The measurements of coordinated patterns of protein abundance using antibody microarrays could be used to gain insight into disease biology and to probe the use of combinations of proteins for ...disease classification. The correct use and interpretation of antibody microarray data requires proper normalization of the data, which has not yet been systematically studied. Therefore we undertook a study to determine the optimal normalization of data from antibody microarray profiling of proteins in human serum specimens. Forty-three serum samples collected from patients with pancreatic cancer and from control subjects were probed in triplicate on microarrays containing 48 different antibodies, using a direct labeling, two-color comparative fluorescence detection format. Seven different normalization methods representing major classes of normalization for antibody microarray data were compared by their effects on reproducibility, accuracy, and trends in the data set. Normalization with ELISA-determined concentrations of IgM resulted in the most accurate, reproducible, and reliable data. The other normalization methods were deficient in at least one of the criteria. Multiparametric classification of the samples based on the combined measurement of seven of the proteins demonstrated the potential for increased classification accuracy compared with the use of individual measurements. This study establishes reliable normalization for antibody microarray data, criteria for assessing normalization performance, and the capability of antibody microarrays for serum-protein profiling and multiparametric sample classification.
Currently, pancreatic cancer is the fourth cause of cancer death. In 2013, it is estimated that ∼38 460 people will die of pancreatic cancer. Early detection of malignant cyst (pancreatic cancer ...precursor) is necessary to help prevent late diagnosis of the tumor. In this study, we characterized glycoproteins and nonglycoproteins on pooled mucinous (n = 10) and nonmucinous (n = 10) pancreatic cyst fluid to identify “proteins of interest” to differentiate between mucinous cyst from nonmucinous cyst and investigate these proteins as potential biomarker targets. An automated multilectin affinity chromatography (M-LAC) platform was utilized for glycoprotein enrichment followed by nano-LC–MS/MS analysis. Spectral count quantitation allowed for the identification of proteins with significant differential levels in mucinous cysts from nonmucinous cysts of which one protein (periostin) was confirmed via immunoblotting. To exhaustively evaluate differentially expressed proteins, we used a number of proteomic tools including gene ontology classification, pathway and network analysis, Novoseek data mining, and chromosome gene mapping. Utilization of complementary proteomic tools revealed that several of the proteins such as mucin 6 (MUC6), bile salt-activated lipase (CEL), and pyruvate kinase lysozyme M1/M2 with significant differential expression have strong association with pancreatic cancer. Furthermore, chromosome gene mapping demonstrated coexpressions and colocalization of some proteins of interest including 14-3-3 protein epsilon (YWHAE), pigment epithelium derived factor (SERPINF1), and oncogene p53.
We used antibody microarrays to probe the associations of multiple serum proteins with pancreatic cancer and to explore the use of combined measurements for sample classification. Serum samples from ...pancreatic cancer patients (n = 61), patients with benign pancreatic disease (n = 31), and healthy control subjects (n = 50) were probed in replicate experiment sets by two-color, rolling circle amplification on microarrays containing 92 antibodies and control proteins. The antibodies that had reproducibly different binding levels between the patient classes revealed different types of alterations, reflecting inflammation (high C-reactive protein, alpha-1-antitrypsin, and serum amyloid A), immune response (high IgA), leakage of cell breakdown products (low plasma gelsolin), and possibly altered vitamin K usage or glucose regulation (high protein-induced vitamin K antagonist-II). The accuracy of the most significant antibody microarray measurements was confirmed through immunoblot and antigen dilution experiments. A logistic-regression algorithm distinguished the cancer samples from the healthy control samples with a 90% and 93% sensitivity and a 90% and 94% specificity in duplicate experiment sets. The cancer samples were distinguished from the benign disease samples with a 95% and 92% sensitivity and an 88% and 74% specificity in duplicate experiment sets. The classification accuracies were significantly improved over those achieved using individual antibodies. This study furthered the development of antibody microarrays for molecular profiling, provided insights into the nature of serum-protein alterations in pancreatic cancer patients, and showed the potential of combined measurements to improve sample classification accuracy.
The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers ...to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications.
The ability to conveniently and rapidly profile a diverse set of proteins has valuable applications. In a step toward further enabling such a capability, we developed the use of rolling-circle ...amplification (RCA) to measure the relative levels of proteins from two serum samples, labeled with biotin and digoxigenin, respectively, that have been captured on antibody microarrays. Two-color RCA produced fluorescence up to 30-fold higher than direct-labeling and indirect-detection methods using antibody microarrays prepared on both polyacrylamide-based hydrogels and nitrocellulose. Replicate RCA measurements of multiple proteins from sets of 24 serum samples were highly reproducible and accurate. In addition, RCA enabled reproducible measurements of distinct expression profiles from lower-abundance proteins that were not measurable using the other detection methods. Two-color RCA on antibody microarrays should allow the convenient acquisition of expression profiles from a great diversity of proteins for a variety of applications.
Glycans are critical to protein biology and are useful as disease biomarkers. Many studies of glycans rely on clinical specimens, but the low amount of sample available for some specimens limits the ...experimental options. Here we present a method to obtain information about protein glycosylation using a minimal amount of protein. We treat proteins that were captured or directly spotted in small microarrays (2.2 mm × 2.2 mm) with exoglycosidases to successively expose underlying features, and then we probe the native or exposed features using a panel of lectins or glycan-binding reagents. We developed an algorithm to interpret the data and provide predictions about the glycan motifs that are present in the sample. We demonstrated the efficacy of the method to characterize differences between glycoproteins in their sialic acid linkages and N-linked glycan branching, and we validated the assignments by comparing results from mass spectrometry and chromatography. The amount of protein used on-chip was about 11 ng. The method also proved effective for analyzing the glycosylation of a cancer biomarker in human plasma, MUC5AC, using only 20 μL of the plasma. A glycan on MUC5AC that is associated with cancer had mostly 2,3-linked sialic acid, whereas other glycans on MUC5AC had a 2,6 linkage of sialic acid. The on-chip glycan modification and probing (on-chip GMAP) method provides a platform for analyzing protein glycosylation in clinical specimens and could complement the existing toolkit for studying glycosylation in disease.
Protein glycosylation represents one of the major post-translational modifications and can have significant effects on protein function. Moreover, changes in the carbohydrate structure are ...increasingly being recognized as an important modification associated with cancer etiology. In this report, we describe the development of a proteomics approach to identify breast cancer related changes in either concentration and/or the carbohydrate structures of glycoprotein(s) present in blood samples. Diseased and healthy serum samples were processed by an optimized sample preparation protocol using multiple lectin affinity chromatography (M-LAC) that partitions serum proteins based on glycan characteristics. Subsequently, three separate procedures, 1D SDS-PAGE, isoelectric focusing and an antibody microarray, were applied to identify potential candidate markers for future study. The combination of these three platforms is illustrated in this report with the analysis of control and cancer glycoproteomic fractions. Firstly, a molecular weight based separation of glycoproteins by 1D SDS-PAGE was performed, followed by protein, glycoprotein staining, lectin blotting and LC–MS analysis. To refine or confirm the list of interesting glycoproteins, isoelectric focusing (targeting sialic acid changes) and an antibody microarray (used to detect neutral glycan shifts) were selected as the orthogonal methods. As a result, several glycoproteins including alpha-1B-glycoprotein, complement C3, alpha-1-antitrypsin and transferrin were identified as potential candidates for further study.
Improved methods for studying glycans could spur significant advances in the understanding and application of glycobiology. The use of affinity reagents such as lectins and glycan-binding antibodies ...is a valuable complement to methods involving mass spectrometry and chromatography. Many lectins, however, are not useful as analytic tools due to low affinity in vitro. As an approach to increasing lectin avidity to targeted glycans, we tested the use of lectin multimerization. Several biotinylated lectins were linked together through streptavidin interactions. The binding of certain lectins for purified glycoproteins and glycoproteins captured directly out of biological solutions was increased using multimerization, resulting in the detection of lower concentrations of glycoprotein than possible using monomeric detection. The analysis of glycoproteins in plasma samples showed that the level of binding enhancement through multimerization was not equivalent across patient samples. Wheat germ agglutinin (WGA) reactive glycans on fibronectin and thrombospondin-5 were preferentially bound by multimers in pancreatic cancer patient samples relative to control samples, suggesting a cancer-associated change in glycan density that could be detected only through lectin multimerization. This strategy could lead to the more sensitive and informative detection of glycans in biological samples and a broader spectrum of lectins that are useful as analytical reagents.
Experiments involving the high-throughput quantification of image data require algorithms for automation. A challenge in the development of such algorithms is to properly interpret signals over a ...broad range of image characteristics, without the need for manual adjustment of parameters. Here we present a new approach for locating signals in image data, called Segment and Fit Thresholding (SFT). The method assesses statistical characteristics of small segments of the image and determines the best-fit trends between the statistics. Based on the relationships, SFT identifies segments belonging to background regions; analyzes the background to determine optimal thresholds; and analyzes all segments to identify signal pixels. We optimized the initial settings for locating background and signal in antibody microarray and immunofluorescence data and found that SFT performed well over multiple, diverse image characteristics without readjustment of settings. When used for the automated analysis of multicolor, tissue-microarray images, SFT correctly found the overlap of markers with known subcellular localization, and it performed better than a fixed threshold and Otsu’s method for selected images. SFT promises to advance the goal of full automation in image analysis.
The glycan array is a powerful tool for investigating the specificities of glycan-binding proteins. By incubating a glycan-binding protein on a glycan array, the relative binding to hundreds of ...different oligosaccharides can be quantified in parallel. Based on these data, much information can be obtained about the preference of a glycan-binding protein for specific subcomponents of oligosaccharides or motifs. In many cases, the analysis and interpretation of glycan array data can be time consuming and imprecise if done manually. Recently we developed software, called GlycoSearch, to facilitate the analysis and interpretation of glycan array data based on the previously developed methods called Motif Segregation and Outlier Motif Analysis. Here we describe the principles behind the software, the use of the software, and an example application. The automated, objective, and precise analysis of glycan array data should enhance the value of the data for a broad range of research applications.
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