A data processing pipeline for mammalian proteome dynamics studies using stable isotope metabolic labeling

In a recent study, in vivo metabolic labeling using (15)N traced the rate of label incorporation among more than 1700 proteins simultaneously and enabled the determination of individual protein turnover rate constants over a dynamic range of three orders of magnitude (Price, J. C., Guan, S., Burlingame, A., Prusiner, S. B., and Ghaemmaghami, S. (2010) Analysis of proteome dynamics in the mouse brain. Proc. Natl. Acad. Sci. U. S. A. 107, 14508-14513). These studies of protein dynamics provide a deeper understanding of healthy development and well-being of complex organisms, as well as the possible causes and progression of disease. In addition to a fully labeled food source and appropriate mass spectrometry platform, an essential and enabling component of such large scale investigations is a robust data processing and analysis pipeline, which is capable of the reduction of large sets of liquid chromatography tandem MS raw data files into the desired protein turnover rate constants. The data processing pipeline described in this contribution is comprised of a suite of software modules required for the workflow that fulfills such requirements. This software platform includes established software tools such as a mass spectrometry database search engine together with several additional, novel data processing modules specifically developed for (15)N metabolic labeling. These fulfill the following functions: (1) cross-extraction of (15)N-containing ion intensities from raw data files at varying biosynthetic incorporation times, (2) computation of peptide (15)N isotopic incorporation distributions, and (3) aggregation of relative isotope abundance curves for multiple peptides into single protein curves. In addition, processing parameter optimization and noise reduction procedures were found to be necessary in the processing modules in order to reduce propagation of errors in the long chain of the processing steps of the entire workflow.     

The 15N isotope effect in Escherichia coli: A neutron can make the difference

Several techniques based on stable isotope labeling are used for quantitative MS. These include stable isotope metabolic labeling methods for cells in culture as well as live organisms with the assumption that the stable isotope has no effect on the proteome. Here, we investigate the ¹⁵N isotope effect on Escherichia coli cultures that were grown in either unlabeled (¹⁴N) or ¹⁵N‐labeled media by LC‐ESI‐MS/MS‐based relative protein quantification. Consistent protein expression level differences and altered growth rates were observed between ¹⁴N and ¹⁵N‐labeled cultures. Furthermore, targeted metabolite analyses revealed altered metabolite levels between ¹⁴N and ¹⁵N‐labeled bacteria. Our data demonstrate for the first time that the introduction of the ¹⁵N isotope affects protein and metabolite levels in E. coli and underline the importance of implementing controls for unbiased protein quantification using stable isotope labeling techniques.     

Hydroponic isotope labelling of entire plants (HILEP) for quantitative plant proteomics; an oxidative stress case study

Hydroponic isotope labelling of entire plants (HILEP) is a cost-effective method enabling metabolic labelling of whole and mature plants with a stable isotope such as (15)N. By utilising hydroponic media that contain (15)N inorganic salts as the sole nitrogen source, near to 100% (15)N-labelling of proteins can be achieved. In this study, it is shown that HILEP, in combination with mass spectrometry, is suitable for relative protein quantitation of seven week-old Arabidopsis plants submitted to oxidative stress. Protein extracts from pooled (14)N- and (15)N-hydroponically grown plants were fractionated by SDS-PAGE, digested and analysed by liquid chromatography electrospray ionisation tandem mass spectrometry (LC-ESI-MS/MS). Proteins were identified and the spectra of (14)N/(15)N peptide pairs were extracted using their m/z chromatographic retention time, isotopic distributions, and the m/z difference between the (14)N and (15)N peptides. Relative amounts were calculated as the ratio of the sum of the peak areas of the two distinct (14)N and (15)N peptide isotope envelopes. Using Mascot and the open source trans-proteomic pipeline (TPP), the data processing was automated for global proteome quantitation down to the isoform level by extracting isoform specific peptides. With this combination of metabolic labelling and mass spectrometry it was possible to show differential protein expression in the apoplast of plants submitted to oxidative stress. Moreover, it was possible to discriminate between differentially expressed isoforms belonging to the same protein family, such as isoforms of xylanases and pathogen-related glucanases (PR 2).     

Find Pairs: The Module for Protein Quantification of the PeakQuant Software Suite

Abstract Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in (14)N/(15)N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as (14)N/(15)N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.     

Study of early leaf senescence in Arabidopsis thaliana by quantitative proteomics using reciprocal 14N/15N labeling and difference gel electrophoresis

Leaf senescence represents the final stage of leaf development and is associated with fundamental changes on the level of the proteome. For the quantitative analysis of changes in protein abundance related to early leaf senescence, we designed an elaborate double and reverse labeling strategy simultaneously employing fluorescent two-dimensional DIGE as well as metabolic (15)N labeling followed by MS. Reciprocal (14)N/(15)N labeling of entire Arabidopsis thaliana plants showed that full incorporation of (15)N into the proteins of the plant did not cause any adverse effects on development and protein expression. A direct comparison of DIGE and (15)N labeling combined with MS showed that results obtained by both quantification methods correlated well for proteins showing low to moderate regulation factors. Nano HPLC/ESI-MS/MS analysis of 21 protein spots that consistently exhibited abundance differences in nine biological replicates based on both DIGE and MS resulted in the identification of 13 distinct proteins and protein subunits that showed significant regulation in Arabidopsis mutant plants displaying advanced leaf senescence. Ribulose 1,5-bisphosphate carboxylase/oxygenase large and three of its four small subunits were found to be down-regulated, which reflects the degradation of the photosynthetic machinery during leaf senescence. Among the proteins showing higher abundance in mutant plants were several members of the glutathione S-transferase family class phi and quinone reductase. Up-regulation of these proteins fits well into the context of leaf senescence since they are generally involved in the protection of plant cells against reactive oxygen species which are increasingly generated by lipid degradation during leaf senescence. With the exception of one glutathione S-transferase isoform, none of these proteins has been linked to leaf senescence before.     

Monitoring of changes in the membrane proteome during stationary phase adaptation of Bacillus subtilis using in vivo labeling techniques

Bacillus subtilis has been developed as a model system for physiological proteomics. However, thus far these studies have mainly been limited to cytoplasmic, extracellular, and cell-wall attached proteins. Although being certainly important for cell physiology, the membrane protein fraction has not been studied in comparable depth due to inaccessibility by traditional 2-DE-based workflows and limitations in reliable quantification. In this study, we now compare the potential of stable isotope labeling with amino acids (SILAC) and (14)N/(15)N-labeling for the analysis of bacterial membrane fractions in physiology-driven proteomic studies. Using adaptation of B. subtilis to amino acid (lysine) and glucose starvation as proof of principle scenarios, we show that both approaches provide similarly valuable data for the quantification of bacterial membrane proteins. Even if labeling with stable amino acids allows a more straightforward analysis of data, the (14)N/(15)N-labeling has some advantages in general such as labeling of all amino acids and thereby increasing the number of peptides for quantification. Both, SILAC as well as (14)N/(15)N-labeling are compatible with 2-DE, 2-D LC-MS/MS, and GeLC-MS/MS and thus will allow comprehensive simultaneous interrogation of cytoplasmic and enriched membrane proteomes.     

Automated comparative proteomics based on multiplex tandem mass spectrometry and stable isotope labeling

Comparative proteomic approaches using isotopic labeling and MS have become increasingly popular. Conventionally quantification is based on MS or extracted ion chromatogram (XIC) signals of differentially labeled peptides. However, in these MS-based experiments, the accuracy and dynamic range of quantification are limited by the high noise levels of MS/XIC data. Here we report a quantitative strategy based on multiplex (derived from multiple precursor ions) MS/MS data. One set of proteins was metabolically labeled with [13C6]lysine and [15N4]arginine; the other set was unlabeled. For peptide analysis after tryptic digestion of the labeled proteins, a wide precursor window was used to include both the light and heavy versions of each peptide for fragmentation. The multiplex MS/MS data were used for both protein identification and quantification. The use of the wide precursor window increased sensitivity, and the y ion pairs in the multiplex MS/MS spectra from peptides containing labeled and unlabeled lysine or arginine offered more information for, and thus the potential for improving, protein identification. Protein ratios were obtained by comparing intensities of y ions derived from the light and heavy peptides. Our results indicated that this method offers several advantages over the conventional XIC-based approach, including increased sensitivity for protein identification and more accurate quantification with more than a 10-fold increase in dynamic range. In addition, the quantification calculation process was fast, fully automated, and independent of instrument and data type. This method was further validated by quantitative analysis of signaling proteins in the EphB2 pathway in NG108 cells.     

15N-metabolic labeling for comparative plasma membrane proteomics in Arabidopsis cells

An important goal for proteomic studies is the global comparison of proteomes from different genotypes, tissues, or physiological conditions. This has so far been mostly achieved by densitometric comparison of spot intensities after protein separation by 2-DE. However, the physicochemical properties of membrane proteins preclude the use of 2-DE. Here, we describe the use of in vivo labeling by the stable isotope 15N as an alternative approach for comparative membrane proteomic studies in plant cells. We confirm that 15N-metabolic labeling of proteins is possible and efficient in Arabidopsis suspension cells. Quantification of 14N versus 15N MS signals reflects the relative abundance of 14N and 15N proteins in the sample analyzed. We describe the use of 15N-metabolic labeling to perform a partial comparative analysis of Arabidopsis cells following cadmium exposure. By focusing our attention on plasma membrane proteins, we were able to confidently identify proteins showing up to 5-fold regulation compared to unexposed cells. This study provides a proof of principle that 15N-metabolic labeling is a useful technique for comparative membrane proteome studies.     

The (15) N isotope effect as a means for correlating phenotypic alterations and affected pathways in a trait anxiety mouse model

Stable isotope labeling techniques hold great potential for accurate quantitative proteomics comparisons by MS. To investigate the effect of stable isotopes in vivo, we metabolically labeled high anxiety-related behavior (HAB) mice with the heavy nitrogen isotope (15) N. (15) N-labeled HAB mice exhibited behavioral alterations compared to unlabeled ((14) N) HAB mice in their depression-like phenotype. To correlate behavioral alterations with changes on the molecular level, we explored the (15) N isotope effect on the brain proteome by comparing protein expression levels between (15) N-labeled and (14) N HAB mouse brains using quantitative MS. By implementing two complementary in silico pathway analysis approaches, we were able to identify altered networks in (15) N-labeled HAB mice, including major metabolic pathways such as the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. Here, we discuss the affected pathways with regard to their relevance for the behavioral phenotype and critically assess the utility of exploiting the (15) N isotope effect for correlating phenotypic and molecular alterations.     

Automated Protein Turnover Calculations from 15N Partial Metabolic Labeling LC/MS Shotgun Proteomics Data

Protein turnover is a well-controlled process in which polypeptides are constantly being degraded and subsequently replaced with newly synthesized copies. Extraction of composite spectral envelopes from complex LC/MS shotgun proteomics data can be a challenging task, due to the inherent complexity of biological samples. With partial metabolic labeling experiments this complexity increases as a result of the emergence of additional isotopic peaks. Automated spectral extraction and subsequent protein turnover calculations enable the analysis of gigabytes of data within minutes, a prerequisite for systems biology high throughput studies. Here we present a fully automated method for protein turnover calculations from shotgun proteomics data. The approach enables the analysis of complex shotgun LC/MS ¹⁵N partial metabolic labeling experiments. Spectral envelopes of 1419 peptides can be extracted within an hour. The method quantifies turnover by calculating the Relative Isotope Abundance (RIA), which is defined as the ratio between the intensity sum of all heavy (¹⁵N) to the intensity sum of all light (¹⁴N) and heavy peaks. To facilitate this process, we have developed a computer program based on our method, which is freely available to download at http://promex.pph.univie.ac.at/protover.     

Automated structure determination of proteins with the SAIL-FLYA NMR method

The labeling of proteins with stable isotopes enhances the NMR method for the determination of 3D protein structures in solution. Stereo-array isotope labeling (SAIL) provides an optimal stereospecific and regiospecific pattern of stable isotopes that yields sharpened lines, spectral simplification without loss of information, and the ability to collect rapidly and evaluate fully automatically the structural restraints required to solve a high-quality solution structure for proteins up to twice as large as those that can be analyzed using conventional methods. Here, we describe a protocol for the preparation of SAIL proteins by cell-free methods, including the preparation of S30 extract and their automated structure analysis using the FLYA algorithm and the program CYANA. Once efficient cell-free expression of the unlabeled or uniformly labeled target protein has been achieved, the NMR sample preparation of a SAIL protein can be accomplished in 3 d. A fully automated FLYA structure calculation can be completed in 1 d on a powerful computer system.     

SILIP: a novel stable isotope labeling method for in planta quantitative proteomic analysis

Due to ease of manipulation, metabolic isotope coding of samples for proteomic analysis is typically performed in cell culture, thus preventing an accurate in vivo quantitative analysis, which is only achievable in intact organisms. To address this issue in plant biology, we developed SILIP (stable isotope labeling in planta) using tomato plants (Solanum lycopersicum cv. Rutgers) as a method that allows soil-grown plants to be efficiently labeled using a 14N/15N isotope coding strategy. After 2 months of growth on 14N- and 15N-enriched nitrogen sources, proteins were extracted from four distinct tomato tissues (roots, stems, leaves and flowers), digested, and analyzed by LC/MS/MS (data-dependent acquisition, DDA) and alternating low- and elevated-energy MS scans (data-independent acquisition, MS(E)). Using a derived relationship to generate a theoretical standard curve, the measured ratio of the M (monoisotopic) and M-1 isotopologues of 70 identified 15N-labeled peptides from 16 different proteins indicated that 15N incorporation was almost 99%, which is in excellent agreement with the 99.3% 15N-enriched nitrate used in the soil-based medium. Values for the various tissues ranged from 98.2 +/- 0.3% 15N incorporation in leaves to 98.8 2 +/- 0.2% in stems, demonstrating uniform labeling throughout the plant. In addition, SILIP is compatible with root-knot nematode (Meloidogyne spp.) development, and thus provides a new quantitative proteomics tool to study both plant and plant-microorganism systems.     

Characterization of an antagonist interleukin-6 dimer by stable isotope labeling,cross-linking,and mass spectrometry

The homodimeric form of a recombinant cytokine interleukin-6 (IL-6(D)) is known to antagonize IL-6 signaling. In this study, spatially proximal residues between IL-6 chains in IL-6(D) were identified using a method for specific recognition of intermolecular cross-linked peptides. Our strategy involved mixing 1:1 (15)N-labeled and unlabeled ((14)N) protein to form a mixture of isotopically labeled and unlabeled homodimers, which was chemically cross-linked. This cross-linked IL-6(D) was subjected to proteolysis by trypsin and the generated peptides were analyzed by electrospray ionization time-of-flight mass spectrometry (MS). Molecular ions from cross-linked peptides of intermolecular origin are labeled with [(15)N/(15)N] + [(15)N/(14)N] + [(14)N/(15)N] + [(14)N/(14)N] yielding readily identified triplet/quadruplet MS peaks. All other peptide species are labeled with [(15)N] + [(14)N] yielding doublet peaks. Intermolecular cross-linked peptides were identified by MS, and cross-linked residues were identified. This intermolecular cross-link detection method, which we have designated "mixed isotope cross-linking" MIX may have more general application to protein-protein interaction studies. The pattern of proximal residues found was consistent with IL-6(D) having a domain-swapped fold similar to IL-10 and interferon-gamma. This fold implies that IL-6(D)-mediated antagonism of IL-6 signaling is caused by obstruction of cooperative gp130 binding on IL-6(D), rather than direct blocking of gp-130-binding sites on IL-6(D).     

Rapid mass spectrometric analysis of 15N-Leu incorporation fidelity during preparation of specifically labeled NMR samples

Advances in NMR spectroscopy have enabled the study of larger proteins that typically have significant overlap in their spectra. Specific (15)N-amino acid incorporation is a powerful tool for reducing spectral overlap and attaining reliable sequential assignments. However, scrambling of the label during protein expression is a common problem. We describe a rapid method to evaluate the fidelity of specific (15)N-amino acid incorporation. The selectively labeled protein is proteolyzed, and the resulting peptides are analyzed using MALDI mass spectrometry. The (15)N incorporation is determined by analyzing the isotopic abundance of the peptides in the mass spectra using the program DEX. This analysis determined that expression with a 10-fold excess of unlabeled amino acids relative to the (15)N-amino acid prevents the scrambling of the (15)N label that is observed when equimolar amounts are used. MALDI TOF-TOF MS/MS data provide additional information that shows where the "extra" (15)N labels are incorporated, which can be useful in confirming ambiguous assignments. The described procedure provides a rapid technique to monitor the fidelity of selective labeling that does not require a lot of protein. These advantages make it an ideal way of determining optimal expression conditions for selectively labeled NMR samples.     

PepLine: a software pipeline for high-throughput direct mapping of tandem mass spectrometry data on genomic sequences

PepLine is a fully automated software which maps MS/MS fragmentation spectra of trypsic peptides to genomic DNA sequences. The approach is based on Peptide Sequence Tags (PSTs) obtained from partial interpretation of QTOF MS/MS spectra (first module). PSTs are then mapped on the six-frame translations of genomic sequences (second module) giving hits. Hits are then clustered to detect potential coding regions (third module). Our work aimed at optimizing the algorithms of each component to allow the whole pipeline to proceed in a fully automated manner using raw nucleic acid sequences (i.e., genomes that have not been "reduced" to a database of ORFs or putative exons sequences). The whole pipeline was tested on controlled MS/MS spectra sets from standard proteins and from Arabidopsis thaliana envelope chloroplast samples. Our results demonstrate that PepLine competed with protein database searching softwares and was fast enough to potentially tackle large data sets and/or high size genomes. We also illustrate the potential of this approach for the detection of the intron/exon structure of genes.     

Novel approach for peptide quantitation and sequencing based on 15N and 13C metabolic labeling

Here we describe a method for protein identification and quantification using stable isotopes via in vivo metabolic labeling of the hyperthermophilic crenarchaeon Sulfolobus solfataricus. Stable isotope labeling for quantitative proteomics is becoming increasingly popular; however, its usefulness in protein identification has not been fully exploited. We use both 15N and 13C labeling to create three different versions of the same peptide, corresponding to the unlabeled, 15N and 13C labeled versions. The peptide then appears as three different peaks in a TOF-MS scan and three corresponding sets of MS/MS spectra are obtained. With this information, the elemental carbon and nitrogen compositions for each peptide and each fragment can be calculated. When this is used as a constraint in database searching and/or de novo sequencing, the confidence of a match is increased (for an example intact peptide from 34 choices to 1). This makes the method a useful proteomic tool for both sequenced and unsequenced organisms. Furthermore, it allows for accurate protein quantitation (standard deviations over >4 peptides per protein were within 10%) of three phenotypes in one MS experiment. Abundances for each peptide are calculated by determining the relative areas of each of the three peaks in the TOF-MS spectrum.     

Identification and quantification of N-linked glycoproteins using hydrazide chemistry,stable isotope labeling and mass spectrometry

Quantitative proteome profiling using stable isotope protein tagging and automated tandem mass spectrometry (MS/MS) is an emerging technology with great potential for the functional analysis of biological systems and for the detection of clinical diagnostic or prognostic marker proteins. Owing to the enormous complexity of proteomes, their comprehensive analysis is an as-yet-unresolved technical challenge. However, biologically or clinically important information can be obtained if specific, information-rich protein classes, or sub-proteomes, are isolated and analyzed. Glycosylation is the most common post-translational modification. Here we describe a method for the selective isolation, identification and quantification of peptides that contain N-linked carbohydrates. It is based on the conjugation of glycoproteins to a solid support using hydrazide chemistry, stable isotope labeling of glycopeptides and the specific release of formerly N-linked glycosylated peptides via peptide- N-glycosidase F (PNGase F). The recovered peptides are then identified and quantified by MS/MS. We applied the approach to the analysis of plasma membrane proteins and proteins contained in human blood serum.     

Mascot file parsing and quantification (MFPaQ), a new software to parse, validate, and quantify proteomics data generated by ICAT and SILAC mass spectrometric analyses: application to the proteomics study of membrane proteins from primary human endothelial cells

Proteomics strategies based on nanoflow (nano-) LC-MS/MS allow the identification of hundreds to thousands of proteins in complex mixtures. When combined with protein isotopic labeling, quantitative comparison of the proteome from different samples can be achieved using these approaches. However, bioinformatics analysis of the data remains a bottleneck in large scale quantitative proteomics studies. Here we present a new software named Mascot File Parsing and Quantification (MFPaQ) that easily processes the results of the Mascot search engine and performs protein quantification in the case of isotopic labeling experiments using either the ICAT or SILAC (stable isotope labeling with amino acids in cell culture) method. This new tool provides a convenient interface to retrieve Mascot protein lists; sort them according to Mascot scoring or to user-defined criteria based on the number, the score, and the rank of identified peptides; and to validate the results. Moreover the software extracts quantitative data from raw files obtained by nano-LC-MS/MS, calculates peptide ratios, and generates a non-redundant list of proteins identified in a multisearch experiment with their calculated averaged and normalized ratio. Here we apply this software to the proteomics analysis of membrane proteins from primary human endothelial cells (ECs), a cell type involved in many physiological and pathological processes including chronic inflammatory diseases such as rheumatoid arthritis. We analyzed the EC membrane proteome and set up methods for quantitative analysis of this proteome by ICAT labeling. EC microsomal proteins were fractionated and analyzed by nano-LC-MS/MS, and database searches were performed with Mascot. Data validation and clustering of proteins were performed with MFPaQ, which allowed identification of more than 600 unique proteins. The software was also successfully used in a quantitative differential proteomics analysis of the EC membrane proteome after stimulation with a combination of proinflammatory mediators (tumor necrosis factor-alpha, interferon-gamma, and lymphotoxin alpha/beta) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.     

Comparison of full versus partial metabolic labeling for quantitative proteomics analysis in Arabidopsis thaliana

In recent years a variety of quantitative proteomics techniques have been developed, allowing characterization of changes in protein abundance in a variety of organisms under various biological conditions. Because it allows excellent control for error at all steps in sample preparation and analysis, full metabolic labeling using (15)N has emerged as an important strategy for quantitative proteomics, having been applied in a variety of organisms from yeast to Arabidopsis and even rats. However, challenges associated with complete replacement of (14)N with (15)N can make its application in many complex eukaryotic systems impractical on a routine basis. Extending a concept proposed by Whitelegge et al. (Whitelegge, J. P., Katz, J. E., Pihakari, K. A., Hale, R., Aguilera, R., Gomez, S. M., Faull, K. F., Vavilin, D., and Vermaas, W. (2004) Subtle modification of isotope ratio proteomics; an integrated strategy for expression proteomics. Phytochemistry 65, 1507-1515), we investigate an alternative strategy for quantitative proteomics that relies upon the subtle changes in isotopic envelope shape that result from partial metabolic labeling to compare relative abundances of labeled and unlabeled peptides in complex mixtures. We present a novel algorithm for the automated quantitative analysis of partial incorporation samples via LC-MS. We then compare the performance of partial metabolic labeling with traditional full metabolic labeling for quantification of controlled mixtures of labeled and unlabeled Arabidopsis peptides. Finally we evaluate the performance of each technique for comparison of light- versus dark-grown Arabidopsis with respect to reproducibility and numbers of peptide and protein identifications under more realistic experimental conditions. Overall full metabolic labeling and partial metabolic labeling prove to be comparable with respect to dynamic range, accuracy, and reproducibility, although partial metabolic labeling consistently allows quantification of a higher percentage of peptide observations across the dynamic range. This difference is especially pronounced at extreme ratios. Ultimately both full metabolic labeling and partial metabolic labeling prove to be well suited for quantitative proteomics characterization.