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Quantitation of proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is complex, with a multiplicity of options ranging from label-free techniques to chemically and metabolically labeling proteins. Increasingly, for clinically relevant analyses, stable isotope-labeled (SIL) internal standards (ISs) represent the "gold standard" for quantitation due to their similar physiochemical properties to the analyte, wide availability, and ability to multiplex to several peptides. However, the purchase of SIL-ISs is a resource-intensive step in terms of cost and time, particularly for screening putative biomarker panels of hundreds of proteins. We demonstrate an alternative strategy utilizing nonhuman sera as the IS for quantitation of multiple human proteins. We demonstrate the effectiveness of this strategy using two high abundance clinically relevant analytes, vitamin D binding protein [Gc globulin] (DBP) and albumin (ALB). We extend this to three putative risk markers for cardiovascular disease: plasma protease C1 inhibitor (SERPING1), annexin A1 (ANXA1), and protein kinase, DNA-activated catalytic subunit (PRKDC). The results show highly specific, reproducible, and linear measurement of the proteins of interest with comparable precision and accuracy to the gold standard SIL-IS technique. This approach may not be applicable to every protein, but for many proteins it can offer a cost-effective solution to LC-MS/MS protein quantitation.

In this chapter, we describe a rapid workflow for the shotgun global phosphoproteomics analysis. The strategy is based on the use of accelerated in-solution trypsin digestion under an ultrasonic field by high-intensity focused ultrasound (HIFU) coupled to titanium dioxide (TiO 2 ) selective phosphopeptide enrichment, fractionation by strong cation exchange chromatography (SCX), and analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) in a high-resolution mass spectrometer (LTQ-Orbitrap XL). The strategy was optimized for the global phosphoproteome analysis of Jurkat T-cells. Using this accelerated workflow, HIFU-TiO 2 -SCX-LC-MS/MS, 15,367 phosphorylation sites from 13,029 different phosphopeptides belonging to 3,163 different phosphoproteins can be efficiently identified in less than 15 h.

Nanoparticles used in biological settings are exposed to proteins that adsorb on the surface forming a protein corona. These adsorbed proteins dictate the subsequent cellular response. A major challenge has been predicting what proteins will adsorb on a given nanoparticle surface. Instead, each new nanoparticle and nanoparticle modification must be tested experimentally to determine what proteins adsorb on the surface. We propose that any future predictive ability will depend on large datasets of protein-nanoparticle interactions. As a first step towards this goal, we have developed an automated workflow using a liquid handling robot to form and isolate protein coronas. As this workflow depends on magnetic separation steps, we test the ability to embed magnetic nanoparticles within a protein nanoparticle. These experiments demonstrate that magnetic separation could be used for any type of nanoparticle in which a magnetic core can be embedded. Higher-throughput corona characterization will also require lower-cost approaches to proteomics. We report a comparison of fast, low-cost, and standard, slower, higher-cost liquid chromatography coupled with mass spectrometry to identify the protein corona. These methods will provide a step forward in the acquisition of the large datasets necessary to predict nanoparticle-protein interactions.

A targeted, bottom-up proteomic assay was developed for the qualitative detection of apolipoprotein L1 (ApoL1) protein variants (G0, G1, and G2) in blood plasma for identification of high and low renal risk genotypes. Following trypsin digestion of liquid or dry plasma, surrogate peptides specific to each ApoL1 variant were detected by liquid chromatography-tandem mass spectrometry along with two surrogate peptides common among all variants which served as internal (positive) controls to verify correct sample processing. Using isotopically labeled peptide internal standards, the presence or absence of each surrogate peptide was determined using multiple objective metrics including: 1) retention time confirmation relative to its internal standard, 2) comparison of the internal standard-normalized response relative to pre-established thresholds for confident detection, and 3) ion ratio monitoring. Based on the pattern of variant-specific surrogate peptides detected, the genotype was accurately inferred. The final, optimized method was fully validated for liquid plasma specimens, as well as dry plasma specimens collected on a laminar flow blood separation device. For both specimen types, the latter which can be self-collected for remote or in-home sampling, the assay was shown to be reproducible, interference-free with the exception of gross hemolysis, and accurate relative to Sanger sequencing (100% agreement). This targeted, qualitative bottom-up proteomic assay for detection of ApoL1 variants in blood provides a high-throughput, cost-effective alternative to molecular methods and has potential implications to improve organ allocation by facilitating screening kidney donors for high-risk ApoL1 genotypes, but could be applicable for genotyping other clinically relevant blood proteins variants., Competing Interests: Declarations of Competing Interest All authors declare they are employed by and/or own stock in Laboratory Corporation of America Holdings., (Copyright © 2020 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.)

In this study, the efficiency of one commercial (ProteoMiner™ -PM) and five simple and cost-effective laboratory chemicals (Acetone, TCA/acetone, DTT, ACN and DTT-ACN) based serum protein pre-fractionation strategies was compared in pig model by label-free quantitation based mass spectrometric approach to find out the most suitable strategy for reducing the complexity of serum proteome for subsequent proteomic studies. The highest serum protein depletion percentage and highest depletion of albumin, the most abundant serum protein, was observed in DTT-ACN method. The maximum number of serum proteins was identified in ACN followed by DTT-ACN method and importantly, detection of more number of low-abundant proteins (LAPs) could also be achieved by these two methods. Although PM method resulted into lowest dynamic range of protein abundance, quite a less number of proteins were identified by this method. Overall, sequential depletion using DTT-ACN and ACN methods provided advantage of simultaneous detection of more number of proteins along with LAPs with a reasonably high dynamic range of protein abundances over other methods and thus emerged as cheaper and effective alternatives to the commercial methods. Further, these methods are species-independent and hence can be applied in human and in any livestock species to simplify the serum proteome., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Trypsin is by far the most commonly used protease in proteomics. Even though the amount of protease used in each experiment is very small, digestion of large amounts of protein prior to enrichment can be rather costly. The price of commercial trypsin is highly dependent on the quality of the enzyme, which is determined by its purity, activity, and chemical modifications. In this study we evaluated several strategies for improving the quality of crude trypsin by reductive methylation and affinity purification. We present a protocol applicable to most proteomics laboratories for obtaining a highly stable and pure trypsin preparation using reductive methylation and purification by benzamidine-sepharose. The entire workflow can be performed within a day and yields ~4 mg per batch but is completely scalable. The methylated product was benchmarked against sequencing grade trypsin from Promega and they were found to be comparable for one hour digestions at elevated temperatures, where residual chymotryptic activity was found to be negligible., Competing Interests: JB works as a senior scientist at Alphalyse A/S. Alphalyse A/S provided support in the form of salary for JB. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Microglia, as the resident brain immune cells, can exhibit a broad range of activation phenotypes, which have been implicated in a multitude of central nervous system disorders. Current widely studied microglial cell lines are mainly derived from neonatal rodent brain that can limit their relevance to homeostatic function and disease-related neuroimmune responses in the adult brain. Recently, an adult mouse brain-derived microglial cell line has been established; however, a comprehensive proteome dataset remains lacking. Here, an optimization method for sensitive and rapid quantitative proteomic analysis of microglia is described that involves suspension trapping (S-Trap) for efficient and reproducible protein extraction from a limited number of microglial cells expected from an adult mouse brain (≈300 000). Using a 2-h gradient on a 75-cm UPLC column with a modified data dependent acquisition method on a hybrid quadrupole-Orbitrap mass spectrometer, 4855 total proteins have been identified where 4698 of which are quantifiable by label-free quantitation with a median and average coefficient of variation (CV) of 6.7% and 10.6%, respectively. This dataset highlights the high depth of proteome coverage and related quantitation precision of the adult-derived microglial proteome including proteins associated with several key pathways related to immune response. Data are available via ProteomeXchange with identifier PXD012006., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Reproducible sample preparation remains a significant challenge in large-scale clinical research using selected reaction monitoring-mass spectrometry (SRM-MS), which enables a highly sensitive multiplexed assay. Although automated liquid-handling platforms have tremendous potential for addressing this issue, the high cost of their consumables is a drawback that renders routine operation expensive. Here we evaluated the performance of a liquid-handling platform in preparing serum samples compared with a standard experiment while reducing the outlay for consumables, such as tips, wasted reagents, and reagent stock plates. A total of 26 multiplex assays were quantified by SRM-MS using four sets of 24 pooled human serum aliquots; the four sets used a fixed number (1, 4, 8, or 24) of tips to dispense digestion reagents. This study demonstrated that the use of 4 or 8 tips is comparable to 24 tips (standard experiment), as evidenced by their coefficients of variation: 13.5% (for 4 and 8 tips) versus 12.0% (24 tips). Thus we can save 37% of the total experimental cost compared with the standard experiment, maintaining nearly equivalent reproducibility. The routine operation of cost-effective liquid-handling platforms can enable researchers to process large-scale samples with high throughput, adding credibility to their findings by minimizing human error.

A simple and fast immunoprecipitation (IP) protocol is designed with the sample preparation incorporated, applicable to both low and high throughput. This new protocol combines two procedures based on magnetic beads in 96-well plate format. Protein complexes are captured by antibodies and magnetic beads conjugated with protein A. Proteins are washed and on-bead digested by using Single-Pot solid-phase sample preparation (SP3). The whole IP-SP3 approach can be completed in one day, which is considerably faster compared to the classical approach. No major quantitative differences are found between SP3 and FASP (filter-aided sample preparation) or a longer incubation protocol. Taken together, the IP-SP3 protocol is a fast and economical approach easily applicable for large-scale protein interactome analysis., (© 2019 The Authors. Proteomics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The soil microbiome is inherently complex with high biological diversity, and spatial heterogeneity typically occurring on the submillimetre scale. To study the microbial ecology of soils, and other microbiomes, biomolecules, that is, nucleic acids and proteins, must be efficiently and reliably co-recovered from the same biological samples. Commercial kits are currently available for the co-extraction of DNA, RNA and proteins but none has been developed for soil samples. We present a new protocol drawing on existing phenol-chloroform-based methods for nucleic acids co-extraction but incorporating targeted precipitation of proteins from the phenol phase. The protocol is cost-effective and robust, and easily implemented using reagents commonly available in laboratories. The method is estimated to be eight times cheaper than using disparate commercial kits for the isolation of DNA and/or RNA, and proteins, from soil. The method is effective, providing good quality biomolecules from a diverse range of soil types, with clay contents varying from 9.5% to 35.1%, which we successfully used for downstream, high-throughput gene sequencing and metaproteomics. Additionally, we demonstrate that the protocol can also be easily implemented for biomolecule co-extraction from other complex microbiome samples, including cattle slurry and microbial communities recovered from anaerobic bioreactors, as well as from Gram-positive and Gram-negative pure cultures., (© 2018 John Wiley & Sons Ltd.)

Recent advances in mass spectrometry (MS)-based technologies are now set to transform translational cancer proteomics from an idea to a practice. Here, we present a robust proteomic workflow for the analysis of clinically relevant human cancer tissues that allows quantitation of thousands of tumor proteins in several hours of measuring time and a total turnaround of a few days. We applied it to a chemorefractory metastatic case of the extremely rare urachal carcinoma. Quantitative comparison of lung metastases and surrounding tissue revealed several significantly upregulated proteins, among them lysine-specific histone demethylase 1 (LSD1/KDM1A). LSD1 is an epigenetic regulator and the target of active development efforts in oncology. Thus, clinical cancer proteomics can rapidly and efficiently identify actionable therapeutic options. While currently described for a single case study, we envision that it can be applied broadly to other patients in a similar condition., (© 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)

High-throughput top-down proteomic experiments directly identify proteoforms in complex mixtures, making high quality tandem mass spectra necessary to deeply characterize proteins with many sources of variation. Collision-based dissociation methods offer expedient data acquisition but often fail to extensively fragment proteoforms for thorough analysis. Electron-driven dissociation methods are a popular alternative approach, especially for precursor ions with high charge density. Combining infrared photoactivation concurrent with electron transfer dissociation (ETD) reactions, i.e., activated ion ETD (AI-ETD), can significantly improve ETD characterization of intact proteins, but benefits of AI-ETD have yet to be quantified in high-throughput top-down proteomics. Here, we report the first application of AI-ETD to LC-MS/MS characterization of intact proteins (<20 kDa), highlighting improved proteoform identification the method offers over higher energy-collisional dissociation (HCD), standard ETD, and ETD followed by supplemental HCD activation (EThcD). We identified 935 proteoforms from 295 proteins from human colorectal cancer cell line HCT116 using AI-ETD compared to 1014 proteoforms, 915 proteoforms, and 871 proteoforms with HCD, ETD, and EThcD, respectively. Importantly, AI-ETD outperformed each of the three other methods in MS/MS success rates and spectral quality metrics (e.g., sequence coverage achieved and proteoform characterization scores). In all, this four-method analysis offers the most extensive comparisons to date and demonstrates that AI-ETD both increases identifications over other ETD methods and improves proteoform characterization via higher sequence coverage, positioning it as a premier method for high-throughput top-down proteomics.

This paper discusses the successful adoption of a subscription-based, open-access model of service delivery for a mass spectrometry and proteomics facility. In 2009, the Mass Spectrometry and Proteomics Facility at the University of Melbourne (Australia) moved away from the standard fee for service model of service provision. Instead, the facility adopted a subscription- or membership-based, open-access model of service delivery. For a low fixed yearly cost, users could directly operate the instrumentation but, more importantly, there were no limits on usage other than the necessity to share available instrument time with all other users. All necessary training from platform staff and many of the base reagents were also provided as part of the membership cost. These changes proved to be very successful in terms of financial outcomes for the facility, instrument access and usage, and overall research output. This article describes the systems put in place as well as the overall successes and challenges associated with the operation of a mass spectrometry/proteomics core in this manner. Graphical abstract ᅟ.

Mass spectrometry (MS) has dominated over other protein analysis methods that aspire to deliver rapid and sensitive protein annotation, due to its ability to acquire high-content biological information from samples of great complexity. Routinely, in-depth analysis of complex biological samples, such as total cell lysates, relies on the high separation power of two-dimensional liquid chromatography-tandem MS (2D LC-MS/MS), often combined with protein pre-fractionation. However, on the basis of recent advances in chromatographic and MS instrumentation, one-dimensional (1D) LC-MS/MS approaches have become the method-of-choice for high-volume/high-throughput protein experiments. Thousands of proteins can be identified in single-run LC-MS/MS experiments. In the present study a 1D LC-MS/MS approach was applied on whole-cell lysates of WM-266-4 human cells leading to identification of more than 5,300 protein groups, 6,000 proteins and 22,00 peptides, in a single run. Using no pre-fractionation steps, method optimization was achieved through experimentation on lysis and protein extraction solutions, as well as nLC gradient parameters., (Copyright © 2016 Elsevier B.V. All rights reserved.)

IEF LC-MS/MS is an analytical method that incorporates a two-step sample separation prior to MS identification of proteins. When analyzing complex samples this preparatory separation allows for higher analytical depth and improved quantification accuracy of proteins. However, cost and analysis time are greatly increased as each analyzed IEF fraction is separately profiled using LC-MS/MS. We propose an approach that selects a subset of IEF fractions for LC-MS/MS analysis that is highly informative in the context of a group of proteins of interest. Specifically, our method allows a significant reduction in cost and instrument time as compared to the standard protocol of running all fractions, with little compromise to coverage. We develop algorithmics to optimize the selection of the IEF fractions on which to run LC-MS/MS. We translate the fraction optimization task to Minimum Set Cover, a well-studied NP-hard problem. We develop heuristic solutions and compare them in terms of effectiveness and running times. We provide examples to demonstrate advantages and limitations of each algorithmic approach. Finally, we test our methodology by applying it to experimental data obtained from IEF LC-MS/MS analysis of yeast and human samples. We demonstrate the benefit of this approach for analyzing complex samples with a focus on different protein sets of interest.

In a comparative study, we investigated the influence of nine sample preparation workflows and seven different lysis buffers for qualitative and quantitative analysis of the human adipose tissue proteome. Adipose tissue is not just a fat depot but also an endocrine organ, which cross-talks with other tissue types and organs throughout the body, like liver, muscle, pancreas, and brain. Its secreted molecules have an influence on the nervous, immune, and vascular system, thus adipose tissue plays an important role in the regulation of whole-body homeostasis. Proteomic analysis of adipose tissue is challenging due to the extremely high lipid content and a variety of different cell types included. We investigated the influence of different detergents to the lysis buffer and compared commonly used methods like protein precipitation and filter-aided sample preparation (FASP) with workflows involving acid labile or precipitable surfactants. The results indicate that a sodium deoxycholate (SDC) based workflow had the highest efficiency and reproducibility for quantitative proteomic analysis. In total 2564 proteins from the adipose tissue of a single person were identified., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity., Results: In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage., Conclusions: The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics.

The rapid spread of vector-borne diseases demands the development of an innovative strategy for arthropod monitoring. The emergence of MALDI-TOF MS as a rapid, low-cost, and accurate tool for arthropod identification is revolutionizing medical entomology. However, as MS spectra from an arthropod can vary according to the body part selected, the sample homogenization method used and the mode and duration of sample storage, standardization of protocols is indispensable prior to the creation and sharing of an MS reference spectra database. In the present study, manual grinding of Anopheles gambiae Giles and Aedes albopictus mosquitoes at the adult and larval (L3) developmental stages was compared to automated homogenization. Settings for each homogenizer were optimized, and glass powder was found to be the best sample disruptor based on its ability to create reproducible and intense MS spectra. In addition, the suitability of common arthropod storage conditions for further MALDI-TOF MS analysis was kinetically evaluated. The conditions that best preserved samples for accurate species identification by MALDI-TOF MS were freezing at -20°C or in liquid nitrogen for up to 6 months. The optimized conditions were objectified based on the reproducibility and stability of species-specific MS profiles. The automation and standardization of mosquito sample preparation methods for MALDI-TOF MS analyses will popularize the use of this innovative tool for the rapid identification of arthropods with medical interest., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Peptide generation by trypsin digestion is typically the first step in mass spectrometry-based proteomics experiments, including 'bottom-up' discovery and targeted proteomics using multiple reaction monitoring. Manual tryptic digest and the subsequent clean-up steps can add variability even before the sample reaches the analytical platform. While specialized filter plates and tips have been designed for automated sample processing, the specialty reagents required may not be accessible or feasible due to their high cost. Here, we report a lower-cost semi-automated protocol for in-gel digestion and GeLC using standard 96-well microplates. Further cost savings were realized by re-using reagent tips with optimized sample ordering. To evaluate the methodology, we compared a simple mixture of 7 proteins and a complex cell-lysate sample. The results across three replicates showed that our semi-automated protocol had performance equal to or better than a manual in-gel digestion with respect to replicate variability and level of contamination. In this paper, we also provide the Agilent Bravo method file, which can be adapted to other liquid handlers. The simplicity, reproducibility, and cost-effectiveness of our semi-automated protocol make it ideal for routine in-gel and GeLC sample preparations, as well as high throughput processing of large clinical sample cohorts., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Protein depletion with acetonitrile and protein equalization with dithiothreitol have been assessed with success as proteomics tools for getting insight into the peritoneal dialysate effluent proteome. The methods proposed are cost-effective, fast and easy of handling, and they match the criteria of analytical minimalism: low sample volume and low reagent consumption. Using two-dimensional gel electrophoresis and peptide mass fingerprinting, a total of 72 unique proteins were identified. Acetonitrile depletes de PDE proteome from high-abundance proteins, such as albumin, and enriches the sample in apolipo-like proteins. Dithiothreitol equalizes the PDE proteome by diminishing the levels of albumin and enriching the extract in immunoglobulin-like proteins. The annotation per gene ontology term reveals the same biological paths being affected for patients undergoing peritoneal dialysis, namely that the largest number of proteins lost through peritoneal dialysate are extracellular proteins involved in regulation processes through binding., Significance: Renal failure is a growing problem worldwide, and particularly in Europe where the population is getting older. Up-to-date there is a focus of interest in peritoneal dialysis (PD), as it provides a better quality of life and autonomy of the patients than other renal replacement therapies such as haemodialysis. However, PD can only be used during a short period of years, as the peritoneum lost its permeability through time. Therefore to make a breakthrough in PD and consequently contribute to better healthcare system it is urgent to find a group of biomarkers of peritoneum degradation. Here we report on two cost-effective methods for protein depletion in peritoneal dialysate effluent (PDE). The use of ACN and DTT over PDE to deplete high abundant proteins or to equalize the concentration of proteins, respectively, performs well and with similar protein profiles than when the same chemicals are used in human plasma samples. ACN depletes de PDE proteome from large proteins, such as albumin, and enriches the sample in apolipoproteins. DTT equalizes the PDE proteome by diminishing the levels of large proteins such as albumin and enriching the extract in immunoglobulins. Although the number and type of proteins identified are different, the annotation per gene ontology term reveals the same biological paths being affected for patients undergoing peritoneal dialysate. Thus, the largest number of proteins lost through peritoneal dialysate belongs to the group of extracellular proteins involved in regulation processes through binding. As for the searching of biomarkers, DTT seems to be the most promising of the two methods because acts as an equalizer and it allows interrogating more proteins in the same sample.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometric imaging (MSI) is a powerful tool to map the spatial distribution of biomolecules on tissue sections. Recent developments of hybrid MS instruments allow combination of different types of data acquisition by various mass analyzers into a single MSI analysis, which reduces experimental time and sample consumptions. Here, using the well-characterized crustacean nervous system as a test-bed, we explore the utility of high resolution and accurate mass (HRAM) MALDI Orbitrap platform for enhanced in situ characterization of the neuropeptidome with improved chemical information. Specifically, we report on a multiplex-MSI method, which combines HRAM MSI with data dependent acquisition (DDA) tandem MS analysis in a single experiment. This method enables simultaneous mapping of neuropeptide distribution, sequence validation, and novel neuropeptide discovery in crustacean neuronal tissues. To enhance the dynamic range and efficiency of in situ DDA, we introduced a novel approach of fractionating full m/z range into several sub-mass ranges and embedding the setup using the multiplex-DDA-MSI scan events to generate pseudo fractionation before MS/MS scans. The division of entire m/z into multiple segments of m/z sub-ranges for MS interrogation greatly decreased the complexity of molecular species from tissue samples and the heterogeneity of the distribution and variation of intensities of m/z peaks. By carefully optimizing the experimental conditions such as the dynamic exclusion, the multiplex-DDA-MSI approach demonstrates better performance with broader precursor coverage, less biased MS/MS scans towards high abundance molecules, and improved quality of tandem mass spectra for low intensity molecular species. Graphical Abstract ᅟ.

The comprehensive MS analysis of the peptidome, the intracellular and intercellular products of protein degradation, has the potential to provide novel insights on endogenous proteolytic processing and its utility in disease diagnosis and prognosis. Along with the advances in MS instrumentation and related platforms, a plethora of proteomics data analysis tools have been applied for direct use in peptidomics; however, an evaluation of the currently available informatics pipelines for peptidomics data analysis has yet to be reported. In this study, we began by evaluating the results of several popular MS/MS database search engines, including MS-GF+, SEQUEST, and MS-Align+, for peptidomics data analysis, followed by identification and label-free quantification using the well-established accurate mass and time (AMT) tag and newly developed informed quantification (IQ) approaches, both based on direct LC-MS analysis. Our results demonstrated that MS-GF+ outperformed both SEQUEST and MS-Align+ in identifying peptidome peptides. Using a database established from MS-GF+ peptide identifications, both the AMT tag and IQ approaches provided significantly deeper peptidome coverage and less missing data for each individual data set than the MS/MS methods, while achieving robust label-free quantification. Besides having an excellent correlation with the AMT tag quantification results, IQ also provided slightly higher peptidome coverage. Taken together, we propose an optimized informatics pipeline combining MS-GF+ for initial database searching with IQ (or AMT tag) approaches for identification and label-free quantification for high-throughput, comprehensive, and quantitative peptidomics analysis. Graphical Abstract ᅟ.

In 2008, the National Institutes of Health's (NIH) Undiagnosed Disease Program (UDP) was initiated to provide diagnoses for individuals who had long sought one without success. As a result of two international conferences (Rome 2014 and Budapest 2015), the Undiagnosed Diseases Network International (UDNI) was established, modeled in part after the NIH UDP. Undiagnosed diseases are a global health issue, calling for an international scientific and healthcare effort. To meet this demand, the UDNI has built a consensus framework of principles, best practices and governance; the Board of Directors reflects its international character, as it includes experts from Australia, Canada, Hungary, Italy, Japan and the USA. The UDNI involves centers with internationally recognized expertise, and its scientific resources and know-how aim to fill the knowledge gaps that impede diagnosis. Consequently, the UDNI fosters the translation of research into medical practice. Active patient involvement is critical; the Patient Advisory Group is expected to play an increasing role in UDNI activities. All information for physicians and patients will be available at the UDNI website., (Copyright © 2015. Published by Elsevier Inc.)

Formalin-fixed paraffin-embedded (FFPE) tissue is a rich source of clinically relevant material that can yield important translational biomarker discovery using proteomic analysis. Protocols for analyzing FFPE tissue by LC-MS/MS exist, but standardization of procedures and critical analysis of data quality is limited. This study compared and characterized data obtained from FFPE tissue using two methods: a urea in-solution digestion method (UISD) versus a commercially available Qproteome FFPE Tissue Kit method (Qkit). Each method was performed independently three times on serial sections of homogenous FFPE tissue to minimize pre-analytical variations and analyzed with three technical replicates by LC-MS/MS. Data were evaluated for reproducibility and physiochemical distribution, which highlighted differences in the ability of each method to identify proteins of different molecular weights and isoelectric points. Each method replicate resulted in a significant number of new protein identifications, and both methods identified significantly more proteins using three technical replicates as compared to only two. UISD was cheaper, required less time, and introduced significant protein modifications as compared to the Qkit method, which provided more precise and higher protein yields. These data highlight significant variability among method replicates and type of method used, despite minimizing pre-analytical variability. Utilization of only one method or too few replicates (both method and technical) may limit the subset of proteomic information obtained., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The amount of sample available for clinical and biological proteomic research is often limited and thus significantly restricts clinical and translational research. Recently, we have integrated pressure cycling technology (PCT) assisted sample preparation and SWATH-MS to perform reproducible proteomic quantification of biopsy-level tissue samples. Here, we further evaluated the minimal sample requirement of the PCT-SWATH method using various types of samples, including cultured cells (HeLa, K562, and U251, 500 000 to 50 000 cells) and tissue samples (mouse liver, heart, brain, and human kidney, 3-0.2 mg). The data show that as few as 50 000 human cells and 0.2-0.5 mg of wet mouse and human tissues produced peptide samples sufficient for multiple SWATH-MS analyses at optimal sample load applied to the system. Generally, the reproducibility of the method increased with decreasing tissue sample amounts. The SWATH maps acquired from peptides derived from samples of varying sizes were essentially identical based on the number, type, and quantity of identified peptides. In conclusion, we determined the minimal sample required for optimal PCT-SWATH analyses, and found smaller sample size achieved higher quantitative accuracy., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The new technology of ultrathroughput MS (uMS) transforms the intrinsic capability of analyte multiplexing in mass spectrometry (MS) to sample multiplexing. Core technological advantages of uMS rely on the decoupled use of isotopic quantitation reference and nonisotopic mass coding of samples. These advantages include: (1) high sample-throughput potential, (2) utilization of minimal amounts of expensive stable isotopes for the quantitation reference, and (3) unleashing of the open-source exploration of the chemical structure diversity of nonisotopic reagents to significantly enhance the MS detectability of analytes. A particular uMS method, ultrathroughput multiple reaction monitoring (uMRM), is reported for one-experiment quantitation of a surrogate peptide (SVILLGR) of prostate specific antigen (PSA) in multiple serum samples. Following derivatization of the pair of spiked, isotopic reference (SVILLGR*) and endogenous, native peptide in each sample, all samples were pooled for a step of simultaneous enrichment and cleanup of derivatized peptide pairs using immobilized antibody. The MS analysis of the pooled sample reported the quantity and sample origin of the surrogate peptide. Several analyses with different sample throughput were presented, with the highest being 15-in-1. Screening of nonisotopic reagents used combinatorial libraries of peptidyl compounds, and the reagent selection was based on the derivatization effectiveness and the capability of MS signal enhancement for the peptide. The precision, accuracy, and linearity of the uMRM MS technology were found to be comparable with standard isotope dilution MRM MS.

Interactions between modular domains and short linear motifs (3-10 amino acids peptide stretches) are crucial for cell signaling. The motifs typically reside in the disordered regions of the proteome and the interactions are often transient, allowing for rapid changes in response to changing stimuli. The properties that make domain-motif interactions suitable for cell signaling also make them difficult to capture experimentally and they are therefore largely underrepresented in the known protein-protein interaction networks. Most of the knowledge on domain-motif interactions is derived from low-throughput studies, although there exist dedicated high-throughput methods for the identification of domain-motif interactions. The methods include arrays of peptides or proteins, display of peptides on phage or yeast, and yeast-two-hybrid experiments. We here provide a survey of scalable methods for domain-motif interaction profiling. These methods have frequently been applied to a limited number of ubiquitous domain families. It is now time to apply them to a broader set of peptide binding proteins, to provide a comprehensive picture of the linear motifs in the human proteome and to link them to their potential binding partners. Despite the plethora of methods, it is still a challenge for most approaches to identify interactions that rely on post-translational modification or context dependent or conditional interactions, suggesting directions for further method development.

Protein chemical cross-linking and mass spectrometry enable the analysis of protein-protein interactions and protein topologies; however, complicated cross-linked peptide spectra require specialized algorithms to identify interacting sites. The Kojak cross-linking software application is a new, efficient approach to identify cross-linked peptides, enabling large-scale analysis of protein-protein interactions by chemical cross-linking techniques. The algorithm integrates spectral processing and scoring schemes adopted from traditional database search algorithms and can identify cross-linked peptides using many different chemical cross-linkers with or without heavy isotope labels. Kojak was used to analyze both novel and existing data sets and was compared to existing cross-linking algorithms. The algorithm provided increased cross-link identifications over existing algorithms and, equally importantly, the results in a fraction of computational time. The Kojak algorithm is open-source, cross-platform, and freely available. This software provides both existing and new cross-linking researchers alike an effective way to derive additional cross-link identifications from new or existing data sets. For new users, it provides a simple analytical resource resulting in more cross-link identifications than other methods.

Improving the ability to predict livestock performance using biomarkers will provide a benefit for livestock genetic evaluation and improvement. The most practical biological sample to screen for development of biomarkers is serum due to the ease of collection. However, protein profiles in serum are complex and dynamic. Strategies are needed to manage variation in serum proteins used for biomarker identification. Albumin is the most abundant protein in serum, comprising over 50% of the overall protein content, and has historically been depleted from serum before biomarker identification. The objective of this study was to investigate the use of gel-based proteomic techniques to evaluate the need for porcine albumin depletion in biomarker identification. Albumin is known to bind many proteins in the blood, thus potential biomarkers could be removed during albumin depletion. Using two-dimensional difference in gel electrophoresis (2D-DIGE), we show whole serum can be used for biomarker discovery. The data obtained show that albumin removal methods are effective for porcine sera. Over 85% of the protein spots resolved on at least half of the gels were changed in abundance between whole and albumin depleted sera. Of the 204 protein spots significantly altered in abundance, 59 were changed over 400%. However, albumin removal also altered the serum proteome in an unpredictable manner; in the depleted sera, 86 protein spots were increased in abundance and 118 were decreased. Furthermore, the abundance of 59.4% of the protein spots in the albumin depleted samples had a larger standard error than whole sera. However, the resolution of albumin in 2D-DIGE analysis of whole sera permitted the detection and quantification of substantial numbers of proteins. Thus, it is proposed that whole serum can be used in a gel-based proteomics system for the identification of porcine biomarkers.

We report automated and time-efficient (2 h per sample) profiling of muscle using ultra-performance LC coupled directly with high-definition MS (HDMS(E)). Soluble proteins extracted from rat gastrocnemius (n = 10) were digested with trypsin and analyzed in duplicate using a 90 min RPLC gradient. Protein identification and label-free quantitation were performed from HDMS(E) spectra analyzed using Progenesis QI for Proteomics software. In total 1514 proteins were identified. Of these, 811 had at least three unique peptides and were subsequently used to assess the dynamic range and precision of LC-HDMS(E) label-free profiling. Proteins analyzed by LC-HDMS(E) encompass the entire complement of glycolytic, β-oxidation, and tricarboxylic acid enzymes. In addition, numerous components of the electron transport chain and protein kinases involved in skeletal muscle regulation were detected. The dynamic range of protein abundances spanned four orders of magnitude. The correlation between technical replicates of the ten biological samples was R(2) = 0.9961 ± 0.0036 (95% CI = 0.9940 - 0.9992) and the technical CV averaged 7.3 ± 6.7% (95% CI = 6.87 - 7.79%). This represents the most sophisticated label-free profiling of skeletal muscle to date., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The PRIME-XS consortium is a pan-European infrastructure for proteomics. As a prologue to this special issue of Molecular & Cellular Proteomics on the research activities of the PRIME-XS consortium, we, as the guest editors of this issue, provide an overview of the structure and activities of this consortium, which is funded by the European Union's 7th Framework Programme for Research and Technological Development., (© 2014 by The American Society for Biochemistry and Molecular Biology, Inc.)

Protein recovery from gel electrophoresis plays a significant role in functional genomics and proteomics. To assist in this, a simple, cost-effective, and efficient apparatus for electroelution of proteins has been designed. The performance of the apparatus was demonstrated using the proteins bovine serum albumin (BSA), phosphorylase, ovalbumin, pepsin, and trypsinogen. In all the cases the yield of elution was found to be consistently greater than 85% and the proteins could be eluted without degradation in less than 15 min. The utility of this method can be extended to protein elution from denatured and native polyacrylamide gels, DNA purification from agarose gels, and oligomeric primers purification from polyacrylamide gels. In addition to this, the method offers an effortless purification and characterization of microbial extracellular proteins. The eluted proteins can be directly used in N-terminal amino acid sequencing, and in amino acid and proteomics analyses.

We describe the use of an isobaric tagging reagent, Deuterium isobaric Amine Reactive Tag (DiART), for quantitative phosphoproteomic experiments. Using DiART tagged custom mixtures of two phosphorylated peptides from alpha casein and their non-phosphorylated counterparts, we demonstrate the compatibility of DiART with TiO2 affinity purification of phosphorylated peptides. Comparison of theoretical vs. experimental reporter ion ratios reveals accurate quantification of phosphorylated peptides over a dynamic range of more than 15-fold. Using DiART labelling and TiO2 enrichment (DiART-TiO2) with large quantities of proteins (8 mg) from the cell lysate of model fungus Aspergillus nidulans, we quantified 744 unique phosphopeptides. Overlap of median values of TiO2 enriched phosphopeptides with theoretical values indicates accurate trends. Altogether these findings confirm the feasibility of performing quantitative phosphoproteomic experiments in a cost-effective manner using isobaric tagging reagents, DiART.

Pancreatic cancer (PC) has a poor prognosis, with a 5-year survival of 3-4%. This is mainly due to late diagnosis because of diffuse symptoms, where 80-85% of the patients are inoperable. Consequently, early diagnosis would be of significant benefit, resulting in a potential 5-year survival of 30-40%. However, new technologies must be carefully evaluated concerning effectiveness and healthcare costs. We have developed a framework for modelling cost and health effects from early detection of PC, which for the first time allowed us to analyse its cost-effectiveness. A probabilistic cohort model for estimating costs and quality adjusted life-years (QALY) arising from screening for PC, compared to a "wait-and-see"-approach, was designed. The test accuracy, Swedish survival and costs by tumour stage, expected life gain from early detection and pretest probabilities in risk groups, were retrieved from previous investigations. In a cohort of newly diagnosed diabetic patient (incidence 0.71%) the incremental cost per QALY gained (ICER) was €13,500, which is considered cost-effective in Europe. Results were mainly sensitive to the incidence with the ICER ranging from €315 to €204,000 (familial PC 35% and general population 0.046%, respectively). This is the first study focusing on clinical implementation of advanced testing and what is required for novel technologies in cancer care to be cost-effective. The model clearly demonstrated the potential of multiplexed proteomic-testing of PC and also identified the requirements for test accuracy. Consequently, it can serve as a model for assessing the possibilities to introduce advanced test platforms also for other cancer indications., (Copyright © 2013 UICC.)

Deuterium isobaric Amine Reactive Tag (DiART) reagents facilitate relative quantification during proteomic analysis in a functionally similar manner to commercially available isobaric tag for relative and absolute quantitation (iTRAQ) and tandem mass tag (TMT) reagents. In contrast to iTRAQ and TMT, DiART reagents incorporate deuterium isotopes which significantly reduce the number of required synthesis steps and hence have potential to significantly reduce reagent production cost. We examined the capability of DiART for performing quantitative proteomic experiments using a linear ion-trap mass spectrometer with Pulsed Q Dissociation (PQD) fragmentation. Using a synthetic peptide tagged with DiART reagent, we observed a precise mass shift of 144.79 Da on the triply charged precursor ion, which shows complete derivatization of the N-terminus and ε-amino group of lysine. A DiART tagged tryptic digest of bovine serum albumin showed a sequence coverage of 57.99% which was very comparable to that showed by iTRAQ, 54.77%. Furthermore, a ten protein mixture tagged with DiART reagents and mixed in 1:1:1:1:1:1 exhibited < 15% error, whereas a linear trend (slope of 1.085) was observed when tagged proteins were mixed in the ratio 2:1:2:4:10:14 and plotted against theoretical ratios. Finally, when complex cell-wall protein mixtures from the model fungus A. nidulans were tagged with DiART reagents and mixed in different ratios, they exhibited similar trends. We conclude that DiART reagents are capable of performing quantitative proteomic experiments using PQD on a linear ion trap mass spectrometer., (Copyright © 2013 John Wiley & Sons, Ltd.)

For the first time, polymer monolithic capillary columns have been employed at ultra-high-pressure liquid chromatographic conditions (UHPLC) to investigate their potential for high-speed separations of peptides and intact proteins. In comparison to conventional flow rates and gradient conditions, a substantial decrease in analysis time (>factor 4) can be achieved when operating monolithic columns such as ultra-high-pressure conditions while scaling the gradient volume. The effects of flow rate and column length on the peak capacity and the gradient performance limits were assessed for the separation of peptide and protein mixtures applying the maximum system pressure (80MPa) and a fixed gradient steepness. The potential for ultra-fast gradient separations of large biomolecules was further demonstrated for very steep gradients (gradient times≪1min). A tryptic digest of cytochrome c was separated using a gradient time of only 1min. Finally, the run-to-run repeatability and column robustness were assessed at ultra-high pressure conditions (after>800 runs) with consecutive steep 1min separations of peptides, yielding RSD values below 0.12% in retention time., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Proteins that are important indicators of physiological or pathological states, can provide information for the identification of early and differential markers for disease. Saliva, contains an abundance of proteins, offers an easy, inexpensive, safe, and non-invasive approach for disease detection, and possesses a high potential to revolutionize the diagnostics. Discovery of salivary biomarkers could be used to scrutinize health and disease surveillance. The impact of human saliva proteome analysis in the search for clinically relevant disease biomarkers will be realized through advances made using proteomic technologies. The advancements of emerging proteomic techniques have benefited biomarker research to the point where saliva is now recognized as an excellent diagnostic medium for the detection of disease. This review presents an overview of the value of saliva as a credible diagnostic tool and we aim to summarize the proteomic technologies currently used for global analysis of saliva proteins and to elaborate on the application of saliva proteomics to the discovery of disease biomarkers, and discuss some of the critical challenges and perspectives in this field., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Urine represents the most easily obtainable body fluid and consequently one of the most common samples in clinical chemistry. The majority of pathological changes in human organs may well be reflected in urine. In this way, urine analysis can aid in disease diagnosis, treatment monitoring, and prognosis. Currently, the most commonly used method for identification of new urine biomarkers involves centrifugation of the urine sample to collect either the soluble urine proteins or the urinary exosomes followed by 1 or 2 protein purification and separation steps before visualization and finally identification of potential biomarkers, usually by mass spectrometry. Here we present a generally applicable, rapid, and robust method for screening large number of urine samples, resulting in a broad spectrum of native peptides, as a tool to be used for biomarker discovery. The method combines online sample pretreatment with a well-established mass spectrometric technique. Native peptides are extracted from urine samples on a miniaturized reverse-phase-strong cation exchange cartridge system. As the proper identification of native peptides often requires combination of data acquired on different mass analyzers, we have aimed at a procedure providing us with sufficient material to identify and characterize the differentially expressed markers.

The combination of MALDI-TOF-mass spectrometry with gel electrophoretic separation using protein visualization by staining procedures involving such as Coomassie Brilliant Blue has been established as a widely used approach in proteomics. Although this approach has been shown to present high detection sensitivity, drawbacks and limitations frequently arise from the significant background in the mass spectrometric analysis. In this chapter we describe an approach for the application of MALDI-MS to the mass spectrometric identification of proteins from one-dimensional (1D) and two-dimensional (2D) gel electrophoretic separation, using stain-free detection and visualization based on native protein fluorescence. Using the native fluorescence of aromatic protein amino acids with UV transmission at 343 nm as a fast gel imaging system, unstained protein spots are localized and, upon excision from gels, can be proteolytically digested and analyzed by MALDI-MS. Following the initial development and testing with standard proteins, applications of the stain-free gel electrophoretic detection approach to mass spectrometric identification of biological proteins from 2D-gel separations clearly show the feasibility and efficiency of this combination, as illustrated by a proteomics study of porcine skeleton muscle proteins. Major advantages of the stain-free gel detection approach with MALDI-MS analysis are (1) rapid analysis of proteins from 1D- and 2D-gel separation without destaining required prior to proteolytic digestion, (2) the low detection limits of proteins attained, and (3) low background in the MALDI-MS analysis.

We have extended the functionality of the Central Proteomics Facilities Pipeline (CPFP) to allow use of remote cloud and high performance computing (HPC) resources for shotgun proteomics data processing. CPFP has been modified to include modular local and remote scheduling for data processing jobs. The pipeline can now be run on a single PC or server, a local cluster, a remote HPC cluster, and/or the Amazon Web Services (AWS) cloud. We provide public images that allow easy deployment of CPFP in its entirety in the AWS cloud. This significantly reduces the effort necessary to use the software, and allows proteomics laboratories to pay for compute time ad hoc, rather than obtaining and maintaining expensive local server clusters. Alternatively the Amazon cloud can be used to increase the throughput of a local installation of CPFP as necessary. We demonstrate that cloud CPFP allows users to process data at higher speed than local installations but with similar cost and lower staff requirements. In addition to the computational improvements, the web interface to CPFP is simplified, and other functionalities are enhanced. The software is under active development at two leading institutions and continues to be released under an open-source license at http://cpfp.sourceforge.net.

Human plasma is dominated by high-abundance proteins which severely impede the detection of low-abundance proteins. Unfortunately, now there is no efficient method for large-scale depletion of high-abundance proteins in human plasma. In this study, we developed a new strategy, strong anion exchange (SAX)/RP 2D LC system, which has potential for large-scale depletion of high-abundance proteins in human plasma. Separation gradients of the system were optimized to ensure an extensive separation of plasma proteins. Plasma was fractionated into 67 fractions by SAX. All these fractions were subjected a thorough separation by the 2D RPLC and 66 peaks with high UV absorption (>20 mAU) at 215 nm were collected. Proteins in these peaks were identified by LC-MS/MS analysis. Results showed that 83 proteins could be identified in these peaks, 68 among them were reported to be high- or middle-abundance proteins in plasma. All these proteins had definite retention times and were mapped in the 2D SAX-RP system, which resulted in accurate depletion of high-abundance proteins with ease. Our studies provide a convenient and effective method for large-scale depletion of high-abundance proteins and in-depth research in human plasma proteomics., (© 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)