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Proteomics experiments based on Selected Reaction Monitoring (SRM, also referred to as Multiple Reaction Monitoring or MRM) are being used to target large numbers of protein candidates in complex mixtures. At present, instrument parameters are often optimized for each peptide, a time and resource intensive process. Large SRM experiments are greatly facilitated by having the ability to predict MS instrument parameters that work well with the broad diversity of peptides they target. For this reason, we investigated the impact of using simple linear equations to predict the collision energy (CE) on peptide signal intensity and compared it with the empirical optimization of the CE for each peptide and transition individually. Using optimized linear equations, the difference between predicted and empirically derived CE values was found to be an average gain of only 7.8% of total peak area. We also found that existing commonly used linear equations fall short of their potential, and should be recalculated for each charge state and when introducing new instrument platforms. We provide a fully automated pipeline for calculating these equations and individually optimizing CE of each transition on SRM instruments from Agilent, Applied Biosystems, Thermo-Scientific and Waters in the open source Skyline software tool (http://proteome.gs.washington.edu/software/skyline). 10.1021/ac102179j

The need for reliable, fast diagnostics is closely linked to the need for safe, effective treatment of the so-called 'neglected' diseases. The list of diseases with no field-adapted diagnostic tools includes leishmaniasis, shigella, typhoid, and bacterial meningitis. Leishmaniasis, in particular, is a parasitic disease caused by Leishmania spp. transmitted by infected phlebotomine sandfly, which remains a public health concern in developing countries with ca. 12 million people infected and 350 million at risk of infection. Despite several attempts, methods for diagnosis are still noneffective, especially with regard to specificity due to false positives with Chagas' disease caused by Trypanosema cruzi. Accepted golden standards for detecting leishmaniasis involve isolation of parasites either microscopically, or by culture, and in beth methods specimens are obtained by invasive means. Here, we show that efficient distinction between cutaneous leishmaniasis and Chagas' disease can be obtained with a low-cost biosenser system made with nanostructured films contraining specific Leishmania amazonensis and T. cruzi antigens and employing impedance spectroscopy as the detection method. This unprecedented selectivity was afforded by antigen-antibody molecular recognition processes inherent in the detection with the immobilized antigens, and by statistically correlating the electrical impedance data, which allowed distinction between real samples that tested positive for Chagas' disease and leishmaniasis. Distinction could be made of blood serum samples containing 10-5 mg/mL of the antibody solution in a few minutes. The methods used here are generic and can be extended to any type of biosensor, which is important for an effective diagnosis of many other diseases. 10.1021/ac101920t

Impedance spectroscopy has been proven a powerful tool for reaching high sensitivity in sensor arrays made with nanostructured films in the so-called electronic tongue systems, whose distinguishing ability may be enhanced with sensing units capable of molecular recognition. In this study we show that for optimized sensors and biosensors the dielectric relaxation processes involved in impedance measurements should also be considered, in addition to an adequate choice of sensing materials. We used sensing units made from layer-by-layer (LbL) films with alternating layers of the polyeletrolytes, poly(ally-lamine) hydrochloride (PAH) and poly(vinyl sulfonate) (PVS), or LbL films of PAH alternated with layers of the enzyme phytase, all adsorbed on gold interdigitate electrodes. Surprisingly, the detection of phytic acid was as effective in the PVS/PAH sensing system as with the PAH/ phytasc system, in spite of the specific interactions of the latter. This was attributed to the dependence of the relaxation processes on nonspecific interactions such as electrostatic cross-linking and possibly on the distinct film architecture as the phytase layers were found to grow as columns on the LbL film, in contrast to the molecularly thin PAH/PVS films. Using projection techniques, we were able to detect phytic acid at the micromolar level with either of the sensing units in a data analysis procedure that allows for further optimization. 10.1021/ac902949h

The integration of nanostructured films containing biomolecules and silicon-based technologies is a promising direction for reaching miniaturized biosensors that exhibit high sensitivity and selectivity. A challenge, however, is to avoid cross talk among sensing units in an array with multiple sensors located on a small area. In this letter, we describe an array of 16 sensing units of a light-addressable potentiometric sensor (LAPS), which was made with layer-by-layer (LbL) films of a poly(amidomine) dendrimer (PAMAM) and single-walled carbon nanotubes (SWNTs), coated with a layer of the enzyme penicillinase. A visual inspection of the data from constant-current measurements with liquid samples containing distinct concentrations of penicillin, glucose, or a buffer indicated a possible cross talk between units that contained penicillinase and those that did not. With the use of multidimensional data projection techniques, normally employed in information visualization methods, we managed to distinguish the results from the modified LAPS, even in cases where the units were adjacent to each other. Furthermore, the plots generated with the interactive document map (IDMAP) projection technique enabled the distinction of the different concentrations of penicillin, from 5 mmol [L.sup.-1] down to 0.5 mmol [L.sup.-1]. Data visualization also confirmed the enhanced performance of the sensing units containing carbon nanotubes, consistent with the analysis of results for LAPS sensors. The use of visual analytics, as with projection methods, may be essential to handle a large amount of data generated in multiple sensor arrays to achieve high performance in miniaturized systems. 10.1021/ac9024076