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

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

Detail of intersections of Brdicka curves for all considered body tissues.

<p>Detail of intersections of Brdicka curves for all considered body tissues.</p

Decision tree of all samples from the cleaned dataset.

<p>Decision tree of all samples from the cleaned dataset.</p

Confusion matrix for decision trees constructed from attributes obtained by a wavelet transform of level 5.

<p>Confusion matrix for decision trees constructed from attributes obtained by a wavelet transform of level 5.</p

A screenshot of the SW tool that was used, depicting all Brdicka curves measured for all the body parts of a single subject, namely P3.

<p>A screenshot of the SW tool that was used, depicting all Brdicka curves measured for all the body parts of a single subject, namely P3.</p

Transformation of DP voltammograms.

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

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