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8 May / June 2021


Capillary Electrophoresis-Mass Spectrometry for Metabolomics: Addressing Perceived Misconceptions


Marlien van Mever1 1


and Rawi Ramautar1 *


Biomedical Microscale Analytics, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, the Netherlands


*Corresponding author: Dr Rawi Ramautar, Email: r.ramautar@lacdr.leidenuniv.nl Abstract


The analytical approaches mainly used in metabolomics for addressing biological questions are based on liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. However, the value of metabolomics, which in essence is obtaining insight into a well-defined biological problem, may be completely overlooked when only these analytical technologies are considered. Notably, for biological questions intrinsically dealing with low sample amounts, but also for the study of ‘difficult’ compound classes, such as low-abundance highly polar ionogenic metabolites. This work aims to highlight the possibilities of capillary electrophoresis-mass spectrometry (CE-MS) for metabolomics by paying attention to some key fundamental and technological aspects to address perceived misconceptions about this analytical technique. With recent examples, we show the utility of CE-MS for special applications and thereby the value of this approach for metabolomics.


Misconception 1: CE-MS lacks reproducibility for metabolomics studies


Compared to other analytical techniques, the use of CE-MS is underrepresented in metabolomics [1], presumably as this analytical technique is perceived as less reproducible, in particular for migration time, by the separation science community [2]. In metabolomics studies, migration- time reproducibility is of pivotal importance as it ensures a reliable comparison of metabolic profiles, including scrutinising samples for subtle changes in patterns. Moreover, it supports the identification of unknown metabolites and is considered complementary to high-resolution MS/MS.


In CE-MS-based metabolomics studies, variability in migration time mainly arises from fluctuations in the electro-osmotic flow (EOF) caused by sample matrix- induced capillary surface interactions. Recently, González-Ruiz et al. addressed this challenge by developing software, designated as ROMANCE, which


converts the migration-time scale into an effective electrophoretic mobility scale [3]. The approach is based on utilising the fundamental separation principle of CE (in this case specifically referring to capillary zone electrophoresis), i.e. the effective electrophoretic mobility of the solute, which in essence depends on the charge and size of each compound (assuming other factors to be constant such as viscosity of the separation buffer). ROMANCE allowed effective correction of migration-time shifts caused by the EOF and as a result, improved the repeatability of the CE-MS analyses. By using this approach based on effective electrophoretic mobility, Drouin et al. constructed a library for 458 endogenous metabolites in order to facilitate metabolite identification by CE-MS [4].


To assess the true power of using effective electrophoretic mobility in CE-MS-based metabolomics studies, Drouin et al. recently set-up a Metabo-ring trial to which 13 independent laboratories from 11 countries contributed [5]. All laboratories used the same batch of samples, comprising


representative metabolite mixtures, human plasma and urine spiked with the same representative metabolites. Each participating lab prepared and employed the same background electrolyte (BGE) based on a protocol. All other parameters, i.e. capillary, interface, injection volume, voltage ramp, temperature, type of instrument used, capillary conditioning and rinsing procedures, etc., were left entirely to the participating labs’ discretion. The critical parameters examined by this Metabo- ring were the reproducibility of relative migration time and effective electrophoretic mobility across the laboratories, which was determined for a set of cationic metabolites in each sample. Despite the huge heterogeneity in experimental conditions and platforms across the 13 independent labs, conversion of migration times into effective electrophoretic mobility reduced variability from 10.9% on relative migration time to 3.1% in effective electrophoretic mobility scale using the same BGE composition. Although this work primarily focused on cationic metabolic profiling,


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