Affinity Proteomics Exploration of Melanoma Identifies Proteins in Serum with Associations to T-Stage and Recurrence

BACKGROUND: Blood-based proteomic profiling may aid and expand our understanding of diseases and their different phenotypes. The aim of the presented study was to profile serum samples from patients with malignant melanoma using affinity proteomic assays to describe proteins in the blood stream that are associated to stage or recurrence of melanoma. MATERIAL AND METHODS: Multiplexed protein analysis was conducted using antibody suspension bead arrays. A total of 232 antibodies against 132 proteins were selected from (i) a screening with 4595 antibodies and 32 serum samples from melanoma patients and controls, (ii) antibodies used for immunohistochemistry, (iii) protein targets previously related with melanoma. The analysis was performed with 149 serum samples from patients with malignant melanoma. Antibody selectivity was then assessed by Western blot, immunocapture mass spectrometry, and epitope mapping. Lastly, indicative antibodies were applied for IHC analysis of melanoma tissues. RESULTS: Serum levels of regucalcin (RGN) and syntaxin 7 (STX7) were found to be lower in patients with both recurring tumors and a high Breslow's thickness (T-stage 3/4) compared to low thickness (T-stage 1/2) without disease recurrence. Serum levels of methylenetetrahydrofolate dehydrogenase 1-like (MTHFD1L) were instead elevated in sera of T3/4 patients with recurrence. The analysis of tissue sections with S100A6 and MTHFD1L showed positive staining in a majority of patients with melanoma, and S100A6 was significantly associated to T-stage. CONCLUSIONS: Our findings provide a starting point to further study RGN, STX7, MTHFD1L and S100A6 in serum to elucidate their involvement in melanoma progression and to assess a possible contribution to support clinical indications.     

A concentration-dependent analysis method for high density protein microarrays

Protein microarray technology is rapidly growing and has the potential to accelerate the discovery of targets of serum antibody responses in cancer, autoimmunity and infectious disease. Analytical tools for interpreting this high-throughput array data, however, are not well-established. We developed a concentration-dependent analysis (CDA) method which normalizes protein microarray data based on the concentration of spotted probes. We show that this analysis samples a data space that is complementary to other commonly employed analyses, and demonstrate experimental validation of 92% of hits identified by the intersection of CDA with other tools. These data support the use of CDA either as a preprocessing step for a more complete proteomic microarray data analysis or as a stand-alone analysis method.     

Affinity separation and enrichment methods in proteomic analysis

Protein separation or enrichment is one of the rate-limiting steps in proteomic studies. Specific capture and removal of highly-abundant proteins (HAP) with large sample-handling capacities are in great demand for enabling detection and analysis of low-abundant proteins (LAP). How to grasp and enrich these specific proteins or LAP in complex protein mixtures is also an outstanding challenge for biomarker discovery and validation. In response to these needs, various approaches for removal of HAP or capture of LAP in biological fluids, particularly in plasma or serum, have been developed. Among them, immunoaffinity subtraction methods based upon polyclonal IgY or IgG antibodies have shown to possess unique advantages for proteomic analysis of plasma, serum and other biological samples. In addition, other affinity methods that use recombinant proteins, lectins, peptides, or chemical ligands have also been developed and applied to LAP capture or enrichment. This review discusses in detail the need to put technologies and methods in affinity subtraction or enrichment into a context of proteomic and systems biology as "Separomics" and provides a prospective of affinity-mediated proteomics. Specific products, along with their features, advantages, and disadvantages will also be discussed.     

Selectivity analysis of single binder assays used in plasma protein profiling

The increasing availability of antibodies toward human proteins enables broad explorations of the proteomic landscape in cells, tissues, and body fluids. This includes assays with antibody suspension bead arrays that generate protein profiles of plasma samples by flow cytometer analysis. However, antibody selectivity is context dependent so it is necessary to corroborate on‐target detection over off‐target binding. To address this, we describe a concept to directly verify interactions from antibody‐coupled beads by analysis of their eluates by Western blots and MS. We demonstrate selective antibody binding in complex samples with antibodies toward a set of chosen proteins with different abundance in plasma and serum, and illustrate the need to adjust sample and bead concentrations accordingly. The presented approach will serve as an important tool for resolving differential protein profiles from antibody arrays within plasma biomarker discoveries.     

Development of a fibrinogen-specific sandwich enzyme-linked immunosorbent assay microarray assay for distinguishing between blood plasma and serum samples

We have developed a fibrinogen-specific sandwich enzyme-linked immunosorbent assay (ELISA) microarray assay for use in qualitatively distinguishing between blood plasma and serum samples. Three capture antibodies (49D2, HPA001900, and F8512) were evaluated in conjunction with 1D6 as the detection antibody. The data show that 49D2 and (to a lesser extent) F8512 successfully identify previously unknown plasma and serum samples based on approximately a 28-fold difference in signal intensity between the sample types. This assay has utility in rapidly identifying previously archived clinical samples with incomplete annotation in a high-throughput manner prior to proteomic analyses.     

CSF proteome: a protein repository for potential biomarker identification

Proteomic analysis is not limited to the analysis of serum or tissues. Synovial, peritoneal, pericardial and cerebrospinal fluid represent unique proteomes for disease diagnosis and prognosis. In particular, cerebrospinal fluid serves as a rich source of putative biomarkers that are not solely limited to neurologic disorders. Peptides, proteolytic fragments and antibodies are capable of crossing the blood-brain barrier, thus providing a repository of pathologic information. Proteomic technologies such as immunoblotting, isoelectric focusing, 2D gel electrophoresis and mass spectrometry have proven useful for deciphering this unique proteome. Cerebrospinal fluid proteins are generally less abundant than their corresponding serum counterparts, necessitating the development and use of sensitive analytical techniques. This review highlights some of the promising areas of cerebrospinal fluid proteomic research and their clinical applications.     

Validation of serum protein profiles by a dual antibody array approach

In recent years, affinity-based technologies have become important tools for serum profiling to uncover protein expression patterns linked to disease state or therapeutic effects. In this study, we describe a path towards the production of an antibody microarray to allow protein profiling of biotinylated human serum samples with reproducible sensitivity in the picomolar range. With the availability of growing numbers of affinity reagents, protein profiles are to be validated in efficient manners and we describe a cross-platform strategy based on data concordance with a suspension bead array to interrogate the identical set of antibodies with the same cohort of serum samples. Comparative analysis enabled to screen for high-performing antibodies, which were displaying consistent results across the two platforms and targeting known serum components. Moreover, data processing methods such as sample referencing and normalization were evaluated for their effects on inter-platform agreement. Our work suggests that mutual validation of protein expression profiles using alternative microarray platforms holds great potential in becoming an important and valuable component in affinity-based high-throughput proteomic screenings as it allows to narrow down the number of discovered targets prior to orthogonal, uniplexed validation approaches.     

Synthesis of polymerized thin films for immobilized ligand display in proteomic analysis

We describe a new material for the display of biomolecular ligands for use in proteomic analysis. We report here on the construction of the first functionalized polymerized diacetylene thin films (PDTFs) for use in displaying immobilized ligands and their application in mass spectral proteomic analysis. Functionalized polymerized thin film surfaces were constructed with diacetylene-containing biotin lipid monomers designed for the capture of proteins (streptavidin) from a complex cellular lysate and detection with mass spectrometry (MS). These materials serve as a prototype for ligand-based spotted arrays amenable to high throughput screening. Functionalized PDTFs can be easily manufactured for customized microarrays and demonstrate high protein specificity and low nonspecific protein adsorption, and the resulting microarrays constructed from these materials are compatible with several different protein analysis platforms. Our results suggest that these materials have broad potential applications for use in mass spectral-based proteomic analysis.     

Clinical cancer proteomics: promises and pitfalls

Proteome analysis promises to be valuable for the identification of tissue and serum biomarkers associated with human malignancies. In addition, proteome technologies offer the opportunity to analyze protein expression profiles and to analyze the activity of signaling pathways. Many published proteomic studies of human tumor tissue are associated with weaknesses in tumor representativity, sample contamination by nontumor cells and serum proteins. Studies often include a moderate number of tumors which may not be representative of clinical materials. It is therefore very important that biomarkers identified by proteomics are validated in representative tumor materials by other techniques, such as immunohistochemistry. Proteome technologies can be used to identify disease markers in human serum. Tumor derived proteins are present at nanomolar to picomolar concentrations in cancer patient sera, 10(6)-10(9)-fold lower than albumin, and will give rise to correspondingly smaller spots/peaks in protein separations. This leads to the need to prefractionate serum samples before analysis. Despite various pitfalls, proteomic analysis is a promising approach to the identification of biomarkers, and for generation of protein expression profiles that can be analyzed by artificial learning methods for improved diagnosis of human malignancy. Recent advances in the field of proteomic analysis of human tumors are summarized in the present review.     

Protein microarrays: a chance to study microorganisms?

Within the last 5 years, protein microarrays have been developed and applied to multiple approaches: identification of protein–protein interactions or protein–small molecule interactions, cancer profiling, detection of microorganisms and toxins, and identification of antibodies due to allergens, autoantigens, and pathogens. Protein microarrays are small size (typically in the microscopy slide format) planar analytical devices with probes arranged in high density to provide the ability to screen several hundred to thousand known substrates (e.g., proteins, peptides, antibodies) simultaneously. Due to their small size, only minute amounts of spotted probes and analytes (e.g., serum) are needed; this is a particularly important feature, for these are limited or expensive. In this review, different types of protein microarrays are reviewed: protein microarrays (PMAs), with spotted proteins or peptides; antibody microarrays (AMAs), with spotted antibodies or antibody fragments (e.g., scFv); reverse phase protein microarrays (RPMAs), a special form of PMA where crude protein mixtures (e.g., cell lysates, fractions) are spotted; and nonprotein microarrays (NPMAs) where macromolecules other than proteins and nucleic acids (e.g., carbohydrates, monosaccharides, lipopolysaccharides) are spotted. In this study, exemplary experiments for all types of protein arrays are discussed wherever applicable with regard to investigations of microorganisms.     

A new approach to the depletion of albumin and immunoglobulin G from human serum

The use of proteomic analysis to find potential diagnostic biomarkers is limited by the presence of serum albumin (HSA) and immunoglobulin (IgG) at high concentrations in patients’ blood; these substances impede the detection of serum proteins with similar molecular weights. Recombinant HSA- and IgG-binding polypeptides are used as ligands in creating sorbents for complete removal of the proteins by affinity chromatography. The binding specificity of the sorbents for HSA and IgG is higher than that of the conventionally used antibodies. A composite sorbent enabling the depletion of HSA and IgG from serum by single-step affinity chromatography was obtained. The developed sorbents were used to prepare serum for proteomic analysis.     

Biomarkers of HIV-1 associated dementia: proteomic investigation of sera

Background  

New, more sensitive and specific biomarkers are needed to support other means of clinical diagnosis of neurodegenerative disorders. Proteomics technology is widely used in discovering new biomarkers. There are several difficulties with in-depth analysis of human plasma/serum, including that there is no one proteomic platform that can offer complete identification of differences in proteomic profiles. Another set of problems is associated with heterogeneity of human samples in addition intrinsic variability associated with every step of proteomic investigation. Validation is the very last step of proteomic investigation and it is very often difficult to validate potential biomarker with desired sensitivity and specificity. Even though it may be possible to validate a differentially expressed protein, it may not necessarily prove to be a valid diagnostic biomarker.     

Using proteomic approach to identify tumor-associated antigens as markers in hepatocellular carcinoma

Many studies have demonstrated that intracellular proteins, which are involved in carcinogenesis, can provoke autoantibody responses. Therefore, autoantibodies can be used clinically for cancer detection and for proteomic analysis in identification of tumor-associated antigens (TAAs) that are potentially involved in malignant transformation. Liver cancer, especially hepatocellular carcinoma (HCC), is one of the most common tumors in the world. The majority of people with HCC will die within 1 year of its detection. This high case fatality rate can partially be attributed to a lack of diagnostic methods that allow early detection. In the present study, sera from 20 patients with HCC, 30 patients with chronic hepatitis (CH), and 30 patients with liver cirrhosis (LC) as well as sera from 10 normal individuals were used in a proteomic approach to identify HCC-related TAAs. Thirty-four immunoreactive protein spots were excised from the two-dimensional gel electrophoresis (2DE), digested with trypsin, and subsequently analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Of 34 immunoreactive protein spots, 28 were identified. Seventeen of them were not only reactive with serum antibodies in HCC but also with antibodies in pre-HCC conditions, and 11 were only reactive with serum antibodies in HCC but not with antibodies in pre-HCC conditions. In the subsequent analysis, two representative proteins, HSP60 and HSP70, were selected as examples for the validation purpose. The results from immunoassay were consistent with the data from proteomic analysis, supporting our hypothesis that proteins identified with autoantibodies that have been present in precancer conditions may be not appropriate to use as TAA markers in cancer detection.     

Analysis of low-abundance, low-molecular-weight serum proteins using mass spectrometry.

To detect diseases early in the general population, new diagnostic approaches are needed that have adequate sensitivity and specificity. Recent studies have used mass spectrometry to identify a serum proteomic pattern for breast and ovarian cancer. Serum contains 60-80 mg/mL protein, but 57-71% of this is serum albumin, and 8-26% are gamma-globulins. These large proteins must be depleted before smaller, less-abundant proteins can be detected using mass spectrometry, but because serum albumin is known to act as a carrier for smaller proteins, removal of these molecules using columns or filtration may result in the loss of molecules of interest. The objective of this study was to develop a reproducible method to deplete serum samples of high-abundance proteins in order to analyze the less-abundant proteins present in serum. We used organic solvents to precipitate the large proteins out of solution. We also predicted that this would cause many smaller proteins to dissociate from their carrier molecules, allowing for detection of a larger number of peptides and small proteins. These treated samples were analyzed using capillary liquid chromatography coupled with electrospray ionization mass spectrometry. Analysis demonstrated reproducible results. Acetonitrile treatment clearly released many carrier-bound molecular species and was superior to ultrafiltration alone for serum proteomic analysis.     

Robust estimation of protein expression ratios with lysate microarray technology

MOTIVATION: The protein lysate microarray is a developing proteomic technology for measuring protein expression levels in a large number of biological samples simultaneously. A challenge for accurate quantification is the relatively narrow dynamic range associated with the commonly used chromogenic signal detection system. To facilitate accurate measurement of the relative expression levels, each sample is serially diluted and each diluted version is spotted on a nitrocellulose-coated slide in triplicate. Thus, each sample yields multiple measurements in different dynamic ranges of the detection system. This study aims to develop suitable algorithms that yield accurate representations of the relative expression levels in different samples from multiple data points. RESULTS: We evaluated two algorithms for estimating relative protein expression in different samples on the lysate microarray by means of a cross-validation procedure. For this purpose as well as for quality control we designed a 1440-spot lysate microarray containing 80 identical samples of purified bovine serum albumin, printed in triplicate with six 2-fold dilutions. Our analysis showed that the algorithm based on a robust least squares estimator provided the most accurate quantification of the protein lysate microarray data. We also demonstrated our methods by estimating relative expression levels of p53 and p21 in either p53(+/+) or p53(-/-) HCT116 colon cancer cells after two drug treatments and their combinations on another lysate microarray. AVAILABILITY: http://www.cs.tut.fi/~mirceanc/lysate_array_bioinformatics.htm     

Hexadecanedionic acid-sepharose 4B: A new tool for preparation of albumin-depleted plasma

Serum and plasma are the major sources of human material for clinical molecular diagnostics and drug discovery. However, due to the high abundance of some proteins, of which serum albumin (SA) is most prominent, lower-abundance proteins often remain undetectable in proteomic analysis of these body fluids. We have used hexadecanedionic acid (HDA) immobilized to Sepharose 4B to develop an affinity resin that is effective in the removal of SA from plasma. Two-dimensional gel analysis of the SA-depleted samples shows a significant enhancement of the low-abundance proteins and highly specific capture of serum albumin. The HDA resin shows better performance in terms of specificity than dye-based resins.     

A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry

There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics.     

Rat plasma proteomics: effects of abundant protein depletion on proteomic analysis

The proteomic analysis of plasma and serum samples represents a formidable challenge due to the presence of a few highly abundant proteins such as albumin and immunoglobulins. Detection of low abundance protein biomarkers requires therefore either the specific depletion of high abundance proteins with immunoaffinity columns and/or optimized protein fractionation methods based on charge, size or hydrophobicity. Here we describe the depletion of seven abundant rat plasma proteins with an immunoaffinity column with coupled antibodies directed against albumin, IgG, transferrin, IgM, haptoglobin, fibrinogen and alpha1-anti-trypsin. The IgY-R7-LC2 (Beckman Coulter) column showed high specificity for the targeted proteins and was able to efficiently remove most of the albumin, IgG and transferrin from rat plasma samples as judged by Western blot analysis. Depleted rat plasma protein samples were analyzed by SELDI-TOF MS, 2D SDS-PAGE and 2D-LC and compared to non-depleted plasma samples as well as to the abundant protein fraction that was eluted from the immunoaffinity column. Analysis of the depleted plasma protein fraction revealed improved signal to noise ratios, regardless of which proteomic method was applied. However, only a small number of new proteins were observed in the depleted protein fraction. Immunoaffinity depletion of abundant plasma proteins results in the significant dilution of the original sample which complicates subsequent analysis. Most proteomic approaches require specialized sample preparation procedures during which significant losses of less abundant proteins and potential biomarkers can occur. Even though abundant protein depletion reduces the dynamic range of the plasma proteome by about 2-3 orders of magnitude, the difference between medium-abundant and low abundant plasma proteins is still in the range of 7-8 orders of magnitude and beyond the dynamic range of current proteomic technologies. Thus, exploring the plasma proteome in greater detail remains a daunting task.     

Antibody microarray profiling of human prostate cancer sera: antibody screening and identification of potential biomarkers

We developed a practical strategy for serum protein profiling using antibody microarrays and applied the method to the identification of potential biomarkers in prostate cancer serum. Protein abundances from 33 prostate cancer and 20 control serum samples were compared to abundances from a common reference pool using a two-color fluorescence assay. Robotically spotted microarrays containing 184 unique antibodies were prepared on two different substrates: polyacrylamide based hydrogels on glass and poly-1-lysine coated glass with a photoreactive cross-linking layer. The hydrogel substrate yielded an average six-fold higher signal-to-noise ratio than the other substrate, and detection of protein binding was possible from a greater number of antibodies using the hydrogels. A statistical filter based on the correlation of data from "reverse-labeled" experiment sets accurately predicted the agreement between the microarray measurements and enzyme-linked immunosorbent assay measurements, showing that this parameter can serve to screen for antibodies that are functional on microarrays. Having defined a set of reliable microarray measurements, we identified five proteins (von Willebrand Factor, immunoglobulinM, Alpha1-antichymotrypsin, Villin and immunoglobulinG) that had significantly different levels between the prostate cancer samples and the controls. These developments enable the immediate use of high-density antibody and protein microarrays in biomarker discovery studies.     

Reverse-Phase Protein Array: Technology,Application, Data Processing,and Integration

Reverse-phase protein array (RPPA) is a high-throughput antibody-based targeted proteomics platform that can quantify hundreds of proteins in thousands of samples derived from tissue or cell lysates, serum, plasma, or other body fluids. Protein samples are robotically arrayed as microspots on nitrocellulose-coated glass slides. Each slide is probed with a specific antibody that can detect levels of total protein expression or post-translational modifications, such as phosphorylation as a measure of protein activity. Here we describe workflow protocols and software tools that we have developed and optimized for RPPA in a core facility setting that includes sample preparation, microarray mapping and printing of protein samples, antibody labeling, slide scanning, image analysis, data normalization and quality control, data reporting, statistical analysis, and management of data. Our RPPA platform currently analyzes ∼240 validated antibodies that primarily detect proteins in signaling pathways and cellular processes that are important in cancer biology. This is a robust technology that has proven to be of value for both validation and discovery proteomic research and integration with other omics data sets.