Protein analysis by shotgun/bottom-up proteomics

Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science

Environmental health science aims to link environmental pollution sources to adverse health outcomes to develop effective exposure intervention strategies that reduce long-term disease risks. Over the past few decades, the public health community recognized that health risk is driven by interaction between the human genome and external environment. Now that the human genetic code has been sequenced, establishing this "G × E" (gene-environment) interaction requires a similar effort to decode the human exposome, which is the accumulation of an individual's environmental exposures and metabolic responses throughout the person's lifetime. The exposome is composed of endogenous and exogenous chemicals, many of which are measurable as biomarkers in blood, breath, and urine. Exposure to pollutants is assessed by analyzing biofluids for the pollutant itself or its metabolic products. New methods are being developed to use a subset of biomarkers, termed bioindicators, to demonstrate biological changes indicative of future adverse health effects. Typically, environmental biomarkers are assessed using noninvasive (excreted) media, such as breath and urine. Blood is often avoided for biomonitoring due to practical reasons such as medical personnel, infectious waste, or clinical setting, despite the fact that blood represents the central compartment that interacts with every living cell and is the most relevant biofluid for certain applications and analyses. The aims of this study were to (1) review the current use of blood samples in environmental health research, (2) briefly contrast blood with other biological media, and (3) propose additional applications for blood analysis in human exposure research.

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.

Rescuing discarded spectra: Full comprehensive analysis of a minimal proteome

A common problem encountered when performing large-scale MS proteome analysis is the loss of information due to the high percentage of unassigned spectra. To determine the causes behind this loss we have analyzed the proteome of one of the smallest living bacteria that can be grown axenically, Mycoplasma pneumoniae (729 ORFs). The proteome of M. pneumoniae cells, grown in defined media, was analyzed by MS. An initial search with both Mascot and a species-specific NCBInr database with common contaminants (NCBImpn), resulted in around 79% of the acquired spectra not having an assignment. The percentage of non-assigned spectra was reduced to 27% after re-analysis of the data with the PEAKS software, thereby increasing the proteome coverage of M. pneumoniae from the initial 60% to over 76%. Nonetheless, 33,413 spectra with assigned amino acid sequences could not be mapped to any NCBInr database protein sequence. Approximately, 1% of these unassigned peptides corresponded to PTMs and 4% to M. pneumoniae protein variants (deamidation and translation inaccuracies). The most abundant peptide sequence variants (Phe-Tyr and Ala-Ser) could be explained by alterations in the editing capacity of the corresponding tRNA synthases. About another 1% of the peptides not associated to any protein had repetitions of the same aromatic/hydrophobic amino acid at the N-terminus, or had Arg/Lys at the C-terminus. Thus, in a model system, we have maximized the number of assigned spectra to 73% (51,453 out of the 70,040 initial acquired spectra). All MS data have been deposited in the ProteomeXchange with identifier PXD002779 (http://proteomecentral.proteomexchange.org/dataset/PXD002779).

Proteomics of cell-cell interactions in health and disease

The mechanisms of cell-cell communications are now under intense study by proteomic approaches. Proteomics has unraveled changes in protein profiling as the result of cell interactions mediated by ligand/receptor, hormones, soluble factors, and the content of extracellular vesicles. Besides being a brief overview of the main and profitable methodologies now available (evaluating theory behind the methods, their usefulness, and pitfalls), this review focuses on-from a proteome perspective-some signaling pathways and post-translational modifications (PTMs), which are essential for understanding ischemic lesions and their recovery in two vital organs in mammals, the heart, and the kidney. Knowledge of misdirection of the proteome during tissue recovery, such as represented by the convergence between fibrosis and cancer, emerges as an important tool in prognosis. Proteomics of cell-cell interaction is also especially useful for understanding how stem cells interact in injured tissues, anticipating clues for rational therapeutic interventions. In the effervescent field of induced pluripotency and cell reprogramming, proteomic studies have shown what proteins from specialized cells contribute to the recovery of infarcted tissues. Overall, we conclude that proteomics is at the forefront in helping us to understand the mechanisms that underpin prevalent pathological processes.

Proteomic profile of seminal plasma in adolescents and adults with treated and untreated varicocele

Varicocele, the most important treatable cause of male infertility, is present in 15% of adult males, 35% of men with primary infertility, and 80% of men with secondary infertility. On the other hand, 80% of these men will not present infertility. Therefore, there is a need to differentiate a varicocele that is exerting a deleterious effect that is treatable from a "silent" varicocele. Despite the growing evidence of the cellular effects of varicocele, its underlying molecular mechanisms are still eluding. Proteomics has become a promising area to determine the reproductive biology of semen as well as to improve diagnosis of male infertility. This review aims to discuss the state-of-art in seminal plasma proteomics in patients with varicocele to discuss the challenges in undertaking these studies, as well as the future outlook derived from the growing body of evidence on the seminal proteome.

Toward the complete proteome of Synechocystis sp. PCC 6803

The proteome of the photosynthetic model organism Synechocystis sp. PCC 6803 has been extensively analyzed in the last 15 years for the purpose of identifying proteins specifically expressed in subcellular compartments or differentially expressed in different environmental or internal conditions. This review summarizes the progress achieved so far with the emphasis on the impact of different techniques, both in sample preparation and protein identification, on the increasing coverage of proteome identification. In addition, this review evaluates the current completeness of proteome identification, and provides insights on the potential factors that could affect the complete identification of the Synechocystis proteome.

Evaluation of dimethyl sulfoxide (DMSO) as a mobile phase additive during top 3 label-free quantitative proteomics

Dimethyl sulfoxide (DMSO) has been advocated as a beneficial additive to electrospray solvents for peptide analysis due to the improved ionisation efficiency conferred. Previous reports have shown that the resultant improvements in peptide ion signal intensities are non-uniform. As a result, it was hypothesised that inclusion of DMSO in electrospray solvents could be detrimental to the outcome of intensity-based label-free absolute quantification approaches, specifically the top 3 method. The effect of DMSO as a mobile phase additive in top 3 label-free quantification was therefore evaluated. We show that inclusion of DMSO enhances data quality, improving the precision and number of proteins quantified, with no significant change to the quantification values observed in its absence.

Chemoproteomic profiling of protein modifications by lipid-derived electrophiles

Lipid-derived electrophiles (LDEs) are a group of endogenous reactive metabolites generated as products of lipid peroxidation when cells are under oxidative stress. LDEs are able to covalently modify nucleophilic residues in proteins to alter their structures and activities, either resulting in irreversible functional damage or triggering aberrant signaling pathways. Traditional biochemical methods have revealed individual protein targets modified by LDEs, however, deciphering the toxicity and/or signaling roles of LDEs requires systematic studies of these modifications in a high-throughput fashion. Here we survey recent progress in developing chemical proteomic strategies to globally profile protein-LDE interactions directly from complex proteomes. These powerful chemoproteomic methods have yielded a rich inventory of proteins and residue sites that are sensitive to LDE modification, serving as valuable resources to investigate mechanisms of their cellular toxicity at the molecular level.

Direct Interface between Digital Microfluidics and High Performance Liquid Chromatography-Mass Spectrometry

We introduce an automated method to facilitate in-line coupling of digital microfluidics (DMF) with HPLC-MS, using a custom, 3D-printed manifold and a custom plugin to the popular open-source control system, DropBot. The method was designed to interface directly with commercial autosamplers (with no prior modification), suggesting that it will be widely accessible for end-users. The system was demonstrated to be compatible with samples dissolved in aqueous buffers and neat methanol and was validated by application to a common steroid-labeling derivatization reaction. We propose that the methods described here will be useful for a wide range of applications, combining the automated sample processing power of DMF with the resolving and analytical capacity of HPLC-MS.

Peptide-level Robust Ridge Regression Improves Estimation, Sensitivity, and Specificity in Data-dependent Quantitative Label-free Shotgun Proteomics

Peptide intensities from mass spectra are increasingly used for relative quantitation of proteins in complex samples. However, numerous issues inherent to the mass spectrometry workflow turn quantitative proteomic data analysis into a crucial challenge. We and others have shown that modeling at the peptide level outperforms classical summarization-based approaches, which typically also discard a lot of proteins at the data preprocessing step. Peptide-based linear regression models, however, still suffer from unbalanced datasets due to missing peptide intensities, outlying peptide intensities and overfitting. Here, we further improve upon peptide-based models by three modular extensions: ridge regression, improved variance estimation by borrowing information across proteins with empirical Bayes and M-estimation with Huber weights. We illustrate our method on the CPTAC spike-in study and on a study comparing wild-type and ArgP knock-out Francisella tularensis proteomes. We show that the fold change estimates of our robust approach are more precise and more accurate than those from state-of-the-art summarization-based methods and peptide-based regression models, which leads to an improved sensitivity and specificity. We also demonstrate that ionization competition effects come already into play at very low spike-in concentrations and confirm that analyses with peptide-based regression methods on peptide intensity values aggregated by charge state and modification status (e.g. MaxQuant's peptides.txt file) are slightly superior to analyses on raw peptide intensity values (e.g. MaxQuant's evidence.txt file).

Current trends in mass spectrometry of peptides and proteins: Application to veterinary and sports-doping control

Detection of misuse of peptides and proteins as growth promoters is a major issue for sport and food regulatory agencies. The limitations of current analytical detection strategies for this class of compounds, in combination with their efficacy in growth-promoting effects, make peptide and protein drugs highly susceptible to abuse by either athletes or farmers who seek for products to illicitly enhance muscle growth. Mass spectrometry (MS) for qualitative analysis of peptides and proteins is well-established, particularly due to tremendous efforts in the proteomics community. Similarly, due to advancements in targeted proteomic strategies and the rapid growth of protein-based biopharmaceuticals, MS for quantitative analysis of peptides and proteins is becoming more widely accepted. These continuous advances in MS instrumentation and MS-based methodologies offer enormous opportunities for detection and confirmation of peptides and proteins. Therefore, MS seems to be the method of choice to improve the qualitative and quantitative analysis of peptide and proteins with growth-promoting properties. This review aims to address the opportunities of MS for peptide and protein analysis in veterinary control and sports-doping control with a particular focus on detection of illicit growth promotion. An overview of potential peptide and protein targets, including their amino acid sequence characteristics and current MS-based detection strategies is, therefore, provided. Furthermore, improvements of current and new detection strategies with state-of-the-art MS instrumentation are discussed for qualitative and quantitative approaches.

Using SEQUEST with theoretically complete sequence databases

SEQUEST has long been used to identify peptides/proteins from their tandem mass spectra and protein sequence databases. The algorithm has proven to be hugely successful for its sensitivity and specificity in identifying peptides/proteins, the sequences of which are present in the protein sequence databases. In this work, we report on work that attempts a new use for the algorithm by applying it to search a complete list of theoretically possible peptides, a de novo-like sequencing. We used freely available mass spectral data and determined a number of unique peptides as identified by SEQUEST. Using masses of these peptides and the mass accuracy of 0.001 Da, we have created a database of all theoretically possible peptide sequences corresponding to the precursor masses. We used our recently developed algorithm for determining all amino acid compositions corresponding to a mass interval, and used a lexicographic ordering to generate theoretical sequences from the compositions. The newly generated theoretical database was many-fold more complex than the original protein sequence database. We used SEQUEST to search and identify the best matches to the spectra from all theoretically possible peptide sequences. We found that SEQUEST cross-correlation score ranked the correct peptide match among the top sequence matches. The results testify to the high specificity of SEQUEST when combined with the high mass accuracy for intact peptides. Graphical Abstract ᅟ.

Pivotal role of computers and software in mass spectrometry - SEQUEST and 20 years of tandem MS database searching

Advances in computer technology and software have driven developments in mass spectrometry over the last 50 years. Computers and software have been impactful in three areas: the automation of difficult calculations to aid interpretation, the collection of data and control of instruments, and data interpretation. As the power of computers has grown, so too has the utility and impact on mass spectrometers and their capabilities. This has been particularly evident in the use of tandem mass spectrometry data to search protein and nucleotide sequence databases to identify peptide and protein sequences. This capability has driven the development of many new approaches to study biological systems, including the use of "bottom-up shotgun proteomics" to directly analyze protein mixtures. Graphical Abstract ᅟ.

Hallmarks of therapeutic management of the cystic fibrosis functional landscape

The cystic fibrosis (CF) transmembrane conductance regulator (CFTR) protein does not operate in isolation, rather in a dynamic network of interacting components that impact its synthesis, folding, stability, intracellular location and function, referred to herein as the 'CFTR Functional Landscape (CFFL)'. For the prominent F508del mutation, many of these interactors are deeply connected to a protein fold management system, the proteostasis network (PN). However, CF encompasses an additional 2000 CFTR variants distributed along its entire coding sequence (referred to as CFTR2), and each variant contributes a differential liability to PN management of CFTR and to a protein 'social network' (SN) that directs the probability of the (patho)physiologic events that impact ion transport in each cell, tissue and patient in health and disease. Recognition of the importance of the PN and SN in driving the unique patient CFFL leading to disease highlights the importance of precision medicine in therapeutic management of disease progression. We take the view herein that it is not CFTR, rather the PN/SN, and their impact on the CFFL, that are the key physiologic forces driving onset and clinical progression of CF. We posit that a deep understanding of each patients PN/SN gained by merging genomic, proteomic (mass spectrometry (MS)), and high-content microscopy (HCM) technologies in the context of novel network learning algorithms will lead to a paradigm shift in CF clinical management. This should allow for generation of new classes of patient specific PN/SN directed therapeutics for personalized management of the CFFL in the clinic.

The Expanding Landscape of the Thiol Redox Proteome

Cysteine occupies a unique place in protein chemistry. The nucleophilic thiol group allows cysteine to undergo a broad range of redox modifications beyond classical thiol-disulfide redox equilibria, including S-sulfenylation (-SOH), S-sulfinylation (-SO(2)H), S-sulfonylation (-SO(3)H), S-nitrosylation (-SNO), S-sulfhydration (-SSH), S-glutathionylation (-SSG), and others. Emerging evidence suggests that these post-translational modifications (PTM) are important in cellular redox regulation and protection against oxidative damage. Identification of protein targets of thiol redox modifications is crucial to understanding their roles in biology and disease. However, analysis of these highly labile and dynamic modifications poses challenges. Recent advances in the design of probes for thiol redox forms, together with innovative mass spectrometry based chemoproteomics methods make it possible to perform global, site-specific, and quantitative analyses of thiol redox modifications in complex proteomes. Here, we review chemical proteomic strategies used to expand the landscape of thiol redox modifications.

Recent progress in urinary proteome analysis for prostate cancer diagnosis and management

Prostate cancer (PCa) is fifth leading cause of cancer-associated deaths in men worldwide. Although the application of the serum prostate-specific antigen (PSA) screening test resulted in an increase in the PCa diagnosed cases, it demonstrated a negligible benefit regarding the associated mortality. Treatment options vary, with active surveillance to be preferable for patients with low-risk PCa and therapy of advanced castration-resistant PCa to rely on α-emitters and cytotoxic chemotherapy. Although recent developments have led to the approval of novel drugs for the treatment of castration-resistant PCa, the optimal sequence and timing of medication have not been yet determined. New screening modalities could improve the discriminatory accuracy between tumors with favorable clinical prognosis. Implementation of proteomic-based biomarkers appears to be a promising improvement, which could enable a more accurate diagnosis, guide treatment and improve patient outcome. Reviewed here are urinary proteome-based approaches for detection of PCa and patient management.

Application of Functional Genomics for Bovine Respiratory Disease Diagnostics

Bovine respiratory disease (BRD) is the most common economically important disease affecting cattle. For developing accurate diagnostics that can predict disease susceptibility/resistance and stratification, it is necessary to identify the molecular mechanisms that underlie BRD. To study the complex interactions among the bovine host and the multitude of viral and bacterial pathogens, as well as the environmental factors associated with BRD etiology, genome-scale high-throughput functional genomics methods such as microarrays, RNA-seq, and proteomics are helpful. In this review, we summarize the progress made in our understanding of BRD using functional genomics approaches. We also discuss some of the available bioinformatics resources for analyzing high-throughput data, in the context of biological pathways and molecular interactions. Although resources for studying host response to infection are avail-able, the corresponding information is lacking for majority of BRD pathogens, impeding progress in identifying diagnostic signatures for BRD using functional genomics approaches.

The identification and characterization of non-coding and coding RNAs and their modified nucleosides by mass spectrometry

The analysis of ribonucleic acids (RNA) by mass spectrometry has been a valuable analytical approach for more than 25 years. In fact, mass spectrometry has become a method of choice for the analysis of modified nucleosides from RNA isolated out of biological samples. This review summarizes recent progress that has been made in both nucleoside and oligonucleotide mass spectral analysis. Applications of mass spectrometry in the identification, characterization and quantification of modified nucleosides are discussed. At the oligonucleotide level, advances in modern mass spectrometry approaches combined with the standard RNA modification mapping protocol enable the characterization of RNAs of varying lengths ranging from low molecular weight short interfering RNAs (siRNAs) to the extremely large 23 S rRNAs. New variations and improvements to this protocol are reviewed, including top-down strategies, as these developments now enable qualitative and quantitative measurements of RNA modification patterns in a variety of biological systems.

Identification of EDIL3 on extracellular vesicles involved in breast cancer cell invasion

Cancer cell-derived extracellular vesicles have been linked to the pathogenesis of various cancers; however, the role of extracellular vesicles in tumorigenesis remains unclear. To identify extracellular vesicle proteins involved in cancer metastasis, quantitative proteomic analyses were performed on extracellular vesicles derived from two representative breast cancer cell lines: the less invasive MCF-7 and the invasive MDA-MB-231. Proteomic analysis allowed for the identification of 270 proteins in the extracellular vesicles. Here we report a new function of EDIL3 on extracellular vesicles, which are sufficient for enhancement of cell invasion and for acceleration of lung metastasis in vivo. This invasion is most likely mediated via the integrin-FAK signaling cascade in breast cancer cells. However, these effects are suppressed when EDIL3 is inactivated, providing evidence for a critical role of EDIL3 in development of cancer. Consistently, in human patients with metastatic breast cancer, the levels of EDIL3 on circulating extracellular vesicles are significantly elevated. This information is a remarkable breakthrough in understanding of the molecular mechanism underlying metastasis of breast cancer as well as in the research for cancer biomarkers using circulating extracellular vesicles. Furthermore, targeting EDIL3 on extracellular vesicles may lead to a new therapeutic option for treatment of breast cancer.

A trial proteomics fingerprint analysis of HepaRG cells by FD-LC-MS/MS

A proteomics profile analysis was performed on a human hepatocyte carcinoma cell line (HepaRG) by using the FD-LC-MS/MS method. One hundred and fifty-eight proteins were newly identified for the first time of which 10 were found to be specific to human hepatocytes. These proteins are a "proteomics fingerprint" that can be used to characterize HepaRG cells.

Direct Capture of Functional Proteins from Mammalian Plasma Membranes into Nanodiscs

Mammalian plasma membrane proteins make up the largest class of drug targets yet are difficult to study in a cell free system because of their intransigent nature. Herein, we perform direct encapsulation of plasma membrane proteins derived from mammalian cells into a functional nanodisc library. Peptide fingerprinting was used to analyze the proteome of the incorporated proteins in nanodiscs and to further demonstrate that the lipid composition of the nanodiscs directly affects the class of protein that is incorporated. Furthermore, the functionality of the incorporated membrane proteome was evaluated by measuring the activity of membrane proteins: Na(+)/K(+)-ATPase and receptor tyrosine kinases. This work is the first report of the successful establishment and characterization of a cell free functional library of mammalian membrane proteins into nanodiscs.

A Survey of the Impact of Deyolking on Biological Processes Covered by Shotgun Proteomic Analyses of Zebrafish Embryos

Deyolking, the removal of the most abundant protein from the zebrafish (Danio rerio) embryo, is a common technique for in-depth exploration of proteome-level changes in vivo due to various environmental stressors or pharmacological impacts during embryonic stage of development. However, the effect of this procedure on the remaining proteome has not been fully studied. Here, we report a label-free shotgun proteomics survey on proteome coverage and biological processes that are enriched and depleted as a result of deyolking. Enriched proteins are involved in cellular energetics and development pathways, specifically implicating enrichment related to mitochondrial function. Although few proteins were removed completely by deyolking, depleted molecular pathways were associated with calcium signaling and signaling events implicating immune system response.

NeuCode Labeling in Nematodes: Proteomic and Phosphoproteomic Impact of Ascaroside Treatment in Caenorhabditis elegans

The nematode Caenorhabditis elegans is an important model organism for biomedical research. We previously described NeuCode stable isotope labeling by amino acids in cell culture (SILAC), a method for accurate proteome quantification with potential for multiplexing beyond the limits of traditional stable isotope labeling by amino acids in cell culture. Here we apply NeuCode SILAC to profile the proteomic and phosphoproteomic response of C. elegans to two potent members of the ascaroside family of nematode pheromones. By consuming labeled E. coli as part of their diet, C. elegans nematodes quickly and easily incorporate the NeuCode heavy lysine isotopologues by the young adult stage. Using this approach, we report, at high confidence, one of the largest proteomic and phosphoproteomic data sets to date in C. elegans: 6596 proteins at a false discovery rate ≤ 1% and 6620 phosphorylation isoforms with localization probability ≥75%. Our data reveal a post-translational signature of pheromone sensing that includes many conserved proteins implicated in longevity and response to stress.

Multiplexed In-cell Immunoassay for Same-sample Protein Expression Profiling

In-cell immunoassays have become a valuable tool for protein expression analysis complementary to established assay formats. However, comprehensive molecular characterization of individual specimens has proven challenging and impractical due to, in part, a singleplex nature of reporter enzymes and technical complexity of alternative assay formats. Herein, we describe a simple and robust methodology for multiplexed protein expression profiling on the same intact specimen, employing a well-characterized enzyme alkaline phosphatase for accurate quantification of all targets of interest, while overcoming fundamental limitations of enzyme-based techniques by implementing the DNA-programmed release mechanism for segregation of sub-sets of target-bound reporters. In essence, this methodology converts same-sample multi-target labeling into a set of isolated singleplex measurements performed in a parallel self-consistent fashion. For a proof-of-principle, multiplexed detection of three model proteins was demonstrated on cultured HeLa cells, and two clinically-relevant markers of dementia, β-amyloid and PHF-tau, were profiled in formalin-fixed paraffin embedded brain tissue sections, uncovering correlated increase in abundance of both markers in the “Alzheimer’s disease” cohort. Featuring an analytically powerful yet technically simple and robust methodology, multiplexed in-cell immunoassay is expected to enable insightful same-sample protein profiling studies and become broadly adopted in biomedical research and clinical diagnostics.

Development and Validation of a Multiplexed Protein Quantitation Assay for the Determination of Three Recombinant Proteins in Soybean Tissues by Liquid Chromatography with Tandem Mass Spectrometry

Currently, traditional immunochemistry technologies such as enzyme-linked immunosorbent assays (ELISA) are the predominant analytical tool used to measure levels of recombinant proteins expressed in genetically engineered (GE) plants. Recent advances in agricultural biotechnology have created a need to develop methods capable of selectively detecting and quantifying multiple proteins in complex matrices because of increasing numbers of transgenic proteins being coexpressed or "stacked" to achieve tolerance to multiple herbicides or to provide multiple modes of action for insect control. A multiplexing analytical method utilizing liquid chromatography with tandem mass spectrometry (LC-MS/MS) has been developed and validated to quantify three herbicide-tolerant proteins in soybean tissues: aryloxyalkanoate dioxygenase (AAD-12), 5-enol-pyruvylshikimate-3-phosphate synthase (2mEPSPS), and phosphinothricin acetyltransferase (PAT). Results from the validation showed high recovery and precision over multiple analysts and laboratories. Results from this method were comparable to those obtained with ELISA with respect to protein quantitation, and the described method was demonstrated to be suitable for multiplex quantitation of transgenic proteins in GE crops.

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of Gi2 (a heterotrimeric G-protein α-subunit) is described in detail.

Label-free quantitative phosphoproteomics with novel pairwise abundance normalization reveals synergistic RAS and CIP2A signaling

Hyperactivated RAS drives progression of many human malignancies. However, oncogenic activity of RAS is dependent on simultaneous inactivation of protein phosphatase 2A (PP2A) activity. Although PP2A is known to regulate some of the RAS effector pathways, it has not been systematically assessed how these proteins functionally interact. Here we have analyzed phosphoproteomes regulated by either RAS or PP2A, by phosphopeptide enrichment followed by mass-spectrometry-based label-free quantification. To allow data normalization in situations where depletion of RAS or PP2A inhibitor CIP2A causes a large uni-directional change in the phosphopeptide abundance, we developed a novel normalization strategy, named pairwise normalization. This normalization is based on adjusting phosphopeptide abundances measured before and after the enrichment. The superior performance of the pairwise normalization was verified by various independent methods. Additionally, we demonstrate how the selected normalization method influences the downstream analyses and interpretation of pathway activities. Consequently, bioinformatics analysis of RAS and CIP2A regulated phosphoproteomes revealed a significant overlap in their functional pathways. This is most likely biologically meaningful as we observed a synergistic survival effect between CIP2A and RAS expression as well as KRAS activating mutations in TCGA pan-cancer data set, and synergistic relationship between CIP2A and KRAS depletion in colony growth assays.

Applying multiple proteases to direct digestion of hundred-scale cell samples for proteome analysis

RATIONALE

Analyzing the proteome on the scale of only several hundred cells with mass spectrometry has great significance for applications with limited sample amounts. We applied multiple proteases to the direct digestion of cells and compared the identified proteins both qualitatively and quantitatively.

METHODS

Three hundred cells were directly digested by trypsin, chymotrypsin, or the combination of trypsin and chymotrypsin. The peptides were identified using a LTQ-Orbitrap XL, and data were analyzed using MaxQuant software.

RESULTS

Different proteases produced different identified protein numbers. Trypsin proved to be the best choice for generating the largest protein number, while other proteases complemented the identification results of trypsin by increasing protein sequence coverage. Concerning the quantitative perspective, using trypsin would produce the biggest number of proteins quantifiable by intensity-based absolute quantification (iBAQ).

CONCLUSIONS

When hundred-scale cell samples are analyzed, an optimum choice of proteases should be made to realize different analytical objectives.

Proteomics in cancer research: Are we ready for clinical practice?

Although genomics has delivered major advances in cancer prognostics, treatment and diagnostics, it still only provides a static image of the situation. To study more dynamic molecular entities, proteomics has been introduced into the cancer research field more than a decade ago. Currently, however, the impact of clinical proteomics on patient management and clinical decision-making is low and the implementations of scientific results in the clinic appear to be scarce. The search for cancer-related biomarkers with proteomics however, has major potential to improve risk assessment, early detection, diagnosis, prognosis, treatment selection and monitoring. In this review, we provide an overview of the transition of oncoproteomics towards translational oncology. We describe which lessons are learned from currently approved protein biomarkers and previous proteomic studies, what the pitfalls and challenges are in clinical proteomics applications, and how proteomic research can be successfully translated into medical practice.

A proteomic profile of synoviocyte lesions microdissected from formalin-fixed paraffin-embedded synovial tissues of rheumatoid arthritis

Background

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of the synovial joints. Early intervention followed by early diagnosis can result in disease remission; however, both early stage diagnosis and provision of effective treatment have been impeded by the heterogeneity of RA, which details of pathological mechanism are unclear. Regardless of numerous investigations of RA by means of genomic and proteomic approaches, proteins interplaying in RA synovial tissues that contain various types of synoviocytes, are not yet sufficiently understood. Hence we have conducted an HPLC/mass spectrometry-based exploratory proteomic analysis focusing on synoviocyte lesions laser-microdissected (LMD) from formalin-fixed paraffin-embedded (FFPE) synovial tissues (RA, n = 15; OA, n = 5), where those of Osteoarthritis (OA) were used as the control.

Results

A total of 508 proteins were identified from the RA and OA groups. With the semi-quantitative comparisons, the spectral index (SpI), log2 protein ratio (R_SC) based on spectral counting, and statistical G-test, 98 proteins were found to be significant (pair-wise p < 0.05) to the RA synovial tissues. These include stromelysin-1 (MMP3), proteins S100-A8 and S100-A9, plastin-2, galectin-3, calreticulin, cathepsin Z, HLA-A, HLA-DRB1, ferritin, neutrophil defensin 1, CD14, MMP9 etc.

Conclusions

Our results confirmed the involvement of known RA biomarkers such as stromelysin-1 (MMP3) and proteins S100-A8 and S100-A9, and also that of leukocyte antigens such as HLA-DRB1. Network analyses of protein–protein interaction for those proteins significant to RA revealed a dominant participation of ribosome pathway (p = 5.91 × 10⁻⁴⁵), and, interestingly, the associations of the p53 signaling (p = 2.34 × 10⁻⁵). An involvement of proteins including CD14, S100-A8/S100-A9 seems to suggest an activation of the NF-kB/MAPK signaling pathway. Our strategy of laser-microdissected FFPE-tissue proteomic analysis in Rheumatoid Arthritis thus demonstrated its technical feasibility in profiling proteins expressed in synovial tissues, which may play important roles in the RA pathogenesis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9091-8) contains supplementary material, which is available to authorized users.

Proteomics, metabolomics, and ionomics perspectives of salinity tolerance in halophytes

Halophytes are plants which naturally survive in saline environment. They account for ∼1% of the total flora of the world. They include both dicots and monocots and are distributed mainly in arid, semi-arid inlands and saline wet lands along the tropical and sub-tropical coasts. Salinity tolerance in halophytes depends on a set of ecological and physiological characteristics that allow them to grow and flourish in high saline conditions. The ability of halophytes to tolerate high salt is determined by the effective coordination between various physiological processes, metabolic pathways and protein or gene networks responsible for delivering salinity tolerance. The salinity responsive proteins belong to diverse functional classes such as photosynthesis, redox homeostasis; stress/defense, carbohydrate and energy metabolism, protein metabolism, signal transduction and membrane transport. The important metabolites which are involved in salt tolerance of halophytes are proline and proline analog (4-hydroxy-N-methyl proline), glycine betaine, pinitol, myo-inositol, mannitol, sorbitol, O-methylmucoinositol, and polyamines. In halophytes, the synthesis of specific proteins and osmotically active metabolites control ion and water flux and support scavenging of oxygen radicals under salt stress condition. The present review summarizes the salt tolerance mechanisms of halophytes by elucidating the recent studies that have focused on proteomic, metabolomic, and ionomic aspects of various halophytes in response to salinity. By integrating the information from halophytes and its comparison with glycophytes could give an overview of salt tolerance mechanisms in halophytes, thus laying down the pavement for development of salt tolerant crop plants through genetic modification and effective breeding strategies.

Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing

Osteoclasts are monocyte-derived multinuclear cells that directly attach to and resorb bone. Sphingosine-1-phosphate (S1P)(1) regulates bone resorption by functioning as both a chemoattractant and chemorepellent of osteoclast precursors through two G-protein coupled receptors that antagonize each other in an S1P-concentration-dependent manner. To quantitatively explore the behavior of this chemosensing pathway, we applied targeted proteomics, transcriptomics, and rule-based pathway modeling using the Simmune toolset. RAW264.7 cells (a mouse monocyte/macrophage cell line) were used as model osteoclast precursors, RNA-seq was used to identify expressed target proteins, and selected reaction monitoring (SRM) mass spectrometry using internal peptide standards was used to perform absolute abundance measurements of pathway proteins. The resulting transcript and protein abundance values were strongly correlated. Measured protein abundance values, used as simulation input parameters, led to in silico pathway behavior matching in vitro measurements. Moreover, once model parameters were established, even simulated responses toward stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrate the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight.

The Negative Mode Proteome with Activated Ion Negative Electron Transfer Dissociation (AI-NETD)

The field of proteomics almost uniformly relies on peptide cation analysis, leading to an underrepresentation of acidic portions of proteomes, including relevant acidic posttranslational modifications. Despite the many benefits negative mode proteomics can offer, peptide anion analysis remains in its infancy due mainly to challenges with high-pH reversed-phase separations and a lack of robust fragmentation methods suitable for peptide anion characterization. Here, we report the first implementation of activated ion negative electron transfer dissociation (AI-NETD) on the chromatographic timescale, generating 7,601 unique peptide identifications from Saccharomyces cerevisiae in single-shot nLC-MS/MS analyses of tryptic peptides-a greater than 5-fold increase over previous results with NETD alone. These improvements translate to identification of 1,106 proteins, making this work the first negative mode study to identify more than 1,000 proteins in any system. We then compare the performance of AI-NETD for analysis of peptides generated by five proteases (trypsin, LysC, GluC, chymotrypsin, and AspN) for negative mode analyses, identifying as many as 5,356 peptides (1,045 proteins) with LysC and 4,213 peptides (857 proteins) with GluC in yeast-characterizing 1,359 proteins in total. Finally, we present the first deep-sequencing approach for negative mode proteomics, leveraging offline low-pH reversed-phase fractionation prior to online high-pH separations and peptide fragmentation with AI-NETD. With this platform, we identified 3,467 proteins in yeast with trypsin alone and characterized a total of 3,730 proteins using multiple proteases, or nearly 83% of the expressed yeast proteome. This work represents the most extensive negative mode proteomics study to date, establishing AI-NETD as a robust tool for large-scale peptide anion characterization and making the negative mode approach a more viable platform for future proteomic studies.

Formation and reshuffling of disulfide bonds in bovine serum albumin demonstrated using tandem mass spectrometry with collision-induced and electron-transfer dissociation

Thermolysin hydrolyzates of freshly isolated, extensively stored (6 years, 6 °C, dry) and heated (60 min, 90 °C, in excess water) bovine serum albumin (BSA) samples were analyzed with liquid chromatography (LC) electrospray ionization (ESI) tandem mass spectrometry (MS/MS) using alternating electron-transfer dissociation (ETD) and collision-induced dissociation (CID). The positions of disulfide bonds and free thiol groups in the different samples were compared to those deduced from the crystal structure of native BSA. Results revealed non-enzymatic posttranslational modifications of cysteine during isolation, extensive dry storage, and heating. Heat-induced extractability loss of BSA was linked to the impact of protein unfolding on the involvement of specific cysteine residues in intermolecular and intramolecular thiol-disulfide interchange and thiol oxidation reactions. The here developed approach holds promise for exploring disulfide bond formation and reshuffling in various proteins under conditions relevant for chemical, biochemical, pharmaceutical and food processing.

ProteinInferencer: Confident protein identification and multiple experiment comparison for large scale proteomics projects

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein-protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable. This article is part of a Special Issue entitled: Computational Proteomics.

Dynamic Proteomics: In Vivo Proteome-Wide Measurement of Protein Kinetics Using Metabolic Labeling

Control of biosynthetic and catabolic rates of polymers, including proteins, stands at the center of phenotype, physiologic adaptation, and disease pathogenesis. Advances in stable isotope-labeling concepts and mass spectrometric instrumentation now allow accurate in vivo measurement of protein synthesis and turnover rates, both for targeted proteins and for unbiased screening across the proteome. We describe here the underlying principles and operational protocols for measuring protein dynamics, focusing on metabolic labeling with (2)H2O (heavy water) combined with tandem mass spectrometric analysis of mass isotopomer abundances in trypsin-generated peptides. The core principles of combinatorial analysis (mass isotopomer distribution analysis or MIDA) are reviewed in detail, including practical advantages, limitations, and technical procedures to ensure optimal kinetic results. Technical factors include heavy water labeling protocols, optimal duration of labeling, clean up and simplification of sample matrices, accurate quantitation of mass isotopomer abundances in peptides, criteria for adequacy of mass spectrometric abundance measurements, and calculation algorithms. Some applications are described, including the noninvasive "virtual biopsy" strategy for measuring molecular flux rates in tissues through measurements in body fluids. In addition, application of heavy water labeling to measure flux lipidomics is noted. In summary, the combination of stable isotope labeling, particularly from (2)H2O, with tandem mass spectrometric analysis of mass isotopomer abundances in peptides, provides a powerful approach for characterizing the dynamics of proteins across the global proteome. Many applications in research and clinical medicine have been achieved and many others can be envisioned.

The role of proteomics in studies of protein moonlighting

The increasing acceptance that proteins may exert multiple functions in the cell brings with it new analytical challenges that will have an impact on the field of proteomics. Many proteomics workflows begin by destroying information about the interactions between different proteins, and the reduction of a complex protein mixture to constituent peptides also scrambles information about the combinatorial potential of post-translational modifications. To bring the focus of proteomics on to the domain of protein moonlighting will require novel analytical and quantitative approaches.