SWATH enables precise label‐free quantification on proteome scale

MS‐based proteomics has emerged as a powerful tool in biological studies. The shotgun proteomics strategy, in which proteolytic peptides are analyzed in data‐dependent mode, enables a detection of the most comprehensive proteome (>10 000 proteins from whole‐cell lysate). The quantitative proteomics uses stable isotopes or label‐free method to measure relative protein abundance. The isotope labeling strategies are more precise and accurate compared to label‐free methods, but labeling procedures are complicated and expensive, and the sample number and types are also limited. Sequential window acquisition of all theoretical mass spectra (SWATH) is a recently developed technique, in which data‐independent acquisition is coupled with peptide spectral library match. In principle SWATH method is able to do label‐free quantification in an MRM‐like manner, which has higher quantification accuracy and precision. Previous data have demonstrated that SWATH can be used to quantify less complex systems, such as spiked‐in peptide mixture or protein complex. Our study first time assessed the quantification performance of SWATH method on proteome scale using a complex mouse‐cell lysate sample. In total 3600 proteins got identified and quantified without sample prefractionation. The SWATH method shows outstanding quantification precision, whereas the quantification accuracy becomes less perfect when protein abundances differ greatly. However, this inaccuracy does not prevent discovering biological correlates, because the measured signal intensities had linear relationship to the sample loading amounts; thus the SWATH method can predict precisely the significance of a protein. Our results prove that SWATH can provide precise label‐free quantification on proteome scale.     

Systematic comparison of label-free, metabolic labeling, and isobaric chemical labeling for quantitative proteomics on LTQ Orbitrap Velos

A variety of quantitative proteomics methods have been developed, including label-free, metabolic labeling, and isobaric chemical labeling using iTRAQ or TMT. Here, these methods were compared in terms of the depth of proteome coverage, quantification accuracy, precision, and reproducibility using a high-performance hybrid mass spectrometer, LTQ Orbitrap Velos. Our results show that (1) the spectral counting method provides the deepest proteome coverage for identification, but its quantification performance is worse than labeling-based approaches, especially the quantification reproducibility; (2) metabolic labeling and isobaric chemical labeling are capable of accurate, precise, and reproducible quantification and provide deep proteome coverage for quantification; isobaric chemical labeling surpasses metabolic labeling in terms of quantification precision and reproducibility; and (3) iTRAQ and TMT perform similarly in all aspects compared in the current study using a CID-HCD dual scan configuration. On the basis of the unique advantages of each method, we provide guidance for selection of the appropriate method for a quantitative proteomics study.     

The use of accurate mass tags for high-throughput microbial proteomics

We describe and review progress towards a global strategy that aims to extend the sensitivity, dynamic range, comprehensiveness, and throughput of proteomic measurements for microbial systems based upon the use of polypeptide accurate mass tags (AMTs) produced by global protein enzymatic digestions. The two-stage strategy exploits high accuracy mass measurements using Fourier transform ion cyclotron resonance mass spectrometry (FTICR) to validate polypeptide AMTs for a specific organism, from potential mass tags tentatively identified using tandem mass spectrometry (MS/MS), providing the basis for subsequent measurements without the need for routine MS/MS. A high-resolution capillary liquid chromatography separation combined with high sensitivity, and high-resolution accurate FTICR measurements is shown to be capable of characterizing polypeptide mixtures of more than 10(5) components, sufficient for broad protein identification using AMTs. Advantages of the approach include the high confidence of protein identification, its broad proteome coverage, and the capability for stable-isotope labeling methods for precise relative protein abundance measurements. The strategy has been initially evaluated using the microorganisms Saccharomyces cerevisiae and Deinococcus radiodurans. Additional developments, including the use of multiplexed-MS/MS capabilities and methods for dynamic range expansion of proteome measurements that promise to further extend the quality of proteomics measurements, are also described.     

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.     

Monitoring Astrocytic Proteome Dynamics by Cell Type-Specific Protein Labeling

The ability of the nervous system to undergo long-term plasticity is based on changes in cellular and synaptic proteomes. While many studies have explored dynamic alterations in neuronal proteomes during plasticity, there has been less attention paid to the astrocytic counterpart. Indeed, progress in identifying cell type-specific proteomes is limited owing to technical difficulties. Here, we present a cell type-specific metabolic tagging technique for a mammalian coculture model based on the bioorthogonal amino acid azidonorleucine and the mutated Mus musculus methionyl-tRNA synthetase^L274G enabling azidonorleucine introduction into de novo synthesized proteins. Azidonorleucine incorporation resulted in cell type-specific protein labeling and retained neuronal or astrocytic cell viability. Furthermore, we were able to label astrocytic de novo synthesized proteins and identified both Connexin-43 and 60S ribosomal protein L10a upregulated upon treatment with Brain-derived neurotrophic factor in astrocytes of a neuron-glia coculture. Taken together, we demonstrate the successful dissociation of astrocytic from neuronal proteomes by cell type-specific metabolic labeling offering new possibilities for the analyses of cell type-specific proteome dynamics.     

Proteome characterization of the unsequenced psychrophile Pedobacter cryoconitis using 15N metabolic labeling, tandem mass spectrometry, and a new bioinformatic workflow

Organisms without a sequenced genome and lacking a complete protein database encounter an added level of complexity to protein identification and quantitation. De novo sequencing, new bioinformatics tools, and mass spectrometry (MS) techniques allow for advances in this area. Here, the proteomic characterization of an unsequenced psychrophilic bacterium, Pedobacter cryoconitis, is presented employing a novel workflow based on (15) N metabolic labelling, 2DE, MS/MS, and bioinformatics tools. Two bioinformatics pipelines, based on nitrogen constraint (N-constraint), ortholog searching, and de novo peptide sequencing with N-constraint similarity database search, are compared based on proteome coverage and throughput. Results demonstrate the effect of different growth temperatures (1°C, 20°C) and different carbon sources (glucose, maltose) on the proteome. Seventy-six and 69 proteins were identified and validated from the glucose- and maltose-grown bacterium, respectively, from which 21 and 22 were differentially expressed at different growth temperatures. Differentially expressed proteins are involved in stress response and carbohydrate metabolism, with higher expression at 20°C than at 1°C, while antioxidants were upregulated at 1°C. This study provides an alternative workflow to identify, validate, and quantify proteins from unsequenced organisms distantly related to other species in the protein database. Furthermore, it provides further understanding on bacterial adaptation mechanisms to cold environments, and a comparative proteomic analyses with other psychrophilic microorganisms.     

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

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

Quantitative profiling of the membrane proteome in a halophilic archaeon

We present a large scale quantitation study of the membrane proteome from Halobacterium salinarum. To overcome problems generally encountered with membrane proteins, we established a membrane preparation protocol that allows the application of most proteomic techniques originally developed for soluble proteins. Proteins were quantified using two complementary approaches. For gel-based quantitation, DIGE labeling was combined with two-dimensional gel electrophoresis on an improved 16-benzyldimethyl-n-hexadecylammonium chloride/SDS system. MS-based quantitation was carried out by combining gel-free separation with the recently developed isotope-coded protein labeling technique. Good correlations between these two independent quantitation strategies were obtained. From computational analysis we conclude that labeling of free amino groups by isotope-coded protein labeling (Lys and free N termini) is better suited for membrane proteins than Cys-based labeling strategies but that quantitation of integral membrane proteins remains cumbersome compared with soluble proteins. Nevertheless we could quantify 155 membrane proteins; 101 of these had transmembrane domains. We compared two growth states that strongly affect the energy supply of the cells: aerobic versus anaerobic/phototrophic conditions. The photosynthetic protein bacteriorhodopsin is the most highly regulated protein. As expected, several other membrane proteins involved in aerobic or anaerobic energy metabolism were found to be regulated, but in total, however, the number of regulated proteins is rather small.     

Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.     

Protein abundance ratios for global studies of prokaryotes

The use of multidimensional capillary HPLC combined with MS/MS has allowed high qualitative and quantitative proteome coverage of prokaryotic organisms. The determination of protein abundance change between two or more conditions has matured to the point that false discovery rates can be very low and for smaller proteomes coverage is sufficiently high to explicitly consider false negative error. Selected aspects of using these methods for global protein abundance assessments are reviewed. These include instrumental issues that influence the reliability of abundance ratios; a comparison of sources of nonlinearity, errors, and data compression in proteomics and spotted cDNA arrays; strengths and weaknesses of spectral counting versus stable isotope metabolic labeling; and a survey of microbiological applications of global abundance analysis at the protein level. Proteomic results for two organisms that have been studied extensively using these methods are reviewed in greater detail. Spectral counting and metabolic labeling data are compared and the utility of proteomics for global gene regulation studies are discussed for the methanogenic Archaeon Methanococcus maripaludis. The oral pathogen Porphyromonas gingivalis is discussed as an example of an organism where a large percentage of the proteome differs in relative abundance between the intracellular and extracellular phenotype.     

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

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

15N metabolic labeling of mammalian tissue with slow protein turnover

We previously reported the metabolic 15N labeling of a rat where enrichment ranged from 94% to 74%. We report here an improved labeling strategy which generates 94% 15N enrichment throughout all tissues of the rat. A high 15N enrichment of the internal standard is necessary for accurate quantitation, and thus, this approach will allow quantitative mass spectrometry analysis of animal models of disease targeting any tissue.     

Combination of online enzyme digestion with stable isotope labeling for high‐throughput quantitative proteome analysis

Various enzyme reactors and online enzyme digestion strategies have been developed in recent years. These reactors greatly enhanced the detection sensitivity and proteome coverage in qualitative proteomics. However, these devices have higher rates of miscleavage in protein digestion. Therefore, we investigated the effect of online enzyme digestion on the quantification accuracy of quantitative proteomics using chemical or metabolic isotope labeling approaches. The incomplete digestion would introduce some unexpected variations in comparative quantification when the samples are digested and then chemically isotope labeled in different aliquots. Even when identical protein aliquots are processed on these devices using post‐digestion chemical isotope labeling and the CVs of the ratios controlled to less than 50% in replicate analyses, about 10% of the quantified proteins have a ratio greater than two‐fold, whereas in theory the ratio is 1:1. Interestingly, the incomplete digestion with enzyme reactor is not a problem when metabolic isotope labeling samples were processed because the proteins are isotopically labeled in vivo prior to their simultaneous digestion within the reactor. Our results also demonstrated that both high quantification accuracy and high proteome coverage can be achieved in comparative proteome quantification using online enzyme digestion even when a limited amount of metabolic isotope labeling samples is used (1683 proteins comparatively quantified from 10⁵ Hela cells).     

Quantitative Proteome Analysis of Leishmania donovani under Spermidine Starvation

We have earlier reported antileishmanial activity of hypericin by spermidine starvation. In the current report, we have used label free proteome quantitation approach to identify differentially modulated proteins after hypericin treatment. A total of 141 proteins were found to be differentially regulated with ANOVA P value less than 0.05 in hypericin treated Leishmania promastigotes. Differentially modulated proteins have been broadly classified under nine major categories. Increase in ribosomal protein S7 protein suggests the repression of translation. Inhibition of proteins related to ubiquitin proteasome system, RNA binding protein and translation initiation factor also suggests altered translation. We have also observed increased expression of Hsp 90, Hsp 83–1 and stress inducible protein 1. Significant decreased level of cyclophilin was observed. These stress related protein could be cellular response of the parasite towards hypericin induced cellular stress. Also, defective metabolism, biosynthesis and replication of nucleic acids, flagellar movement and signalling of the parasite were observed as indicated by altered expression of proteins involved in these pathways. The data was analyzed rigorously to get further insight into hypericin induced parasitic death.     

High‐performance hybrid Orbitrap mass spectrometers for quantitative proteome analysis: Observations and implications

We present basic workups and quantitative comparisons for two current generation Orbitrap mass spectrometers, the Q Exactive Plus and Orbitrap Fusion Tribrid, which are widely considered two of the highest performing instruments on the market. We assessed the performance of two quantitative methods on both instruments, namely label‐free quantitation and stable isotope labeling using isobaric tags, for studying the heat shock response in Escherichia coli. We investigated the recently reported MS3 method on the Fusion instrument and the potential of MS3‐based reporter ion isolation Synchronous Precursor Selection (SPS) and its impact on quantitative accuracy. We confirm that the label‐free approach offers a more linear response with a wider dynamic range than MS/MS‐based isobaric tag quantitation and that the MS3/SPS approach alleviates but does not eliminate dynamic range compression. We observed, however, that the choice of quantitative approach had little impact on the ability to statistically evaluate the E. coli heat shock response. We conclude that in the experimental conditions tested, MS/MS‐based reporter ion quantitation provides reliable biological insight despite the issue of compressed dynamic range, an observation that significantly impacts the choice of instrument.     

Sequential protein extraction as an efficient method for improved proteome coverage in larvae of Atlantic salmon (Salmo salar)

Understanding diet‐ and environmentally induced physiological changes in fish larvae is a major goal for the aquaculture industry. Proteomic analysis of whole fish larvae comprising multiple tissues offers considerable potential but is challenging due to the very large dynamic range of protein abundance. To extend the coverage of the larval phase of the Atlantic salmon (Salmo salar) proteome, we applied a two‐step sequential extraction (SE) method, based on differential protein solubility, using a nondenaturing buffer containing 150 mM NaCl followed by a denaturing buffer containing 7 M urea and 2 M thiourea. Extracts prepared using SE and one‐step direct extraction were characterized via label‐free shotgun proteomics using nanoLC‐MS/MS (LTQ‐Orbitrap). SE partitioned the proteins into two fractions of approximately equal amounts, but with very distinct protein composition, leading to identification of ～40% more proteins than direct extraction. This fractionation strategy enabled the most detailed characterization of the salmon larval proteome to date and provides a platform for greater understanding of physiological changes in whole fish larvae. The MS data are available via the ProteomeXchange Consortium PRIDE partner repository, dataset PXD003366.     

Proteomic analysis of mouse astrocytes and their secretome by a combination of FASP and StageTip‐based,high pH,reversed‐phase fractionation

Astrocytes are the most abundant cells in the CNS, but their function remains largely unknown. Characterization of the whole‐cell proteome and secretome in astrocytes would facilitate the study of their functions in various neurodegenerative diseases and astrocyte–neuron communication. To build a reference proteome, we established a C8‐D1A astrocyte proteome to a depth of 7265 unique protein groups using a novel strategy that combined two‐step digestion, filter‐aided sample preparation, StageTip‐based high pH fractionation, and high‐resolution MS. Nearly, 6000 unique protein groups were identified from conditioned media of astrocyte cultures, constituting the largest astrocyte secretome that has been reported. High‐confidence whole‐cell proteomes and secretomes are valuable resources in studying astrocyte function by label‐free quantitation and bioinformatics analysis. All MS data have been deposited in the ProteomeXchange with identifier PXD000501 ( http://proteomecentral.proteomexchange.org/dataset/PXD000501 ).     

Measurement of human plasma proteome dynamics with (2)H(2)O and liquid chromatography tandem mass spectrometry

Dysfunction of protein turnover is a feature of many human diseases, and proteins are substrates in important biological processes. Currently, no method exists for the measurement of global protein turnover (i.e., proteome dynamics) that can be applied in humans. Here we describe the use of metabolic labeling with deuterium ((2)H) from (2)H(2)O and liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of mass isotopomer patterns to measure protein turnover. We show that the positions available for (2)H label incorporation in vivo can be calculated using peptide sequence. The isotopic incorporation values calculated by combinatorial analysis of mass isotopomer patterns in peptides correlate very closely with values established for individual amino acids. Inpatient and outpatient heavy water labeling protocols resulted in (2)H label incorporation sufficient for reproducible quantitation in humans. Replacement rates were similar for peptides deriving from the same protein. Using a kinetic model to account for the time course of each individual's (2)H(2)O enrichment curves, dynamics of approximately 100 proteins with half-lives ranging from 0.4 to 40 days were measured using 8 μl of plasma. The measured rates were consistent with literature values. This method can be used to measure in vivo proteome homeostasis in humans in disease and during therapeutic interventions.     

A novel strategy for quantitative proteomics using isotope-coded protein labels

Stable isotope labelling in combination with mass spectrometry has emerged as a powerful tool to identify and relatively quantify thousands of proteins within complex protein mixtures. Here we describe a novel method, termed isotope-coded protein label (ICPL), which is capable of high-throughput quantitative proteome profiling on a global scale. Since ICPL is based on stable isotope tagging at the frequent free amino groups of isolated intact proteins, it is applicable to any protein sample, including extracts from tissues or body fluids, and compatible to all separation methods currently employed in proteome studies. The method showed highly accurate and reproducible quantification of proteins and yielded high sequence coverage, indispensable for the detection of post-translational modifications and protein isoforms. The efficiency (e.g. accuracy, dynamic range, sensitivity, speed) of the approach is demonstrated by comparative analysis of two differentially spiked proteomes.