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1.  Protease- and acid-catalyzed labeling workflows employing 18O-enriched water 
Short abstract
Stable isotope labeling workflows employing 18O-enriched water (LeO-workflows) are versatile tools for quantitative and qualitative proteomics studies. In protease-assisted (PALeO) workflows, 18O-atoms are introduced by proteolytic cleavage and carboxyl oxygen exchange reactions mediated by proteases. In the acid-catalyzed (ALeO) workflow, 18O-atoms are introduced by carboxyl oxygen exchange at low pH.
Long abstract
Stable isotopes are essential tools in biological mass spectrometry. Historically, 18O-stable isotopes have been extensively used to study the catalytic mechanisms of proteolytic enzymes1–3. With the advent of mass spectrometry-based proteomics, the enzymatically-catalyzed incorporation of 18O-atoms from stable isotopically enriched water has become a popular method to quantitatively compare protein expression levels (reviewed by Fenselau and Yao4, Miyagi and Rao5 and Ye et al.6). 18O-labeling constitutes a simple and low-cost alternative to chemical (e.g., iTRAQ, ICAT) and metabolic (e.g., SILAC) labeling techniques7. Depending on the protease utilized, 18O-labeling can result in the incorporation of up to two 18O-atoms in the C-terminal carboxyl group of the cleavage product3. The labeling reaction can be subdivided into two independent processes, the peptide bond cleavage and the carboxyl oxygen exchange reaction8. In our PALeO (protease-assisted labeling employing 18O-enriched water) adaptation of enzymatic 18O-labeling, we utilized 50% 18O-enriched water to yield distinctive isotope signatures. In combination with high-resolution matrix-assisted laser desorption ionization time-of-flight tandem mass spectrometry (MALDI-TOF/TOF MS/MS), the characteristic isotope envelopes can be used to identify cleavage products with a high level of specificity. We previously have used the PALeO-methodology to detect and characterize endogenous proteases9 and monitor proteolytic reactions10–11. Since PALeO encodes the very essence of the proteolytic cleavage reaction, the experimental setup is simple and biochemical enrichment steps of cleavage products can be circumvented. The PALeO-method can easily be extended to (i) time course experiments that monitor the dynamics of proteolytic cleavage reactions and (ii) the analysis of proteolysis in complex biological samples that represent physiological conditions. PALeO-TimeCourse experiments help identifying rate-limiting processing steps and reaction intermediates in complex proteolytic pathway reactions. Furthermore, the PALeO-reaction allows us to identify proteolytic enzymes such as the serine protease trypsin that is capable to rebind its cleavage products and catalyze the incorporation of a second 18O-atom. Such “double-labeling” enzymes can be used for postdigestion 18O-labeling, in which peptides are exclusively labeled by the carboxyl oxygen exchange reaction. Our third strategy extends labeling employing 18O-enriched water beyond enzymes and uses acidic pH conditions to introduce 18O-stable isotope signatures into peptides.
doi:10.3791/3891
PMCID: PMC3605716  PMID: 23462971
MALDI-TOF mass spectrometry; proteomics; proteolysis; quantification; stable isotope labeling
2.  Acid-Catalyzed Oxygen-18 Labeling of Peptides for Proteomics Applications 
Analytical chemistry  2009;81(7):2804-2809.
In enzymatic 18O-labeling strategies for quantitative proteomics, the exchange of carboxyl oxygens at low pH is a common, undesired side reaction. We asked if acid-catalyzed back exchange could interfere with quantitation and whether the reaction itself could be used as an alternative method for introducing 18O label into peptides. Several synthetic amino acid sequences were dissolved in dilute acid containing 50% (v/v) H218O and incubated at room temperature. Aliquots were removed over a period of 3 weeks and analyzed by tandem mass spectrometry (MS/MS). 18O-incorporation ratios were determined by linear regression analysis that allowed for multiple stable isotope incorporations. At low pH, peptides exchanged their carboxyl oxygen atoms with the aqueous solvent. The isotope patterns gradually shifted to higher masses until they reached the expected binomial distribution at equilibrium after ~11 days. Reaction rates were residue and sequence specific. Due to its slow nature, the acid-based back exchange is expected to minimally interfere with enzymatic 18O-labeling studies provided that storage and analysis conditions minimize low pH exposure times. On its own, acid-catalyzed 18O labeling is a general tagging strategy that is an alternative to the chemical, metabolic and enzymatic isotope-labeling schemes currently used in quantitative proteomics.
doi:10.1021/ac802484d
PMCID: PMC2892872  PMID: 19243188
3.  An Optimized Method for Computing 18O/16O Ratios of Differentially Stable-isotope Labeled Peptides in the Context of Post-digestion 18O Exchange/Labeling 
Analytical chemistry  2010;82(13):5878-5886.
Differential 18O/16O stable isotope labeling of peptides that relies on enzyme-catalyzed oxygen exchange at their carboxyl termini in the presence of H218O has been widely used for relative quantitation of peptides/proteins. The role of tryptic proteolysis in bottom-up shotgun proteomics and low reagent costs, has made trypsin-catalyzed 18O post-digestion exchange a convenient and affordable stable isotope labeling approach. However, it is known that trypsin-catalyzed 18O exchange at the carboxyl terminus is in many instances inhomogeneous/incomplete. The extent of the 18O exchange/incorporation fluctuates from peptide to peptide mostly due to variable enzyme-substrate affinity. Thus, accurate calculation and interpretation of peptide ratios are analytically complicated and in some regard deficient. Therefore, a computational approach capable of improved measurement of actual 18O incorporation for each differentially labeled peptide pair is needed. In this regard, we have developed an algorithmic method that relies on the trapezoidal rule to integrate peak intensities of all detected isotopic species across a particular peptide ion over the retention time, which fits the isotopic manifold to Poisson distributions. Optimal values for manifold fitting were calculated and then 18O/16O ratios derived via evolutionary programming. The algorithm is tested using trypsin–catalyzed 18O post-digestion exchange to differentially label bovine serum albumin (BSA) at a priori determined ratios. Both, accuracy and precision are improved utilizing this rigorous mathematical approach. Utilizing this algorithmic technique, we demonstrate the effectiveness of this method to accurately calculate 18O/16O ratios for differentially labeled BSA peptides, by accounting for artifacts caused by a variable degree of post-digestion 18O exchange. We further demonstrate the effectiveness of this method to accurately calculate 18O/16O ratios in a large scale proteomic quantitation of detergent resistant membrane microdomains (DRMMs) isolated from cells expressing wild-type HIV-1 Gag and its non myristylated mutant.
doi:10.1021/ac101284c
PMCID: PMC3479679  PMID: 20540505
quantitation; 18O/16O stable isotope labeling; variable/incomplete 18O exchange
4.  LC–MS Based Detection of Differential Protein Expression 
While several techniques are available in proteomics, LC-MS based analysis of complex protein/peptide mixtures has turned out to be a mainstream analytical technique for quantitative proteomics. Significant technical advances at both sample preparation/separation and mass spectrometry levels have revolutionized comprehensive proteome analysis. Moreover, automation and robotics for sample handling process permit multiple sampling with high throughput.
For LC-MS based quantitative proteomics, sample preparation turns out to be critical step, as it can significantly influence sensitivity of downstream analysis. Several sample preparation strategies exist, including depletion of high abundant proteins or enrichment steps that facilitate protein quantification but with a compromise of focusing on a smaller subset of a proteome. While several experimental strategies have emerged, certain limitations such as physiochemical properties of a peptide/protein, protein turnover in a sample, analytical platform used for sample analysis and data processing, still imply challenges to quantitative proteomics. Other aspects that make analysis of a proteome a challenging task include dynamic nature of a proteome, need for efficient and fast analysis of protein due to its constant modifications inside a cell, concentration range of proteins that exceed dynamic range of a single analytical method, and absence of appropriate bioinformatics tools for analysis of large volume and high dimensional data.
This paper gives an overview of various LC-MS methods currently used in quantitative proteomics and their potential for detecting differential protein expression. Fundamental steps such as sample preparation, LC separation, mass spectrometry, quantitative assessment and protein identification are discussed.
For quantitative assessment of protein expression, both label and label free approaches are evaluated for their set of merits and demerits. While most of these methods edge on providing “relative abundance” information, absolute quantification is achieved with limitation as it caters to fewer proteins. Isotope labeling is extensively used for quantifying differentially expressed proteins, but is severely limited by successful incorporation of its heavy label. Lengthy labeling protocols restrict the number of samples that can be labeled and processed. Alternatively, label free approach appears promising as it can process many samples with any number of comparisons possible but entails reproducible experimental data for its application.
doi:10.4172/jpb.1000102
PMCID: PMC2867618  PMID: 20473349
Liquid chromatography-mass spectrometry (LC-MS); Quantitative proteomics; Labeling; Label-free; Tandem mass spectrometry (MS/MS)
5.  EP6 Quantitative Proteomics 
There are numerous approaches to study the proteome in a quantitative manner. All rely heavily on optimized sample preparation and appropriate statistical analysis of resulting datasets. This session will cover the following aspects of quantitative proteomics approaches:
Quantitative profiling of the membrane proteome requires special considerations not addressed in typical mass-spectrometry analyses. Optimized sample preparation and separation strategies will be discussed in the context of enriched membrane fractions and a quantitative proteomics platform using stable isotopes.In shotgun proteomics, a complex protein mixture is first digested to peptides, which are then analyzed by a combination of nanoflow chromatography and tandem mass spectrometry. The effects of subtle changes in sample preparation and chromatographic conditions in the characterization of complex mixtures will be presented. A discovery-based mass spectrometry approach using a bench-top LTQ linear ion trap and in-house written software for label-free differential protein profiling will be presented. This approach is quite comprehensive and is compatible with even the most inexpensive mass spectrometers. For proteins not detected routinely using our discovery-based approaches, we have applied selected reaction monitoring using a TSQ Quantum Ultra. This approach has been used to identify and quantify proteins at the low ng/mL level in plasma without any prior fractionation. A software pipeline has been developed to go from hypothesized proteins of interest derived from the literature to predicted hSRM transitions, collision offsets, and predicted chromatographic retention times. The combination of both discovery- and hypothesis-driven proteomics using nanoflow separations and tandem mass spectrometry provides us with unparalleled sensitivity and dynamic range in characterizing complex mixtures.Spectrum counting is an appealing and relatively straightforward approach for quantitative proteomics. Since the spectrum count of a protein in a proteomic analysis is the total number of peptides, not just unique peptides detected and identified for a given protein, searching criteria and false-positive minimization is important. There are several different versions of spectral counting currently in use, but each approach has shared core characteristics. An additional important consideration for quantitative proteomic analysis is the use of replicates for statistical analysis and determining the proper statistical test to use based on the overall structure of the datasets. This presentation will describe the foundation of spectral counting and the modifications to this approach used by different researchers. In addition, selected examples of the biological implementation of these approaches will be described.
PMCID: PMC2292016
6.  ABRF-sPRG 2013 Study: Development and Characterization of a Proteomics Normalization Standard Consisting of 1000 Stable Isotope Labeled Peptides and a Qualitative Stability Study of Peptides from the ABRF-sPRG 2012 Study 
The Proteomics Standards Research Group (sPRG) is reporting the first year progress in a two-year sPRG 2012-2013 study which focuses on the generation of a standard that can be used for interassay, interspecies, and interlaboratory normalization in both label-free and stable isotope label-based quantitative proteomics analysis. The standard has been formulated as two mixtures: 1000 stable isotope 13C/15N-labeled synthetic tryptic peptides alone, and peptides mixed with a tryptic digest from a HEK 293 cell lysate. The sequences of the synthetic peptides were derived from approximately 400 proteins and were conserved across proteomes of the most commonly analyzed species: Homo sapiens, Mus musculus and Rattus norvegicus. The selected peptides represent the full range of hydrophobicities and isoelectric points typical to tryptic peptides from complex proteomic samples. The standard was designed to represent proteins of various concentrations, spanning three orders of magnitude. This year we focused our efforts on selection of appropriate protein and peptide candidates, peptide synthesis, quality assessment and LC-MS evaluation by several sPRG member laboratories. The sPRG study design and initial results of a thorough characterization of the standard using a variety of instrumental configurations and bioinformatics approaches will be presented in this talk.
The sPRG is hopeful that the designed formulation will become a valuable resource in various mass spectrometry-based proteomic applications, including quantitative and differential profiling, as well as general benchmarking (e.g. chromatographic retention time). The sPRG plans to start recruiting study participants in April 2013, complete the study by the end of the year 2013, and present the results at the ABRF 2014 meeting. The sPRG encourages proteomics laboratories to participate in the study and sign in at www.abrf.org/sprg.
The second half of the session will discuss the qualitative stability study performed using purified synthetic peptides containing a variety of modifications selected from the 2012 sPRG ABRF sample. The stability of the selected synthetic peptides was evaluated by the sPRG using different storage conditions over a three-month period. After storage at either at room temperature, +4°C or −80°C for one week, one month, or three months. Quantitative LC-MS/MS analysis was used to monitor the stability and degradation of the peptides, and to determine the effect of modifications and storage conditions on peptide degradation rates. The data presented have been built on the quantitative study that was presented at both the 2012 ABRF and ASMS conferences. All forms of degraded peptides were separated and identified using nano-LC tandem mass spectrometry on a Thermo Scientific Q-Exactive hybrid mass spectrometer. Integrated extracted ion chromatograms were used to measure relative amounts of degradation to identify which pathways are most prevalent during storage.
PMCID: PMC3635292
7.  [No title available] 
The Proteomics Standards Research Group (sPRG) is reporting the first year progress in a two-year sPRG 2012–2013 study which focuses on the generation of a standard that can be used for interassay, interspecies, and interlaboratory normalization in both label-free and stable isotope label-based quantitative proteomics analysis. The standard has been formulated as two mixtures: 1000 stable isotope 13C/15N-labeled synthetic tryptic peptides alone, and peptides mixed with a tryptic digest from a HEK 293 cell lysate. The sequences of the synthetic peptides were derived from approximately 400 proteins and were conserved across proteomes of the most commonly analyzed species: Homo sapiens, Mus musculus and Rattus norvegicus. The selected peptides represent the full range of hydrophobicities and isoelectric points typical to tryptic peptides from complex proteomic samples. The standard was designed to represent proteins of various concentrations, spanning three orders of magnitude. This year we focused our efforts on selection of appropriate protein and peptide candidates, peptide synthesis, quality assessment and LC-MS evaluation by several sPRG member laboratories. The sPRG study design and initial results of a thorough characterization of the standard using a variety of instrumental configurations and bioinformatics approaches will be presented in this talk.
The sPRG is hopeful that the designed formulation will become a valuable resource in various mass spectrometry-based proteomic applications, including quantitative and differential profiling, as well as general benchmarking (e.g. chromatographic retention time). The sPRG plans to start recruiting study participants in April 2013, complete the study by the end of the year 2013, and present the results at the ABRF 2014 meeting. The sPRG encourages proteomics laboratories to participate in the study and sign in at www.abrf.org/sprg.
The second half of the session will discuss the qualitative stability study performed using purified synthetic peptides containing a variety of modifications selected from the 2012 sPRG ABRF sample. The stability of the selected synthetic peptides was evaluated by the sPRG using different storage conditions over a three-month period. After storage at either at room temperature, +4°C or -80°C for one week, one month, or three months. Quantitative LC-MS/MS analysis was used to monitor the stability and degradation of the peptides, and to determine the effect of modifications and storage conditions on peptide degradation rates. The data presented have been built on the quantitative study that was presented at both the 2012 ABRF and ASMS conferences. All forms of degraded peptides were separated and identified using nano-LC tandem mass spectrometry on a Thermo Scientific Q-Exactive hybrid mass spectrometer. Integrated extracted ion chromatograms were used to measure relative amounts of degradation to identify which pathways are most prevalent during storage.
PMCID: PMC3635369
8.  P42-T Analysis of Complex Carbohydrates Using a Hybrid Ion Trap and TOF Mass Spectrometer Coupling with MALDI 
Living systems are dynamic, each developing, surviving, and proliferating in a different way. A very important element in understanding system dynamics is to quantify change. Quantitative measurement of proteins is therefore increasingly requested by life science researchers as a means of characterizing complex biological systems, including human cells, tissues, and body fluids. Mass spectrometry has became a major tool for proteomics applications, and allowed the quantitative measurement of protein expression in many biological systems when combined with stable isotope coding. This presentation shows an approach to quantification and identification in proteomics applications, for the first time using a hybrid ion trap and TOF mass spectrometer combined with stable isotope coding. The mass spectrometer is equipped with LC MALDI to meet separation needs, in-source decay (ISD), and collision-induced dissociation (CID) to generate MSn spectra. The quantification is achieved in two ways. Peptide mass shift resulting from the stable isotope coding can be measured directly in MS mode, and relative abundance of coded and non-coded peptides is read out from the MS spectra. The samples coded with the tandem mass tags (TMTs)1 strategy are analyzed with ISD or MSn mode, and then the relative abundance of the reporter ions from TMTs are measured for the quantification. Metabolic incorporation of stable isotope (15N) labeled nutrients in growth media into cultured cells is used for global coding of proteomes. Labeled and non-labeled proteins were separated and purified from cell lysates, tryptically digested, and then mixed in different ratios for MS-mode measurement. Standard proteins labeled with TMTs were used for ISD and MSn-mode measurement. The results demonstrate that high signal-to-noise ratio is achieved and both the identity and relative abundances are obtained simultaneously with MSn-based detection; PTM information could be obtained as well.
PMCID: PMC2291835
9.  Quantitative Proteomics Targeting Classes of Motif-containing Peptides Using Immunoaffinity-based Mass Spectrometry* 
The development of high-performance technology platforms for generating detailed protein expression profiles, or protein atlases, is essential. Recently, we presented a novel platform that we termed global proteome survey, where we combined the best features of affinity proteomics and mass spectrometry, to probe any proteome in a species independent manner while still using a limited set of antibodies. We used so called context-independent-motif-specific antibodies, directed against short amino acid motifs. This enabled enrichment of motif-containing peptides from a digested proteome, which then were detected and identified by mass spectrometry. In this study, we have demonstrated the quantitative capability, reproducibility, sensitivity, and coverage of the global proteome survey technology by targeting stable isotope labeling with amino acids in cell culture-labeled yeast cultures cultivated in glucose or ethanol. The data showed that a wide range of motif-containing peptides (proteins) could be detected, identified, and quantified in a highly reproducible manner. On average, each of six different motif-specific antibodies was found to target about 75 different motif-containing proteins. Furthermore, peptides originating from proteins spanning in abundance from over a million down to less than 50 copies per cell, could be targeted. It is worth noting that a significant set of peptides previously not reported in the PeptideAtlas database was among the profiled targets. The quantitative data corroborated well with the corresponding data generated after conventional strong cation exchange fractionation of the same samples. Finally, several differentially expressed proteins, with both known and unknown functions, many relevant for the central carbon metabolism, could be detected in the glucose- versus ethanol-cultivated yeast. Taken together, the study demonstrated the potential of our immunoaffinity-based mass spectrometry platform for reproducible quantitative proteomics targeting classes of motif-containing peptides.
doi:10.1074/mcp.M111.016238
PMCID: PMC3412966  PMID: 22543061
10.  Matching isotopic distributions from metabolically labeled samples 
Bioinformatics  2008;24(13):i339-i347.
Motivation: In recent years stable isotopic labeling has become a standard approach for quantitative proteomic analyses. Among the many available isotopic labeling strategies, metabolic labeling is attractive for the excellent internal control it provides. However, analysis of data from metabolic labeling experiments can be complicated because the spacing between labeled and unlabeled forms of each peptide depends on its sequence, and is thus variable from analyte to analyte. As a result, one generally needs to know the sequence of a peptide to identify its matching isotopic distributions in an automated fashion. In some experimental situations it would be necessary or desirable to match pairs of labeled and unlabeled peaks from peptides of unknown sequence. This article addresses this largely overlooked problem in the analysis of quantitative mass spectrometry data by presenting an algorithm that not only identifies isotopic distributions within a mass spectrum, but also annotates matches between natural abundance light isotopic distributions and their metabolically labeled counterparts. This algorithm is designed in two stages: first we annotate the isotopic peaks using a modified version of the IDM algorithm described last year; then we use a probabilistic classifier that is supplemented by dynamic programming to find the metabolically labeled matched isotopic pairs. Such a method is needed for high-throughput quantitative proteomic metabolomic experiments measured via mass spectrometry.
Results: The primary result of this article is that the dynamic programming approach performs well given perfect isotopic distribution annotations. Our algorithm achieves a true positive rate of 99% and a false positive rate of 1% using perfect isotopic distribution annotations. When the isotopic distributions are annotated given ‘expert’ selected peaks, the same algorithm gets a true positive rate of 77% and a false positive rate of 1%. Finally, when annotating using machine selected peaks, which may contain noise, the dynamic programming algorithm gives a true positive rate of 36% and a false positive rate of 1%. It is important to mention that these rates arise from the requirement of exact annotations of both the light and heavy isotopic distributions. In our evaluations, a match is considered ‘entirely incorrect’ if it is missing even one peak or containing an extraneous peak. If we only require that the ‘monoisotopic’ peaks exist within the two matched distributions, our algorithm obtains a positive rate of 45% and a false positive rate of 1% on the ‘machine’ selected data. Changes to the algorithm's scoring function and training example generation improves our ‘monoisotopic’ peak score true positive rate to 65% while obtaining a false positive rate of 2%. All results were obtained within 10-fold cross-validation of 41 mass spectra with a mass-to-charge range of 800–4000m/z. There are a total of 713 isotopic distributions and 255 matched isotopic pairs that are hand-annotated for this study.
Availability: Programs are available via http://www.cs.wisc.edu/~mcilwain/IDM/
Contact:mcilwain@cs.wisc.edu
doi:10.1093/bioinformatics/btn190
PMCID: PMC2718665  PMID: 18586733
11.  A Quantitative Proteomics Analysis of Subcellular Proteome Localization and Changes Induced by DNA Damage* 
A major challenge in cell biology is to identify the subcellular distribution of proteins within cells and to characterize how protein localization changes under different cell growth conditions and in response to stress and other external signals. Protein localization is usually determined either by microscopy or by using cell fractionation combined with protein blotting techniques. Both these approaches are intrinsically low throughput and limited to the analysis of known components. Here we use mass spectrometry-based proteomics to provide an unbiased, quantitative, and high throughput approach for measuring the subcellular distribution of the proteome, termed “spatial proteomics.” The spatial proteomics method analyzes a whole cell extract created by recombining differentially labeled subcellular fractions derived from cells in which proteins have been mass-labeled with heavy isotopes. This was used here to measure the relative distribution between cytoplasm, nucleus, and nucleolus of over 2,000 proteins in HCT116 cells. The data show that, at steady state, the proteome is predominantly partitioned into specific subcellular locations with only a minor subset of proteins equally distributed between two or more compartments. Spatial proteomics also facilitates a proteome-wide comparison of changes in protein localization in response to a wide range of physiological and experimental perturbations, shown here by characterizing dynamic changes in protein localization elicited during the cellular response to DNA damage following treatment of HCT116 cells with etoposide. DNA damage was found to cause dissociation of the proteasome from inhibitory proteins and assembly chaperones in the cytoplasm and relocation to associate with proteasome activators in the nucleus.
doi:10.1074/mcp.M900429-MCP200
PMCID: PMC2849709  PMID: 20026476
12.  The Use of a Quantitative Cysteinyl-peptide Enrichment Technology for High-Throughput Quantitative Proteomics 
Quantitative proteomic measurements are of significant interest in studies aimed at discovering disease biomarkers and providing new insights into biological pathways. A quantitative cysteinyl-peptide enrichment technology (QCET) can be employed to achieve higher efficiency, greater dynamic range, and higher throughput in quantitative proteomic studies based upon the use of stable-isotope labeling techniques combined with high-resolution capillary or nano-scale liquid chromatography (LC)-mass spectrometry (MS) measurements. The QCET approach involves specific 16O/18O labeling of tryptic peptides, high-efficiency enrichment of cysteinyl-peptides, and confident protein identification and quantification using high mass accuracy LC-Fourier transform ion cyclotron resonance mass spectrometry (FTICR) measurements and a previously established database of accurate mass and LC elution time information for the labeled peptides. This methodology has been initially demonstrated by using proteome profiling of naïve and in vitro-differentiated human mammary epithelial cells (HMEC) as an example, which initially resulted in the identification and quantification of 603 proteins in a single LC-FTICR analysis. QCET provides not only highly efficient enrichment of cysteinyl-peptides for more extensive proteome coverage and improved labeling efficiency for better quantitative measurements, but more importantly, a high-throughput strategy suitable for quantitative proteome analysis where extensive or parallel proteomic measurements are required, such as in time course studies of specific pathways and clinical sample analyses for biomarker discovery.
PMCID: PMC3292281  PMID: 17484113
Quantitative proteomics; QCET; 18O labeling; cysteinyl-peptide enrichment; FTICR; AMT
13.  Absolute quantification of microbial proteomes at different states by directed mass spectrometry 
The developed, directed mass spectrometry workflow allows to generate consistent and system-wide quantitative maps of microbial proteomes in a single analysis. Application to the human pathogen L. interrogans revealed mechanistic proteome changes over time involved in pathogenic progression and antibiotic defense, and new insights about the regulation of absolute protein abundances within operons.
The developed, directed proteomic approach allowed consistent detection and absolute quantification of 1680 proteins of the human pathogen L. interrogans in a single LC–MS/MS experiment.The comparison of 25 extensive, consistent and quantitative proteome maps revealed new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans, and about the regulation of protein abundances within operons.The generated time-resolved data sets are compatible with pattern analysis algorithms developed for transcriptomics, including hierarchical clustering and functional enrichment analysis of the detected profile clusters.This is the first study that describes the absolute quantitative behavior of any proteome over multiple states and represents the most comprehensive proteome abundance pattern comparison for any organism to date.
Over the last decade, mass spectrometry (MS)-based proteomics has evolved as the method of choice for system-wide proteome studies and now allows for the characterization of several thousands of proteins in a single sample. Despite these great advances, redundant monitoring of protein levels over large sample numbers in a high-throughput manner remains a challenging task. New directed MS strategies have shown to overcome some of the current limitations, thereby enabling the acquisition of consistent and system-wide data sets of proteomes with low-to-moderate complexity at high throughput.
In this study, we applied this integrated, two-stage MS strategy to investigate global proteome changes in the human pathogen L. interrogans. In the initial discovery phase, 1680 proteins (out of around 3600 gene products) could be identified (Schmidt et al, 2008) and, by focusing precious MS-sequencing time on the most dominant, specific peptides per protein, all proteins could be accurately and consistently monitored over 25 different samples within a few days of instrument time in the following scoring phase (Figure 1). Additionally, the co-analysis of heavy reference peptides enabled us to obtain absolute protein concentration estimates for all identified proteins in each perturbation (Malmström et al, 2009). The detected proteins did not show any biases against functional groups or protein classes, including membrane proteins, and span an abundance range of more than three orders of magnitude, a range that is expected to cover most of the L. interrogans proteome (Malmström et al, 2009).
To elucidate mechanistic proteome changes over time involved in pathogenic progression and antibiotic defense of L. interrogans, we generated time-resolved proteome maps of cells perturbed with serum and three different antibiotics at sublethal concentrations that are currently used to treat Leptospirosis. This yielded an information-rich proteomic data set that describes, for the first time, the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date. Using this unique property of the data set, we could quantify protein components of entire pathways across several time points and subject the data sets to cluster analysis, a tool that was previously limited to the transcript level due to incomplete sampling on protein level (Figure 4). Based on these analyses, we could demonstrate that Leptospira cells adjust the cellular abundance of a certain subset of proteins and pathways as a general response to stress while other parts of the proteome respond highly specific. The cells furthermore react to individual treatments by ‘fine tuning' the abundance of certain proteins and pathways in order to cope with the specific cause of stress. Intriguingly, the most specific and significant expression changes were observed for proteins involved in motility, tissue penetration and virulence after serum treatment where we tried to simulate the host environment. While many of the detected protein changes demonstrate good agreement with available transcriptomics data, most proteins showed a poor correlation. This includes potential virulence factors, like Loa22 or OmpL1, with confirmed expression in vivo that were significantly up-regulated on the protein level, but not on the mRNA level, strengthening the importance of proteomic studies. The high resolution and coverage of the proteome data set enabled us to further investigate protein abundance changes of co-regulated genes within operons. This suggests that although most proteins within an operon respond to regulation synchronously, bacterial cells seem to have subtle means to adjust the levels of individual proteins or protein groups outside of the general trend, a phenomena that was recently also observed on the transcript level of other bacteria (Güell et al, 2009).
The method can be implemented with standard high-resolution mass spectrometers and software tools that are readily available in the majority of proteomics laboratories. It is scalable to any proteome of low-to-medium complexity and can be extended to post-translational modifications or peptide-labeling strategies for quantification. We therefore expect the approach outlined here to become a cornerstone for microbial systems biology.
Over the past decade, liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) has evolved into the main proteome discovery technology. Up to several thousand proteins can now be reliably identified from a sample and the relative abundance of the identified proteins can be determined across samples. However, the remeasurement of substantially similar proteomes, for example those generated by perturbation experiments in systems biology, at high reproducibility and throughput remains challenging. Here, we apply a directed MS strategy to detect and quantify sets of pre-determined peptides in tryptic digests of cells of the human pathogen Leptospira interrogans at 25 different states. We show that in a single LC–MS/MS experiment around 5000 peptides, covering 1680 L. interrogans proteins, can be consistently detected and their absolute expression levels estimated, revealing new insights about the proteome changes involved in pathogenic progression and antibiotic defense of L. interrogans. This is the first study that describes the absolute quantitative behavior of any proteome over multiple states, and represents the most comprehensive proteome abundance pattern comparison for any organism to date.
doi:10.1038/msb.2011.37
PMCID: PMC3159967  PMID: 21772258
absolute quantification; directed mass spectrometry; Leptospira interrogans; microbiology; proteomics
14.  The quantitative proteomes of human-induced pluripotent stem cells and embryonic stem cells 
An in-depth proteomic comparison of human-induced pluripotent stem cells, and their parent fibroblast cells, with embryonic stem cells shows that the reprogramming process comprehensively remodels protein expression levels, creating cells that closely resemble natural stem cells.
We present here a large proteomic characterization of human embryonic stem cells, human-induced pluripotent stem cells and their parental fibroblasts cell lines.Overall, 97.8% of the 2683 quantified proteins in four experiments showed no significant differences in abundance between hESC and hiPSC highlighting the high similarity of these pluripotent cell lines.In total, 58 proteins were found significantly differentially expressed between hiPSCs and hESCs. The observed low overlap of these proteins with previous transcriptomic studies suggests that those differences do no reflect a recurrent molecular signature.
Human embryonic stem cells (hESCs) are capable of self-renewal and multi-lineage differentiation. However, the use of hESCs for clinical treatment entails ethical issues as they are derived from human embryos. Recently, reprogramming of somatic cells to an embryonic stem cell-like state, named induced pluripotent stem cells (iPSCs), was achieved through ectopic expression of defined factors. In addition to their clinical potential, hiPSCs represent a unique tool to develop cellular models for human diseases as well. Although current functional assays (e.g., tetraploid complementation) have confirmed the pluripotency of hiPSCs, there might still be significant differences (e.g., differentiation potential) when compared with their natural hESCs counterparts. Consequently, an extensive molecular characterization to address differences and similarities between these two pluripotent cell lines seems to be a prerequisite before any clinical application is conducted. Despite that great efforts, mainly at the genomic levels, have been made to address how similar hESCs and hiPSCs are, the definite answer to this fundamental question is currently still debated. Direct assessment of protein levels has yet to be incorporated into these integrative systems-level analyses. Protein levels are tuned by intricate mechanisms of gene expression regulation and it has recently been documented that mRNA and protein levels poorly correlate in mouse ESCs. Here, we use in-depth quantitative proteomics to gain insights into the differences and similarities in the protein content of two hiPS cell lines, their precursor IMR90 and 4Skin fibroblast cell lines and one hES cell line, providing novel molecular signatures that may assist in filling a gap in the understanding of pluripotency.
To study the degree of similarity, at the protein level, between hiPSCs and hESCs, four MS-based proteomic experiments were designed that use our in-house developed triplex dimethyl labeling chemistry followed by extensive fractionation by strong cation exchange (SCX) chromatography to reduce the sample complexity. High-resolution LC-MS/MS with dedicated fragmentation schemes (i.e., electron transfer dissociation, collision-induced dissociation and higher-energy collision dissociation) was subsequently used to maximize peptide identification rates. A total of 348 LC-MS/MS analyses (including technical and biological replicates) were performed. We confidently identified 1 593 446 peptide spectrum matches (peptide FDR<1%) corresponding to 10 628 unique protein groups (protein FDR∼4%). Using the extracted ion chromatograms, we also estimated the absolute abundance of the proteins within the samples spanning six orders of magnitude. To the best of our knowledge, the coverage obtained in this study represents the largest achieved by any proteomics screen on pluripotent cells.
Most importantly, our results indicate that the reprogramming process remodeled the proteome of both fibroblast cell lines to a profile that closely resembles the pluripotent hESCs proteome: 97.8% of the quantified proteins (2638 proteins in all four experiments) showed nonsignificant changes. Nevertheless, a small fraction of 58 proteins, mainly related to metabolism, antigen processing and cell adhesion, was found significantly regulated between hiPSCs and hESCs. A comparison of the regulated proteins to previously published transcriptomic studies showed a low overlap, highlighting the emerging notion that differences between both pluripotent cell lines rather reflect experimental conditions than a recurrent molecular signature. On the other side, the inclusion of the two parental fibroblast cell lines in our analysis allowed us to study changes in the proteome at both the starting and end points of the reprogramming process. As expected, the vast majority of the proteins (73.4%) showed differential expression between the parental fibroblasts and the reprogrammed pluripotent cells.
To find out if the differences observed in our study were a consequence of transcriptional or translational regulation, we performed paired genome-wide gene expression analyses on the same six samples that were used for the proteomic profiling. Overall, we observed a good correlation between mRNA and protein levels (r∼0.7). These results further authenticated the proteomic measurements and implied a high degree of control at the transcriptional level. Nevertheless, numerous genes were found uncorrelated highlighting the necessity of complementing transcriptomic-based approaches with proteomics.
Assessing relevant molecular differences between human-induced pluripotent stem cells (hiPSCs) and human embryonic stem cells (hESCs) is important, given that such differences may impact their potential therapeutic use. Controversy surrounds recent gene expression studies comparing hiPSCs and hESCs. Here, we present an in-depth quantitative mass spectrometry-based analysis of hESCs, two different hiPSCs and their precursor fibroblast cell lines. Our comparisons confirmed the high similarity of hESCs and hiPSCS at the proteome level as 97.8% of the proteins were found unchanged. Nevertheless, a small group of 58 proteins, mainly related to metabolism, antigen processing and cell adhesion, was found significantly differentially expressed between hiPSCs and hESCs. A comparison of the regulated proteins with previously published transcriptomic studies showed a low overlap, highlighting the emerging notion that differences between both pluripotent cell lines rather reflect experimental conditions than a recurrent molecular signature.
doi:10.1038/msb.2011.84
PMCID: PMC3261715  PMID: 22108792
human embryonic stem cells; human-induced pluripotent stem cells; proteomics; quantitation
15.  Proteome-wide systems analysis of a cellulosic biofuel-producing microbe 
We apply mass spectrometry-based ReDi proteomics to quantify the Clostridium phytofermentans proteome during fermentation of cellulosic substrates. ReDi proteomics gives accurate, low-cost quantification of an extra and intracellular microbial proteome. When combined with physiological measurements, these methods form a general systems biology strategy to evaluate the efficiency of cellulosic bioconversion and to identify enzyme targets to engineer for improving this process.C. phytofermentans expressed more than 100 carbohydrate-active enzymes, of which distinct subsets were upregulated on cellulose and hemicellulose. Numerous extracellular enzymes cleave insoluble plant polysaccharides into oligosaccharides, which are transported into the cell to be further degraded by intracellular carbohydratases. Sugars are catabolized by EMP glycolysis incorporating alternative glycolytic enzymes to maximize the ATP yield of anaerobic metabolism.During cellulosic fermentation, cells adhered to the substrate and altered metabolic processes such as upregulation of tryptophan and nicotinamide synthesis proteins and repression of proteins for fatty acid metabolism and cell motility. These diverse metabolic changes highlight how a systems approach can identify novel ways to optimize cellulosic fermentation.
Cellulose is the world's most abundant renewable, biological energy source (Leschine, 1995). Microbial fermentation of cellulosic biomass could sustainably provide enough ethanol for 65% of US ground transportation fuel at current levels (Somerville, 2006). However, cellulose in plant biomass is packaged into a crystalline matrix, making biomass deconstruction a key roadblock to using it as a feedstock (Houghton et al, 2006). A promising strategy to overcome biomass recalcitrance is consolidated bioprocessing (Lynd et al, 2002), which uses microbes such as Clostridium phytofermentans to both secrete enzymes to depolymerize biomass and then ferment the resulting hexose and pentose sugars to a biofuel such as ethanol. The C. phytofermentans genome encodes 161 carbohydrate-active enzymes (CAZy) including 108 glycoside hydrolases spread across 39 families (Cantarel et al, 2009), highlighting the elaborate set of enzymes needed to breakdown different cellulosic polysaccharides. Faced with the complexity of metabolizing biomass, systems biology strategies are needed to comprehensively identify which cellulolytic and metabolic enzymes are used to ferment different cellulosic substrates.
This study presents a systems-level analysis of how C. phytofermentans ferments different cellulosic substrates that incorporates quantitative mass spectrometry-based proteomics of over 2500 proteins. Protein concentrations within each carbon source treatment were calculated by machine learning-supported spectral counting (Absolute Protein EXpression, APEX) (Lu et al, 2007). Protein levels on hemicellulose and cellulose relative to glucose were determined using reductive methylation (Hsu et al, 2003; Boersema et al, 2009), here called ReDi labeling, to chemically incorporate hydrogen or deuterium isotopes at lysines and N-terminal amines of tryptic peptides. We show that ReDi proteomics gives accurate, low-cost quantification of a microbial proteome and can be used to discern extracellular proteins. Further, we combine these quantitative proteomics with detailed measurements of growth, biomass consumption, fermentation product analyses, and electron microscopy. Together, these methods form a general strategy to evaluate the efficiency of cellulosic bioconversion and to identify enzyme targets to engineer for improving this process (Figure 1).
We found that fermentation of cellulosic substrates by C. phytofermentans involves secretion of numerous CAZy as well as proteins for binding of extracellular solutes, proteolysis, and motility. The most highly expressed protein in the proteome is a secreted protein that appears to compose a surface layer to support the cell and anchor cell surface proteins, including some enzymes for plant degradation. Once the secreted CAZy cleave insoluble plant polysaccharides into oligosaccharides, they are taken into the cell to be further degraded by intracellular CAZy, enabling more efficient sugar transport, conserving energy by phosphorolytic cleavage, and ensuring the sugar monomers were not available to competing microbes. Sugars are catabolized by EMP glycolysis incorporating reversible, PPi-dependent glycolytic enzymes, and pyruvate ferredoxin oxidoreductase. The genome encodes seven alcohol dehydrogenases, among which two iron-dependent enzymes are highly expressed and likely facilitate the high ethanol yields. Growth on cellulose also resulted in indirect changes such as increased tryptophan and nicotinamide synthesis and repression of fatty acid synthesis. We distilled the data into a model showing the highly expressed enzymes enabling efficient cellulosic fermentation by C. phytofermentans (Figure 7). Collectively, these data help understand how bacteria recycle plant biomass works towards enabling the use of plant biomass as a low-cost chemical feedstock.
Fermentation of plant biomass by microbes like Clostridium phytofermentans recycles carbon globally and can make biofuels from inedible feedstocks. We analyzed C. phytofermentans fermenting cellulosic substrates by integrating quantitative mass spectrometry of more than 2500 proteins with measurements of growth, enzyme activities, fermentation products, and electron microscopy. Absolute protein concentrations were estimated using Absolute Protein EXpression (APEX); relative changes between treatments were quantified with chemical stable isotope labeling by reductive dimethylation (ReDi). We identified the different combinations of carbohydratases used to degrade cellulose and hemicellulose, many of which were secreted based on quantification of supernatant proteins, as well as the repertoires of glycolytic enzymes and alcohol dehydrogenases (ADHs) enabling ethanol production at near maximal yields. Growth on cellulose also resulted in diverse changes such as increased expression of tryptophan synthesis proteins and repression of proteins for fatty acid metabolism and cell motility. This study gives a systems-level understanding of how this microbe ferments biomass and provides a rational, empirical basis to identify engineering targets for industrial cellulosic fermentation.
doi:10.1038/msb.2010.116
PMCID: PMC3049413  PMID: 21245846
bioenergy; clostridium; proteomics
16.  MaXIC-Q Web: a fully automated web service using statistical and computational methods for protein quantitation based on stable isotope labeling and LC–MS 
Nucleic Acids Research  2009;37(Web Server issue):W661-W669.
Isotope labeling combined with liquid chromatography–mass spectrometry (LC–MS) provides a robust platform for analyzing differential protein expression in proteomics research. We present a web service, called MaXIC-Q Web (http://ms.iis.sinica.edu.tw/MaXIC-Q_Web/), for quantitation analysis of large-scale datasets generated from proteomics experiments using various stable isotope-labeling techniques, e.g. SILAC, ICAT and user-developed labeling methods. It accepts spectral files in the standard mzXML format and search results from SEQUEST, Mascot and ProteinProphet as input. Furthermore, MaXIC-Q Web uses statistical and computational methods to construct two kinds of elution profiles for each ion, namely, PIMS (projected ion mass spectrum) and XIC (extracted ion chromatogram) from MS data. Toward accurate quantitation, a stringent validation procedure is performed on PIMSs to filter out peptide ions interfered with co-eluting peptides or noise. The areas of XICs determine ion abundances, which are used to calculate peptide and protein ratios. Since MaXIC-Q Web adopts stringent validation on spectral data, it achieves high accuracy so that manual validation effort can be substantially reduced. Furthermore, it provides various visualization diagrams and comprehensive quantitation reports so that users can conveniently inspect quantitation results. In summary, MaXIC-Q Web is a user-friendly, interactive, robust, generic web service for quantitation based on ICAT and SILAC labeling techniques.
doi:10.1093/nar/gkp476
PMCID: PMC2703943  PMID: 19528069
17.  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 14N/15N 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 14N/15N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.
doi:10.1089/omi.2011.0140
PMCID: PMC3437042  PMID: 22909347
18.  Quantification of mRNA and protein and integration with protein turnover in a bacterium 
Determination of the average cellular copy number of 400 proteins under different growth conditions and integration with protein turnover and absolute mRNA levels reveals the dynamics of protein expression in the genome-reduced bacterium Mycoplasma pneumoniae.
Our study provides a fine-grained, quantitative picture to unprecedented detail in an established model organism for systems-wide studies.Our integrative approach reveals a novel, dynamic view on the processes, interactions and regulations underlying the central dogma pathway and the composition of protein complexes.Simulations using our quantitative data on mRNA, protein and turnover show how an organism copes with stochastic noise in gene expression in vivo.Our data serve as an important resource for colleagues both within our field of research and in related disciplines.
A hallmark of Systems Biology is the integration of diverse, large quantitative data sets with the aim to gain novel insights into how biological processes work. We measured individual mRNA and protein abundances as well as protein turnover in the bacterium Mycoplasma pneumoniae. This human pathogen is an ideal model organism for organism-wide studies. It can be readily cultured under laboratory conditions and it has a very small genome with only 690 protein-coding genes. This comparably low complexity allows for the exhaustive analysis of major cellular biomolecules avoiding constrains introduced by limitations of available analysis techniques.
Using a recently developed mass spectrometry-based approach, we determined the average cellular copy number for over 400 individual proteins under different growth and stress conditions. The 20 most abundant proteins, including Elongation factor Tu, cellular chaperones, and proteins involved in metabolizing glucose, the major energy source of M. pneumoniae account for nearly 44% of the total cellular protein mass. We observed abundance changes of many expected and several unexpected proteins in response to cellular stress, such as heat shock, DNA damage and osmotic stress, as well as along batch culture growth over 4 days.
Integration of the protein abundance data with quantitative mRNA measurements revealed a modest correlation between these two classes of biomolecules. However, for several classical stress-induced proteins, we observed a correlated induction of mRNA and protein in response to heat shock. A focused analysis of mRNA–protein abundance dynamics during batch culture growth suggested that the regulation of gene expression is largely decoupled from protein dynamics in M. pneumoniae, indicating extensive post-transcriptional and post-translational regulation influencing the cellular mRNA–protein ratios.
To investigate the factors influencing the cellular protein abundance, we measured individual protein turnover rates by mass spectrometry using a label-chase approach involving stable isotope-labelled amino acids. The average half-life of a protein in M. pneumoniae is 23 h. Based on the measured quantitative mRNA data, the protein abundances and their half-lives, we established an ordinary differential equations model for the estimation of individual in vivo protein degradation and translation efficiency rates. We found out that translation efficiency rather than protein turnover is the dominating factor influencing protein abundance. Using our abundance and turnover data, we additionally performed stochastic simulations of gene expression. We observed that long protein half-life and low translational efficiency buffers gene expression noise propagating from low cellular mRNA levels in vivo.
We compared the abundance ratios of proteins associating into complexes in vivo with their expected functional stoichiometries. We observed that for stable protein complexes, such as the GroEL/ES chaperonin or DNA gyrase, our measured abundance ratios reflected the expected subunit stoichiometries. More dynamic protein complexes, such as the DnaK/J/GrpE chaperone system or RNA polymerase, showed several unusual subunit ratios, pointing towards transient interaction of sub-stoichiometric subunits for function. A detailed, quantitative analysis of the ribosome, the largest cellular protein complex, revealed large abundance differences of the 51 subunits. This observation indicates a multi-functionality for several, abundant ribosomal proteins.
Finally, a comparison of the determined average cellular protein abundances with a different pathogenic bacterium, Leptospira interrogans, revealed that cellular protein abundances closely reflect their respective lifestyles.
Our study represents an organism-wide, quantitative analysis of cellular protein abundances. Integrating our proteomics data with determined mRNA levels and protein turnover rates reveals insights into the dynamic interplay and regulation of mRNA and proteins, the central biomolecules of a cell.
Biological function and cellular responses to environmental perturbations are regulated by a complex interplay of DNA, RNA, proteins and metabolites inside cells. To understand these central processes in living systems at the molecular level, we integrated experimentally determined abundance data for mRNA, proteins, as well as individual protein half-lives from the genome-reduced bacterium Mycoplasma pneumoniae. We provide a fine-grained, quantitative analysis of basic intracellular processes under various external conditions. Proteome composition changes in response to cellular perturbations reveal specific stress response strategies. The regulation of gene expression is largely decoupled from protein dynamics and translation efficiency has a higher regulatory impact on protein abundance than protein turnover. Stochastic simulations using in vivo data show how low translation efficiency and long protein half-lives effectively reduce biological noise in gene expression. Protein abundances are regulated in functional units, such as complexes or pathways, and reflect cellular lifestyles. Our study provides a detailed integrative analysis of average cellular protein abundances and the dynamic interplay of mRNA and proteins, the central biomolecules of a cell.
doi:10.1038/msb.2011.38
PMCID: PMC3159969  PMID: 21772259
mRNA–protein; Mycoplasma pneumoniae; protein homeostasis; protein turnover; quantitative proteomics
19.  Mass Spectrometry-Based Label-Free Quantitative Proteomics 
In order to study the differential protein expression in complex biological samples, strategies for rapid, highly reproducible and accurate quantification are necessary. Isotope labeling and fluorescent labeling techniques have been widely used in quantitative proteomics research. However, researchers are increasingly turning to label-free shotgun proteomics techniques for faster, cleaner, and simpler results. Mass spectrometry-based label-free quantitative proteomics falls into two general categories. In the first are the measurements of changes in chromatographic ion intensity such as peptide peak areas or peak heights. The second is based on the spectral counting of identified proteins. In this paper, we will discuss the technologies of these label-free quantitative methods, statistics, available computational software, and their applications in complex proteomics studies.
doi:10.1155/2010/840518
PMCID: PMC2775274  PMID: 19911078
20.  Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics 
BMC Bioinformatics  2008;9:542.
Background
Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics.
Results
We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling.
Conclusion
The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.
doi:10.1186/1471-2105-9-542
PMCID: PMC2651178  PMID: 19087345
21.  Temporal profiling of the adipocyte proteome during differentiation using a 5-plex SILAC based strategy 
Journal of proteome research  2009;8(1):48-58.
The adipose tissue has important secretory and endocrine functions in humans. The regulation of adipocyte differentiation has been actively pursued using transcriptomic methods over the last several years. Quantitative proteomics has emerged as a promising approach to obtain temporal profiles of biological processes such as differentiation. Stable isotope labeling with amino acids in cell culture (SILAC) is a simple and robust method for labeling proteins in vivo. Here, we describe the development and application of a five-plex SILAC experiment using four different heavy stable isotopic forms of arginine to study the nuclear proteome and the secretome during the course of adipocyte differentiation. Tandem mass spectrometry analysis using a quadrupole time-of-flight instrument resulted in identification of a total 882 proteins from these two proteomes. Of these proteins, 427 were identified on the basis of one or more arginine containing peptides that allowed quantitation. In addition to previously reported molecules that are differentially expressed during the process of adipogenesis (e.g. adiponectin and lipoprotein lipase), we identified several proteins whose differential expression during adipocyte differentiation has not been documented previously. For example, THO complex 4, a context-dependent transcriptional activator in the T-cell receptor alpha enhancer complex, showed highest expression at middle stage of adipogenesis while SNF2 alpha, a chromatin remodeling protein, was downregulated upon initiation of adipogenesis and remained so during subsequent time points. This study using a 5-plex SILAC to investigate dynamics illustrates the power of this approach to identify differentially expressed proteins in a temporal fashion.
doi:10.1021/pr800650r
PMCID: PMC2642533  PMID: 18947249
Adipocyte; adipogenesis; proteomics; SILAC
22.  Envelope: interactive software for modeling and fitting complex isotope distributions 
BMC Bioinformatics  2008;9:446.
Background
An important aspect of proteomic mass spectrometry involves quantifying and interpreting the isotope distributions arising from mixtures of macromolecules with different isotope labeling patterns. These patterns can be quite complex, in particular with in vivo metabolic labeling experiments producing fractional atomic labeling or fractional residue labeling of peptides or other macromolecules. In general, it can be difficult to distinguish the contributions of species with different labeling patterns to an experimental spectrum and difficult to calculate a theoretical isotope distribution to fit such data. There is a need for interactive and user-friendly software that can calculate and fit the entire isotope distribution of a complex mixture while comparing these calculations with experimental data and extracting the contributions from the differently labeled species.
Results
Envelope has been developed to be user-friendly while still being as flexible and powerful as possible. Envelope can simultaneously calculate the isotope distributions for any number of different labeling patterns for a given peptide or oligonucleotide, while automatically summing these into a single overall isotope distribution. Envelope can handle fractional or complete atom or residue-based labeling, and the contribution from each different user-defined labeling pattern is clearly illustrated in the interactive display and is individually adjustable. At present, Envelope supports labeling with 2H, 13C, and 15N, and supports adjustments for baseline correction, an instrument accuracy offset in the m/z domain, and peak width. Furthermore, Envelope can display experimental data superimposed on calculated isotope distributions, and calculate a least-squares goodness of fit between the two. All of this information is displayed on the screen in a single graphical user interface. Envelope supports high-quality output of experimental and calculated distributions in PNG or PDF format. Beyond simply comparing calculated distributions to experimental data, Envelope is useful for planning or designing metabolic labeling experiments, by visualizing hypothetical isotope distributions in order to evaluate the feasibility of a labeling strategy. Envelope is also useful as a teaching tool, with its real-time display capabilities providing a straightforward way to illustrate the key variable factors that contribute to an observed isotope distribution.
Conclusion
Envelope is a powerful tool for the interactive calculation and visualization of complex isotope distributions for comparison to experimental data. It is available under the GNU General Public License from .
doi:10.1186/1471-2105-9-446
PMCID: PMC2605472  PMID: 18937869
23.  ABRF Research Group Development and Characterization of a Proteomics Normalization Standard Consisting of 1,000 Stable Isotope Labeled Peptides 
The ABRF Proteomics Standards Research Group (sPRG) is reporting the progress of a two-year study (2012–2014) which focuses on the generation of interassay, interspecies, and interlaboratory peptide standard that can be used for normalization of protein abundance measurements in mass spectrometry based quantitative proteomics analyses. The standard has been formulated as two mixtures: 1,000 stable isotope 13C/15N-labeled (SIL) synthetic peptides alone, and peptides mixed with a tryptic digest of a HEK 293 cell lysate. The sequences of the synthetic peptides were derived from 552 proteins conserved across proteomes of commonly analyzed species: Homo sapiens, Mus musculus and Rattus norvegicus. The selected peptides represent a full range of hydrophobicities and isoelectric points, typical of tryptic peptides derived from complex proteomic samples. The standard was designed to represent proteins of various concentrations, spanning three orders of magnitude. First year efforts were focused on selection of appropriate protein and peptide candidates, peptide synthesis, quality assessment and LC-MS/MS evaluation conducted in laboratories of sPRG members. Using a variety of instrumental configurations and bioinformatics approaches, a thorough characterization of all 1,000 peptides was established. In the second year, the group launched the study to the entire proteomics community. A lyophilized mixture of HEK 293 tryptic digest cell lysate spiked with the 1,000 SIL peptide standards was provided to each participant. Also provided were a Skyline tutorial, tutorial datasets, three MS/MS spectral libraries generated from linear ion-trap (CID), Q-TOF/QQQ (CID), or Orbitrap (HCD) instrumentation, and a Panorama data repository. Participants were asked to analyze the sample in triplicate and calculate ratios of the spiked SIL to endogenous peptides and coefficients of variance for each peptide. Over 40 datasets were returned, and results following thorough characterization of the standard using various instrumental configurations will be reported.
PMCID: PMC4162257
24.  Stable isotope dimethyl labeling combined with LTQ mass spectrometric detection, a quantitative proteomics technology used in liver cancer research 
Biomedical Reports  2013;1(4):549-554.
Liver cancer is a common malignant disease, with high incidence and mortality rates. The study on the proteomics of liver cancer has attracted particular attention. The quantitative study method of proteomics depends predominantly on two-dimensional (2D) gel electrophoresis. In the present study we reported a rapid and accurate proteomics quantitative study method of high repeatability that includes the use of stable isotope labeling for the extraction of proteins and peptides via enzymolysis to achieve new type 2D capillary liquid chromatography-mass spectrometry separation using the separation mode of cation-exchange chromatography in conjunction with reversed-phase chromatography. LTQ OrbiTrap mass spectrometry detection was also performed. A total of 188 differential proteins were analyzed, including 122 upregulating [deuterium/hydrogen ratio (D/H) >1.5)] and 66 downregulating proteins (D/H<0.67). These proteins may play an important role in the occurrence, drug resistance, metastasis and recurrence of cancer or other pathological processes. Such a proteomics technology may provide biological data as well as a new methodological basis for liver cancer research.
doi:10.3892/br.2013.100
PMCID: PMC3917736  PMID: 24648984
stable isotope labeling; liver cancer; quantitative proteomics; mass spectrum
25.  Quantitative proteomic analysis of amniocytes reveals potentially dysregulated molecular networks in Down syndrome 
Clinical proteomics  2013;10(1):2.
Background
Down syndrome (DS), caused by an extra copy of chromosome 21, affects 1 in 750 live births and is characterized by cognitive impairment and a constellation of congenital defects. Currently, little is known about the molecular pathogenesis and no direct genotype-phenotype relationship has yet been confirmed. Since DS amniocytes are expected to have a distinct biological behaviour compared to normal amniocytes, we hypothesize that relative quantification of proteins produced from trisomy and euploid (chromosomally normal) amniocytes will reveal dysregulated molecular pathways.
Results
Chromosomally normal- and Trisomy 21-amniocytes were quantitatively analyzed by using Stable Isotope Labeling of Amino acids in Cell culture and tandem mass spectrometry. A total of 4919 unique proteins were identified from the supernatant and cell lysate proteome. More specifically, 4548 unique proteins were identified from the lysate, and 91% of these proteins were quantified based on MS/MS spectra ratios of peptides containing isotope-labeled amino acids. A total of 904 proteins showed significant differential expression and were involved in 25 molecular pathways, each containing a minimum of 16 proteins. Sixty of these proteins consistently showed aberrant expression from trisomy 21 affected amniocytes, indicating their potential role in DS pathogenesis. Nine proteins were analyzed with a multiplex selected reaction monitoring assay in an independent set of Trisomy 21-amniocyte samples and two of them (SOD1 and NES) showed a consistent differential expression.
Conclusions
The most extensive proteome of amniocytes and amniotic fluid has been generated and differentially expressed proteins from amniocytes with Trisomy 21 revealed molecular pathways that seem to be most significantly affected by the presence of an extra copy of chromosome 21.
doi:10.1186/1559-0275-10-2
PMCID: PMC3626793  PMID: 23394617
Down syndrome; Trisomy 21; Amniocyte; Amniotic fluid cells; Quantitative proteomics

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