Portable capillary-based (non-chip) capillary electrophoresis

Miniaturized, portable instrumentation has been gaining popularity in all areas of analytical chemistry. Capillary electrophoresis (CE), due to its main strengths of high separation efficiency, relatively short analysis time and low consumption of chemicals, is a particularly suitable technique for use in portable analytical instrumentation. In line with the general trend in miniaturization in chemistry utilizing microfluidic chips, the main thrust of portable CE (P—CE) systems development is towards chip-based miniaturized CE. Despite this, capillary-based (non-chip) P—CE systems have certain unmatched advantages, especially in the relative simplicity of the regular cylindrical geometry of the CE capillary, maximal volume-to-surface ratio, no need to design and to fabricate a chip, the low costs of capillary compared to chip, and better performance with some detection techniques. This review presents an overview of the state of the art of P—CE and literature relevant to futuredevelopments. We pay particular attention to the development and the potential of miniaturization of functional parts for P—CE. These include components related to sample introduction, separation and detection, which are the key elements in P—CE design. The future of P—CE may be in relatively simple, rugged designs (e.g., using a short piece of capillary fixed to a chip-sized platform on which injection and detection parts can be mounted). Electrochemical detection is well suited for miniaturization, so is probably the most suitable detection technique for P—CE, but optical detection is gaining interest, especially due to miniaturized light sources (e.g., light-emitting diodes)

Tissue specific electrochemical fingerprinting.

BACKGROUND: Proteomics and metalloproteomics are rapidly developing interdisciplinary fields providing enormous amounts of data to be classified, evaluated and interpreted. Approaches offered by bioinformatics and also by biostatistical data analysis and treatment are therefore of extreme interest. Numerous methods are now available as commercial or open source tools for data processing and modelling ready to support the analysis of various datasets. The analysis of scientific data remains a big challenge, because each new task sets its specific requirements and constraints that call for the design of a targeted data pre-processing approach. METHODOLOGY/PRINCIPAL FINDINGS: This study proposes a mathematical approach for evaluating and classifying datasets obtained by electrochemical analysis of metallothionein in rat 9 tissues (brain, heart, kidney, eye, spleen, gonad, blood, liver and femoral muscle). Tissue extracts were heated and then analysed using the differential pulse voltammetry Brdicka reaction. The voltammograms were subsequently processed. Classification models were designed making separate use of two groups of attributes, namely attributes describing local extremes, and derived attributes resulting from the level=5 wavelet transform. CONCLUSIONS/SIGNIFICANCE: On the basis of our results, we were able to construct a decision tree that makes it possible to distinguish among electrochemical analysis data resulting from measurements of all the considered tissues. In other words, we found a way to classify an unknown rat tissue based on electrochemical analysis of the metallothionein in this tissue

ELECTROCHEMICAL SCIENCE Femtogram Electroanalytical Detection of Prostatic Specific Antigen by Brdicka Reaction

Prostatic-specific antigen is considered as the best marker for prostate cancer. Due to the importance of PSA for diagnostic purposes it is not surprising that there are tested and optimized various methods for its determination. In spite of such intensive research in the field of electrochemical detection of some by-products connected with concentration of PSA, electrochemical behaviour of PSA has not been studied yet. The aim of this study was to investigate electrochemical catalytic signals of PSA using differential pulse voltammetry Brdicka reaction. The catalytic signals were studied using adsorptive transfer stripping technique as well as directly in the electrochemical cell. Nevertheless, we primarily tested detection of PSA by standard immunoanalysis and by gel and capillary chip electrophoresis to investigate behaviour of this protein in electric field. Both electrophoretic methods showed that the most intensive band of PSA was determined at 37 kDa under reducing conditions and at 26 kDa under non-reducing. Band at 37 kDa corresponds to a reduced, and at 26 kDa to non-reduced PSA. Studying of basic electrochemical behaviour of PSA was primarily carried out using standard electrochemical cell and HMDE as a working electrode. Co(NH 3

Similarities in DP voltammograms.

<p>(A) RadViz image for the <i>w5coef24 … w5coef27</i> projection of selected wavelet coefficients. (B) Approximation of the Brdicka curve between points Cat2 and Max3 by the line <i>y = kx+q</i>. (C) Cross point for the set of kidney and (D) spleen.</p

Transformation of DP voltammograms.

<p>(A) Haar’s Simple Wavelet transformation – brain. (B) Haar’s Simple Wavelet transformation – eye.</p

Suggested Tissue Electrochemical Fingerprinting is based on sampling a tissue, which is then extracted and heat treated.

<p>The denatured sample is further electrochemically analysed using the differential pulse voltammetry Brdicka reaction. The data that is obtained is processed using an optimized protocol, and a tissue is identified.</p

Brdicka reaction of metallothionein.

<p>(A) Presumable scheme of the sequence of electrochemical reactions at the mercury electrode when the Brdicka reaction is applied for MT analysis. (B) A typical DPV voltammogram of MT measured in the presence of a supporting electrolyte containing 1 mM Co(NH₃)₆Cl₃ and 1 M NH₃(aq)+NH₄Cl, pH = 9.6; dotted line: voltammogram of a supporting electrolyte without MT. During MT analysis four peaks, Co1, RS₂Co, Cat1 and Cat2 that correspond to the MT level can be observed. Heights of (C) Cat2, (D) RS₂Co and (E) Cat1 peaks measured in extracts of various rat tissues. (C) Metallothionein level in single tissues. The highest level is in liver and kidney, i.e. organs providing detoxification, and in brain. In comparison, the level of MT in muscle and in heart is almost 50% lower.</p