Virtual Quantification of Metabolites by Capillary Electrophoresis-Electrospray Ionization-Mass Spectrometry: Predicting Ionization Efficiency Without Chemical Standards Kenneth R. Chalcraft, Richard Lee, Casandra Mills, and Philip Britz-McKibbin* Department of Chemistry, McMaster University, Hamilton, Ontario, L8S 4M1, Canada A major obstacle in metabolomics remains the identifica- tion and quantification of a large fraction of unknown metabolites in complex biological samples when purified standards are unavailable. Herein we introduce a multi- variate strategy for de novo quantification of cationic/ zwitterionic metabolites using capillary electrophoresis- electrospray ionization-mass spectrometry (CE-ESI-MS) based on fundamental molecular, thermodynamic, and electrokinetic properties of an ion. Multivariate calibration was used to derive a quantitative relationship between the measured relative response factor (RRF) of polar metabo- lites with respect to four physicochemical properties associated with ion evaporation in ESI-MS, namely, mo- lecular volume (MV), octanol-water distribution coef- ficient (log D), absolute mobility (µ o ), and effective charge (z eff ). Our studies revealed that a limited set of intrinsic solute properties can be used to predict the RRF of various classes of metabolites (e.g., amino acids, amines, peptides, acylcarnitines, nucleosides, etc.) with reasonable accuracy and robustness provided that an appropriate training set is validated and ion re- sponses are normalized to an internal standard(s). The applicability of the multivariate model to quantify micromolar levels of metabolites spiked in red blood cell (RBC) lysates was also examined by CE-ESI-MS without significant matrix effects caused by involatile salts and/or major co-ion interferences. This work demonstrates the feasibility for virtual quantification of low-abundance metabolites and their isomers in real-world samples using physicochemical properties estimated by computer modeling, while providing deeper insight into the wide disparity of solute re- sponses in ESI-MS. New strategies for predicting ionization efficiency in silico allow for rapid and semi- quantitative analysis of newly discovered biomarkers and/or drug metabolites in metabolomics research when chemical standards do not exist. There is growing interest in developing metabolomics as a complement to functional genomic studies that are required for new advances in drug development, 1 disease diagnosis, 2 environ- mental toxicology, 3 and agriculture. 4 Two instrumental platforms widely used in metabolomics research include nuclear magnetic resonance (NMR) and mass spectrometry (MS), which provide quantitative and qualitative information suitable for comprehensive metabolite analyses. 5,6 Electrospray ionization-mass spectrometry (ESI-MS) is the method of choice for direct analysis of polar metabolites due to its high sensitivity and direct compatibility with separation techniques, such as liquid chromatography (LC) 7 and capillary electrophoresis (CE). 8 A major challenge in metabolo- mics remains the identification of a large fraction of unknown yet biologically relevant metabolites that do not correspond to known candidates within conserved metabolic pathways. In cases when no match is found within public databases, 9 several empirical candidate structures can be deduced from accurate mass, isotopic composition, and fragmentation information. 10,11 However, reliable quantification of novel metabolites remains elusive while they are not commercially available, difficult to synthesize, or costly to purify. This dilemma is considerable when less than 10% of total metabolite peaks detected in biological samples can be quantified due to limited access to chemical standards. 12-14 Thus, new strategies that permit direct quantification of recently identified metabolites (e.g., biomarkers, xenobiotics, etc.) based on their * To whom correspondence should be addressed. Fax: +1-905-522-2509. E-mail: britz@mcmaster.ca. (1) Lindon, J. C.; Holmes, E.; Nicholson, J. K. FEBS J. 2007, 274, 1140–1151. (2) Dunn, W. B.; Broadhurst, D. I.; Deepak, S. M.; Buch, M. H.; McDowell, G.; Spasic, I.; Ellis, D. I.; Brooks, N.; Kell, D. B.; Neyses, L. 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