Systematical Optimization of Reverse-Phase Chromatography for Shotgun Proteomics

We report the optimization of a common LC−MS/MS platform to maximize the number of proteins identified from a complex biological sample. The platform uses digested yeast lysate on a 75 μm internal diameter × 12 cm reverse-phase column that is combined with an LTQ-Orbitrap mass spectrometer. We first generated a yeast peptide mix that was quantified by multiple methods including the strategy of stable isotope labeling with amino acids in cell culture (SILAC). The peptide mix was analyzed on a highly reproducible, automated nanoLC−MS/MS system with systematic adjustment of loading amount, flow rate, elution gradient range and length. Interestingly, the column was found to be almost saturated by loading ∼1 μg of the sample. Whereas the optimal flow rate (∼0.2 μL/min) and elution buffer range (13−32% of acetonitrile) appeared to be independent of the loading amount, the best gradient length varied according to the amount of samples: 160 min for 1 μg of the peptide mix, but 40 min for 10 ng of the same sample. The effect of these parameters on elution peptide peak width is evaluated. After full optimization, 1012 proteins (clustered in 806 groups) with an estimated protein false discovery rate of ∼3% were identified in 1 μg of yeast lysate in a single 160-min LC−MS/MS run

Systematical Optimization of Reverse-Phase Chromatography for Shotgun Proteomics

We report the optimization of a common LC−MS/MS platform to maximize the number of proteins identified from a complex biological sample. The platform uses digested yeast lysate on a 75 μm internal diameter × 12 cm reverse-phase column that is combined with an LTQ-Orbitrap mass spectrometer. We first generated a yeast peptide mix that was quantified by multiple methods including the strategy of stable isotope labeling with amino acids in cell culture (SILAC). The peptide mix was analyzed on a highly reproducible, automated nanoLC−MS/MS system with systematic adjustment of loading amount, flow rate, elution gradient range and length. Interestingly, the column was found to be almost saturated by loading ∼1 μg of the sample. Whereas the optimal flow rate (∼0.2 μL/min) and elution buffer range (13−32% of acetonitrile) appeared to be independent of the loading amount, the best gradient length varied according to the amount of samples: 160 min for 1 μg of the peptide mix, but 40 min for 10 ng of the same sample. The effect of these parameters on elution peptide peak width is evaluated. After full optimization, 1012 proteins (clustered in 806 groups) with an estimated protein false discovery rate of ∼3% were identified in 1 μg of yeast lysate in a single 160-min LC−MS/MS run

Systematical Optimization of Reverse-Phase Chromatography for Shotgun Proteomics

We report the optimization of a common LC−MS/MS platform to maximize the number of proteins identified from a complex biological sample. The platform uses digested yeast lysate on a 75 μm internal diameter × 12 cm reverse-phase column that is combined with an LTQ-Orbitrap mass spectrometer. We first generated a yeast peptide mix that was quantified by multiple methods including the strategy of stable isotope labeling with amino acids in cell culture (SILAC). The peptide mix was analyzed on a highly reproducible, automated nanoLC−MS/MS system with systematic adjustment of loading amount, flow rate, elution gradient range and length. Interestingly, the column was found to be almost saturated by loading ∼1 μg of the sample. Whereas the optimal flow rate (∼0.2 μL/min) and elution buffer range (13−32% of acetonitrile) appeared to be independent of the loading amount, the best gradient length varied according to the amount of samples: 160 min for 1 μg of the peptide mix, but 40 min for 10 ng of the same sample. The effect of these parameters on elution peptide peak width is evaluated. After full optimization, 1012 proteins (clustered in 806 groups) with an estimated protein false discovery rate of ∼3% were identified in 1 μg of yeast lysate in a single 160-min LC−MS/MS run

Systematical Optimization of Reverse-Phase Chromatography for Shotgun Proteomics

We report the optimization of a common LC−MS/MS platform to maximize the number of proteins identified from a complex biological sample. The platform uses digested yeast lysate on a 75 μm internal diameter × 12 cm reverse-phase column that is combined with an LTQ-Orbitrap mass spectrometer. We first generated a yeast peptide mix that was quantified by multiple methods including the strategy of stable isotope labeling with amino acids in cell culture (SILAC). The peptide mix was analyzed on a highly reproducible, automated nanoLC−MS/MS system with systematic adjustment of loading amount, flow rate, elution gradient range and length. Interestingly, the column was found to be almost saturated by loading ∼1 μg of the sample. Whereas the optimal flow rate (∼0.2 μL/min) and elution buffer range (13−32% of acetonitrile) appeared to be independent of the loading amount, the best gradient length varied according to the amount of samples: 160 min for 1 μg of the peptide mix, but 40 min for 10 ng of the same sample. The effect of these parameters on elution peptide peak width is evaluated. After full optimization, 1012 proteins (clustered in 806 groups) with an estimated protein false discovery rate of ∼3% were identified in 1 μg of yeast lysate in a single 160-min LC−MS/MS run

Middle-Down Proteomics Reveals Dense Sites of Methylation and Phosphorylation in Arginine-Rich RNA-Binding Proteins

Post-translational modifications (PTMs) within arginine (Arg)-rich RNA-binding proteins, such as phosphorylation and methylation, regulate multiple steps in RNA metabolism. However, the identification of PTMs within Arg-rich domains with complete trypsin digestion is extremely challenging due to the high density of Arg residues within these proteins. Here, we report a middle-down proteomic approach coupled with electron-transfer dissociation (ETD) mass spectrometry to map previously unknown sites of phosphorylation and methylation within the Arg-rich domains of U1-70K and structurally similar RNA-binding proteins from nuclear extracts of human embryonic kidney (HEK)-293T cells. Notably, the Arg-rich domains in RNA-binding proteins are densely modified by methylation and phosphorylation compared with the remainder of the proteome, with methylation and phosphorylation favoring RSRS motifs. Although they favor a common motif, analysis of combinatorial PTMs within RSRS motifs indicates that phosphorylation and methylation do not often co-occur, suggesting that they may functionally oppose one another. Furthermore, we show that phosphorylation may modify interactions between Arg-rich proteins, as serine–arginine splicing factor 2 (SRSF2) has a stronger association with U1-70K and LUC7L3 upon dephosphorylation. Collectively, these findings suggest that the level of PTMs within Arg-rich domains may be among the highest in the proteome and a possible unexplored regulator of RNA-binding protein interactions

Systematic Approach for Validating the Ubiquitinated Proteome

Protein ubiquitination plays an essential regulatory role within all eukaryotes. Large-scale analyses of ubiquitinated proteins are usually performed by combining affinity purification strategies with mass spectrometry. However, there is no reliable method to systematically differentiate ubiquitinated species from copurified unmodified components. Here we report a simple strategy for the large-scale validation of ubiquitination by reconstructing virtual Western blots for proteins analyzed by gel electrophoresis and mass spectrometry. Because protein ubiquitination, especially polyubiquitination, causes a dramatic shift of molecular weight, the difference between experimental and expected molecular weight was used to confirm the status of ubiquitination. Experimental molecular weight of putative yeast ubiquitin-conjugates was computed from the value and distribution of spectral counts in the gel using a Gaussian curve fitting approach. Unmodified proteins in yeast cell lysate were also analyzed as a control to assess the accuracy of the method. Multiple thresholds that incorporated the mass of ubiquitin and/or experimental variations were evaluated with respect to sensitivity and specificity. Ultimately, only ∼30% of the candidate ubiquitin-conjugates were accepted based on the stringent filtering criteria, although they were purified under denaturing conditions. These accepted conjugates had an estimated false discovery rate of ∼8% and primarily consisted of proteins larger than 100 kDa. Compared with another validation method (i.e., identification of ubiquitinated lysine sites), ∼95% of the proteins with defined modification sites showed a convincing increase in molecular weight on the virtual Western blots. A second independent analysis indicated that the method can be simplified by excising fewer than ten gel bands. Therefore, this strategy establishes criteria necessary for the interpretation of ubiquitinated proteins

Systematic Approach for Validating the Ubiquitinated Proteome

Protein ubiquitination plays an essential regulatory role within all eukaryotes. Large-scale analyses of ubiquitinated proteins are usually performed by combining affinity purification strategies with mass spectrometry. However, there is no reliable method to systematically differentiate ubiquitinated species from copurified unmodified components. Here we report a simple strategy for the large-scale validation of ubiquitination by reconstructing virtual Western blots for proteins analyzed by gel electrophoresis and mass spectrometry. Because protein ubiquitination, especially polyubiquitination, causes a dramatic shift of molecular weight, the difference between experimental and expected molecular weight was used to confirm the status of ubiquitination. Experimental molecular weight of putative yeast ubiquitin-conjugates was computed from the value and distribution of spectral counts in the gel using a Gaussian curve fitting approach. Unmodified proteins in yeast cell lysate were also analyzed as a control to assess the accuracy of the method. Multiple thresholds that incorporated the mass of ubiquitin and/or experimental variations were evaluated with respect to sensitivity and specificity. Ultimately, only ∼30% of the candidate ubiquitin-conjugates were accepted based on the stringent filtering criteria, although they were purified under denaturing conditions. These accepted conjugates had an estimated false discovery rate of ∼8% and primarily consisted of proteins larger than 100 kDa. Compared with another validation method (i.e., identification of ubiquitinated lysine sites), ∼95% of the proteins with defined modification sites showed a convincing increase in molecular weight on the virtual Western blots. A second independent analysis indicated that the method can be simplified by excising fewer than ten gel bands. Therefore, this strategy establishes criteria necessary for the interpretation of ubiquitinated proteins

Galectin-3 Is a Candidate Biomarker for Amyotrophic Lateral Sclerosis: Discovery by a Proteomics Approach

The discovery of biomarkers for neurodegenerative diseases will have a major impact on the efficiency of therapeutic clinical trials and may be important for understanding basic pathogenic mechanisms. We have approached the discovery of protein biomarkers for amyotrophic lateral sclerosis (ALS) by profiling affected tissues in a relevant animal model and then validating the findings in human tissues. Ventral roots from SOD1G93A “ALS” mice were analyzed by label-free quantitative mass spectrometry, and the resulting data were compared with data for matched samples from nontransgenic littermates and transgenic mice carrying wild-type human SOD1 (SOD1WT). Of 1299 proteins, statistical inference of the data in the three groups identified 14 proteins that were dramatically altered in the ALS mice compared with the two control groups. The protein galectin-3 emerged as a lead biomarker candidate on the basis of its differential expression as assessed by immunoblot and immunocytochemistry in SOD1G93A mice as compared to controls and because it is a secreted protein that could potentially be measured in human biofluids. Spinal cord tissue from ALS patients also exhibited increased levels of galectin-3 when compared to controls. Further measurement of galectin-3 in cerebrospinal fluid samples showed that ALS patients had approximately twice as much galectin-3 as normal and disease controls. These results provide the proof of principle that biomarker identification in relevant and well-controlled animal models can be translated to human disease. The challenge is to validate our biomarker candidate proteins as true biomarkers for ALS that will be useful for diagnosis and/or monitoring disease activity in future clinical trials

Specific Proteomes of Hippocampal Regions CA2 and CA1 Reveal Proteins Linked to the Unique Physiology of Area CA2

The hippocampus is well established as an essential brain center for learning and memory. Within the hippocampus, recent studies show that area CA2 is important for social memory and is an anomaly compared to its better-understood neighboring region, CA1. Unlike CA1, CA2 displays a lack of typical synaptic plasticity, enhanced calcium buffering and extrusion, and resilience to cell death following injury. Although recent studies have identified multiple molecular markers of area CA2, the proteins that mediate the unique physiology, signaling, and resilience of this region are unknown. Using a transgenic GFP-reporter mouse line that expresses eGFP in CA2, we were able to perform targeted dissections of area CA2 and CA1 for proteomic analysis. We identified over 100 proteins with robustly enriched expression in area CA2 compared to CA1. Many of these proteins, including RGS14 and NECAB2, have already been shown to be enriched in CA2 and important for its function, while many more merit further study in the context of enhanced expression in this enigmatic brain region. Furthermore, we performed a comprehensive analysis of the entire data set (>2300 proteins) using a weighted protein co-expression network analysis. This identified eight distinct co-expressed patterns of protein co-enrichment associated with increased expression in area CA2 tissue (compared to CA1). The novel data set we present here reveals a specific CA2 hippocampal proteome, laying the groundwork for future studies and a deeper understanding of area CA2 and the proteins mediating its unique physiology and signaling