Minimizing resolution of isotopically coded peptides in comparative proteomics

Stable isotopes are now widely used to quantify concentration changes in proteomics. This paper focuses on the resolution of isotopically coded peptides and how isotope effects occurring during chromatographic separations can be minimized. Heavy isotope derivatizing agents used in this work were the commercially available 2H8-ICAT reagent and 13C4-succinic anhydride. The ICAT reagent derivatizes cysteine-containing peptides, whereas the succinic anhydride reacts with primary amine groups in peptides. It was observed during reversed-phase chromatography of peptides from a BSA tryptic digest differentially labeled with the 2Hr and 2H8-ICAT reagents that resolution of the isoforms exceeded 0.5 with 20% of the peptides in the digest. Three-fourths of the peptides in this group contained two cysteine residues and were doubly labeled. Only 23% of the peptides labeled with a single ICAT residue had a resolution greater than 0.4. The resolution of peptides differentially labeled with 13C- and 12C-succinate never exceeded +/- 0.01, even in the case of peptides from the BSA digest labeled with 2 mol of succinate. Because this value is within the limits of the method used to determine resolution, it was concluded the 13C- and 12C-coded isoforms of labeled peptides did not resolve. The isotope ratio in the case of 13C/12C coding could be determined from a single mass spectrum taken at any point in the elution profile. This enabled isotope ratio analysis to be completed early in the elution of a peptide from chromatography columns.     

Characterisation of global protein expression by two-dimensional electrophoresis and mass spectrometry: proteomics of Toxoplasma gondii.

The development of tools for the analysis of global gene expression is vital for the optimal exploitation of the data on parasite genomes that are now being generated in abundance. Recent advances in two-dimensional electrophoresis (2-DE), mass spectrometry and bioinformatics have greatly enhanced the possibilities for mapping and characterisation of protein populations. We have employed these developments in a proteomics approach for the analysis of proteins expressed in the tachyzoite stage of Toxoplasma gondii. Over 1000 polypeptides were reproducibly separated by high-resolution 2-DE using the pH ranges 4-7 and 6-11. Further separations using narrow range gels suggest that at least 3000-4000 polypeptides should be resolvable by 2-DE using multiple single pH unit gels. Mass spectrometry was used to characterise a variety of protein spots on the 2-DE gels. Peptide mass fingerprints, acquired by matrix-assisted laser desorption/ionisation-(MALDI) mass spectrometry, enabled unambiguous protein identifications to be made where full gene sequence information was available. However, interpretation of peptide mass fingerprint data using the T. gondii expressed sequence tag (EST) database was less reliable. Peptide fragmentation data, acquired by post-source decay mass spectrometry, proved a more successful strategy for the putative identification of proteins using the T. gondii EST database and protein databases from other organisms. In some instances, several protein spots appeared to be encoded by the same gene, indicating that post-translational modification and/or alternative splicing events may be a common feature of functional gene expression in T. gondii. The data demonstrate that proteomic analyses are now viable for T. gondii and other protozoa for which there are good EST databases, even in the absence of complete genome sequence. Moreover, proteomics is of great value in interpreting and annotating EST databases.     

UNiquant, a program for quantitative proteomics analysis using stable isotope labeling

Stable isotope labeling (SIL) methods coupled with nanoscale liquid chromatography and high resolution tandem mass spectrometry are increasingly useful for elucidation of the proteome-wide differences between multiple biological samples. Development of more effective programs for the sensitive identification of peptide pairs and accurate measurement of the relative peptide/protein abundance are essential for quantitative proteomic analysis. We developed and evaluated the performance of a new program, termed UNiquant, for analyzing quantitative proteomics data using stable isotope labeling. UNiquant was compared with two other programs, MaxQuant and Mascot Distiller, using SILAC-labeled complex proteome mixtures having either known or unknown heavy/light ratios. For the SILAC-labeled Jeko-1 cell proteome digests with known heavy/light ratios (H/L = 1:1, 1:5, and 1:10), UNiquant quantified a similar number of peptide pairs as MaxQuant for the H/L = 1:1 and 1:5 mixtures. In addition, UNiquant quantified significantly more peptides than MaxQuant and Mascot Distiller in the H/L = 1:10 mixtures. UNiquant accurately measured relative peptide/protein abundance without the need for postmeasurement normalization of peptide ratios, which is required by the other programs.     

The use of a novel quantitation strategy based on Reductive Isotopic Di-Ethylation (RIDE) to evaluate the effect of glufosinate on the unicellular algae Ostreococcus tauri

We report a novel stable-isotope labeling strategy for quantitative proteomics analysis. The method consists of labeling N-termini and lysine ε-amino groups through reductive amination using acetaldehyde. This allows isotope labeling using pairs of either 2H/1H or 13C/12C without mass spectrum overlap. Our labeling procedure, which is significantly different than that developed for dimethylation, can be completed with little trace of partial ethylation; non-labeled peptides represent less than 0.05% of all peptides. Co-elution of both isotopic 13C/12C peptide pairs was observed in all cases, simplifying data analysis, which can be performed using standard commercial software such as Mascot Distiller. A 13C/12C labeled mix in a 1:1 ratio from a complex extract digest of the unicellular algae Ostreococcus tauri, showed a relative standard deviation of less than 14%. This quantitative method was used to characterize O. tauri in the presence of glufosinate, an herbicide which inhibits glutamine synthetase. Blocking glutamine synthetase significantly reduced the expression of several enzymes and transporters involved in nitrogen assimilation and the expression of a number of proteins involved in various stresses including oxidative damage response were up-regulated.     

Mass spectrometry-based proteomics turns quantitative

The field of proteomics is built on technologies to analyze large numbers of proteins--ideally the entire proteome--in the same experiment. Mass spectrometry (MS) has been successfully used to characterize proteins in complex mixtures, but results so far have largely been qualitative. Two recently developed methodologies offer the opportunity to obtain quantitative proteomic information. Comparing the signals from the same peptide under different conditions yields a rough estimate of relative protein abundance between two proteomes. Alternatively, and more accurately, peptides are labeled with stable isotopes, introducing a predictable mass difference between peptides from two experimental conditions. Stable isotope labels can be incorporated 'post-harvest', by chemical approaches or in live cells through metabolic incorporation. This isotopic handle facilitates direct quantification from the mass spectra. Using these quantitative approaches, precise functional information as well as temporal changes in the proteome can be captured by MS.     

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.     

A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen

In order to assess the biological function of proteins and their modifications for understanding signaling mechanisms within cells as well as specific biomarkers to disease, it is important that quantitative information be obtained under different experimental conditions. Stable isotope labeling is a powerful method for accurately determining changes in the levels of proteins and PTMs; however, isotope labeling experiments suffer from limited dynamic range resulting in signal change ratios of less than approximately 20:1 using most commercial mass spectrometers. Label-free approaches to relative quantification in proteomics such as spectral counting have gained popularity since no additional chemistries are needed. Here, we show a label-free method for relative quantification based on the TIC from peptide MS/MS spectra collected from data-dependent runs can be used effectively as a quantitative measure and expands the dynamic range over isotope labeling experiments allowing for abundance differences up to approximately 60:1 in a screen for proteins that bind to phosphotyrosine residues.     

Stable isotope labeling of Arabidopsis thaliana cells and quantitative proteomics by mass spectrometry

Quantitative analysis of protein expression is an important tool for the examination of complex biological systems. Albeit its importance, quantitative proteomics is still a challenging task because of the high dynamic range of protein amounts in the cell and the variation in the physical properties of proteins. Stable isotope labeling by amino acids in cell culture (SILAC) has been successfully used in yeast and mammalian cells to measure relative protein abundance by mass spectrometry. Here we show for the first time that proteins from Arabidopsis thaliana cell cultures can be selectively isotope-labeled in vivo by growing cells in the presence of a single stable isotope-labeled amino acid. Among the tested amino acids ([2H3]-leucine, [13C6]arginine, and [2H4]lysine), [13C6]arginine proved to be the most suitable. Incorporation of [13C6]arginine into the proteome was homogeneous and reached efficiencies of about 80%. [13C6]Arginine-labeled A. thaliana suspension cells were used to study the regulation of glutathione S-transferase expression in response to abiotic stress caused by salicylic acid and to identify proteins that bind specifically to phosphorylated 14-3-3 binding motifs on synthesized bait peptides in affinity purification experiments. In conclusion, the combination of stable isotope labeling of plant cells and mass spectrometry is a powerful technology that can be applied to study complex biological processes that involve changes in protein expression such as cellular responses to various kinds of stress or activation of cell signaling.     

A method to determine 18 O kinetic isotope effects in the hydrolysis of nucleotide triphosphates

A method to determine 18 O kinetic isotope effects (KIEs) in the hydrolysis of GTP that is generally applicable to reactions involving other nucleotide triphosphates is described. Internal competition, where the substrate of the reaction is a mixture of 18 O-labeled and unlabeled nucleotides, is employed, and the change in relative abundance of the two species in the course of the reaction is used to calculate KIE. The nucleotide labeled with 18 O at sites of mechanistic interest also contains 13C at all carbon positions, whereas the 16 O-labeled nucleotide is depleted of 13C. The relative abundance of the labeled and unlabeled substrates or products is reflected in the carbon isotope ratio (13C/12C) in GTP or GDP, which is determined by the use of a liquid chromatography-coupled isotope ratio mass spectrometer (LC-coupled IRMS). The LC is coupled to the IRMS by an Isolink interface. Carbon isotope ratios can be determined with accuracy and precision greater than 0.04% and are consistent over an order of magnitude in sample amount. KIE values for Ras/NF1(333)-catalyzed hydrolysis of [beta18 O3,13C]GTP were determined by change in the isotope ratio of GTP or GDP or the ratio of the isotope ratio of GDP to that of GTP. KIE values computed in the three ways agree within 0.1%, although the method using the ratio of isotope ratios of GDP and GTP gives superior precision (<0.1%). A single KIE measurement can be conducted in 25 min with less than 5 microg nucleotide reaction product.     

Mass spectrometry for high throughput quantitative proteomics in plant research: lessons from thylakoid membranes

Proteomics seeks to monitor the flux of protein through cells under variable developmental and environmental influences as programmed by the genome. Consequently, it is necessary to measure changes in protein abundance and turnover rate as faithfully as possible. In the absence of non-invasive technologies, the majority of proteomics approaches involve destructive sampling at various time points to obtain 'snapshots' that periodically report the genomes's product. The work has fallen to separations technologies coupled to mass spectrometry, for high throughput protein identification. Quantitation has become the major challenge facing proteomics as the field matures. Because of the variability of day-to-day measurements of protein quantities by mass spectrometry, a common feature of quantitative proteomics is the use of stable isotope coding to distinguish control and experimental samples in a mixture that can be profiled in a single experiment. To address limitations with separation technologies such as 2D-gel electrophoresis, alternative systems are being introduced including multi-dimensional chromatography. Strategies that accelerate throughput for mass spectrometry are also emerging and the benefits of these 'shotgun' protocols will be considered in the context of the thylakoid membrane and photosynthesis. High resolution Fourier-transform mass spectrometry is bringing increasingly accurate mass measurements to peptides and a variety of gas-phase dissociation mechanisms are permitting 'top-down' sequencing of intact proteins. Finally, a versatile workflow for sub-cellular compartments including membranes is presented that allows for intact protein mass measurements, localization of post-translational modifications and relative quantitation or turnover measurement.     

Halobacterium salinarum NRC-1 PeptideAtlas: toward strategies for targeted proteomics and improved proteome coverage

The relatively small numbers of proteins and fewer possible post-translational modifications in microbes provide a unique opportunity to comprehensively characterize their dynamic proteomes. We have constructed a PeptideAtlas (PA) covering 62.7% of the predicted proteome of the extremely halophilic archaeon Halobacterium salinarum NRC-1 by compiling approximately 636 000 tandem mass spectra from 497 mass spectrometry runs in 88 experiments. Analysis of the PA with respect to biophysical properties of constituent peptides, functional properties of parent proteins of detected peptides, and performance of different mass spectrometry approaches has highlighted plausible strategies for improving proteome coverage and selecting signature peptides for targeted proteomics. Notably, discovery of a significant correlation between absolute abundances of mRNAs and proteins has helped identify low abundance of proteins as the major limitation in peptide detection. Furthermore, we have discovered that iTRAQ labeling for quantitative proteomic analysis introduces a significant bias in peptide detection by mass spectrometry. Therefore, despite identifying at least one proteotypic peptide for almost all proteins in the PA, a context-dependent selection of proteotypic peptides appears to be the most effective approach for targeted proteomics.     

Hydrogen, carbon and nitrogen isotopic fractionations during chlorophyll biosynthesis in C3 higher plants

We determined hydrogen, carbon and nitrogen isotopic compositions of chlorophylls a and b isolated from leaves of five C3 higher plant species (Benthamidia japonica, Prunus japonica, Acer carpinifolium, Acer argutum and Querus mongloica), and hydrogen and carbon isotopic compositions of phytol and chlorophyllides in the chlorophylls to understand isotopic fractionations associated with chlorophyll biosynthesis in these species. Chlorophylls are depleted in D relative to ambient water by approximately 189 per thousand and enriched in (13)C relative to bulk tissue by approximately 1.6 per thousand. These data can be explained by the contribution of isotopic fractionations during phytol and chlorophyllide biosyntheses. Phytol is more depleted in both D (by approximately 308 per thousand) and (13)C (by approximately 4.3 per thousand), while chlorophyllides are less depleted in D (by approximately 44 per thousand) and enriched in (13)C (by approximately 4.8 per thousand). Such inhomogeneous distribution of isotopes in chlorophylls suggests that (1) the phytol in chlorophylls reflects strong D- and (13)C-depletions due to the isotopic fractionations during the methylerythritol phosphate pathway followed by hydrogenation, and (2) the chlorophyllides reflect D- and (13)C-enrichments in tricarboxylic acid cycle. On the other hand, chlorophylls are slightly ( approximately 1.2 per thousand) depleted in (15)N relative to the bulk tissue, indicating that net isotopic fractionation of nitrogen during chlorophyll biosynthesis is small compared with those of hydrogen and carbon.     

Quantitative analysis of SILAC data sets using spectral counting

We report a new quantitative proteomics approach that combines the best aspects of stable isotope labeling of amino acids in cell culture (SILAC) labeling and spectral counting. The SILAC peptide count ratio analysis (SPeCtRA, http://proteomics.mcw.edu/visualize ) method relies on MS² spectra rather than ion chromatograms for quantitation and therefore does not require the use of high mass accuracy mass spectrometers. The inclusion of a stable isotope label allows the samples to be combined before sample preparation and analysis, thus avoiding many of the sources of variability that can plague spectral counting. To validate the SPeCtRA method, we have analyzed samples constructed with known ratios of protein abundance. Finally, we used SPeCtRA to compare endothelial cell protein abundances between high (20 mM) and low (11 mM) glucose culture conditions. Our results demonstrate that SPeCtRA is a protein quantification technique that is accurate and sensitive as well as easy to automate and apply to high‐throughput analysis of complex biological samples.     

Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass spectrometry

MOTIVATION: Using stable isotopes in global proteome scans, labeled molecules from one sample are pooled with unlabeled molecules from another sample and subsequently subjected to mass-spectral analysis. Stable-isotope methodologies make use of the fact that identical molecules of different stable-isotope compositions are differentiated in a mass spectrometer and are represented in a mass spectrum as distinct isotopic clusters with a known mass shift. We describe two multivariable linear regression models for (16)O/(18)O stable-isotope labeled data that jointly model pairs of resolved isotopic clusters from the same peptide and quantify the abundance present in each of the two biological samples while concurrently accounting for peptide-specific incorporation rates of the heavy isotope. The abundance measure for each peptide from the two biological samples is then used in down-stream statistical analyses, e.g. differential expression analysis. Because the multivariable regression models are able to correct for the abundance of the labeled peptide that appear as an unlabeled peptide due to the inability to exchange the natural C-terminal oxygen for the heavy isotope, they are particularly advantageous for a two-step digestion/labeling procedure. We discuss how estimates from the regression model are used to quantify the variability of the estimated abundance measures for the paired samples. Although discussed in the context of (16)O/(18)O stable-isotope labeled data, the multivariable regression models are generalizable to other stable-isotope labeled technologies.     

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.     

ANIBAL, stable isotope-based quantitative proteomics by aniline and benzoic acid labeling of amino and carboxylic groups

Identification and relative quantification of hundreds to thousands of proteins within complex biological samples have become realistic with the emergence of stable isotope labeling in combination with high throughput mass spectrometry. However, all current chemical approaches target a single amino acid functionality (most often lysine or cysteine) despite the fact that addressing two or more amino acid side chains would drastically increase quantifiable information as shown by in silico analysis in this study. Although the combination of existing approaches, e.g. ICAT with isotope-coded protein labeling, is analytically feasible, it implies high costs, and the combined application of two different chemistries (kits) may not be straightforward. Therefore, we describe here the development and validation of a new stable isotope-based quantitative proteomics approach, termed aniline benzoic acid labeling (ANIBAL), using a twin chemistry approach targeting two frequent amino acid functionalities, the carboxylic and amino groups. Two simple and inexpensive reagents, aniline and benzoic acid, in their (12)C and (13)C form with convenient mass peak spacing (6 Da) and without chromatographic discrimination or modification in fragmentation behavior, are used to modify carboxylic and amino groups at the protein level, resulting in an identical peptide bond-linked benzoyl modification for both reactions. The ANIBAL chemistry is simple and straightforward and is the first method that uses a (13)C-reagent for a general stable isotope labeling approach of carboxylic groups. In silico as well as in vitro analyses clearly revealed the increase in available quantifiable information using such a twin approach. ANIBAL was validated by means of model peptides and proteins with regard to the quality of the chemistry as well as the ionization behavior of the derivatized peptides. A milk fraction was used for dynamic range assessment of protein quantification, and a bacterial lysate was used for the evaluation of relative protein quantification in a complex sample in two different biological states.     

ICPLQuant – A software for non‐isobaric isotopic labeling proteomics

The main goal of many proteomics experiments is an accurate and rapid quantification and identification of regulated proteins in complex biological samples. The bottleneck in quantitative proteomics remains the availability of efficient software to evaluate and quantify the tremendous amount of mass spectral data acquired during a proteomics project. A new software suite, ICPLQuant, has been developed to accurately quantify isotope‐coded protein label (ICPL)‐labeled peptides on the MS level during LC‐MALDI and peptide mass fingerprint experiments. The tool is able to generate a list of differentially regulated peptide precursors for subsequent MS/MS experiments, minimizing time‐consuming acquisition and interpretation of MS/MS data. ICPLQuant is based on two independent units. Unit 1 performs ICPL multiplex detection and quantification and proposes peptides to be identified by MS/MS. Unit 2 combines MASCOT MS/MS protein identification with the quantitative data and produces a protein/peptide list with all the relevant information accessible for further data mining. The accuracy of quantification, selection of peptides for MS/MS‐identification and the automated output of a protein list of regulated proteins are demonstrated by the comparative analysis of four different mixtures of three proteins (Ovalbumin, Horseradish Peroxidase and Rabbit Albumin) spiked into the complex protein background of the DGPF Proteome Marker.     

Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC-FTICR-MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics.