New advances in separation science for metabolomics: resolving chemical diversity in a post-genomic era

Capacitively coupled contactless conductivity detection (C4D) is presented in a progressively detailed approach. Through different levels of theoretical and practical complexity, several aspects related to this kind of detection are addressed, which should be helpful to understand the results as well as to design a detector or plan experiments. Simulations and experimental results suggest that sensitivity depends on: 1) the electrolyte co-ion and counter-ion; 2) cell geometry and its positioning; 3) operating frequency. Undesirable stray capacitance formed due to the close placement of the electrodes is of great importance to the optimization of the operating frequency and must be minimized.

Understanding Capacitively Coupled Contactless Conductivity Detection in Capillary and Microchip Electrophoresis. Part 1. Fundamentals

One of the central challenges to metabolomics is metabolite identification. Regardless of whether one uses so-called 'targeted' or 'untargeted' metabolomics, eventually all paths lead to the requirement of identifying (and quantifying) certain key metabolites. Indeed, without metabolite identification, the results of any metabolomic analysis are biologically and chemically uninterpretable. Given the chemical diversity of most metabolomes and the character of most metabolomic data, metabolite identification is intrinsically difficult. Consequently a great deal of effort in metabolomics over the past decade has been focused on making metabolite identification better, faster and cheaper. This review describes some of the newly emerging techniques or technologies in metabolomics that are making metabolite identification easier and more robust. In particular, it focuses on advances in metabolite identification that have occurred over the past 2 to 3 years concerning the technologies, methodologies and software as applied to NMR, MS and separation science. The strengths and limitations of some of these approaches are discussed along with some of the important trends in metabolite identification.

Advances in metabolite identification

A review of the recent achievements in capacitively coupled contactless conductivity detection

Contactless conductivity detection is universal for CE in that all charged species can be quantified, and it is particularly attractive for those inorganic and organic ions that are not directly accessible by optical means. It is not necessary to make precise alignments or to create an optical window into the capillary. Commercial detectors that can be retrofitted to existing instruments are available at a cost that is lower than that of any other electrophoresis detector. The approach is also suited for lab-on-chip devices. It is attempted in this review to summarize some of the expertise accumulated since the introduction of the axial contactless conductivity detector to CE 10 years ago.

Ten years of axial capacitively coupled contactless conductivity detection for CZE--a review.

We introduce the mathematical model of electromigration of electrolytes in free solution together with free software Simul, version 5, designed for simulation of electrophoresis. The mathematical model is based on principles of mass conservation, acid-base equilibria, and electroneutrality. It accounts for any number of multivalent electrolytes or ampholytes and yields a complete picture about dynamics of electromigration and diffusion in the separation channel. Additionally, the model accounts for the influence of ionic strength on ionic mobilities and electrolyte activities. The typical use of Simul is: inspection of system peaks (zones), stacking and preconcentrating analytes, resonance phenomena, and optimization of separation conditions, in either CZE, ITP, or IEF.

Simul 5 – Free dynamic simulator of electrophoresis

We are introducing a computer implementation of the mathematical model of zone electrophoresis (CZE) described in Stedry, M., Jaros, M., Hruska, V., Gas, B., Electrophoresis 2004, 25, 3071-3079 program PeakMaster. The computer model calculates eigenmobilities, which are the eigenvalues of the matrix tied to the linearized continuity equations, and which are responsible for the presence of system eigenzones (system zones, system peaks). The model also calculates other parameters of the background electrolyte (BGE)-pH, conductivity, buffer capacity, ionic strength, etc., and parameters of the separated analytes--effective mobility, transfer ratio, molar conductivity detection response, and relative velocity slope. This allows the assessment of the indirect detection, conductivity detection and peak broadening (peak distortion) due to electromigration dispersion. The computer model requires the input of the BGE composition, the list of analytes to be separated, and the system instrumental configuration. The output parameters of the model are directly comparable with experiments; the model also simulates electropherograms in a user-friendly way. We demonstrate a successful application of PeakMaster for inspection of BGEs having no stationary injection zone.

Eigenmobilities in background electrolytes for capillary zone electrophoresis: IV. Computer program PeakMaster.

A mathematical and computational model described in the previous paper (Gas, B., Coufal, P., Jaros, M., Muzikar, J., Jelinek, L., J. Chromatogr. A 2001, 905, 269-279) is adapted, algorithmized, and a computer program PeakMaster having a status of freeware (http://natur.cuni.cz/ approximately gas) is introduced. The model enables optimization of background electrolyte (BGE) systems for capillary zone electrophoresis. The model allows putting to use uni- or di- or trivalent electrolytes and allows also for modeling highly acidic or alkaline BGEs. It takes into account the dependence of ionic mobilities and dissociation of weak electrolytes on the ionic strength. The model calculates the effective mobility of analytes and predicts parameters of the system that are experimentally available, such as the transfer ratio, which is a measure of the sensitivity in the indirect UV detection or the molar conductivity detection response, which expresses the sensitivity of the conductivity detection. Further, the model enables evaluation of a tendency of the analyte to undergo electromigration dispersion or peak broadening. The suitability of the model is verified by comparison of the predicted results with experiments, even under conditions that are far from ideal (under extreme pH and a high ionic strength).

Optimization of background electrolytes for capillary electrophoresis: II. Computer simulation and comparison with experiments.

Optimization of background electrolytes for capillary electrophoresis I. Mathematical and computational model.

A mathematical model of capillary zone electrophoresis (CZE) based on the conception of eigenmobilities, which are the eigenvalues of a matrix M tied to the linearized governing equations is presented. The model considers CZE systems, where constituents, either analytes or components of the background electrolyte (BGE), are weak electrolytes – acids, bases, or ampholytes. There is no restriction on the number of components nor on the valence of the constituents nor on pH of the BGE. An electrophoretic system with N constituents has N eigenmobilities. In most BGEs one or two eigenmobilities are very close to zero so their corresponding eigenzones move very slowly. However, there are BGEs where no eigenmobility is close to zero. The mathematical model further provides: the transfer ratio, the molar conductivity detection response, and the relative velocity slope. This allows the assessment of the indirect detection, conductivity detection and peak broadening (distortion) due to electromigration dispersion. Also, we present a spectral decomposition of the matrix M to matrices allowing the assessment of the amplitudes of system eigenpeaks (system peaks). Our model predicted the existence of BGEs having no stationary injection zone (or water zone, gap, peak, dip). A common practice of using the injection zone as a marker of the electroosmotic flow must fail in such electrolytes.

Michal Jaroš

Papers

Simul 5 – Free dynamic simulator of electrophoresis

Eigenmobilities in background electrolytes for capillary zone electrophoresis: IV. Computer program PeakMaster.

Optimization of background electrolytes for capillary electrophoresis: II. Computer simulation and comparison with experiments.

Optimization of background electrolytes for capillary electrophoresis I. Mathematical and computational model.

Eigenmobilities in background electrolytes for capillary zone electrophoresis: III. Linear theory of electromigration