Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC-FTICR-MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics.

Given the growing interest in applying genomic and proteomic approaches for studying the mammalian brain using mouse models, we hereby present a global proteomic approach for analyzing brain tissue and for the first time a comprehensive characterization of the whole mouse brain proteome. Preparation of the whole brain sample incorporated a highly efficient cysteinyl-peptide enrichment (CPE) technique to complement a global enzymatic digestion method. Both the global and the cysteinyl-enriched peptide samples were analyzed by SCX fractionation coupled with reversed phase LC-MS/MS analysis. A total of 48,328 different peptides were confidently identified (>98% confidence level), covering 7792 non-redundant proteins (∼34% of the predicted mouse proteome). 1564 and 1859 proteins were identified exclusively from the cysteinyl-peptide and the global peptide samples, respectively, corresponding to 25% and 31% improvements in proteome coverage compared to analysis of only the global peptide or cysteinyl-peptide samples. The identified proteins provide a broad representation of the mouse proteome with little bias evident due to protein pI, molecular weight, and/or cellular localization. Approximately 26% of the identified proteins with gene ontology (GO) annotations were membrane proteins, with 1447 proteins predicted to have transmembrane domains, and many of the membrane proteins were found to be involved in transport and cell signaling. The MS/MS spectrum count information for the identified proteins was used to provide a measure of relative protein abundances. The mouse brain peptide/protein database generated from this study represents the most comprehensive proteome coverage for the mammalian brain to date, and the basis for future quantitative brain proteomic studies using mouse models. The proteomic approach presented here may have broad applications for rapid proteomic analyses of various mouse models of human brain diseases.

An accurate mass and time (AMT) tag approach for proteomic analyses has been developed over the past several years to facilitate comprehensive high-throughput proteomic measurements. An AMT tag database for an organism, tissue, or cell line is established by initially performing standard shotgun proteomic analysis and, most importantly, by validating peptide identifications using the mass measurement accuracy of Fourier transform ion cyclotron resonance (FTICR) mass spectrometry (MS) and liquid chromatography (LC) elution time constraint. Creation of an AMT tag database largely obviates the need for subsequent MS/MS analyses, and thus facilitates high-throughput analyses. The strength of this technology resides in the ability to achieve highly efficient and reproducible one-dimensional reversed-phased LC separations in conjunction with highly accurate mass measurements using FTICR MS. Recent improvements allow for the analysis of as little as picrogram amounts of proteome samples by minimizing sample handling and maximizing peptide recovery. The nanoproteomics platform has also demonstrated the ability to detect >10(6) differences in protein abundances and identify more abundant proteins from subpicogram amounts of samples. The AMT tag approach is poised to become a new standard technique for the in-depth and high-throughput analysis of complex organisms and clinical samples, with the potential to extend the analysis to a single mammalian cell.

A nanoparticle-based electrochemical immunosensor has been developed for the detection of phosphorylated acetylcholinesterase (AChE), which is a potential biomarker of exposure to organophosphate (OP) pesticides and chemical warfare nerve agents. Zirconia nanoparticles (ZrO(2) NPs) were used as selective sorbents to capture the phosphorylated AChE adduct, and quantum dots (ZnS@CdS, QDs) were used as tags to label monoclonal anti-AChE antibody to quantify the immunorecognition events. The sandwich-like immunoreactions were performed among the ZrO(2) NPs, which were pre-coated on a screen printed electrode (SPE) by electrodeposition, phosphorylated AChE and QD-anti-AChE. The captured QD tags were determined on the SPE by electrochemical stripping analysis of its metallic component (cadmium) after an acid-dissolution step. Paraoxon was used as the model OP insecticide to prepare the phosphorylated AChE adducts to demonstrate proof of principle for the sensor. The phosphorylated AChE adduct was characterized by Fourier transform infrared spectroscopy (FTIR) and mass spectroscopy. The binding affinity of anti-AChE to the phosphorylated AChE was validated with an enzyme-linked immunosorbent assay. The parameters (e.g., amount of ZrO(2) NP, QD-anti-AChE concentration,) that govern the electrochemical response of immunosensors were optimized. The voltammetric response of the immunosensor is highly linear over the range of 10 pM to 4 nM phosphorylated AChE, and the limit of detection is estimated to be 8.0 pM. The immunosensor also successfully detected phosphorylated AChE in human plasma. This new nanoparticle-based electrochemical immunosensor provides an opportunity to develop field-deployable, sensitive, and quantitative biosensors for monitoring exposure to a variety of OP pesticides and nerve agents.