Antibody-based Proteomics for Human Tissue Profiling

Gut dysbiosis and neurological modalities: An engineering approach via proteomic analysis of gut-brain axis

The human gut microbiota is a complex and dynamic community of microorganisms, that influence metabolic, neurodevelopmental, and immune pathways. Microbial dysbiosis, characterized by changes in microbial diversity and relative abundances, is implicated in the development of various chronic neurological and neurodegenerative disorders. These disorders are marked by the accumulation of pathological protein aggregates, leading to the progressive loss of neurons and behavioural functions. Dysregulations in protein-protein interaction networks and signalling complexes, critical for normal brain function, are common in neurological disorders but challenging to unravel, particularly at the neuron and synapse-specific levels. To advance therapeutic strategies, a deeper understanding of neuropathogenesis, especially during the progressive disease phase, is needed. Biomarkers play a crucial role in identifying disease pathophysiology and monitoring disease progression. Proteomics, a powerful technology, shows promise in accelerating biomarker discovery and aiding in the development of novel treatments. In this chapter, we provide an in-depth overview of how proteomic techniques, utilizing various biofluid samples from patients with neurological conditions and diverse animal models, have contributed valuable insights into the pathogenesis of numerous neurological disorders. We also discuss the current state of research, potential challenges, and future directions in proteomic approaches to unravel neuro-pathological conditions.

Chemical Sensors using Single-Molecule Electrical Measurements

Driven by the digitization and informatization of contemporary society, electrical sensors are developing toward minimal structure, intelligent function, and high detection resolution. Single-molecule electrical measurement techniques have been proven to be capable of label-free molecular recognition and detection, which opens a new strategy for the design of efficient single-molecule detection sensors. In this review, we outline the main advances and potentials of single-molecule electronics for qualitative identification and recognition assays at the single-molecule level. Strategies for single-molecule electro-sensing and its main applications are reviewed, mainly in the detection of ions, small molecules, oligomers, genetic materials, and proteins. This review summarizes the remaining challenges in the current development of single-molecule electrical sensing and presents some potential perspectives for this field.

Selective Schiff base formation via gas-phase ion/ion reactions to enable differentiation of isobaric lipids in imaging mass spectrometry

The separation and identification of lipids in complex mixtures are critical to deciphering their cellular functions. Failure to resolve isobaric compounds (e.g., via high mass resolution or tandem mass spectrometry) can result in incorrect identifications in mass spectrometry experiments. In imaging mass spectrometry, unresolved peaks can also result in composite images of multiple compounds, giving inaccurate depictions of molecular distributions. Gas-phase ion/ion reactions can be used to selectively react with specific chemical functional groups on a target analyte, thereby extracting it from a complex mixture and shifting its m/z value to an unobstructed region of the mass range. Herein, we use selective Schiff base formation via a novel charge inversion ion/ion reaction to purify phosphatidylserines from other isobaric (i.e., same nominal mass) lipids and reveal their singular distributions in imaging mass spectrometry. The selective Schiff base formation between singly deprotonated phosphatidylserine (PS) lipid anions and doubly charged N,N,N',N'-tetramethyl-N,N'-bis(6-oxohexyl)hexane-1,6-diaminium (TMODA) cations is performed using a modified commercial dual source hybrid Fourier transform ion cyclotron resonance (FTICR) mass spectrometer. This process is demonstrated using the isobaric lipids [PS 40:6 - H]- (m/z 834.528) and [SHexCer d38:1 - H]- (m/z 834.576), which produces [PS 40:6 + TMODA - H - H2O]+ (m/z 1186.879), and [SHexCer d38:1 + TMODA - H]+ (m/z 1204.938) product ions following the gas-phase charge inversion reaction. These product ions differ by roughly 18 Da in mass and are easily separated by low mass resolution analysis, while the isobaric precursor ions require roughly 45,000 mass resolving power (full-width at half maximum) to separate. Imaging mass spectrometry using targeted gas-phase ion/ion reactions shows distinct spatial distributions for the separated lipid product ions relative to the composite images of the unseparated precursor ions.

Tutorial: using nanoneedles for intracellular delivery

Intracellular delivery of advanced therapeutics, including biologicals and supramolecular agents, is complex because of the natural biological barriers that have evolved to protect the cell. Efficient delivery of therapeutic nucleic acids, proteins, peptides and nanoparticles is crucial for clinical adoption of emerging technologies that can benefit disease treatment through gene and cell therapy. Nanoneedles are arrays of vertical high-aspect-ratio nanostructures that can precisely manipulate complex processes at the cell interface, enabling effective intracellular delivery. This emerging technology has already enabled the development of efficient and non-destructive routes for direct access to intracellular environments and delivery of cell-impermeant payloads. However, successful implementation of this technology requires knowledge of several scientific fields, making it complex to access and adopt by researchers who are not directly involved in developing nanoneedle platforms. This presents an obstacle to the widespread adoption of nanoneedle technologies for drug delivery. This tutorial aims to equip researchers with the knowledge required to develop a nanoinjection workflow. It discusses the selection of nanoneedle devices, approaches for cargo loading and strategies for interfacing to biological systems and summarises an array of bioassays that can be used to evaluate the efficacy of intracellular delivery.

Accelerating biological insight for understudied genes

The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The vast majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

Antibody Identification for Antigen Detection in Formalin-Fixed Paraffin-Embedded Tissue Using Phage Display and Naïve Libraries

Immunohistochemistry is a widely used technique for research and diagnostic purposes that relies on the recognition by antibodies of antigens expressed in tissues. However, tissue processing and particularly formalin fixation affect the conformation of these antigens through the formation of methylene bridges. Although antigen retrieval techniques can partially restore antigen immunoreactivity, it is difficult to identify antibodies that can recognize their target especially in formalin-fixed paraffin-embedded tissues. Most of the antibodies currently used in immunohistochemistry have been obtained by animal immunization; however, in vitro display techniques represent alternative strategies that have not been fully explored yet. This review provides an overview of phage display-based antibody selections using naïve antibody libraries on various supports (fixed cells, dissociated tissues, tissue fragments, and tissue sections) that have led to the identification of antibodies suitable for immunohistochemistry.

Ethical Principles, Constraints and Opportunities in Clinical Proteomics

Recent advances in mass spectrometry (MS)-based proteomics have vastly increased the quality and scope of biological information that can be derived from human samples. These advances have rendered current workflows increasingly applicable in biomedical and clinical contexts. As proteomics is poised to take an important role in the clinic, associated ethical responsibilities increase in tandem with impacts on the health, privacy, and wellbeing of individuals. We conducted and here report a systematic literature review of ethical issues in clinical proteomics. We add our perspectives from a background of bioethics, the results of our accompanying paper extracting individual-sensitive results from patient samples, and the literature addressing similar issues in genomics. The spectrum of potential issues ranges from patient re-identification to incidental findings of clinical significance. The latter can be divided into actionable and unactionable findings. Some of these have the potential to be employed in discriminatory or privacy-infringing ways. However, incidental findings may also have great positive potential. A plasma proteome profile, for instance, could inform on the general health or disease status of an individual regardless of the narrow diagnostic question that prompted it. We suggest that early discussion of ethical issues in clinical proteomics can ensure that eventual healthcare practices and regulations reflect the considered judgment of the community and anticipate opportunities and problems that may arise as the technology matures.

Review of proteomics approach to eye diseases affecting the anterior segment

Visual impairment and blindness is a major health burden worldwide, and major ocular diseases causing visual impairment pertain to the anterior segment of the eye. Anterior segment ocular diseases are common, yet complex entities. Although many treatment options and surgical techniques are available for these ailments, the underlying cause and pathogenesis is still unclear. Finding ways to fundamentally treat these patients and rectify the underlying dysregulations leading to the disease may help cure patients completely without major complications. Proteomics approaches are a novel way to distinguish dysregulated proteins in a variety of biological tissues in a hypothesis-free manner, thus helping to find the responsible pathways leading to a certain disease. The aim of the current study is to review the available knowledge in scientific literature regarding the proteomics studies done on anterior segment eye diseases and suggest potential clinical implications to exploit the results of these studies. SIGNIFICANCE: Anterior segment ocular diseases are responsible for a major proportion of visual impairment and blindness worldwide. Although ophthalmologists have several treatment options that can alleviate or control the progression of these diseases, no definite cure is available for most of them. Moreover, because these diseases are progressive, prompt diagnosis is of utmost important. Proteomics studies enable us to identify and quantify the dysregulated proteins in a biological specimen in a hypothesis-free manner. Understanding the dysregulated protein pathways shines a light on the pathogenesis of the disease. Moreover, these dysregulated proteins may act as biomarkers to help in diagnosis and treatment follow-up. Hence, in this article we sought out to review the available scientific literature regarding the proteomics studies of anterior segment ocular diseases and to identify potential applications of proteomic studies in clinic.

Genes Regulated by HPV 16 E6 and High Expression of NFX1-123 in Cervical Cancers

Purpose

High-risk human papillomaviruses (HR HPV) cause cervical cancer, and in these cancers, HPV type 16 is the most common HR type. The HR viral oncogenes E6 and E7 partner with cellular proteins to drive cancer and modulate immune pathways; previously, we demonstrated in keratinocytes that HPV 16 E6 and high expression of the endogenous host protein partner NFX1-123 led to the increased expression of multiple genes, including Notch1, secretory leukocyte peptidase inhibitor (SLPI), and retinoic acid early transcript 1G (RAET1G). The present study was conducted to determine if NFX1-123 was highly expressed in cervical cancer and if genes increased by NFX1-123 and 16E6 in keratinocytes were also increased in cervical cancers.

Materials and Methods

The Cancer Genome Atlas (TCGA) database and The Human Protein Atlas database were used to compare relative mRNA and protein gene expression, respectively, in the normal cervix and cervical cancers. Formalin-fixed paraffin-embedded (FFPE) normal cervix and HPV 16 positive cervical cancer samples were analyzed for relative protein expression by immunohistochemical staining. Protein expression of a subset of regulated genes was quantified by Western blot of HPV positive and negative cell lines.

Results

Immunohistochemical staining of HPV 16 positive cervical dysplasias and cancers revealed high NFX1-123, Ki67, and Notch1 expression. NFX1 and NFX1L1 mRNA levels were increased in cervical cancers compared to normal cervix in the TCGA database. Fourteen genes previously identified as upregulated in keratinocytes with 16E6 and overexpressed NFX1-123 also had high mRNA expression and selected genes had high protein expression in cervical cancers and cell lines.

Conclusion

In cervical cancer, NFX1-123 is highly expressed, and 16E6 and NFX1-123 together alter the expression of a wide set of genes. The involvement of these genes in cell proliferation, differentiation, invasion, and metastasis provides further insight into potential ways that HR HPVs promote cancer initiation and maintenance.

Met-Flow, a strategy for single-cell metabolic analysis highlights dynamic changes in immune subpopulations

A complex interaction of anabolic and catabolic metabolism underpins the ability of leukocytes to mount an immune response. Their capacity to respond to changing environments by metabolic reprogramming is crucial to effector function. However, current methods lack the ability to interrogate this network of metabolic pathways at single-cell level within a heterogeneous population. We present Met-Flow, a flow cytometry-based method capturing the metabolic state of immune cells by targeting key proteins and rate-limiting enzymes across multiple pathways. We demonstrate the ability to simultaneously measure divergent metabolic profiles and dynamic remodeling in human peripheral blood mononuclear cells. Using Met-Flow, we discovered that glucose restriction and metabolic remodeling drive the expansion of an inflammatory central memory T cell subset. This method captures the complex metabolic state of any cell as it relates to phenotype and function, leading to a greater understanding of the role of metabolic heterogeneity in immune responses.

Patricia Ahl et al. present Met-Flow, a flow cytometry-based approach for capturing the metabolic state of immune cells across multiple pathways. The authors apply Met-Flow to a central memory T cell subset, showing the importance of glucose restriction and metabolic reprogramming to T cell polarization and expansion.

Maspin as a Prognostic Marker for Early Stage Colorectal Cancer With Microsatellite Instability

Colorectal cancers are among the most common cancers and a leading cause of cancer death. In our pursuit to discover molecular markers for better characterization and precision theranostics of these cancers, we first conducted global deep proteome analyses and identified maspin (serpin B5, peptidase inhibitor 5) as an upregulated protein in tumor tissue. We then validated its expression in a large cohort of 743 patients with colorectal cancers of all stages and found that both cytoplasmic and nuclear expression varied widely between different patients. Comparison with clinicopathological features revealed that maspin expression levels correlate significantly only with mismatch repair (MMR) status but not with other features. To elucidate the prognostic significance of maspin, we analyzed two outcome-annotated cohorts, one of 572 early stage cancer patients and another of 93 late stage cancer patients. Kaplan–Meier survival, univariate, and multivariate analyses revealed that maspin overexpression predicts longer overall and disease-free survival for early stage microsatellite instability (MSI) subtype colorectal cancer, but there is no correlation with survival for patients with early stage cancer of the microsatellite stability (MSS) subtype or late stage cancer. Our study identifies maspin expression as an independent prognostic marker for risk stratification of early stage MSI subtype colorectal cancer and may provide guidance for improved therapeutic management.

Application of Solid-State Nanopore in Protein Detection

A protein is a kind of major biomacromolecule of life. Its sequence, structure, and content in organisms contains quite important information for normal or pathological physiological process. However, research of proteomics is facing certain obstacles. Only a few technologies are available for protein analysis, and their application is limited by chemical modification or the need for a large amount of sample. Solid-state nanopore overcomes some shortcomings of the existing technology, and has the ability to detect proteins at a single-molecule level, with its high sensitivity and robustness of device. Many works on detection of protein molecules and discriminating structure have been carried out in recent years. Single-molecule protein sequencing techniques based on solid-state nanopore are also been proposed and developed. Here, we categorize and describe these efforts and progress, as well as discuss their advantages and drawbacks.

Boosting Natural Killer Cell-Mediated Targeting of Sarcoma Through DNAM-1 and NKG2D

Sarcomas are malignancies of mesenchymal origin that occur in bone and soft tissues. Many are chemo- and radiotherapy resistant, thus conventional treatments fail to increase overall survival. Natural Killer (NK) cells exert anti-tumor activity upon detection of a complex array of tumor ligands, but this has not been thoroughly explored in the context of sarcoma immunotherapy. In this study, we investigated the NK cell receptor/ligand immune profile of primary human sarcoma explants. Analysis of tumors from 32 sarcoma patients identified the proliferative marker PCNA and DNAM-1 ligands CD112 and/or CD155 as commonly expressed antigens that could be efficiently targeted by genetically modified (GM) NK cells. Despite the strong expression of CD112 and CD155 on sarcoma cells, characterization of freshly dissociated sarcomas revealed a general decrease in tumor-infiltrating NK cells compared to the periphery, suggesting a defect in the endogenous NK cell response. We also applied a functional screening approach to identify relevant NK cell receptor/ligand interactions that induce efficient anti-tumor responses using a panel NK-92 cell lines GM to over-express 12 different activating receptors. Using GM NK-92 cells against primary sarcoma explants (n = 12) revealed that DNAM-1 over-expression on NK-92 cells led to efficient degranulation against all tested explants (n = 12). Additionally, NKG2D over-expression showed enhanced responses against 10 out of 12 explants. These results show that DNAM-1⁺ or NKG2D⁺ GM NK-92 cells may be an efficient approach in targeting sarcomas. The degranulation capacity of GM NK-92 cell lines was also tested against various established tumor cell lines, including neuroblastoma, Schwannoma, melanoma, myeloma, leukemia, prostate, pancreatic, colon, and lung cancer. Enhanced degranulation of DNAM-1⁺ or NKG2D⁺ GM NK-92 cells was observed against the majority of tumor cell lines tested. In conclusion, DNAM-1 or NKG2D over-expression elicited a dynamic increase in NK cell degranulation against all sarcoma explants and cancer cell lines tested, including those that failed to induce a notable response in WT NK-92 cells. These results support the broad therapeutic potential of DNAM-1⁺ or NKG2D⁺ GM NK-92 cells and GM human NK cells for the treatment of sarcomas and other malignancies.

RAB6C is an independent prognostic factor of estrogen receptor-positive/progesterone receptor-negative breast cancer

The majority of breast cancer tumors are estrogen receptor-positive (ER⁺) and can be treated with endocrine therapy. However, certain patients may exhibit a good prognosis without systemic treatment. The aim of the present study was to identify novel prognostic factors for patients with ER⁺ breast cancer tumors using gene copy data, and to investigate if these factors have prognostic value in subgroups categorized by progesterone receptor status (PR). Public data, including the whole genome gene copy data of 199 systemically untreated patients with ER⁺ tumors, were utilized in the present study. To assess prognostic value, patients were divided into two groups using the median gene copy number as a cut-off for the SNPs that were the most variable. One SNP was identified, which indicated that the Ras-related protein Rab-6C (RAB6C) gene may exhibit prognostic significance. Therefore, RAB6C protein expression was subsequently investigated in a second independent cohort, consisting of 469 systematically untreated patients (of which 310 were ER⁺) who received long term follow-up. In the public data set, a distant recurrence risk reduction of 55% was determined for copy numbers above the median value of RAB6C compared with numbers below [multivariable adjusted hazard ratio (HR), 0.45; 95% CI 0.28–0.72; P=0.001)]. It was also more pronounced in the ER⁺/PR⁻ subgroup (HR, 0.15; 95% CI, 0.05–0.46; P=0.001). In the second cohort, patients of the ER⁺/PR⁻ subgroup who exhibited high RAB6C expression had a reduced distant recurrence risk (HR, 0.17; 95% CI, 0.05–0.60; P=0.006). However, this was not identified among ER⁺/PR⁺ tumors (HR, 1.31; 95% CI, 0.69–2.48; P=0.41). The results of the present study indicated that RAB6C serves as an independent prognostic factor of distant recurrence risk in systemically untreated patients with an ER⁺/PR⁻ tumor.

Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry

Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces-interfaces-of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.

Intact Transition Epitope Mapping - Thermodynamic Weak-force Order (ITEM - TWO)

We have developed an electrospray mass spectrometry method which is capable to determine antibody affinity in a gas phase experiment. A solution with the immune complex is electrosprayed and multiply charged ions are translated into the gas phase. Then, the intact immune-complex ions are separated from unbound peptide ions. Increasing the voltage difference in a collision cell results in collision induced dissociation of the immune-complex by which bound peptide ions are released. When analyzing a peptide mixture, measuring the mass of the complex-released peptide ions identifies which of the peptides contains the epitope. A step-wise increase in the collision cell voltage difference changes the intensity ratios of the surviving immune complex ions, the released peptide ions, and the antibody ions. From all the ions´ normalized intensity ratios are deduced the thermodynamic quasi equilibrium dissociation constants (K_Dm0g^#) from which are calculated the apparent gas phase Gibbs energies of activation over temperature (ΔG_m0g^#T). The order of the apparent gas phase dissociation constants of four antibody - epitope peptide pairs matched well with those obtained from in-solution measurements. The determined gas phase values for antibody affinities are independent from the source of the investigated peptides and from the applied instrument. Data are available via ProteomeXchange with identifier PXD016024. SIGNIFICANCE: ITEM - TWO enables rapid epitope mapping and determination of apparent dissociation energies of immune complexes with minimal in-solution handling. Mixing of antibody and antigen peptide solutions initiates immune complex formation in solution. Epitope binding strengths are determined in the gas phase after electrospraying the antibody / antigen peptide mixtures and mass spectrometric analysis of immune complexes under different collision induced dissociation conditions. Since the order of binding strengths in the gas phase is the same as that in solution, ITEM - TWO characterizes two most important antibody properties, specificity and affinity.

Dissecting the expression landscape of cytochromes P450 in hepatocellular carcinoma: towards novel molecular biomarkers

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths around the world. Recent advances in genomic technologies have allowed the identification of various molecular signatures in HCC tissues. For instance, differential gene expression levels of various cytochrome P450 genes (CYP450) have been reported in studies performed on limited numbers of HCC tissue samples, or focused on a small subset on CYP450s. In the present study, we monitored the expression landscape of all the members of the CYP450 family (57 genes) in more than 200 HCC tissues using RNA-Seq data from The Cancer Genome Atlas. Using stringent statistical filters and data from paired tissues, we identified significantly dysregulated CYP450 genes in HCC. Moreover, the expression level of selected CYP450s was validated by qPCR on cDNA samples from an independent cohort. Threshold values (sensitivity and specificity) based on dysregulated gene expression were also determined to allow for confident identification of HCC tissues. Finally, a global look at expression levels of the 57 members of the CYP450 family across ten different cancer types revealed specific expression signatures. Overall, this study provides useful information on the transcriptomic landscape of CYP450 genes in HCC and on new potential HCC biomarkers.

Targeted Analysis of Serum Proteins Encoded at Known Inflammatory Bowel Disease Risk Loci

BACKGROUND

Few studies have investigated the blood proteome of inflammatory bowel disease (IBD). We characterized the serum abundance of proteins encoded at 163 known IBD risk loci and tested these proteins for their biomarker discovery potential.

METHODS

Based on the Human Protein Atlas (HPA) antibody availability, 218 proteins from genes mapping at 163 IBD risk loci were selected. Targeted serum protein profiles from 49 Crohn's disease (CD) patients, 51 ulcerative colitis (UC) patients, and 50 sex- and age-matched healthy individuals were obtained using multiplexed antibody suspension bead array assays. Differences in relative serum abundance levels between disease groups and controls were examined. Replication was attempted for CD-UC comparisons (including disease subtypes) by including 64 additional patients (33 CD and 31 UC). Antibodies targeting a potentially novel risk protein were validated by paired antibodies, Western blot, immuno-capture mass spectrometry, and epitope mapping.

RESULTS

By univariate analysis, 13 proteins mostly related to neutrophil, T-cell, and B-cell activation and function were differentially expressed in IBD patients vs healthy controls, 3 in CD patients vs healthy controls and 2 in UC patients vs healthy controls (q < 0.01). Multivariate analyses further differentiated disease groups from healthy controls and CD subtypes from UC (P < 0.05). Extended characterization of an antibody targeting a novel, discriminative serum marker, the laccase (multicopper oxidoreductase) domain containing 1 (LACC1) protein, provided evidence for antibody on-target specificity.

CONCLUSIONS

Using affinity proteomics, we identified a set of IBD-associated serum proteins encoded at IBD risk loci. These candidate proteins hold the potential to be exploited as diagnostic biomarkers of IBD.

Direct Profiling the Post-Translational Modification Codes of a Single Protein Immobilized on a Surface Using Cu-free Click Chemistry

Combinatorial post-translational modifications (PTMs), which can serve as dynamic "molecular barcodes", have been proposed to regulate distinct protein functions. However, studies of combinatorial PTMs on single protein molecules have been hindered by a lack of suitable analytical methods. Here, we describe erasable single-molecule blotting (eSiMBlot) for combinatorial PTM profiling. This assay is performed in a highly multiplexed manner and leverages the benefits of covalent protein immobilization, cyclic probing with different antibodies, and single molecule fluorescence imaging. Especially, facile and efficient covalent immobilization on a surface using Cu-free click chemistry permits multiple rounds (>10) of antibody erasing/reprobing without loss of antigenicity. Moreover, cumulative detection of coregistered multiple data sets for immobilized single-epitope molecules, such as HA peptide, can be used to increase the antibody detection rate. Finally, eSiMBlot enables direct visualization and quantitative profiling of combinatorial PTM codes at the single-molecule level, as we demonstrate by revealing the novel phospho-codes of ligand-induced epidermal growth factor receptor. Thus, eSiMBlot provides an unprecedentedly simple, rapid, and versatile platform for analyzing the vast number of combinatorial PTMs in biological pathways.

Mass spectrometric epitope mapping

Mass spectrometric epitope mapping has become a versatile method to precisely determine a soluble antigen's partial structure that directly interacts with an antibody in solution. Typical lengths of investigated antigens have increased up to several 100 amino acids while experimentally determined epitope peptides have decreased in length to on average 10-15 amino acids. Since the early 1990s more and more sophisticated methods have been developed and have forwarded a bouquet of suitable approaches for epitope mapping with immobilized, temporarily immobilized, and free-floating antibodies. While up to now monoclonal antibodies have been mostly used in epitope mapping experiments, the applicability of polyclonal antibodies has been proven. The antibody's resistance towards enzymatic proteolysis has been of key importance for the two mostly applied methods: epitope excision and epitope extraction. Sample consumption has dropped to low pmol amounts on both, the antigen and the antibody. While adequate in-solution sample handling has been most important for successful epitope mapping, mass spectrometric analysis has been found the most suitable read-out method from early on. The rapidity by which mass spectrometric epitope mapping nowadays is executed outperforms all alternative methods. Thus, it can be asserted that mass spectrometric epitope mapping has reached a state of maturity, which allows it to be used in any mass spectrometry laboratory. After 25 years of constant and steady improvements, its application to clinical samples, for example, for patient characterization and stratification, is anticipated in the near future. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:229-241, 2018.

Visualizing Single-Cell Secretion Dynamics with Single-Protein Sensitivity

Cellular secretion of proteins into the extracellular environment is an essential mediator of critical biological mechanisms, including cell-to-cell communication, immunological response, targeted delivery, and differentiation. Here, we report a novel methodology that allows for the real-time detection and imaging of single unlabeled proteins that are secreted from individual living cells. This is accomplished via interferometric detection of scattered light (iSCAT) and is demonstrated with Laz388 cells, an Epstein-Barr virus (EBV)-transformed B cell line. We find that single Laz388 cells actively secrete IgG antibodies at a rate of the order of 100 molecules per second. Intriguingly, we also find that other proteins and particles spanning ca. 100 kDa-1 MDa are secreted from the Laz388 cells in tandem with IgG antibody release, likely arising from EBV-related viral proteins. The technique is general and, as we show, can also be applied to studying the lysate of a single cell. Our results establish label-free iSCAT imaging as a powerful tool for studying the real-time exchange between cells and their immediate environment with single-protein sensitivity.

A PLA-iRoCS Pipeline for the Localization of Protein-Protein Interactions In Situ

In plants as well as other organisms, protein localization alone is insufficient to provide a mechanistic link between stimulus and process regulation. This is because protein-protein interactions are central to the regulation of biological processes. However, they remain very difficult to detect in situ, with the choice of tools for the detection of protein-protein interaction in situ still in need of expansion. Here, we provide a protocol for the detection and accurate localization of protein interactions based on the combination of a whole-mount proximity ligation assay and iRoCS, a coordinate system able to standardize subtle differences between the architecture of individual Arabidopsis roots.

The roles of intrinsic disorder-based liquid-liquid phase transitions in the "Dr. Jekyll-Mr. Hyde" behavior of proteins involved in amyotrophic lateral sclerosis and frontotemporal lobar degeneration

Pathological developments leading to amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are associated with misbehavior of several key proteins, such as SOD1 (superoxide dismutase 1), TARDBP/TDP-43, FUS, C9orf72, and dipeptide repeat proteins generated as a result of the translation of the intronic hexanucleotide expansions in the C9orf72 gene, PFN1 (profilin 1), GLE1 (GLE1, RNA export mediator), PURA (purine rich element binding protein A), FLCN (folliculin), RBM45 (RNA binding motif protein 45), SS18L1/CREST, HNRNPA1 (heterogeneous nuclear ribonucleoprotein A1), HNRNPA2B1 (heterogeneous nuclear ribonucleoprotein A2/B1), ATXN2 (ataxin 2), MAPT (microtubule associated protein tau), and TIA1 (TIA1 cytotoxic granule associated RNA binding protein). Although these proteins are structurally and functionally different and have rather different pathological functions, they all possess some levels of intrinsic disorder and are either directly engaged in or are at least related to the physiological liquid-liquid phase transitions (LLPTs) leading to the formation of various proteinaceous membrane-less organelles (PMLOs), both normal and pathological. This review describes the normal and pathological functions of these ALS- and FTLD-related proteins, describes their major structural properties, glances at their intrinsic disorder status, and analyzes the involvement of these proteins in the formation of normal and pathological PMLOs, with the ultimate goal of better understanding the roles of LLPTs and intrinsic disorder in the "Dr. Jekyll-Mr. Hyde" behavior of those proteins.

Intact Transition Epitope Mapping (ITEM)

Intact transition epitope mapping (ITEM) enables rapid and accurate determination of protein antigen-derived epitopes by either epitope extraction or epitope excision. Upon formation of the antigen peptide-containing immune complex in solution, the entire mixture is electrosprayed to translate all constituents as protonated ions into the gas phase. There, ions from antibody-peptide complexes are separated from unbound peptide ions according to their masses, charges, and shapes either by ion mobility drift or by quadrupole ion filtering. Subsequently, immune complexes are dissociated by collision induced fragmentation and the ion signals of the "complex-released peptides," which in effect are the epitope peptides, are recorded in the time-of-flight analyzer of the mass spectrometer. Mixing of an antibody solution with a solution in which antigens or antigen-derived peptides are dissolved is, together with antigen proteolysis, the only required in-solution handling step. Simplicity of sample handling and speed of analysis together with very low sample consumption makes ITEM faster and easier to perform than other experimental epitope mapping methods. Graphical Abstract ᅟ.

Channel of viral DNA packaging motor for real time kinetic analysis of peptide oxidation states

Nanopore technology has become a powerful tool in single molecule sensing, and protein nanopores appear to be more advantageous than synthetic counterparts with regards to channel amenability, structure homogeneity, and production reproducibility. However, the diameter of most of the well-studied protein nanopores is too small to allow the passage of protein or peptides that are typically in multiple nanometers scale. The portal channel from bacteriophage SPP1 has a large channel size that allows the translocation of peptides with higher ordered structures. Utilizing single channel conductance assay and optical single molecule imaging, we observed translocation of peptides and quantitatively analyzed the dynamics of peptide oligomeric states in real-time at single molecule level. The oxidative and the reduced states of peptides were clearly differentiated based on their characteristic electronic signatures. A similar Gibbs free energy (ΔG0) was obtained when different concentrations of substrates were applied, suggesting that the use of SPP1 nanopore for real-time quantification of peptide oligomeric states is feasible. With the intrinsic nature of size and conjugation amenability, the SPP1 nanopore has the potential for development into a tool for the quantification of peptide and protein structures in real time.

Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview and Best Practices

Here, we present two perspectives on the task of predicting post translational modifications (PTMs) from local sequence fragments using machine learning algorithms. The first is the description of the fundamental steps required to construct a PTM predictor from the very beginning. These steps include data gathering, feature extraction, or machine-learning classifier selection. The second part of our work contains the detailed discussion of more advanced problems which are encountered in PTM prediction task. Probably the most challenging issues which we have covered here are: (1) how to address the training data class imbalance problem (we also present statistics describing the problem); (2) how to properly set up cross-validation folds with an approach which takes into account the homology of protein data records, to address this problem we present our folds-over-clusters algorithm; and (3) how to efficiently reach for new sources of learning features. Presented techniques and notes resulted from intense studies in the field, performed by our and other groups, and can be useful both for researchers beginning in the field of PTM prediction and for those who want to extend the repertoire of their research techniques.

Multiplexed and scalable super-resolution imaging of three-dimensional protein localization in size-adjustable tissues

The biology of multicellular organisms is coordinated across multiple size scales, from the subnanoscale of molecules to the macroscale, tissue-wide interconnectivity of cell populations. Here we introduce a method for super-resolution imaging of the multiscale organization of intact tissues. The method, called magnified analysis of the proteome (MAP), linearly expands entire organs fourfold while preserving their overall architecture and three-dimensional proteome organization. MAP is based on the observation that preventing crosslinking within and between endogenous proteins during hydrogel-tissue hybridization allows for natural expansion upon protein denaturation and dissociation. The expanded tissue preserves its protein content, its fine subcellular details, and its organ-scale intercellular connectivity. We use off-the-shelf antibodies for multiple rounds of immunolabeling and imaging of a tissue's magnified proteome, and our experiments demonstrate a success rate of 82% (100/122 antibodies tested). We show that specimen size can be reversibly modulated to image both inter-regional connections and fine synaptic architectures in the mouse brain.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

Antibodies as means for selective mass spectrometry

For protein analysis of biological samples, two major strategies are used today; mass spectrometry (MS) and antibody-based methods. Each strategy offers advantages and drawbacks. However, combining the two using an immunoenrichment step with MS analysis brings together the benefits of each method resulting in increased sensitivity, faster analysis and possibility of higher degrees of multiplexing. The immunoenrichment can be performed either on protein or peptide level and quantification standards can be added in order to enable determination of the absolute protein concentration in the sample. The combination of immunoenrichment and MS holds great promise for the future in both proteomics and clinical diagnostics. This review describes different setups of immunoenrichment coupled to mass spectrometry and how these can be utilized in various applications.

GenomewidePDB 2.0: A Newly Upgraded Versatile Proteogenomic Database for the Chromosome-Centric Human Proteome Project

Since the launch of the Chromosome-centric Human Proteome Project (C-HPP) in 2012, the number of "missing" proteins has fallen to 2932, down from ∼5932 since the number was first counted in 2011. We compared the characteristics of missing proteins with those of already annotated proteins with respect to transcriptional expression pattern and the time periods in which newly identified proteins were annotated. We learned that missing proteins commonly exhibit lower levels of transcriptional expression and less tissue-specific expression compared with already annotated proteins. This makes it more difficult to identify missing proteins as time goes on. One of the C-HPP goals is to identify alternative spliced product of proteins (ASPs), which are usually difficult to find by shot-gun proteomic methods due to their sequence similarities with the representative proteins. To resolve this problem, it may be necessary to use a targeted proteomics approach (e.g., selected and multiple reaction monitoring [S/MRM] assays) and an innovative bioinformatics platform that enables the selection of target peptides for rarely expressed missing proteins or ASPs. Given that the success of efforts to identify missing proteins may rely on more informative public databases, it was necessary to upgrade the available integrative databases. To this end, we attempted to improve the features and utility of GenomewidePDB by integrating transcriptomic information (e.g., alternatively spliced transcripts), annotated peptide information, and an advanced search interface that can find proteins of interest when applying a targeted proteomics strategy. This upgraded version of the database, GenomewidePDB 2.0, may not only expedite identification of the remaining missing proteins but also enhance the exchange of information among the proteome community. GenomewidePDB 2.0 is available publicly at http://genomewidepdb.proteomix.org/.

Transformative Impact of Proteomics on Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association

The year 2014 marked the 20th anniversary of the coining of the term proteomics. The purpose of this scientific statement is to summarize advances over this period that have catalyzed our capacity to address the experimental, translational, and clinical implications of proteomics as applied to cardiovascular health and disease and to evaluate the current status of the field. Key successes that have energized the field are delineated; opportunities for proteomics to drive basic science research, facilitate clinical translation, and establish diagnostic and therapeutic healthcare algorithms are discussed; and challenges that remain to be solved before proteomic technologies can be readily translated from scientific discoveries to meaningful advances in cardiovascular care are addressed. Proteomics is the result of disruptive technologies, namely, mass spectrometry and database searching, which drove protein analysis from 1 protein at a time to protein mixture analyses that enable large-scale analysis of proteins and facilitate paradigm shifts in biological concepts that address important clinical questions. Over the past 20 years, the field of proteomics has matured, yet it is still developing rapidly. The scope of this statement will extend beyond the reaches of a typical review article and offer guidance on the use of next-generation proteomics for future scientific discovery in the basic research laboratory and clinical settings.

The beta-cell in type 1 diabetes: What have we learned from proteomic studies?

Pancreatic beta-cells have a crucial role in the regulation of blood glucose homeostasis by the production and secretion of insulin. In type 1 diabetes (T1D), an autoimmune reaction against the beta-cells together with the presence of inflammatory cytokines and ROS in the islets leads to beta-cell dysfunction and death. This review gives an overview of proteomic studies that lead to better understanding of beta-cell functioning in T1D. Protein profiling of isolated islets and beta-cell lines in health and T1D contributed to the unraveling of pathways involved in cytokine-induced cell death. In addition, by studying the serological proteome of T1D patients, new biomarkers and beta-cell autoantigens were discovered, which may improve screening tests and follow-up of T1D development. Interestingly, an important role for PTMs was demonstrated in the generation of beta-cell autoantigens. To conclude, proteomic techniques are of indispensable value to improve the knowledge on beta-cell function in T1D and the search toward therapeutic targets.

Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells--a benchmarking study

BACKGROUND

Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples.

RESULTS

Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs.

CONCLUSIONS

Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.

Analysis of candidate genes for lineage-specific expression changes in humans and primates

RUNX2, a gene involved in skeletal development, has previously been shown to be potentially affected by positive selection during recent human evolution. Here we have used antibody-based proteomics to characterize potential differences in expression patterns of RUNX2 interacting partners during primate evolution. Tissue microarrays consisting of a large set of normal tissues from human and macaque were used for protein profiling of 50 RUNX2 partners with immunohistochemistry. Eleven proteins (AR, CREBBP, EP300, FGF2, HDAC3, JUN, PRKD3, RUNX1, SATB2, TCF3, and YAP1) showed differences in expression between humans and macaques. These proteins were further profiled in tissues from chimpanzee, gorilla, and orangutan, and the corresponding genes were analyzed with regard to genomic features. Moreover, protein expression data were compared with previously obtained RNA sequencing data from six different organs. One gene (TCF3) showed significant expression differences between human and macaque at both the protein and RNA level, with higher expression in a subset of germ cells in human testis compared with macaque. In conclusion, normal tissues from macaque and human showed differences in expression of some RUNX2 partners that could be mapped to various defined cell types. The applied strategy appears advantageous to characterize the consequences of altered genes selected during evolution.

Big data analytics in immunology: a knowledge-based approach

With the vast amount of immunological data available, immunology research is entering the big data era. These data vary in granularity, quality, and complexity and are stored in various formats, including publications, technical reports, and databases. The challenge is to make the transition from data to actionable knowledge and wisdom and bridge the knowledge gap and application gap. We report a knowledge-based approach based on a framework called KB-builder that facilitates data mining by enabling fast development and deployment of web-accessible immunological data knowledge warehouses. Immunological knowledge discovery relies heavily on both the availability of accurate, up-to-date, and well-organized data and the proper analytics tools. We propose the use of knowledge-based approaches by developing knowledgebases combining well-annotated data with specialized analytical tools and integrating them into analytical workflow. A set of well-defined workflow types with rich summarization and visualization capacity facilitates the transformation from data to critical information and knowledge. By using KB-builder, we enabled streamlining of normally time-consuming processes of database development. The knowledgebases built using KB-builder will speed up rational vaccine design by providing accurate and well-annotated data coupled with tailored computational analysis tools and workflow.

Single-molecule spectroscopy of amino acids and peptides by recognition tunnelling

The human proteome has millions of protein variants due to alternative RNA splicing and post-translational modifications, and variants that are related to diseases are frequently present in minute concentrations. For DNA and RNA, low concentrations can be amplified using the polymerase chain reaction, but there is no such reaction for proteins. Therefore, the development of single-molecule protein sequencing is a critical step in the search for protein biomarkers. Here, we show that single amino acids can be identified by trapping the molecules between two electrodes that are coated with a layer of recognition molecules, then measuring the electron tunnelling current across the junction. A given molecule can bind in more than one way in the junction, and we therefore use a machine-learning algorithm to distinguish between the sets of electronic 'fingerprints' associated with each binding motif. With this recognition tunnelling technique, we are able to identify D and L enantiomers, a methylated amino acid, isobaric isomers and short peptides. The results suggest that direct electronic sequencing of single proteins could be possible by sequentially measuring the products of processive exopeptidase digestion, or by using a molecular motor to pull proteins through a tunnel junction integrated with a nanopore.

Immunohistochemical LRIG3 expression in cervical intraepithelial neoplasia and invasive squamous cell cervical cancer: association with expression of tumor markers, hormones, high-risk HPV-infection, smoking and patient outcome

The novel biomarker LRIG3 is a member of the LRIG family (LRIG1-3). While LRIG1 has been associated with favorable prognosis and LRIG2 with poor prognosis in invasive cervical cancer, little is known about the role of LRIG3. The aim of this study was to investigate the expression of LRIG3 in invasive cancer and cervical intraepithelial neoplasia (CIN) for possible correlation with other tumor markers, to hormones and smoking, as a diagnostic adjunct in CIN, and prognostic value in invasive cancer. Cervical biopsies from 129 patients with invasive squamous cell carcinoma and 170 biopsies showing low grade and high grade CIN, or normal epithelium were stained for LRIG3 and 17 additional tumor markers. Among other variables the following were included: smoking habits, hormonal contraceptive use, serum progesterone, serum estradiol, high-risk HPV-infection, menopausal status and ten-year survival. In CIN, high expression of the tumor suppressors retinoblastoma protein, p53, and p16, and Ecadherin (cell-cell interaction), or low expression of CK10, correlated to LRIG3 expression. In addition, progestogenic contraceptive use correlated to high expression of LRIG3. In invasive cancer there was a correlation between expression of the major tumor promoter c-myc and high LRIG3 expression. High LRIG3 expression correlated significantly to presence of high-risk HPV infection in patients with normal epithelium and CIN. There was no correlation between LRIG3 expression and 10-year survival in patients with invasive cell cervical cancer. LRIG3 expression is associated with a number of molecular events in CIN. Expression also correlates to hormonal contraceptive use. The results on expression of other tumor markers suggest that LRIG3 is influenced by or influences a pattern of tumor markers in cancer and precancerous cells. Further studies are needed to elucidate if LRIG3 expression might be clinically useful.

The Use of Antibodies in Small-Molecule Drug Discovery

Antibodies are powerful research tools that can be used in many areas of biology to probe, measure, and perturb various biological structures. Successful drug discovery is dependent on the correct identification of a target implicated in disease, coupled with the successful selection, optimization, and development of a candidate drug. Because of their specific binding characteristics, with regard to specificity, affinity, and avidity, coupled with their amenability to protein engineering, antibodies have become a key tool in drug discovery, enabling the quantification, localization, and modulation of proteins of interest. This review summarizes the application of antibodies and other protein affinity reagents as specific research tools within the drug discovery process.

Alternative mammalian target of rapamycin (mTOR) signal activation in sorafenib-resistant hepatocellular carcinoma cells revealed by array-based pathway profiling

Sorafenib is a multi-kinase inhibitor that has been proven effective for the treatment of unresectable hepatocellular carcinoma (HCC). However, its precise mechanisms of action and resistance have not been well established. We have developed high-density fluorescence reverse-phase protein arrays and used them to determine the status of 180 phosphorylation sites of signaling molecules in the 120 pathways registered in the NCI-Nature curated database in 23 HCC cell lines. Among the 180 signaling nodes, we found that the level of ribosomal protein S6 phosphorylated at serine residue 235/236 (p-RPS6 S235/236) was most significantly correlated with the resistance of HCC cells to sorafenib. The high expression of p-RPS6 S235/236 was confirmed immunohistochemically in biopsy samples obtained from HCC patients who responded poorly to sorafenib. Sorafenib-resistant HCC cells showed constitutive activation of the mammalian target of rapamycin (mTOR) pathway, but whole-exon sequencing of kinase genes revealed no evident alteration in the pathway. p-RPS6 S235/236 is a potential biomarker that predicts unresponsiveness of HCC to sorafenib. The use of mTOR inhibitors may be considered for the treatment of such tumors.

Proteomic profiling of the tumor microenvironment: recent insights and the search for biomarkers

Although gain of oncogene functions and loss of tumor suppressor functions are driving forces in tumor development, the tumor microenvironment, comprising the extracellular matrix, surrounding stroma, signaling molecules and infiltrating immune and other cell populations, is now also recognized as crucial to tumor development and metastasis. Many interactions at the tumor cell-environment interface occur at the protein level. Proteomic approaches are contributing to the definition of the protein constituents of the microenvironment and their sources, modifications, interactions and turnover, as well as providing information on how these features relate to tumor development and progression. Recently, proteomic studies have revealed how cancer cells modulate the microenvironment through their secreted proteins and how they can alter their protein constituents to adapt to the microenvironment. Moreover, the release of proteins from the microenvironment into the circulatory system has relevance for the development of blood-based cancer diagnostics. Here, we review how proteomic approaches are being applied to studies of the tumor microenvironment to decipher tumor-stroma interactions and to elucidate the role of host cells in the tumor microenvironment.

A chromosome-centric analysis of antibodies directed toward the human proteome using Antibodypedia

Antibodies are crucial for the study of human proteins and have been defined as one of the three pillars in the human chromosome-centric Human Proteome Project (C-HPP). In this article the chromosome-centric structure has been used to analyze the availability of antibodies as judged by the presence within the portal Antibodypedia, a database designed to allow comparisons and scoring of publicly available antibodies toward human protein targets. This public database displays antibody data from more than one million antibodies toward human protein targets. A summary of the content in this knowledge resource reveals that there exist more than 10 antibodies to over 70% of all the putative human genes, evenly distributed over the 24 human chromosomes. The analysis also shows that at present, less than 10% of the putative human protein-coding genes (n = 1882) predicted from the genome sequence lack antibodies, suggesting that focused efforts from the antibody-based and mass spectrometry-based proteomic communities should be encouraged to pursue the analysis of these missing proteins. We show that Antibodypedia may be used to track the development of available and validated antibodies to the individual chromosomes, and thus the database is an attractive tool to identify proteins with no or few antibodies yet generated.

Protein Quantification by MRM for Biomarker Validation

In this chapter we describe how mass spectrometry-based quantitative protein measurements by multiple reaction monitoring (MRM) have opened up the opportunity for the assembly of large panels of candidate protein biomarkers that can be simultaneously validated in large clinical cohorts to identify diagnostic protein biomarker signatures. We outline a workflow in which candidate protein biomarker panels are initially assembled from multiple diverse sources of discovery data, including proteomics and transcriptomics experiments, as well as from candidates found in the literature. Subsequently, the individual candidates in these large panels may be prioritised by application of a range of bioinformatics tools to generate a refined panel for which MRM assays may be developed. We describe a process for MRM assay design and implementation, and illustrate how the data generated from these multiplexed MRM measurements of prioritised candidates may be subjected to a range of statistical tools to create robust biomarker signatures for further clinical validation in large patient sample cohorts. Through this overall approach MRM has the potential to not only support individual biomarker validation but also facilitate the development of clinically useful protein biomarker signatures.

Proteomic approaches and identification of novel therapeutic targets for alcoholism

Recent studies have shown that gene regulation is far more complex than previously believed and does not completely explain changes at the protein level. Therefore, the direct study of the proteome, considerably different in both complexity and dynamicity to the genome/transcriptome, has provided unique insights to an increasing number of researchers. During the past decade, extraordinary advances in proteomic techniques have changed the way we can analyze the composition, regulation, and function of protein complexes and pathways underlying altered neurobiological conditions. When combined with complementary approaches, these advances provide the contextual information for decoding large data sets into meaningful biologically adaptive processes. Neuroproteomics offers potential breakthroughs in the field of alcohol research by leading to a deeper understanding of how alcohol globally affects protein structure, function, interactions, and networks. The wealth of information gained from these advances can help pinpoint relevant biomarkers for early diagnosis and improved prognosis of alcoholism and identify future pharmacological targets for the treatment of this addiction.

Quantitative and qualitative proteome characteristics extracted from in-depth integrated genomics and proteomics analysis

Quantitative and qualitative protein characteristics are regulated at genomic, transcriptomic, and posttranscriptional levels. Here, we integrated in-depth transcriptome and proteome analyses of liver tissues from two rat strains to unravel the interactions within and between these layers. We obtained peptide evidence for 26,463 rat liver proteins. We validated 1,195 gene predictions, 83 splice events, 126 proteins with nonsynonymous variants, and 20 isoforms with nonsynonymous RNA editing. Quantitative RNA sequencing and proteomics data correlate highly between strains but poorly among each other, indicating extensive nongenetic regulation. Our multilevel analysis identified a genomic variant in the promoter of the most differentially expressed gene Cyp17a1, a previously reported top hit in genome-wide association studies for human hypertension, as a potential contributor to the hypertension phenotype in SHR rats. These results demonstrate the power of and need for integrative analysis for understanding genetic control of molecular dynamics and phenotypic diversity in a system-wide manner.