Increased proteome coverage by combining PAGE and peptide isoelectric focusing: Comparative study of gel‐based separation approaches

The in‐depth analysis of complex proteome samples requires fractionation of the sample into subsamples prior to LC‐MS/MS in shotgun proteomics experiments. We have established a 3D workflow for shotgun proteomics that relies on protein separation by 1D PAGE, gel fractionation, trypsin digestion, and peptide separation by in‐gel IEF, prior to RP‐HPLC‐MS/MS. Our results show that applying peptide IEF can significantly increase the number of proteins identified from PAGE subfractionation. This method delivers deeper proteome coverage and provides a large degree of flexibility in experimentally approaching highly complex mixtures by still relying on protein separation according to molecular weight in the first dimension.     

Combination of peptide OFFGEL fractionation and label-free quantitation facilitated proteomics profiling of extraocular muscle

Several label-free quantitation strategies have been introduced that obliterate the need for expensive isotopically labeled molecules. However label-free approaches have considerably higher demands in respect of repeatability of sample preparation and fractionation than multiplexing isotope labeling-based strategies. OFFGEL fractionation promises the necessary separation efficiency and repeatability. To test this platform, 12-fraction peptide OFFGEL electrophoresis and online reversed-phase LC connected to a quadrupole TOF mass spectrometer were used to determine differences of the physiological, pathological and biochemical distinct extraocular muscle allotype in comparison to hind-limb muscle. Close to 70% of the peptides separated by OFFGEL electrophoresis were detected only in a single fraction. To determine the separation repeatability of four samples, we compared the ion volumes of multiple peptides deriving from the thick filament-associated protein titin over several fractions and determined a coefficient of variation below 20%. Of the 474 proteins identified, 61 proteins were differently expressed between the two muscle allotypes and were involved in metabolism, muscle contraction, stress response, or gene expression. Several expression differences were validated using immunohistochemistry and Western blot analysis. We therefore consider peptide OFFGEL fractionation an effective and efficient addition to our label-free quantitative proteomics workflow.     

Rapid verification of candidate serological biomarkers using gel-based, label-free multiple reaction monitoring

Stable isotope dilution-multiple reaction monitoring-mass spectrometry (SID-MRM-MS) has emerged as a promising platform for verification of serological candidate biomarkers. However, cost and time needed to synthesize and evaluate stable isotope peptides, optimize spike-in assays, and generate standard curves quickly becomes unattractive when testing many candidate biomarkers. In this study, we demonstrate that label-free multiplexed MRM-MS coupled with major protein depletion and 1D gel separation is a time-efficient, cost-effective initial biomarker verification strategy requiring less than 100 μL of serum. Furthermore, SDS gel fractionation can resolve different molecular weight forms of targeted proteins with potential diagnostic value. Because fractionation is at the protein level, consistency of peptide quantitation profiles across fractions permits rapid detection of quantitation problems for specific peptides from a given protein. Despite the lack of internal standards, the entire workflow can be highly reproducible, and long-term reproducibility of relative protein abundance can be obtained using different mass spectrometers and LC methods with external reference standards. Quantitation down to ~200 pg/mL could be achieved using this workflow. Hence, the label-free GeLC-MRM workflow enables rapid, sensitive, and economical initial screening of large numbers of candidate biomarkers prior to setting up SID-MRM assays or immunoassays for the most promising candidate biomarkers.     

A hybrid LC-Gel-MS method for proteomics research and its application to protease functional pathway mapping

Two-dimensional (2D) gel electrophoresis is the most common protein separation method in proteomics research. It can provide high resolution and high sensitivity. However, 2D gel methods have several limitations, such as labor-intensive procedures, poor reproducibility, and limited dynamic range of detection. In fact, many investigators have returned to couple the one-dimensional (1D) SDS-PAGE with mass spectrometry for protein identification. The limitation of this approach is the increased protein complexity in each one-dimensional gel band. To overcome this problem and provide reproducible quantitative information, we describe here a 2D method for protein mixture separation using a combination of high performance liquid chromatography (HPLC) and 1D SDS-PAGE. The study shows that the step-gradient fractionation method we have applied provides excellent reproducibility. In addition, high mass accuracy of LC-FTICR-MS can allow more confident protein identifications by high resolution and ultra-high mass measurement accuracy. This approach was applied to comparative proteomics since protein abundance level changes can be easily visualized with side-by-side vertical comparison in one gel. Furthermore, separation of multi-samples in the same gel significantly reduces run-to-run variation, as is shown with differential image gel electrophoresis (DIGE). Finally, this approach readily incorporates immunological methods to normalize relative abundances of multiple samples within a single gel. This paper presents the results of our developments and our initial application of this strategy for mapping protease function of beta amyloid cleaving enzyme (BACE) in biological systems.     

Label-free quantitative analysis of one-dimensional PAGE LC/MS/MS proteome: application on angiotensin II-stimulated smooth muscle cells secretome

A widely used method for protein identification couples prefractionation of protein samples by one-dimensional (1D) PAGE with LC/MS/MS. We developed a new label-free quantitative algorithm by combining measurements of spectral counting, ion intensity, and peak area on 1D PAGE-based proteomics. This algorithm has several improvements over other label-free quantitative algorithms: (i) Errors in peak detection are reduced because the retention time is based on each LC/MS/MS run and actual precursor m/z. (ii) Detection sensitivity is increased because protein quantification is based on the combination of peptide count, ion intensity, and peak area. (iii) Peak intensity and peak area are calculated in each LC/MS/MS run for all slices from 1D PAGE for every single identified protein and visualized as a Western blot image. The sensitivity and accuracy of this algorithm were demonstrated by using standard curves (17.4 fmol to 8.7 pmol), complex protein mixtures (30 fmol to 1.16 pmol) of known composition, and spiked protein (34.8 fmol to 17.4 pmol) in complex proteins. We studied the feasibility of this approach using the secretome of angiotensin II (Ang II)-stimulated vascular smooth muscle cells (VSMCs). From the VSMC-conditioned medium, 629 proteins were identified including 212 putative secreted proteins. 26 proteins were differently expressed in control and Ang II-stimulated VSMCs, including 18 proteins not previously reported. Proteins related to cell growth (CYR61, protein NOV, and clusterin) were increased, whereas growth arrest-specific 6 (GAS6) and growth/differentiation factor 6 were decreased by Ang II stimulation. Ang II-stimulated changes of plasminogen activator inhibitor-1, GAS6, cathepsin B, and periostin were validated by Western blot. In conclusion, a novel label-free quantitative analysis of 1D PAGE-LC/MS/MS-based proteomics has been successfully applied to the identification of new potential mediators of Ang II action and may provide an alternative to traditional protein staining methods.     

Protein identification and quantification from riverbank grape,Vitis riparia: Comparing SDS‐PAGE and FASP‐GPF techniques for shotgun proteomic analysis

Protein sample preparation optimisation is critical for establishing reproducible high throughput proteomic analysis. In this study, two different fractionation sample preparation techniques (in‐gel digestion and in‐solution digestion) for shotgun proteomics were used to quantitatively compare proteins identified in Vitis riparia leaf samples. The total number of proteins and peptides identified were compared between filter aided sample preparation (FASP) coupled with gas phase fractionation (GPF) and SDS‐PAGE methods. There was a 24% increase in the total number of reproducibly identified proteins when FASP‐GPF was used. FASP‐GPF is more reproducible, less expensive and a better method than SDS‐PAGE for shotgun proteomics of grapevine samples as it significantly increases protein identification across biological replicates. Total peptide and protein information from the two fractionation techniques is available in PRIDE with the identifier PXD001399 ( http://proteomecentral.proteomexchange.org/dataset/PXD001399 ).     

Comparison of sample preparation techniques for large‐scale proteomics

Numerous workflows exist for large‐scale bottom‐up proteomics, many of which achieve exceptional proteome depth. Herein, we evaluated the performance of several commonly used sample preparation techniques for proteomic characterization of HeLa lysates [unfractionated in‐solution digests, SDS‐PAGE coupled with in‐gel digestion, gel‐eluted liquid fraction entrapment electrophoresis (GELFrEE) technology, SCX StageTips and high‐/low‐pH reversed phase fractionation (HpH)]. HpH fractionation was found to be superior in terms of proteome depth (>8400 proteins detected) and fractionation efficiency compared to other techniques. SCX StageTip fractionation required minimal sample handling and was also a substantial improvement over SDS‐PAGE separation and GELFrEE technology. Sequence coverage of the HeLa proteome increased to 38% when combining all workflows, however, total proteins detected improved only slightly to 8710. In summary, HpH fractionation and SCX StageTips are robust techniques and highly suited for complex proteome analysis.     

Identification and quantification of basic and acidic proteins using solution-based two-dimensional protein fractionation and label-free or 18O-labeling mass spectrometry

Reduction in sample complexity enables more thorough proteomic analysis using mass spectrometry (MS). A solution-based two-dimensional (2D) protein fractionation system, ProteomeLab PF 2D, has recently become available for sample fractionation and complexity reduction. PF 2D resolves proteins by isoelectric point (pI) and hydrophobicity in the first and second dimensions, respectively. It offers distinctive advantages over 2D gel electrophoresis with respects to automation of the fractionation processes and characterization of proteins having extreme pIs. Besides fractionation, PF 2D is equipped with built-in UV detectors intended for relative quantification of proteins in contrasting samples using its software tools. In this study, we utilized PF 2D for the identification of basic and acidic proteins in mammalian cells, which are generally under-characterized. In addition, mass spectrometric methods (label-free and 18O-labeling) were employed to complement protein quantification based on UV absorbance. Our studies indicate that the selection of chromatographic fractions could impact protein identification and that the UV-based quantification for contrasting complex proteomes is constrained by coelution or partial coelution of proteins. In contrast, the quantification post PF 2D chromatography based on label-free or 18O-labeling mass spectrometry provides an alternative platform for basic/acidic protein identification and quantification. With the use of HCT116 colon carcinoma cells, a total of 305 basic and 183 acidic proteins was identified. Quantitative proteomics revealed that 17 of these proteins were differentially expressed in HCT116 p53-/- cells.     

Label-free quantitation, an extension to 2DB

Determining the differential expression of proteins under different conditions is of major importance in proteomics. Since mass spectrometry-based proteomics is often used to quantify proteins, several labelling strategies have been developed. While these are generally more precise than label-free quantitation approaches, they imply specifically designed experiments which also require knowledge about peptides that are expected to be measured and need to be modified. We recently designed the 2DB database which aids storage, analysis, and publication of data from mass spectrometric experiments to identify proteins. This database can aid identifying peptides which can be used for quantitation. Here an extension to the database application, named MSMAG, is presented which allows for more detailed analysis of the distribution of peptides and their associated proteins over the fractions of an experiment. Furthermore, given several biological samples in the database, label-free quantitation can be performed. Thus, interesting proteins, which may warrant further investigation, can be identified en passant while performing high-throughput proteomics studies.     

A review on recent developments in mass spectrometry instrumentation and quantitative tools advancing bacterial proteomics

Proteomics has evolved substantially since its early days, some 20 years ago. In this mini-review, we aim to provide an overview of general methodologies and more recent developments in mass spectrometric approaches used for relative and absolute quantitation of proteins. Enhancement of sensitivity of the mass spectrometers as well as improved sample preparation and protein fractionation methods are resulting in a more comprehensive analysis of proteomes. We also document some upcoming trends for quantitative proteomics such as the use of label-free quantification methods. Hopefully, microbiologists will continue to explore proteomics as a tool in their research to understand the adaptation of microorganisms to their ever changing environment. We encourage them to incorporate some of the described new developments in mass spectrometry to facilitate their analyses and improve the general knowledge of the fascinating world of microorganisms.     

Less label, more free: approaches in label-free quantitative mass spectrometry

In this review we examine techniques, software, and statistical analyses used in label-free quantitative proteomics studies for area under the curve and spectral counting approaches. Recent advances in the field are discussed in an order that reflects a logical workflow design. Examples of studies that follow this design are presented to highlight the requirement for statistical assessment and further experiments to validate results from label-free quantitation. Limitations of label-free approaches are considered, label-free approaches are compared with labelling techniques, and forward-looking applications for label-free quantitative data are presented. We conclude that label-free quantitative proteomics is a reliable, versatile, and cost-effective alternative to labelled quantitation.     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.     

Intact proteome fractionation strategies compatible with mass spectrometry

Proteome fractionation refers to separation at the level of intact proteins. Proteome fractionation may precede sample digestion and subsequent peptide-level separation and detection (i.e., bottom-up mass spectrometry [MS]). For top-down MS, proteome fractionation acts as a stand-alone separation platform, since intact proteins are directly analyzed by the mass spectrometer. Regardless of the MS identification strategy, separation of intact proteins has clear benefits as a result of decreasing sample complexity. However, this stage of the workflow also creates considerable challenges, which are generally absent from the counterpart peptide separation experiment. For example, maintaining protein solubility is a key concern before, during and after separation. To this end, surfactants such as sodium dodecyl sulfate may be employed during fractionation, so long as they are eliminated prior to MS. In this article, current strategies for proteome fractionation in a MS-compatible format are reviewed, illustrating the challenges and outlooks on this important aspect of proteomics.     

A simple peak detection and label-free quantitation algorithm for chromatography-mass spectrometry

Background

Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study.

Results

We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools.

Conclusions

AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0376-0) contains supplementary material, which is available to authorized users.     

Comparing SILAC and two-dimensional gel electrophoresis image analysis for profiling urokinase plasminogen activator signaling in ovarian cancer cells

Two-dimensional gel electrophoresis (2-DE) image analysis is conventionally used for comparative proteomics. However, there are a number of technical difficulties associated with 2-DE protein separation that limit the depth of proteome coverage, and the image analysis steps are typically labor-intensive and low-throughput. Recently, mass spectrometry-based quantitation strategies have been described as alternative differential proteome analysis techniques. In this study, we investigated changes in protein expression using an ovarian cancer cell line, OVMZ6, 24 h post-stimulation with the relatively weak agonist, urokinase-type plasminogen activator (uPA). Quantitative protein profiles were obtained by MALDI-TOF/TOF from stable isotope-labeled cells in culture (SILAC), and these results were compared to the quantitative ratios obtained using 2-DE gel image analysis. MALDI-TOF/TOF mass spectrometry showed that differential quantitation using SILAC was highly reproducible (approximately 8% coefficient of variation (CV)), and this variance was considerably lower than that achieved using automated 2-DE image analysis strategies (CV approximately 25%). Both techniques revealed subtle alterations in cellular protein expression following uPA stimulation. However, due to the lower variances associated with the SILAC technique, smaller changes in expression of uPA-inducible proteins could be found with greater certainty.     

Aluminum modifies the viscosity of filamentous actin solutions as measured by optical displacement microviscometry

Two-dimensional electrophoresis (2-DE) is a highly resolving technique for arraying proteins by isoelectric point and molecular mass. To date, the resolving ability of 2-DE for protein separation is unsurpassed, thus ensuring its use as the fundamental separation method for proteomics. When immobilized pH gradients (IPGs) are used for isoelectric focusing in the first dimension, excellent reproducibility and high protein load capacity can be achieved. While this has been beneficial for separations of soluble and mildly hydrophobic proteins, separations of membrane proteins and other hydrophobic proteins with IPGs have often been poor. Stimulated by the growing interest in proteomics, recent developments in 2-DE methodology have been aimed at rectifying this situation. Improvements have been made in the area of protein solubilization and sample fractionation, leading to a revamp of traditional approaches for 2-DE of membrane proteins. This review explores these developments.     

Identification and characterization of the Sulfolobus solfataricus P2 proteome

Via combined separation approaches, a total of 1399 proteins were identified, representing 47% of the Sulfolobus solfataricus P2 theoretical proteome. This includes 1323 proteins from the soluble fraction, 44 from the insoluble fraction and 32 from the extra-cellular or secreted fraction. We used conventional 2-dimensional gel electrophoresis (2-DE) for the soluble fraction, and shotgun proteomics for all three cell fractions (soluble, insoluble, and secreted). Two gel-based fractionation methods were explored for shotgun proteomics, namely: (i) protein separation utilizing 1-dimensional gel electrophoresis (1-DE) followed by peptide fractionation by iso-electric focusing (IEF), and (ii) protein and peptide fractionation both employing IEF. Results indicate that a 1D-IEF fractionation workflow with three replicate mass spectrometric analyses gave the best overall result for soluble protein identification. A greater than 50% increment in protein identification was achieved with three injections using LC-ESI-MS/MS. Protein and peptide fractionation efficiency; together with the filtration criteria are also discussed.     

Combinatorial use of offline SCX and online RP-RP liquid chromatography for iTRAQ-based quantitative proteomics applications

Extensive front-end separation is usually required for complex samples in bottom-up proteomics to alleviate the problem of peptide undersampling. Isobaric Tags for Relative and Absolute Quantification (iTRAQ)-based experiments have particularly higher demands, in terms of the number of duty cycles and the sensitivity, to confidently quantify protein abundance. Strong cation exchange (SCX)/reverse phase (RP) liquid chromatography (LC) is currently used routinely to separate iTRAQ-labeled peptides because of its ability to simultaneously clean up the iTRAQ reagents and byproducts and provide first-dimension separation; nevertheless, the low resolution of SCX means that peptides can be redundantly sampled across fractions, leading to loss of usable duty cycles. In this study, we explored the combinatorial application of offline SCX fractionation with online RP-RP applied to iTRAQ-labeled chloroplast proteins to evaluate the effect of three-dimensional LC separation on the overall performance of the quantitative proteomics experiment. We found that the higher resolution of RP-RP can be harnessed to complement SCX-RP and increase the quality of protein identification and quantification, without significantly impacting instrument time and reproducibility.     

Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering

Comprehensive comparisons of quantitative proteomics techniques are rare in the literature, yet they are crucially important for optimal selection of approaches and methodologies that are ideal for a given proteomics initiative. In this study, two LC-based quantitative proteomics approaches--iTRAQ and label-free--were implemented using the LTQ-Orbitrap Velos platform. For this comparison, the model used was the total protein content from two Chlamydomonas reinhardtii strains in the context of alternative biofuels production. The strain comparison includes sta6 (a starch-less mutant of cw15) that produces twice as many lipid bodies (LB) containing triacylglycerols (TAGs) as its parental strain cw15 (a cell wall-deficient C. reinhardtii strain) under nitrogen starvation. Internal standard addition was used to rigorously assess the quantitation accuracy and precision of each method. Results from iTRAQ-4plex labeling using HCD (higher energy collision-induced dissociation) fragmentation were compared to those obtained using a label-free approach based on the peak area of intact peptides and collision-induced dissociation. The accuracy and precision, number of identified/quantified proteins and statistically significant protein differences detected, as well as efficiency of these two quantitative proteomics methods were evaluated and compared. Four technical and three biological replicates of each strain were performed to assess both the technical and biological variation of both approaches. A total of 896 and 639 proteins were identified with high confidence, and 329 and 124 proteins were quantified significantly with label-free and iTRAQ, respectively, using biological replicates. The results showed that both iTRAQ labeling and label-free methods provide high quality quantitative and qualitative data using nano-LC coupled with the LTQ-Orbitrap Velos mass spectrometer, but the selection of the optimal approach is dependent on experimental design and the biological question to be addressed. The functional categorization of the differential proteins between cw15 and sta6 reveals already known but also new mechanisms likely responsible for the production of lipids in sta6 and sets the baseline for future studies aimed at engineering these strains for high oil production.