Dual spatial maps of transcript and protein abundance in the mouse brain

Integrating quantitative proteomic and transcriptomic datasets promises valuable insights in unraveling the molecular mechanisms of the brain. We concentrate on recent studies using mass spectrometry and microarray data to investigate transcript and protein abundance in normal and diseased neural tissues. Highlighted are dual spatial maps of these molecules obtained using voxelation of the mouse brain. We demonstrate that the relationship between transcript and protein levels displays a specific anatomical distribution, with greatest fidelity in midline structures and the hypothalamus. Genes are also identified that have strong correlations between mRNA and protein abundance. In addition, transcriptomic and proteomic analysis of mouse models of Parkinson's disease are discussed.

Mapping protein abundance patterns in the brain using voxelation combined with liquid chromatography and mass spectrometry

Voxelation creates expression atlases by high-throughput analysis of spatially registered cubes or voxels harvested from the brain. The modality independence of voxelation allows a variety of bioanalytical techniques to be used to map abundance. Protein expression patterns in the brain can be obtained using liquid chromatography (LC) combined with mass spectrometry (MS). Here we describe the methodology of voxelation as it pertains particularly to LC-MS proteomic analysis: sample preparation, instrumental set up and analysis, peptide identification and protein relative abundance quantitation. We also briefly describe some of the advantages, limitations and insights into the brain that can be obtained using combined proteomic and transcriptomic maps.

Large-scale multiplexed quantitative discovery proteomics enabled by the use of an (18)O-labeled "universal" reference sample

The quantitative comparison of protein abundances across a large number of biological or patient samples represents an important proteomics challenge that needs to be addressed for proteomics discovery applications. Herein, we describe a strategy that incorporates a stable isotope (18)O-labeled "universal" reference sample as a comprehensive set of internal standards for analyzing large sample sets quantitatively. As a pooled sample, the (18)O-labeled "universal" reference sample is spiked into each individually processed unlabeled biological sample and the peptide/protein abundances are quantified based on (16)O/(18)O isotopic peptide pair abundance ratios that compare each unlabeled sample to the identical reference sample. This approach also allows for the direct application of label-free quantitation across the sample set simultaneously along with the labeling-approach (i.e., dual-quantitation) since each biological sample is unlabeled except for the labeled reference sample that is used as internal standards. The effectiveness of this approach for large-scale quantitative proteomics is demonstrated by its application to a set of 18 plasma samples from severe burn patients. When immunoaffinity depletion and cysteinyl-peptide enrichment-based fractionation with high resolution LC-MS measurements were combined, a total of 312 plasma proteins were confidently identified and quantified with a minimum of two unique peptides per protein. The isotope labeling data was directly compared with the label-free (16)O-MS intensity data extracted from the same data sets. The results showed that the (18)O reference-based labeling approach had significantly better quantitative precision compared to the label-free approach. The relative abundance differences determined by the two approaches also displayed strong correlation, illustrating the complementary nature of the two quantitative methods. The simplicity of including the (18)O-reference for accurate quantitation makes this strategy especially attractive when a large number of biological samples are involved in a study where label-free quantitation may be problematic, for example, due to issues associated with instrument platform robustness. The approach will also be useful for more effectively discovering subtle abundance changes in broad systems biology studies.

Analysis of non-enzymatically glycated peptides: neutral-loss-triggered MS(3) versus multi-stage activation tandem mass spectrometry

Non-enzymatic glycation of tissue proteins has important implications in the development of complications of diabetes mellitus. While electron transfer dissociation (ETD) has been shown to outperform collision-induced dissociation (CID) in sequencing glycated peptides by tandem mass spectrometry, ETD instrumentation is not yet widely available and often suffers from significantly lower sensitivity than CID. In this study, we evaluated different advanced CID techniques (i.e., neutral-loss-triggered MS(3) and multi-stage activation) during liquid chromatography/multi-stage mass spectrometric (LC/MS(n)) analyses of Amadori-modified peptides enriched from human serum glycated in vitro. During neutral-loss-triggered MS(3) experiments, MS(3) scans triggered by neutral losses of 3 H(2)O or 3 H(2)O + HCHO produced similar results in terms of glycated peptide identifications. However, neutral losses of 3 H(2)O resulted in significantly more glycated peptide identifications during multi-stage activation experiments. Overall, the multi-stage activation approach produced more glycated peptide identifications, while the neutral-loss-triggered MS(3) approach resulted in much higher specificity. Both techniques are viable alternatives to ETD for identifying glycated peptides.

Characterization of the mouse pancreatic islet proteome and comparative analysis with other mouse tissues

The pancreatic islets of Langerhans, and especially the insulin-producing beta cells, play a central role in the maintenance of glucose homeostasis. Alterations in the expression of multiple proteins in the islets that contribute to the maintenance of islet function are likely to underlie the pathogenesis of types 1 and 2 diabetes. To identify proteins that constitute the islet proteome, we provide the first comprehensive proteomic characterization of pancreatic islets for mouse, the most commonly used animal model in diabetes research. Using strong cation exchange fractionation coupled with reversed phase LC-MS/MS we report the confident identification of 17,350 different tryptic peptides covering 2612 proteins having at least two unique peptides per protein. The data set also identified approximately 60 post-translationally modified peptides including oxidative modifications and phosphorylation. While many of the identified phosphorylation sites corroborate those previously known, the oxidative modifications observed on cysteinyl residues reveal potentially novel information suggesting a role for oxidative stress in islet function. Comparative analysis with 15 available proteomic data sets from other mouse tissues and cells revealed a set of 133 proteins predominantly expressed in pancreatic islets. This unique set of proteins, in addition to those with known functions such as peptide hormones secreted from the islets, contains several proteins with as yet unknown functions. The mouse islet protein and peptide database accessible at (http://ncrr.pnl.gov), provides an important reference resource for the research community to facilitate research in the diabetes and metabolism fields.

Mitochondrial dysfunction, oxidative stress, and apoptosis revealed by proteomic and transcriptomic analyses of the striata in two mouse models of Parkinson's disease

The molecular mechanisms underlying the changes in the nigrostriatal pathway in Parkinson's disease (PD) are not completely understood. Here, we use mass spectrometry and microarrays to study the proteomic and transcriptomic changes in the striatum of two mouse models of PD, induced by the distinct neurotoxins 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and methamphetamine (METH). Proteomic analyses resulted in the identification and relative quantification of 912 proteins with two or more unique peptides and 86 proteins with significant abundance changes following neurotoxin treatment. Similarly, microarray analyses revealed 181 genes with significant changes in mRNA, following neurotoxin treatment. The combined protein and gene list provides a clearer picture of the potential mechanisms underlying neurodegeneration observed in PD. Functional analysis of this combined list revealed a number of significant categories, including mitochondrial dysfunction, oxidative stress response, and apoptosis. These results constitute one of the largest descriptive data sets integrating protein and transcript changes for these neurotoxin models with many similar end point phenotypes but distinct mechanisms.

DAnTE: a statistical tool for quantitative analysis of -omics data

Data Analysis Tool Extension (DAnTE) is a statistical tool designed to address challenges associated with quantitative bottom-up, shotgun proteomics data. This tool has also been demonstrated for microarray data and can easily be extended to other high-throughput data types. DAnTE features selected normalization methods, missing value imputation algorithms, peptide-to-protein rollup methods, an extensive array of plotting functions and a comprehensive hypothesis-testing scheme that can handle unbalanced data and random effects. The graphical user interface (GUI) is designed to be very intuitive and user friendly.

AVAILABILITY

DAnTE may be downloaded free of charge at http://omics.pnl.gov/software/.

SUPPLEMENTARY INFORMATION

An example dataset with instructions on how to perform a series of analysis steps is available at http://omics.pnl.gov/software/

Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, it is necessary to limit systematic mass measurement errors. It is well known that errors in m/z measurements can be affected by experimental parameters that include, for example, outdated calibration coefficients, ion intensity, and temperature changes during the measurement. Traditionally, these variations have been corrected through the use of internal calibrants (well-characterized standards introduced with the sample being analyzed). In this paper, we describe an alternative approach where the calibration is provided through the use of a priori knowledge of the sample being analyzed. Such an approach has previously been demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluated using several commercially available instruments. The applied approach is shown to remove any noticeable biases from the overall mass measurement errors and decreases the overall standard deviation of the mass measurement error distribution by 1.2-2-fold, depending on instrument type. Subsequent reduction of the random errors based on multiple measurements over consecutive spectra further improves accuracy and results in an overall decrease of the standard deviation by 1.8-3.7-fold. This new procedure will decrease the false discovery rates for peptide identifications using high-accuracy mass measurements.

A genome-scale map of expression for a mouse brain section obtained using voxelation

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed two-dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation, and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genes with unexpected patterns were identified, and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.

Spatial mapping of protein abundances in the mouse brain by voxelation integrated with high-throughput liquid chromatography-mass spectrometry

Temporally and spatially resolved mapping of protein abundance patterns within the mammalian brain is of significant interest for understanding brain function and molecular etiologies of neurodegenerative diseases; however, such imaging efforts have been greatly challenged by complexity of the proteome, throughput and sensitivity of applied analytical methodologies, and accurate quantitation of protein abundances across the brain. Here, we describe a methodology for comprehensive spatial proteome mapping that addresses these challenges by employing voxelation integrated with automated microscale sample processing, high-throughput liquid chromatography (LC) system coupled with high-resolution Fourier transform ion cyclotron resonance (FTICR) mass spectrometer, and a "universal" stable isotope labeled reference sample approach for robust quantitation. We applied this methodology as a proof-of-concept trial for the analysis of protein distribution within a single coronal slice of a C57BL/6J mouse brain. For relative quantitation of the protein abundances across the slice, an 18O-isotopically labeled reference sample, derived from a whole control coronal slice from another mouse, was spiked into each voxel sample, and stable isotopic intensity ratios were used to obtain measures of relative protein abundances. In total, we generated maps of protein abundance patterns for 1028 proteins. The significant agreement of the protein distributions with previously reported data supports the validity of this methodology, which opens new opportunities for studying the spatial brain proteome and its dynamics during the course of disease progression and other important biological and associated health aspects in a discovery-driven fashion.

Mass Measurement Accuracy in Analyses of Highly Complex Mixtures Based Upon Multidimensional Recalibration

Mass spectrometry combined with a range of on-line separation techniques has become a powerful tool for characterization of complex mixtures, including protein digests in proteomics studies. Accurate mass measurements can be compromised due to variations that occur in the course of an on-line separation, e.g., due to excessive space charge in an ion trap, temperature changes, or other sources of instrument "drift". We have developed a multidimensional recalibration approach that utilizes existing information on the likely mixture composition, taking into account variable conditions of mass measurements, and that corrects the mass calibration for sets of individual peaks binned by, for example, the total ion count for the mass spectrum, the individual peak abundance, m/z value, and liquid chromatography separation time. The multidimensional recalibration approach uses a statistical matching of measured masses in such measurements, often exceeding 105, to a significant number of putative known species likely to be present in the mixture (i.e., having known accurate masses), to identify a subset of the detected species that serve as effective calibrants. The recalibration procedure involves optimization of the mass accuracy distribution (histogram), to provide a more confident distinction between true and false identifications. We report the mass accuracy improvement obtained for data acquired using a TOF and several FTICR mass spectrometers. We show that the multidimensional recalibration better compensates for systematic mass measurement errors and also significantly reduces the mass error spread: i.e., both the accuracy and precision of mass measurements are improved. The mass measurement improvement is found to be virtually independent of the initial instrument calibration, allowing, for example, less frequent calibration. We show that this recalibration can provide sub-ppm mass measurement accuracy for measurements of a complex fungal proteome tryptic digest and provide improved confidence or numbers of peptide identifications.

Robust Algorithm for Alignment of Liquid Chromatography−Mass Spectrometry Analyses in an Accurate Mass and Time Tag Data Analysis Pipeline

Liquid chromatography coupled to mass spectrometry (LC-MS) and tandem mass spectrometry (LC-MS/MS) has become a standard technique for analyzing complex peptide mixtures to determine composition and relative abundance. Several high-throughput proteomics techniques attempt to combine complementary results from multiple LC-MS and LC-MS/MS analyses to provide more comprehensive and accurate results. To effectively collate and use results from these techniques, variations in mass and elution time measurements between related analyses need to be corrected using algorithms designed to align the various types of data: LC-MS/MS versus LC-MS/MS, LC-MS versus LC-MS/MS, and LC-MS versus LC-MS. Described herein are new algorithms referred to collectively as liquid chromatography-based mass spectrometric warping and alignment of retention times of peptides (LCMSWARP), which use a dynamic elution time warping approach similar to traditional algorithms that correct for variations in LC elution times using piecewise linear functions. LCMSWARP is compared to the equivalent approach based upon linear transformation of elution times. LCMSWARP additionally corrects for temporal drift in mass measurement accuracies. We also describe the alignment of LC-MS results and demonstrate their application to the alignment of analyses from different chromatographic systems, showing the suitability of the present approach for more complex transformations.

Characterization of the mouse brain proteome using global proteomic analysis complemented with cysteinyl-peptide enrichment

We report 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 nonredundant proteins ( approximately 34% of the predicted mouse proteome). A total of 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.

Hybridization of antisense oligonucleotides with alpha-sarcin loop region of Escherichia coli 23S rRNA

Binding of complementary oligonucleotides (ONs) with alpha-sarcin loop region (2638-2682) of Escherichia coli 23S rRNA was investigated. Four of the tested pentadecanucleotides efficiently bound to target sequences with association rate and equilibrium constants approximately 10(3) M(-1)s(-1) and 10(7) M(-1), respectively. ON S5 (CGAGAGGACCGGAGU) complementary to the sequence 2658-2672 displayed the highest affinity to the target. Activation energy for binding of ON S5 was measured to be 11 kcal/mol; this value corresponds to approximately 10% of the calculated enthalpy of the local RNA structure unfolding in the presence of this oligonucleotide. The activation energy value is evidence for the heteroduplex formation to occur via strand displacement pathway; the initiation of heteroduplex formation requires disruption of 1-2 base pairs in RNA hairpin.

Hybridization of antisense oligonucleotides with the 3'part of tRNA(Phe)

The interaction of antisense oligodeoxyribonucleotides with yeast tRNA(Phe) was investigated. 14-15-mers complementary to the 3'-terminal sequence including the ACCA end bind to the tRNA under physiological conditions. At low oligonucleotide concentrations the binding occurs at the unique complementary site. At higher oligonucleotide concentrations, the second oligonucleotide molecule binds to the complex due to non-perfect duplex formation in the T-loop stabilized by stacking between the two bound oligonucleotides. In these complexes the acceptor stem is open and the 5'-terminal sequence of the tRNA is accessible for binding of a complementary oligonucleotide. The results prove that the efficient binding of oligonucleotides to the 3'-terminal sequence of the tRNA occurs through initial binding to the single-stranded sequence ACCA followed by invasion in the acceptor stem and strand displacement.