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通过超高性能液体染色学-轨道轨道轨道天体质量谱仪进行大规模脂管学数据分析的新型等价碳数策略

Yao Chen^1,2,3, Xinxin Wang^1,2,3, Rui Zhong^1,4

¹State Key Laboratory of Medical Proteomics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China.

Analytical chemistry

|July 17, 2025

概括

一个新的策略是将MS-DIAL和LipidSearch结合起来,使用相当碳数 (ECN) 模型来准确的脂管学. 这种方法增强了脂质识别,并减少了质谱数据中的假阳性.

科学领域:

利皮多米克 (Lipidomics) 是一种消化剂.
质谱测量质量谱测量
分析化学分析化学

背景情况:

脂质学研究了健康和疾病中的脂质变化.
带有Astral分析仪的轨道飞行器质谱学提高了MS/MS的获取率.
同时分析脂质保留和结构注释是具有挑战性的.

研究的目的:

制定一项综合战略,将MS-DIAL和LipidSearch整合在一起,使用相当碳素数 (ECN) 模型.
为了提高脂质保留行为和MS/MS光谱信息分析的准确性.
在大规模的脂管学研究中实现高效准确的脂质识别.

主要方法:

采用了等价碳计数 (ECN) 模型来整合MS-DIAL和LipidSearch.
研究了34个脂质标准,入NIST SRM 1950等离子体中.
在各种生物样本 (HeLa细胞,血,肝脏,酵母) 中分析了Astral-MS的数据.

主要成果:

该ECN策略在保留时间预测方面取得了高准确性 (RSD<±5%对于90%的脂质在正离子模式下,100%在负离子模式下).
从LipidSearch显著减少了虚假阳性 (例如,酵母减少了68.8%和80.1%).
在各种样本中注释了大量的脂质,并与QE-MS相比,在Astral-MS中显示出更高的脂质识别率.

结论:

该ECN策略有效地整合了脂质保留和MS/MS数据,以准确识别脂质.
这种方法通过提供精确的保留时间和可靠的注释来增强脂管学研究.
在各种生物环境中为大规模脂管学提供广泛的适用性.

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