Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between samples using mass spectrometry. ReDi labeling is performed using either regular (light) or deuterated (heavy) forms of formaldehyde and sodium cyanoborohydride to add two methyl groups to each free amine. Here we demonstrate a robust protocol for ReDi labeling and quantitative comparison of complex protein mixtures. Protein samples for comparison are digested into peptides, labeled to carry either light or heavy methyl tags, mixed, and co-analyzed by LC-MS/MS. Relative protein abundances are quantified by comparing the ion chromatogram peak areas of heavy and light labeled versions of the constituent peptide extracted from the full MS spectra. The method described here includes sample preparation by reversed-phase solid phase extraction, on-column ReDi labeling of peptides, peptide fractionation by basic pH reversed-phase (BPRP) chromatography, and StageTip peptide purification. We discuss advantages and limitations of ReDi labeling with respect to other methods for stable isotope incorporation. We highlight novel applications using ReDi labeling as a fast, inexpensive, and accurate method to compare protein abundances in nearly any type of sample.     

In-depth Qualitative and Quantitative Profiling of Tyrosine Phosphorylation Using a Combination of Phosphopeptide Immunoaffinity Purification and Stable Isotope Dimethyl Labeling

Several mass spectrometry-based assays have emerged for the quantitative profiling of cellular tyrosine phosphorylation. Ideally, these methods should reveal the exact sites of tyrosine phosphorylation, be quantitative, and not be cost-prohibitive. The latter is often an issue as typically several milligrams of (stable isotope-labeled) starting protein material are required to enable the detection of low abundance phosphotyrosine peptides. Here, we adopted and refined a peptidecentric immunoaffinity purification approach for the quantitative analysis of tyrosine phosphorylation by combining it with a cost-effective stable isotope dimethyl labeling method. We were able to identify by mass spectrometry, using just two LC-MS/MS runs, more than 1100 unique non-redundant phosphopeptides in HeLa cells from about 4 mg of starting material without requiring any further affinity enrichment as close to 80% of the identified peptides were tyrosine phosphorylated peptides. Stable isotope dimethyl labeling could be incorporated prior to the immunoaffinity purification, even for the large quantities (mg) of peptide material used, enabling the quantification of differences in tyrosine phosphorylation upon pervanadate treatment or epidermal growth factor stimulation. Analysis of the epidermal growth factor-stimulated HeLa cells, a frequently used model system for tyrosine phosphorylation, resulted in the quantification of 73 regulated unique phosphotyrosine peptides. The quantitative data were found to be exceptionally consistent with the literature, evidencing that such a targeted quantitative phosphoproteomics approach can provide reproducible results. In general, the combination of immunoaffinity purification of tyrosine phosphorylated peptides with large scale stable isotope dimethyl labeling provides a cost-effective approach that can alleviate variation in sample preparation and analysis as samples can be combined early on. Using this approach, a rather complete qualitative and quantitative picture of tyrosine phosphorylation signaling events can be generated.Reversible tyrosine phosphorylation plays an important role in numerous cellular processes like growth, differentiation, and migration. Phosphotyrosine signaling is tightly controlled by the balanced action of protein-tyrosine kinases and protein-tyrosine phosphatases. Aberrant tyrosine phosphorylation has been suggested to be an underlying cause in multiple cancers (1). Therefore, the identification of tyrosine phosphorylated proteins and the investigation into their involvement in signaling pathways are important. Several groups have attempted to comprehensively study tyrosine phosphorylation by proteomics means (2–5). However, large scale identification of tyrosine phosphorylation sites by MS can be hindered by the low abundance of tyrosine phosphorylated proteins. Especially, signaling intermediates are usually low abundance proteins that show substoichiometric phosphorylation levels. In addition, the identification by mass spectrometry of phosphopeptides from a complex cellular lysate digest is often complicated by ion suppression effects due to a high background of non-phosphorylated peptides. Enrichment of tyrosine phosphorylated proteins or peptides prior to mass spectrometric detection is therefore essential. Traditionally, antibodies against phosphorylated tyrosine have been used to immunoprecipitate tyrosine phosphorylated proteins from cultured cells (2–4, 6–8). This phosphoprotein immunoaffinity purification method has for example been used to study the global dynamics of phosphotyrosine signaling events after EGF¹ stimulation using stable isotope labeling by amino acids in cell culture (SILAC) (2). This approach led to the identification of known and previously unidentified signaling proteins in the EGF receptor (EGFR) pathway, including their temporal activation profile after stimulation of the EGFR, providing crucial information for modeling signaling events in the cell. However, as the identification and quantification of these phosphorylated proteins in these studies were not necessarily based on tyrosine phosphorylated peptides but largely on non-phosphorylated peptides, little information is derived on the exact site(s) of tyrosine phosphorylation. Also, binding partners of tyrosine phosphorylated proteins, which themselves are not tyrosine phosphorylated, might be co-precipitated and impair the tyrosine phosphorylation quantification. Immunoaffinity purification of phosphotyrosine peptides, rather than proteins, using anti-phosphotyrosine antibodies (5, 9–16) significantly facilitates the identification of the site(s) of phosphorylation as it greatly alleviates most of the above mentioned problems because the tyrosine phosphorylated site can be directly identified and quantified.Accurate MS-based quantification is typically performed by stable isotope labeling. The isotopes can be incorporated metabolically during cell culture as in SILAC (17) or chemically as in an isobaric tag for relative and absolute quantitation (iTRAQ) (18) or stable isotope dimethyl labeling (19–21). Typically, the most precise quantification can be obtained by metabolic labeling as the different samples can be combined at the level of intact cells (22). However, metabolic labeling is somewhat limited to biological systems that can be grown in culture, and the medium may have an effect on the growth and development of the cells. iTRAQ has been used in conjunction with phosphotyrosine peptide immunoprecipitation (5). As the chemical labeling is performed before immunoprecipitation, the differentially labeled samples can be precipitated together, thereby neutralizing the potentially largest source of variation. However, as this phosphotyrosine peptide immunoprecipitation is typically performed on several hundreds of micrograms to milligrams of protein sample, iTRAQ provides in these cases a rather cost-prohibitive means.Here, we present an optimized immunoaffinity purification approach for the analysis of tyrosine phosphorylation combined with stable isotope dimethyl labeling (19–21, 23). We efficiently enriched and identified by MS 1112 unique phosphopeptides derived from 4 mg of starting protein material without any further affinity chromatographic enrichment whereby up to 80% of the peptides analyzed in the final LC run were phosphotyrosine peptides. We further advanced the method by introducing triplex stable isotope dimethyl labeling prior to immunoprecipitation. We quantified differences in tyrosine phosphorylation upon pervanadate treatment or EGF stimulation to detect site-specific changes in tyrosine phosphorylation. 128 unique phosphotyrosine peptides were identified and quantified upon pervanadate treatment. By using an internal standard comprising both mock and pervanadate-treated samples, we could more confidently identify and quantify phosphorylation sites that are strongly regulated and on-off situations. Analysis of EGF-stimulated HeLa cells resulted in the quantification of 73 unique phosphotyrosine peptides. Most of the up-regulated phosphotyrosine peptides that were identified have been reported previously to be involved in the EGFR signaling pathway, validating our approach. However, for the first time, we found TFG to also become highly tyrosine phosphorylated upon EGF stimulation together with some tyrosine phosphorylation sites on for example IRS2, SgK269, and DLG3 that have not been firmly established earlier to be involved in EGFR signaling.In general, we show that the combination of immunoaffinity purification of tyrosine phosphorylated peptides with large scale chemical stable isotope dimethyl labeling provides a cost-effective approach that can alleviate variation in immunoprecipitation and LC-MS as samples can be combined before immunoprecipitation and the necessity of performing additional enrichment is removed by an optimization of the protocol. With only a single LC-MS run, already a rather complete qualitative and quantitative picture of a signaling event can be generated.     

质谱MRM技术结合稳定同位素标记法的应用进展

定量检测是生物化学研究中最常见的一种分析方法,可以显示蛋白质的差异表达及翻译后修饰情况,有助于了解酶的活性状态、信号通路及活性分子间相互作用网络。生物质谱以其高通量、高灵敏度、高分辨率等特点使定量检测技术有了质的飞跃,促进了蛋白质组学各个领域的发展。近年来出现的质谱多反应监测技术引起了众多研究者的关注,已被广泛应用在目标定量蛋白质组学研究中。通过查阅文献资料,综述了质谱多反应监测技术结合稳定同位素标记法在蛋白质组学研究中的应用原理及特点,总结了近年来的一些研究进展,最后分析了该技术的局限性及可能优化改进的方面,以便使该项技术能够被相关研究人员很好利用。     

Stable Isotope Labeling in Zebrafish Allows in Vivo Monitoring of Cardiac Morphogenesis

Quantitative proteomics is an important tool to study biological processes, but so far it has been challenging to apply to zebrafish. Here, we describe a large scale quantitative analysis of the zebrafish proteome using a combination of stable isotope labeling and liquid chromatography-mass spectrometry (LC-MS). Proteins derived from the fully labeled fish were used as a standard to quantify changes during embryonic heart development. LC-MS-assisted analysis of the proteome of activated leukocyte cell adhesion molecule zebrafish morphants revealed a down-regulation of components of the network required for cell adhesion and maintenance of cell shape as well as secondary changes due to arrest of cellular differentiation. Quantitative proteomics in zebrafish using the stable isotope-labeling technique provides an unprecedented resource to study developmental processes in zebrafish.Over the past years, mass spectrometry-based proteomics has been widely used to analyze complex biological samples (1). Although the latest generation of MS instrumentation allows proteome-wide analysis, protein quantitation is still a challenge (2, 3). Metabolic labeling using stable isotopes has been used for almost a century. Today, the most commonly used techniques for relative protein quantification are based on ¹⁵N labeling and stable isotope labeling by amino acids in cell culture (SILAC)¹ (4, 5). SILAC was initially developed for cell culture experiments, and recent approaches extended labeling to living organisms, including bacteria (6), yeast (7), flies (8), worms (9), and rodents (10, 11). In addition, several pulsed SILAC (also known as dynamic SILAC) experiments were performed to assess protein dynamics in cell culture and living animals (12–15).The zebrafish (Danio rerio) has proved to be an important model organism to study developmental processes. It also serves as a valuable tool to investigate basic pathogenic principles of human diseases such as cardiovascular disorders and tissue regeneration (16). So far, most researchers rely on immunohistochemistry and Western blots for semi-quantitative protein analysis, an approach that is hampered by the paucity of reliable antibodies in zebrafish. Proteomics approaches that depend on two-dimensional gel approaches (17–19) have not gained wide popularity because of issues with workload, reproducibility, and sensitivity (20, 21).Another approach for protein quantitation is the chemical modification of peptides, and so far several isobaric tagging methods, including ICAT (22), iTRAQ (23), ¹⁸O (24), and dimethyl labeling (25), have been proven to be successful methods.Recently, a quantitative phosphopeptide study based on dimethyl labeling in zebrafish showed the consequences of a morpholino-based kinase knockdown (26). However, each chemical modification bears the risk of nonspecific and incomplete labeling, which complicates mass spectrometric data interpretation.Alternatively, a metabolic labeling study with stable isotopes was recently performed on adult zebrafish by the administration of a mouse diet containing [¹³C₆]lysine (Lys-6) (27). Feeding adult zebrafish with the Lys-6-containing mouse chow leads to an incorporation rate of 40%, and SILAC labeling was used to investigate protein and tissue turnover.Here, we have developed a SILAC fish diet made in-house for the complete SILAC labeling of zebrafish. We established a Lys-6-containing diet as a universal fish food for larval and adult zebrafish. The method allows accurate quantitation of large numbers of proteins, and we proved our approach by the analysis of embryonic heart development. In addition, we investigated the consequences of the morpholino-based activated leukocyte cell adhesion molecule (ALCAM) knockdown during development and identified the lipid anchor protein Paralemmin as a down-regulated protein during heart development. Our approach yielded a huge resource of protein expression data for zebrafish development and provided the basis for more refined studies depending on accurate SILAC protein quantification.     

激光捕获显微切割技术结合^18O标记定量蛋白质组技术在胃癌标志物筛查中的应用研究

建立一种更加精确地分离鉴定胃癌特异肿瘤标志物的定量蛋白质组学技术.首先采用激光捕获显微切割技术(LCM)纯化胃腺癌细胞及胃黏膜良性上皮细胞,将裂解的样本总蛋白经过1D SDS-PAGE预分离,然后采用17O/16O分别标记两种样本酶切后的多肽混合物.结合纳升级液相色谱(Nano-HPLC-MS/MS)定量地鉴定胃癌细胞和胃黏膜良性上皮细胞的差异表达蛋白.共筛选出78个差异表达蛋白,其中42个蛋白质在胃癌组织中表达上调,36个蛋白质下调.Western blot技术验证了其中几个差异蛋白(moesin,periostin,annexin A2,annexin A4)的表达,与蛋白质组学研究的结果一致.LOM技术结合18O稳定同位素标记的定量蛋白质组学技术,为研究胃癌发生机制、筛选胃癌的分子标志物提供了新的思路,亦为诸如胃癌等复杂体系蛋白质的分离鉴定提供了新的技术选择.     

A Software Toolkit and Interface for Performing Stable Isotope Labeling and Top3 Quantification Using Progenesis LC-MS

Abstract Numerous software packages exist to provide support for quantifying peptides and proteins from mass spectrometry (MS) data. However, many support only a subset of experimental methods or instrument types, meaning that laboratories often have to use multiple software packages. The Progenesis LC-MS software package from Nonlinear Dynamics is a software solution for label-free quantitation. However, many laboratories using Progenesis also wish to employ stable isotope-based methods that are not natively supported in Progenesis. We have developed a Java programming interface that can use the output files produced by Progenesis, allowing the basic MS features quantified across replicates to be used in a range of different experimental methods. We have developed post-processing software (the Progenesis Post-Processor) to embed Progenesis in the analysis of stable isotope labeling data and top3 pseudo-absolute quantitation. We have also created export ability to the new data standard, mzQuantML, produced by the Proteomics Standards Initiative to facilitate the development and standardization process. The software is provided to users with a simple graphical user interface for accessing the different features. The underlying programming interface may also be used by Java developers to develop other routines for analyzing data produced by Progenesis.     

An Isotope Labeling Strategy for Methyl TROSY Spectroscopy

Recently we have shown that HMQC spectra of protonated methyl groups in high molecular weight, highly deuterated proteins have large enhancements in sensitivity and resolution relative to HSQC-generated data sets. These enhancements derive from a TROSY effect in which complete cancellation of intra-methyl (1)H-(1)H and (1)H-(13)C dipolar interactions occurs for 50% of the signal in the case of HMQC, so long as the methyl is attached to a molecule tumbling in the macromolecular limit (Tugarinov, V., Hwang, P.M., Ollerenshaw, J.E., Kay, L.E. J. Am. Chem. Soc. (2003) 125, 10420-10428; Ollerenshaw, J.E., Tugarinov, V. and Kay, L.E. Magn. Reson. Chem. (2003) 41, 843-852. The first demonstration of this effect was made for isoleucine delta1 methyl groups in a highly deuterated 82 kDa protein, malate synthase G. As with (1)H-(15)N TROSY spectroscopy high levels of deuteration are critical for maximizing the TROSY effect. Here we show that excellent quality methyl TROSY spectra can be recorded on U-[(2)H] Iledelta1-[(13)CH(3)] Leu,Val-[(13)CH(3)/(12)CD(3)] protein samples, significantly extending the number of probes available for structural and dynamic studies of high molecular weight systems.     

Stable Isotope Metabolic Labeling with a Novel 15N-Enriched Bacteria Diet for Improved Proteomic Analyses of Mouse Models for Psychopathologies

The identification of differentially regulated proteins in animal models of psychiatric diseases is essential for a comprehensive analysis of associated psychopathological processes. Mass spectrometry is the most relevant method for analyzing differences in protein expression of tissue and body fluid proteomes. However, standardization of sample handling and sample-to-sample variability are problematic. Stable isotope metabolic labeling of a proteome represents the gold standard for quantitative mass spectrometry analysis. The simultaneous processing of a mixture of labeled and unlabeled samples allows a sensitive and accurate comparative analysis between the respective proteomes. Here, we describe a cost-effective feeding protocol based on a newly developed ¹⁵N bacteria diet based on Ralstonia eutropha protein, which was applied to a mouse model for trait anxiety. Tissue from ¹⁵N-labeled vs. ¹⁴N-unlabeled mice was examined by mass spectrometry and differences in the expression of glyoxalase-1 (GLO1) and histidine triad nucleotide binding protein 2 (Hint2) proteins were correlated with the animals'' psychopathological behaviors for methodological validation and proof of concept, respectively. Additionally, phenotyping unraveled an antidepressant-like effect of the incorporation of the stable isotope ¹⁵N into the proteome of highly anxious mice. This novel phenomenon is of considerable relevance to the metabolic labeling method and could provide an opportunity for the discovery of candidate proteins involved in depression-like behavior. The newly developed ¹⁵N bacteria diet provides researchers a novel tool to discover disease-relevant protein expression differences in mouse models using quantitative mass spectrometry.     

Determination of an Angiotensin II-regulated Proteome in Primary Human Kidney Cells by Stable Isotope Labeling of Amino Acids in Cell Culture (SILAC)

Angiotensin II (AngII), the major effector of the renin-angiotensin system, mediates kidney disease progression by signaling through the AT-1 receptor (AT-1R), but there are no specific measures of renal AngII activity. Accordingly, we sought to define an AngII-regulated proteome in primary human proximal tubular cells (PTEC) to identify potential AngII activity markers in the kidney. We utilized stable isotope labeling with amino acids (SILAC) in PTECs to compare proteomes of AngII-treated and control cells. Of the 4618 quantified proteins, 83 were differentially regulated. SILAC ratios for 18 candidates were confirmed by a different mass spectrometry technique called selected reaction monitoring. Both SILAC and selected reaction monitoring revealed heme oxygenase-1 (HO-1) as the most significantly up-regulated protein in response to AngII stimulation. AngII-dependent regulation of the HO-1 gene and protein was further verified in PTECs. To extend these in vitro observations, we overlaid a network of significantly enriched gene ontology terms from our AngII-regulated proteins with a dataset of differentially expressed kidney genes from AngII-treated wild type mice and AT-1R knock-out mice. Five gene ontology terms were enriched in both datasets and included HO-1. Furthermore, HO-1 kidney expression and urinary excretion were reduced in AngII-treated mice with PTEC-specific AT-1R deletion compared with AngII-treated wild-type mice, thus confirming AT-1R-mediated regulation of HO-1. Our in vitro approach identified novel molecular markers of AngII activity, and the animal studies demonstrated that these markers are relevant in vivo. These interesting proteins hold promise as specific markers of renal AngII activity in patients and in experimental models.     

Stable Isotope Techniques and Applications for Primatologists

Stable isotope biogeochemistry is useful for quantifying the feeding ecology of modern and extinct primates. Over the past three decades, substantial advances have been made in our knowledge of the physiological causes of isotopic patterns as well as effective methodology to prepare samples for isotopic analysis. Despite these advances, the potential of stable isotope biogeochemistry has yet to be fully exploited by primate researchers, perhaps due to the prolific and somewhat daunting nature of the isotopic literature. I here aim to present a cogent overview of stable isotope applications to nonhuman primate feeding ecology. I review the factors that influence ecological patterns in carbon, nitrogen, and oxygen stable isotopes. I present methods for collecting and preparing samples of tooth enamel and bone mineral hydroxyapatite, bone collagen, fur and hair keratin, blood, feces, and urine for isotope analysis. I discuss both the existing and potential applications of these isotopic patterns to primate feeding ecology. Lastly, I point out some of the pitfalls to avoid when interpreting and comparing isotopic results.     

Nic1 Inactivation Enables Stable Isotope Labeling with 13C615N4-Arginine in Schizosaccharomyces pombe

Stable Isotope Labeling by Amino Acids (SILAC) is a commonly used method in quantitative proteomics. Because of compatibility with trypsin digestion, arginine and lysine are the most widely used amino acids for SILAC labeling. We observed that Schizosaccharomyces pombe (fission yeast) cannot be labeled with a specific form of arginine, ¹³C₆¹⁵N₄-arginine (Arg-10), which limits the exploitation of SILAC technology in this model organism. We hypothesized that in the fission yeast the guanidinium group of ¹³C₆¹⁵N₄-arginine is catabolized by arginase and urease activity to ¹⁵N₁-labeled ammonia that is used as a precursor for general amino acid biosynthesis. We show that disruption of Ni²⁺-dependent urease activity, through deletion of the sole Ni²⁺ transporter Nic1, blocks this recycling in ammonium-supplemented EMMG medium to enable ¹³C₆¹⁵N₄-arginine labeling for SILAC strategies in S. pombe. Finally, we employed Arg-10 in a triple-SILAC experiment to perform quantitative comparison of G1 + S, M, and G2 cell cycle phases in S. pombe.Stable Isotope Labeling by Amino acids in Cell culture (SILAC)¹ is one of the most widely used methods in quantitative proteomics (1). It involves in vivo metabolic labeling of cell cultures (or small organisms) with different versions of stable isotope-labeled amino acids (2). To maximize the number of peptides that can be quantified after proteome digestion with trypsin, proteins are usually differentially labeled with different forms of lysine and arginine (3): l-lysine (Lys-0) and l-arginine (Arg-0); ²H₄-lysine(Lys-4) and ¹³C₆-arginine (Arg-6); or ¹³C₆-¹⁵N₂-lysine (Lys-8) and ¹³C₆-¹⁵N₄-arginine (Arg-10). The availability of multiple forms of labeled lysine and arginine support the application of SILAC in duplex (comparison of two states) or triplex (comparison of three states) formats. Efficient anabolic pathways mean that lysine and arginine are not essential for growth of wild type yeast cells. Auxotrophic mutants that are defective in these pathways can be used to switch yeast to an absolute dependence upon the provision of these amino acids in the external medium. Consequently, mutations in arginine and lysine biosynthesis pathways can be used to drive the complete labeling of all tryptic peptides with specific forms of these amino acids (4, 5). SILAC has been used in quantitative proteomics in several yeast species, but most widely in Saccharomyces cerevisiae (6) (budding yeast) and Schizosaccharomyces pombe (fission yeast). S. pombe is extensively exploited to study cell cycle control (7), heterochromatin (8), and differentiation (9) and is increasingly the subject of large-scale quantitative proteomic studies (10, 11).A major challenge that is faced when using SILAC in fission yeast, is metabolic conversion of arginine to other amino acids such as proline, glutamine, and lysine (5). This partial labeling of additional amino acids after the conversion event produces spectra with complex isotope clusters that makes the downstream analysis challenging and error-prone. Inactivation of the “arginine conversion pathway” by removal of the orthinine transferase, Car2, effectively overcomes this problem to support the use of arginine labeling in SILAC-based experiments (5). Although this exploitation of the car2.Δ mutation now enables SILAC technology in fission yeast, the choice of amino acids that can be employed remains limited. Only one form of heavy arginine (R6) is currently used alongside three forms of heavy lysine (Lys-4, Lys-6, and Lys-8) (5, 12). Surprisingly, we could not find any studies that use arginine (Arg-10) in fission yeast, even though this is a widely exploited reagent for labeling other cell types (1).Here, we show that labeling of fission yeast with Arg-10 leads to a general misincorporation of the stable isotope label that prevents the identification of labeled peptides. We hypothesize that successive arginase and urease activities catabolize the guanidinium group of Arg-10 to ¹⁵N₁-labeled ammonia. This labeled ammonium is then used as a general precursor for amino acid biosynthesis. Disruption of Ni²⁺-dependent urease activity through deletion of Ni²⁺ transporter Nic1 in ammonium-supplemented medium, blocked this recycling to support ¹³C₆¹⁵N₄-arginine labeling SILAC strategies. As a proof of principle we employ Arg-10 in a triple-SILAC experiment to perform quantitative comparison of G1 + S, M, and G2 cell cycle phases in S. pombe.     

Quantification of the Dynamic Phosphorylation Process of ERK Using Stable Isotope Dilution Selective Reaction Monitoring Mass Spectrometry

Mitogen‐activated protein (MAP) kinase signaling is critical for various cellular responses, including cell proliferation, differentiation, and cell death. The MAP kinase cascade is conserved in the eukaryotic kingdom as a three‐tiered kinase module—MAP kinase kinase kinase, MAP kinase kinase, and MAP kinase—that transduces signals via sequential phosphorylation upon stimulation. Dual phosphorylation of MAP kinase on the conserved threonine‐glutamic acid‐tyrosine (TEY) motif is essential for its catalytic activity and signal activation; however, the molecular mechanism by which the two residues are phosphorylated remains elusive. In the present study, the pattern of dual phosphorylation of extracellular signal‐regulated kinase (ERK) is profiled on the TEY motif using stable isotope dilution (SID)‐selective reaction monitoring (SRM) mass spectrometry (MS) to elucidate the order and magnitude of endogenous ERK phosphorylation in cellular model systems. The SID‐SRM‐MS analysis of phosphopeptides demonstrates that tyrosine phosphorylation in the TEY motif is dynamic, while threonine phosphorylation is static. Analyses of the mono‐phosphorylatable mutants ERK^T202A and ERK^Y204F indicate that phosphorylation of tyrosine is not affected by the phosphorylation state of threonine, while threonine phosphorylation depends on tyrosine phosphorylation. The data suggest that dual phosphorylation of ERK is a highly ordered and restricted mechanism determined by tyrosine phosphorylation.     

激光捕获显微切割技术结合18O标记定量蛋白质组技术在胃癌标志物筛查中的应用研究

建立一种更加精确地分离鉴定胃癌特异肿瘤标志物的定量蛋白质组学技术．首先采用激光捕获显微切割技术(LCM)纯化胃腺癌细胞及胃黏膜良性上皮细胞,将裂解的样本总蛋白经过1D SDS-PAGE预分离,然后采用¹⁸O/¹⁶O分别标记两种样本酶切后的多肽混合物．结合纳升级液相色谱(Nano-HPLC-MS/MS)定量地鉴定胃癌细胞和胃黏膜良性上皮细胞的差异表达蛋白．共筛选出78个差异表达蛋白,其中42个蛋白质在胃癌组织中表达上调,36个蛋白质下调．Western blot 技术验证了其中几个差异蛋白(moesin, periostin, annexin A2, annexin A4)的表达,与蛋白质组学研究的结果一致．LCM技术结合¹⁸O稳定同位素标记的定量蛋白质组学技术,为研究胃癌发生机制、筛选胃癌的分子标志物提供了新的思路,亦为诸如胃癌等复杂体系蛋白质的分离鉴定提供了新的技术选择．     

Rapid Validation of Mascot Search Results via Stable Isotope Labeling, Pair Picking, and Deconvolution of Fragmentation Patterns

Conventional LC-MS/MS data analysis matches each precursor ion and fragmentation pattern to their best fit within databases of theoretical spectra, yielding a peptide identification. Confidence is estimated by a score but can be validated by statistics, false discovery rates, and/or manual validation. A weakness is that each ion is evaluated independently, discarding potentially useful cross-correlations. In a classical approach to de novo sequence analysis, mixtures of peptides differing only in a carboxyl-terminal isotopic label yield fragmentation spectra with single, unlabeled b-type ions but pairs of isotope-labeled y-type ions, facilitating confident assignments. To apply this principle to identification by fragmentation pattern matching, we developed Validator, software that recognizes isotopic peptide pairs and compares their identifications and fragmentation patterns. Testing Validator 1 on a Mascot results file from FT-ICR LC-MS/MS of ¹⁶O/¹⁸O-labeled yeast cell lysate peptides yielded 2,775 peptide pairs sharing a common identification but differing in carboxyl-terminal label. Comparing observed b- and y-ions with the predicted fragmentation pattern improved the threshold Mascot score for 5% false discovery from 36 to 22, significantly increasing both sensitivity and specificity. Validator 2, which identifies pairs by precursor mass difference alone before comparing observed fragmentation with that predicted by Mascot, found 2,021 isotopic pairs, similarly achieving improved sensitivity and specificity. Finally Validator 3, which finds pairs based on mass difference alone and then deconvolutes fragmentation patterns independently of Mascot, found 964 predicted peptides. Validator 3 allowed raw mass spectrometry data to be mined not only to validate Mascot results but also to discover peptides missed by Mascot. Using standard desktop hardware, the Validator 1–3 software processed the 11,536 spectra in the 93-MB Mascot .DAT file in less than 6 min (32 spectra/s), revealing high confidence peptide identifications without regard to Mascot score, far faster than manual or other independent validation methods.MS/MS combined with informatics analysis is now a uniquely powerful approach for identifying the components of complex protein samples (1–3). Although new technologies have dramatically enhanced the speed, sensitivity, and precision of LC-MS/MS instrumentation (4), data analysis has neither kept pace with nor taken full advantage of these advances. Determining peptide sequences from fragment ion spectra remains a difficult problem, and three main strategies have matured (5). In de novo sequencing, the peptide sequence is inferred directly from the fragment ion spectra, and many algorithms have been developed to automate this process, including Lutefisk (6), PepNovo (7), NovoHMM (8), Peptide Identification via Integer linear Optimization (PILOT) (9), and others (10–13). Incomplete fragmentation patterns and low signal to noise (10) make this method difficult to implement as an exclusive means of peptide identification.The most commonly used method involves comparing experimental MS/MS spectra to theoretical peptide fragmentation patterns derived from protein sequence databases (4) and reporting the best peptide match, which is then propagated forward through the process of determining likely protein components. Several programs are commonly used, including SEQUEST (14, 15), Mascot (16), and X! Tandem (17, 18). What these algorithms share is the determination of a score for a spectrum-peptide match and subsequently a protein identification, and it is the way in which these scores are assigned and interpreted that distinguishes them (19).The third method for spectrum-peptide matching is a hybrid of de novo and database searching (5) in which small lengths of sequence are generated directly from the fragment ion spectra, and these “sequence tags” (20) are used to corroborate spectrum-database matches. Popular implementations of this strategy include DirecTag (21), GutenTag (22), and MultiTag (23). The limitations to this method include the requirement for consecutive fragmentation ions and the reliance on de novo algorithms to identify sequence tags.Database search is highly susceptible to both overreporting false positives (low specificity) and underreporting true positives (low sensitivity). The search engines provide different scoring systems that cannot be directly compared, as the rankings of spectral quality are often based on arbitrary cutoff values. Recent research has focused less on the sequence matching algorithms themselves but more on the statistics used to evaluate the resulting match scores (24). PeptideProphet was one of the first algorithms developed to evaluate match scores and assign probabilities by evaluating each match with respect to all other peptide assignments. By using machine learning techniques (an expectation-maximization algorithm), PeptideProphet was shown to have high discriminating power for database search results (25). Initially developed for SEQUEST search results, PeptideProphet has been subsequently adapted for use with database search results from Mascot and X! Tandem. These components are combined in Scaffold, a commercial software suite developed by Proteome Software. An alternative approach is to filter the primary data to exclude poor quality MS/MS scans prior to the database search (26), thereby enhancing the likely significance of each reported match.Using a false discovery rate instead of a false-positive rate is now the standard statistical measure for reporting error rates in data sets with large numbers of features (e.g. proteomics or genomics data) (5, 27). Target-decoy searching as an estimate of false discovery rate (FDR)¹ involves first constructing a database of decoy peptides (28, 29), and this strategy is being incorporated into PeptideProphet (30, 31). For each peptide-spectrum match, the target spectrum is queried against a second (decoy) database with characteristics similar to those of the first (e.g. a database of reversed or random peptides). Matches to the decoy database are considered false discoveries, and the number of matches above a particular cutoff score threshold is reported. The target-decoy search option is now available in the newest version (version 2.2) of the database search engine Mascot (Matrix Science).Despite these advances in mass spectrometry, database searching, and statistical approaches to validating matches, the process of analyzing mass spectrometry data remains time-consuming and computer processor-intensive, often requiring several steps and various data transformations (19). To overcome these limitations, we developed a fast and efficient method for peptide identification validation that minimizes the false discovery rate. Our algorithm relies on data from stable isotopic labeling, which is a standard method for quantifying relative protein abundance in complex mixtures (see Ref. 32 and references therein). Carboxyl-terminal labeling methods, including trypsin-catalyzed ¹⁸O exchange (33), result in a mixture of pairs of chemically identical but isotopically distinct peptides. The “light” and “heavy” peptides co-elute from HPLC but are readily distinguished by precursor mass (Fig. 1A). Each peptide also has an isotopic envelope comprised of isotopologues, molecules that are identical in composition except they can contain any number of isotopes. In the case of trypsin-catalyzed ¹⁸O exchange, two ¹⁸O atoms are substituted for the two carboxyl-terminal ¹⁶O atoms. Comparison of CID fragmentation patterns of carboxyl terminus-labeled light and heavy precursors (or isotopologues) distinguishes b-type and y-type ions (34, 35). The carboxyl-terminal fragments (y-ions) appear as light (¹⁶O) and heavy (¹⁸O-substituted) forms, but the amino-terminal fragments (b-ions) display a single shared mass (Fig. 1, B–D).Open in a separate windowFig. 1.Peptide pair identification strategy. A, shown is an example of experimental spectra of a ¹⁶O/¹⁸O-peptide pair. Each peptide has an isotopic envelope comprised of three to four different isotopologues containing zero to three molecules of ¹³C, ¹⁵N, or other naturally occurring stable isotopes. The ¹⁸O envelope is shifted by about 2.0 Da, reflecting the difference in mass due to the substitution of two ¹⁸O atoms. Note that the difference of 2.0 Da is due to the peptide having a 2+ charge state. Peptide pairs with a 1+ charge would be separated by about 4.0 Da. B, the b-type and y-type ions from the collision-induced dissociation of a peptide are shown. Any carboxyl-terminal substitution (as in ¹⁸O, indicated by *) will affect the y-ions exclusively. C, idealized sample MS/MS spectra from the peptide and ions in B. The spectra from the ¹⁶O- and ¹⁸O-peptide forms have similar patterns, although the peak heights may be different. D, top, the two spectra from C are overlaid to demonstrate that the b-ions will have a nearly identical mass-to-charge ratio, whereas the y-ions will have a shift reflective of the stable isotope substitution. In the example given, peaks “a” and “k” from C are both b-ions and therefore overlap, whereas peaks “b” and “l” are y-ions with l being shifted due to the substitution of two ¹⁸O atoms. Shifted ions are indicated with a horizontal bar underneath. By observing which ions overlap and which have shifted, the identities of the b- and y-ions can be inferred (D, bottom).The technique of using isotopic pairs to enhance peptide identification is not new, and several authors have recognized that isotopic labeling could be used to differentiate carboxyl-terminal from amino-terminal peptide fragments to facilitate peptide sequence analysis (2, 33, 35–38). This method has been productively applied to de novo analysis (12, 39–45) and peptide mass fingerprinting (46). In addition, analogous techniques have been applied to the analysis of mixtures of modified and unmodified peptides by probing for peptide mass differences that match known post-translational modifications (47); other groups have used MS/MS spectra information to corroborate these matches and remove noise (48, 49). Finally, isotopic labeling with ¹⁸O has been used for manual validation of peptide identifications by observing the predicted mass shift of y-ions (50). Nevertheless, this strategy has yet to be harnessed as a means for automated data analysis and peptide search validation.The goal of this study was to develop a set of software tools designed to provide rapid and automatic validation of peptide assignments by Mascot and to determine the relative benefit of reducing false discovery and the magnitude of loss of bona fide identifications. We hypothesized that the characteristic shifting of y-type ions between fragmentation spectra of light and heavy precursors might provide a robust check for validity of peptide assignment by database search. Here we demonstrate the feasibility of quickly and efficiently analyzing searched mass spectrometry data, determining within minutes which peptide and protein assignments are likely valid. In its simplest form, Validator 1, identified isotopic pairs in a Mascot results file and improved the 5% FDR cutoff from a Mascot score of 36 to 22, thereby capturing many true identifications that would otherwise have been discarded. A more advanced algorithm, Validator 3, that considers only precursor ion mass, charge, and fragmentation spectral data to identify isotopic pairs independently of any peptide identifications, not only rapidly validated the Mascot results but also discovered peptides that Mascot had failed to match. Our software suite, Validator 1–3, provides new and robust tools for rapid validation of searched LC-MS/MS data obtained in stable isotope experiments, offering improved sensitivity and specificity over database searching alone.     

Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC)-Based Strategy for Proteome-Wide Thermodynamic Analysis of Protein-Ligand Binding Interactions

Described here is a quantitative mass spectrometry-based proteomics method for the large-scale thermodynamic analysis of protein-ligand binding interactions. The methodology utilizes a chemical modification strategy termed, Stability of Proteins from Rates of Oxidation (SPROX), in combination with a Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) approach to compare the equilibrium folding/unfolding properties of proteins in the absence and presence of target ligands. The method, which is general with respect to ligand, measures the ligand-induced changes in protein stability associated with protein-ligand binding. The methodology is demonstrated in a proof-of-principle study in which the well-characterized protein-drug interaction between cyclosporine A (CsA) and cyclophilin A was successfully analyzed in the context of a yeast cell lysate. A control experiment was also performed to assess the method''s false positive rate of ligand discovery, which was found to be on the order of 0.4 - 3.5%. The new method was utilized to characterize the adenosine triphosphate (ATP)-interactome in Saccharomyces cerevisiae using the nonhydrolyzable ATP analog, adenylyl imidodiphosphate (AMP-PNP), and the proteins in a yeast cell lysate. The new methodology enabled the interrogation of 526 yeast proteins for interactions with ATP using 2035 peptide probes. Ultimately, 325 peptide hits from 139 different proteins were identified. Approximately 70% of the hit proteins identified in this work were not previously annotated as ATP binding proteins. However, nearly two-thirds of the newly discovered ATP interacting proteins have known interactions with other nucleotides and co-factors (e.g. NAD and GTP), DNA, and RNA based on GO-term analyses. The current work is the first proteome-wide profile of the yeast ATP-interactome, and it is the largest proteome-wide profile of any ATP-interactome generated, to date, using an energetics-based method. The data is available via ProteomeXchange with identifiers PXD000858, DOI 10.6019/PXD000858, and PXD000860.The characterization of protein-ligand interactions is important in many areas of biochemical research from fundamental studies of biological processes to understanding drug action. Currently, the most widely used methods for proteome-wide analyses of protein-ligand binding interactions are those that combine an affinity purification step with a mass spectrometry-based proteomics analysis. Such methods have provided a wealth of information about protein-protein interaction networks in different proteomes (1–4), and they have helped identify the protein targets of small molecules (5–7). However, a significant drawback to their use is the need for specially designed ligands to facilitate the affinity purification. This has prompted the development of more general methods for protein-ligand binding analyses that can be performed directly in solution and do not require derivatization and/or immobilization of the ligand. Several such methods involving the use of chromatography co-elution (8), protease susceptibility (9), and energetics-based approaches (10–15) have recently been reported.Energetics-based approaches are especially attractive for protein-ligand binding analyses because they can be both quantitative and general with respect to ligand class. Two energetics-based approaches, the stability of proteins from rates of oxidation (SPROX)¹ (10, 16, 17) and pulse proteolysis techniques (13, 18), have shown promise for protein-ligand binding analyses on the proteomic scale, but so far have been limited in their proteomic coverage. Although the pulse proteolysis technique does utilize targeted mass spectrometry-based proteomics analyses for the identification of hit proteins, the technique relies on gel-based strategies for the resolution, detection, and quantitation of potential protein targets (13, 18). This reliance on gel-based strategies for protein resolution, detection, and quantitation, ultimately limits the complexity of protein samples that can be interrogated for ligand binding. In contrast, the SPROX technique has been interfaced with conventional bottom-up shotgun proteomics platforms that exploit the capabilities of modern LC-MS/MS systems to resolve, detect, and quantify the protein components of complex biological mixtures (10, 16, 17).A key limitation to the bottom-up shotgun proteomics protocols developed for SPROX analyses, to date, is that they require the detection and quantitation of methionine-containing peptides to report on the thermodynamic stability of the proteins to which they map. Although the frequency of methionine residues in proteins is relatively low (∼2.5%) (19), the large majority of proteins have at least one methionine. Because one methionine residue can report on the global equilibrium folding/unfolding properties of the protein or protein domain to which it maps, the scope of SPROX is not fundamentally limited by the relatively low frequency of methionine residues in proteins. Rather, the protein coverage in proteome-wide SPROX experiments is limited by the practicalities associated with the comprehensive detection and quantitation of methionine-containing peptides in the bottom-up shotgun proteomics experiment.The SPROX protocol described here utilizes a stable isotope labeling with amino acids in cell culture (SILAC)-based strategy to expand the protein coverage in proteome-wide SPROX experiments by enabling any peptide (i.e. methionine-containing or not) that is identified and quantified in a bottom-up shotgun proteomics experiment to report on the stability of the protein to which it maps. As part of the work described here the capabilities of this new method for protein-ligand binding analysis (referred to hereafter as SILAC-SPROX) are demonstrated and benchmarked in two protein-ligand binding studies. In the first part of this work, the endogenous proteins in a yeast cell lysate are analyzed for binding to cyclosporine A (CsA), an immunosuppressant with well-characterized protein targets (5, 20). In the second part of this work, the endogenous proteins in a yeast cell lysate are analyzed for binding to adenylyl imidodiphosphate (AMP-PNP), a nonhydrolyzable analog of the ubiquitous enzyme co-factor, adenosine triphosphate (ATP), which has less well-characterized protein targets. In the CsA binding study, the already well-characterized tight-binding interaction between CsA and cyclophilin A (21–23) was successfully detected and quantified using the methodology. A number of known and unknown protein binding interactions of ATP were identified and quantified in the ATP-binding experiments described here. The SILAC-SPROX approach shows promise for future studies of protein-ligand interactions at the systems level (e.g. in cellular processes and disease states).     

鸟类组织稳定同位素分析样品的预处理方法

稳定同位素分析样品在进行同位素质谱分析之前需要适当的处理,然而如何处理尚未形成统一的标准。本文结合鸟类组织稳定同位素分析样品的测试流程,介绍了鸟类学研究领域稳定同位素技术应用中涉及的分析样品解冻清洗、干燥、分离纯化、研磨和储存等前处理方法,并对不同组织常用的处理方法及分析样品测定中有待解决的问题展开了讨论。旨在抛砖引玉,为利用稳定同位素技术开展鸟类学方面的研究提供科学参考和技术支持。