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Two major challenges in proteomics are the large number of proteins and their broad dynamic range in the cell. We exploited the abundance-dependent Michaelis-Menten kinetics of trypsin digestion to selectively digest and deplete abundant proteins with a method we call DigDeAPr. We validated the depletion mechanism with known yeast protein abundances, and we observed greater than threefold improvement in low-abundance human-protein identification and quantitation metrics. This methodology should be broadly applicable to many organisms, proteases and proteomic pipelines.

We describe a solid phase microextraction (SPME), multistep elution, transient isotachophoresis (tITP) capillary electrophoresis-tandem mass spectrometry (CE-MS/MS) procedure which employs a high sensitivity porous electrospray ionization (ESI) sprayer for the proteomic analysis of a moderately complex protein mixture. In order to improve comprehensiveness and sensitivity over a previously reported proteomic application of the ESI sprayer, we evaluated preconcentration with SPME and multistep elution prior to tITP stacking and CE separation. To maximize separation efficiency, we primarily employed electrokinetic methods for elution and separation after loading the sample by application of pressure. Conditions were developed for optimum simultaneous electrokinetic elution and sample stacking using a tryptic digest of 16 proteins to maximize peptide identifications and minimize band broadening. We performed comparative proteomic analysis of a dilution series using CE and nanoflow liquid chromatography (nLC). We found complementary peptide and protein identifications with larger quantities (100 ng) of a Pyrococcus furiosus tryptic digest, but with mass-limited amounts (5 ng) CE was 3 times more effective at identifying proteins. We attribute these gains in sensitivity to lower noise levels with the porous CE sprayer, illustrated by better signal-to-noise ratios of peptide precursor ions and associated higher XCorr values of identified peptides when compared directly to nLC. From comparative analysis of SPME-tITP-CE with direct injection CE, the SPME-tITP process improved comprehensiveness and sensitivity.

Herein we report the characterization and optimization of single-step inline enrichment of phosphopeptides directly from small amounts of whole cell and tissue lysates (100-500 μg) using a hydroxyapatite (HAP) microcolumn and Multidimensional Protein Identification Technology (MudPIT). In comparison to a triplicate HILIC-IMAC phosphopeptide enrichment study, ∼80% of the phosphopeptides identified using HAP-MudPIT were unique. Similarly, analysis of the consensus phosphorylation motifs between the two enrichment methods illustrates the complementarity of calcium- and iron-based enrichment methods and the higher sensitivity and selectivity of HAP-MudPIT for acidic motifs. We demonstrate how the identification of more multiply phosphorylated peptides from HAP-MudPIT can be used to quantify phosphorylation cooperativity. Through optimization of HAP-MudPIT on a whole cell lysate we routinely achieved identification and quantification of ca. 1000 phosphopeptides from a ∼1 h enrichment and 12 h MudPIT analysis on small quantities of material. Finally, we applied this optimized method to identify phosphorylation sites from a mass-limited mouse brain region, the amygdala (200-500 μg), identifying up to 4000 phosphopeptides per run.

Ideally, shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors that facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.

In the postgenomic era, proteomics has become a dominant field for identifying and quantifying the complex protein machinery of the cell. The expression levels, posttranslational modifications, and specific interactions of proteins control the biology of such processes as development, differentiation, and signal transduction. Studies of the proteins involved in these processes often lead to a better understanding of biology and of human disease. Powerful separation techniques and sensitive detection methods enable researchers to untangle these complicated networks of processes. CE coupled with either MS or LIF are two of the techniques that make this possible. This review will cover proven CE-based methods for proteomics on the cell and tissue level and their application in biological and clinical studies, relevant new developments in enabling technology such as microfluidic CE-MS demonstrated on model systems, and comment on the future of CE in proteomics.

The article discusses the technique of shotgun/ bottom-up proteomics for the analysis of protein. It states that bottom-up process characterizes protein by analyzing the peptides released from the protein through proteolysis. It discusses several techniques related to shotgun proteomics which include two dimensional polyacrylamide gel electrophoresis (20-PAGE), chromatographic methods and post-translational modification (PTM) analysis by Mass Spectrometry.

We describe a solid phase microextraction (SPME), multistep elution, transient isotachophoresis (tITP) capillary electrophoresis-tandem mass spectrometry (CE-MS/MS) procedure which employs a high sensitivity porous electrospray ionization (ESI) sprayer for the proteomic analysis of a moderately complex protein mixture. In order to improve comprehensiveness and sensitivity over a previously reported proteomic application of the ESI sprayer, we evaluated preconcentration with SPME and multistep elution prior to tITP stacking and CE separation. To maximize separation efficiency, we primarily employed electrokinetic methods for elution and separation after loading the sample by application of pressure. Conditions were developed for optimum simultaneous electrokinetic elution and sample stacking using a tryptic digest of 16 proteins to maximize peptide identifications and minimize band broadening. We performed comparative proteomic analysis of a dilution series using CE and nanoflow liquid chromatography (nLC). We found complementary peptide and protein identifications with larger quantities (100 ng) of a Pyrococcus furiosus tryptic digest, but with mass-limited amounts (5 ng) CE was 3 times more effective at identifying proteins. We attribute these gains in sensitivity to lower noise levels with the porous CE sprayer, illustrated by better signal-to-noise ratios of peptide precursor ions and associated higher XCorr values of identified peptides when compared directly to nLC. From comparative analysis of SPME-tITP-CE with direct injection CE, the SPME-tITP process improved comprehensiveness and sensitivity. [ABSTRACT FROM AUTHOR]

Global metabolomics has emerged as a powerful tool to interrogate cellular biochemistry at the systems level by tracking alterations in the levels of small molecules. One approach to define cellular dynamics with respect to this dysregulation of small molecules has been to consider metabolic flux as a function of time. While flux measurements have proven effective for model organisms, acquiring multiple time points at appropriate temporal intervals for many sample types (e.g., clinical specimens) is challenging. As an alternative, meta-analysis provides another strategy for delineating metabolic cause and effect perturbations. That is, the combination of untargeted metabolomic data from multiple pairwise comparisons enables the association of specific changes in small molecules with unique phenotypic alterations. We recently developed metabolomic software called metaXCMS to automate these types of higher order comparisons. Here we discuss the potential of metaXCMS for analyzing proteomic datasets and highlight the biological value of combining meta-results from both metabolomic and proteomic analyses. The combined meta-analysis has the potential to facilitate efforts in functional genomics and the identification of metabolic disruptions related to disease pathogenesis. [ABSTRACT FROM AUTHOR]