Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD)
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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.
Our aim was to rationally and efficiently design complementary HILIC-based polar metabolomics methods on multiple instruments using design of experiments (DoE).
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 Comprehensive optimization of LC–MS metabolomics methods using design of experiments (COLMeD). Multivariate statistical analysis guided our decision process in the method optimizations.
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.
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.
KeywordsDesign of experiments HILIC LC–MS Method development Multivariate statistical analysis
Funding was provided by National Institute of Health (Grant No. T32 GM008076), and [National Center for Research Resources (Grant No.UL1RR024134)].The authors would also like to thank Saikumari Krishnaiah for assistance with qTOF data acquisition and Barry Slaff for fruitful discussions regarding qTOF data analysis. S.D.R. is supported through a Pharmacology T32 Training Grant (T32 GM008076). Supported in part by the Institute for Translational Medicine and Therapeutics (ITMAT) Transdisciplinary Program in Translational Medicine and Therapeutics
Compliance with ethical standard
Conflict of interest
The authors declare no competing financial interest.
This article does not contain any experiments with human participants or animals as performed by the authors.
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