利用实验设计(COLMeD)对LC-MS代谢组学方法进行综合优化。

Seth D Rhoades, Aalim M Weljie
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引用次数: 26

摘要

反相化学和HILIC化学都用于液相色谱-质谱(LC-MS)代谢组学分析,但是HILIC方法在可重复性和多功能性方面落后于反相方法。综合代谢组学分析由于代谢产物的理化多样性和一系列可调的分析参数而变得更加复杂。目的:利用实验设计(design of Experiments, DoE)在多台仪器上合理、高效地设计互补的hilic极性代谢组学方法。方法:通过称为COLMeD(利用实验设计对LC-MS代谢组学方法进行综合优化)的多轮工作流程,我们迭代调整离子开关三重四极杆(QqQ)和四极杆飞行时间(qTOF)质谱仪的LC和MS条件。多元统计分析指导了我们在方法优化中的决策过程。结果:LC-MS/MS对QqQ方法进行调整后,血清代谢物的中位响应提高了161.5% (p)结论:COLMeD方法阐明了响应权衡,在不影响等柱分离的情况下,促进了色谱和MS响应的改进。COLMeD是高效的,在给定的DoE轮中不需要超过20次注射,并且灵活,能够通过QqQ方法中的酰基肉碱优化来进行特定类别的优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comprehensive Optimization of LC-MS Metabolomics Methods Using Design of Experiments (COLMeD).

Comprehensive Optimization of LC-MS Metabolomics Methods Using Design of Experiments (COLMeD).

Introduction: Both reverse-phase and HILIC chemistries are deployed for liquid-chromatography mass spectrometry (LC-MS) metabolomics analyses, however HILIC methods lag behind reverse-phase methods in reproducibility and versatility. Comprehensive metabolomics analysis is additionally complicated by the physiochemical diversity of metabolites and array of tunable analytical parameters.

Objective: Our aim was to rationally and efficiently design complementary HILIC-based polar metabolomics methods on multiple instruments using Design of Experiments (DoE).

Methods: We iteratively tuned LC and MS conditions on ion-switching triple quadrupole (QqQ) and quadrupole-time-of-flight (qTOF) mass spectrometers through multiple rounds of a workflow we term COLMeD (Comprehensive optimization of LC-MS metabolomics methods using design of experiments). Multivariate statistical analysis guided our decision process in the method optimizations.

Results: LC-MS/MS tuning for the QqQ method on serum metabolites yielded a median response increase of 161.5% (p<0.0001) over initial conditions with a 13.3% increase in metabolite coverage. The COLMeD output was benchmarked against two widely used polar metabolomics methods, demonstrating total ion current increases of 105.8% and 57.3%, with median metabolite response increases of 106.1% and 10.3% (p<0.0001 and p<0.05 respectively). For our optimized qTOF method, 22 solvent systems were compared on a standard mix of physiochemically diverse metabolites, followed by COLMeD optimization, yielding a median 29.8% response increase (p<0.0001) over initial conditions.

Conclusions: The COLMeD process elucidated response tradeoffs, facilitating improved chromatography and MS response without compromising separation of isobars. COLMeD is efficient, requiring no more than 20 injections in a given DoE round, and flexible, capable of class-specific optimization as demonstrated through acylcarnitine optimization within the QqQ method.

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