Seth D Rhoades1, Aalim M Weljie1. 1. Department of Systems Pharmacology and Translational Therapeutics, Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.
Abstract
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.
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.
Authors: Elizabeth J Want; Ian D Wilson; Helen Gika; Georgios Theodoridis; Robert S Plumb; John Shockcor; Elaine Holmes; Jeremy K Nicholson Journal: Nat Protoc Date: 2010-06 Impact factor: 13.491
Authors: Ioannis Sampsonidis; Michael Witting; Wendelin Koch; Christina Virgiliou; Helen G Gika; Philippe Schmitt-Kopplin; Georgios A Theodoridis Journal: J Chromatogr A Date: 2015-06-14 Impact factor: 4.759
Authors: Mattias Eliasson; Stefan Rännar; Rasmus Madsen; Magdalena A Donten; Emma Marsden-Edwards; Thomas Moritz; John P Shockcor; Erik Johansson; Johan Trygg Journal: Anal Chem Date: 2012-07-26 Impact factor: 6.986
Authors: Helen G Gika; Ian D Wilson; Georgios A Theodoridis Journal: J Chromatogr B Analyt Technol Biomed Life Sci Date: 2014-02-08 Impact factor: 3.205
Authors: Saikumari Y Krishnaiah; Gang Wu; Brian J Altman; Jacqueline Growe; Seth D Rhoades; Faith Coldren; Anand Venkataraman; Anthony O Olarerin-George; Lauren J Francey; Sarmistha Mukherjee; Saiveda Girish; Christopher P Selby; Sibel Cal; Ubeydullah Er; Bahareh Sianati; Arjun Sengupta; Ron C Anafi; I Halil Kavakli; Aziz Sancar; Joseph A Baur; Chi V Dang; John B Hogenesch; Aalim M Weljie Journal: Cell Metab Date: 2017-04-04 Impact factor: 27.287
Authors: Jennifer R Goldschmied; Arjun Sengupta; Anup Sharma; Lynne Taylor; Knashawn H Morales; Michael E Thase; Michael E Thase; Aalim Weljie; Matthew S Kayser Journal: Psychopharmacol Bull Date: 2021-06-01
Authors: Annie L Hsieh; Xiangzhong Zheng; Zhifeng Yue; Zachary E Stine; Anthony Mancuso; Seth D Rhoades; Rebekah Brooks; Aalim M Weljie; Robert N Eisenman; Amita Sehgal; Chi V Dang Journal: Cell Rep Date: 2019-11-12 Impact factor: 9.423
Authors: Ratnasekhar Ch; Guillaume Rey; Sandipan Ray; Pawan K Jha; Paul C Driscoll; Mariana Silva Dos Santos; Dania M Malik; Radoslaw Lach; Aalim M Weljie; James I MacRae; Utham K Valekunja; Akhilesh B Reddy Journal: Nat Commun Date: 2021-01-15 Impact factor: 14.919