Automation and low-cost proteomics for characterization of the protein corona: experimental methods for big data.

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.

Improved Methodology for Sensitive and Rapid Quantitative Proteomic Analysis of Adult-Derived Mouse Microglia: Application to a Novel In Vitro Mouse Microglial Cell Model.

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.

A Fast and Economical Sample Preparation Protocol for Interaction Proteomics Analysis.

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.

Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics.

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.

Systematic evaluation of species-independent serum pre-fractionation strategies revealed cost-effective methods to reduce proteome complexity.

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.

The Value of Activated Ion Electron Transfer Dissociation for High-Throughput Top-Down Characterization of Intact Proteins.

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.

Proteomics of hydrophobic samples: Fast, robust and low-cost workflows for clinical approaches.

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.

Standardization of sample homogenization for mosquito identification using an innovative proteomic tool based on protein profiling.

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.

MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

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.

PRIME-XS, a European infrastructure for proteomics.

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.

Minimal sample requirement for highly multiplexed protein quantification in cell lines and tissues by PCT-SWATH mass spectrometry.

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.

Two methods for proteomic analysis of formalin-fixed, paraffin embedded tissue result in differential protein identification, data quality, and cost.

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.

Kojak: efficient analysis of chemically cross-linked protein complexes.

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.

Nonisotopic reagents for a cost-effective increase in sample throughput of targeted quantitative proteomics.

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.

Undiagnosed Diseases Network International (UDNI): White paper for global actions to meet patient needs.

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.

High-throughput methods for identification of protein-protein interactions involving short linear motifs.

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.

Label-free profiling of skeletal muscle using high-definition mass spectrometry.

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, ß-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.

How proteomic ApoE serotyping could impact Alzheimer's disease risk assessment: genetic testing by proteomics.

Humans have three major apolipoprotein E (ApoE) alleles (APOE; ε2, ε3 and ε4) that produce three ApoE protein isoforms. The ε2 allele encodes the ApoE2 isoform (Cys112, Cys158), whereas ε3 encodes the wild-type ApoE3 isoform (Cys112, Arg158) and ε4 encodes the ApoE4 isoform (Arg112, Arg158). Because the type of ApoE expressed is related to sporadic Alzheimer's disease risk and familial hyperlipidemia, many clinical studies have utilized ApoE typing in recent years. ApoE serotyping is based on the correlation between ApoE genotype and isoform; it is therefore possible to determine the genotype from the blood ApoE isoform combination. Serotyping ApoE using mass spectrometry promises highly accurate results while requiring minimal amounts of blood and reagents, resulting in lower costs, which suggest that proteomic-based ApoE serotyping may eventually become a routine clinical laboratory test. Not limited to ApoE, proteomic analysis of human samples could be used to intentionally determine - and perhaps unintentionally reveal - personal genetic information.

Cost-effective and efficient apparatus for electroelution of micro- and macrogram quantities of proteins from polyacrylamide gels.

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.