Spectrometry

|May 21, 2025

概括

碰撞截面 (CCS) 改善了在癌的质谱成像 (MSI) 中的代谢物识别. 高精度的CCS数据增强了脂质注释,并有助于识别未知的化合物,从空间代谢学中解锁新的生物见解.

科学领域:

空间代谢学空间代谢学
质谱成像 (MSI) 的成像
离子移动性光谱仪离子移动性光谱仪

背景情况:

准确的代谢物注释对于理解非目标空间代谢学中的生物作用和空间模式至关重要.
不完整的MS2质谱成像 (MSI) 覆盖率导致许多未注释的特征,代表丢失的生物信息.
碰撞截面 (CCS) 为确认代谢物注释,区分异构体和阐明未知的结构提供了有价值的数据.

研究的目的:

调查碰撞横截面 (CCS) 测量如何提高MSI脂质注释信心.
评估机器学习CCS预测和SIRIUS分析与实验CCS数据的联合使用.
探索CCS在识别人类细胞癌 (RCC) 组织中未知的特征方面的实用性.

主要方法:

在人体RCC组织上利用了脱电喷雾电离循环离子运动质谱成像 (DESI-cIM-MSI).
进行了多通道离子移动 (IM) 实验,以获得高精度的CCS测量 (精度<0.4%).
集成的实验CCS数据与机器学习CCS预测和SIRIUS分析MS2数据进行注释.

主要成果:

高精度的多通路CCS测量成功注释了同位素脂质数据库匹配,即使没有MS2数据.
实验CCS数据有效地过了SIRIUS预测中的不太可能的候选人.
在RCC组织中确定了两个未知的,与空间相关的特征,即罗库,这是MSI研究中以前未报告的物质.

结论:

高精度的CCS测量显著提高了对MSI的代谢物注释信心.
实验性CCS与SIRIUS等计算工具和机器学习的整合改善了复杂的MSI数据的分析.
这种方法具有很大的潜力,可以通过允许对以前未被描述的特征进行注释来推进空间代谢学.

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