Brian B. Haab

Protein microarrays for highly parallel detection and quantitation of specific proteins and antibodies in complex solutions

BackgroundWe describe a method for printing protein microarrays, and using these microarrays in a comparative fluorescence assay to measure the abundance of many specific proteins in complex solutions. A robotic device was used to print hundreds of specific antibody or antigen solutions in an array on the surface of derivatized microscope slides. Two complex protein samples, one serving as a standard for comparative quantitation, and the other representing an experimental sample in which the concentrations of specific proteins were to be measured, were labeled by covalent attachment of spectrally-resolvable fluorescent dyes. Specific antibody-antigen interactions localized specific components of the complex mixtures to defined cognate spots in the array, where the relative intensity of the fluorescent signals representing the experimental sample and the reference standard provided a measure of each proteins abundance in the experimental sample. To characterize the specificity, sensitivity and accuracy of this assay, we analyzed the performance of 115 antibody/antigen pairs.Results50% of the arrayed antigens, and 20% of the arrayed antibodies, provided specific and accurate measurements of their cognate ligands at or below concentrations of 1.6 µg/ml and 0.34 µg/ml, respectively. Some of the antibody/antigen pairs allowed detection of the cognate ligands at absolute concentrations below 1 ng/ml, and partial concentrations of less than 1 part in 106, sensitivities sufficient for measurement of many clinically important proteins in patient blood samples.ConclusionsProtein microarrays can provide a simple and practical means to characterize patterns of variation in hundreds or thousands of different proteins, in clinical or research applications.

Autoantigen microarrays for multiplex characterization of autoantibody responses

We constructed miniaturized autoantigen arrays to perform large-scale multiplex characterization of autoantibody responses directed against structurally diverse autoantigens, using submicroliter quantities of clinical samples. Autoantigen microarrays were produced by attaching hundreds of proteins, peptides and other biomolecules to the surface of derivatized glass slides using a robotic arrayer. Arrays were incubated with patient serum, and spectrally resolvable fluorescent labels were used to detect autoantibody binding to specific autoantigens on the array. We describe and characterize arrays containing the major autoantigens in eight distinct human autoimmune diseases, including systemic lupus erythematosus and rheumatoid arthritis. This represents the first report of application of such technology to multiple human disease sera, and will enable validated detection of antibodies recognizing autoantigens including proteins, peptides, enzyme complexes, ribonucleoprotein complexes, DNA and post-translationally modified antigens. Autoantigen microarrays represent a powerful tool to study the specificity and pathogenesis of autoantibody responses, and to identify and define relevant autoantigens in human autoimmune diseases.

Gene expression profiling of clear cell renal cell carcinoma: Gene identification and prognostic classification

To better understand the molecular mechanisms that underlie the tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC), we studied the gene expression profiles of 29 ccRCC tumors obtained from patients with diverse clinical outcomes by using 21,632 cDNA microarrays. We identified gene expression alterations that were both common to most of the ccRCC studied and unique to clinical subsets. There was a significant distinction in gene expression profile between patients with a relatively nonaggressive form of the disease [100% survival after 5 years with the majority (15/17 or 88%) having no clinical evidence of metastasis] versus patients with a relatively aggressive form of the disease (average survival time 25.4 months with a 0% 5-year survival rate). Approximately 40 genes most accurately make this distinction, some of which have previously been implicated in tumorigenesis and metastasis. To test the robustness and potential clinical usefulness of this molecular distinction, we simulated its use as a prognostic tool in the clinical setting. In 96% of the ccRCC cases tested, the prediction was compatible with the clinical outcome, exceeding the accuracy of prediction by staging. These results suggest that two molecularly distinct forms of ccRCC exist and that the integration of expression profile data with clinical parameters could serve to enhance the diagnosis and prognosis of ccRCC. Moreover, the identified genes provide insight into the molecular mechanisms of aggressive ccRCC and suggest intervention strategies.

Antibody Arrays in Cancer Research

Antibody arrays have valuable applications in cancer research. Many different antibody array technologies have been developed, each with particular advantages, disadvantages, and optimal applications. The methods have been demonstrated on various sample types, such as serum, plasma, and other bodily fluids; cell culture supernatants; tissue culture lysates; and resected tumor specimens. The applications to cancer research have included profiling proteins to identify candidate biomarkers, characterizing signaling pathways, and the measurement of changes in modification or expression level of cancer-related proteins. Further innovations in the methods and experimental strategies are broadening the scope of the applications and the type of information that can be gathered. These alternate formats and uses of antibody arrays include arrays to measure whole cells, arrays to measure enzyme activities, reverse phase arrays, and bead-based arrays. This article reviews the various types of antibody array methods and their applications to cancer research.

Multiplexed analysis of glycan variation on native proteins captured by antibody microarrays.

Carbohydrate post-translational modifications on proteins are important determinants of protein function in both normal and disease biology. We have developed a method to allow the efficient, multiplexed study of glycans on individual proteins from complex mixtures, using antibody microarray capture of multiple proteins followed by detection with lectins or glycan-binding antibodies. Chemical derivatization of the glycans on the spotted antibodies prevented lectin binding to those glycans. Multiple lectins could be used as detection probes, each targeting different glycan groups, to build up lectin binding profiles of captured proteins. By profiling both protein and glycan variation in multiple samples using parallel sandwich and glycan-detection assays, we found cancer-associated glycan alteration on the proteins MUC1 and CEA in the serum of pancreatic cancer patients. Antibody arrays for glycan detection are highly effective for profiling variation in specific glycans on multiple proteins and should be useful in diverse areas of glycobiology research.

Distinctive serum protein profiles involving abundant proteins in lung cancer patients based upon antibody microarray analysis

BackgroundCancer serum protein profiling by mass spectrometry has uncovered mass profiles that are potentially diagnostic for several common types of cancer. However, direct mass spectrometric profiling has a limited dynamic range and difficulties in providing the identification of the distinctive proteins. We hypothesized that distinctive profiles may result from the differential expression of relatively abundant serum proteins associated with the host response.MethodsEighty-four antibodies, targeting a wide range of serum proteins, were spotted onto nitrocellulose-coated microscope slides. The abundances of the corresponding proteins were measured in 80 serum samples, from 24 newly diagnosed subjects with lung cancer, 24 healthy controls, and 32 subjects with chronic obstructive pulmonary disease (COPD). Two-color rolling-circle amplification was used to measure protein abundance.ResultsSeven of the 84 antibodies gave a significant difference (p < 0.01) for the lung cancer patients as compared to healthy controls, as well as compared to COPD patients. Proteins that exhibited higher abundances in the lung cancer samples relative to the control samples included C-reactive protein (CRP; a 13.3 fold increase), serum amyloid A (SAA; a 2.0 fold increase), mucin 1 and α-1-antitrypsin (1.4 fold increases). The increased expression levels of CRP and SAA were validated by Western blot analysis. Leave-one-out cross-validation was used to construct Diagonal Linear Discriminant Analysis (DLDA) classifiers. At a cutoff where all 56 of the non-tumor samples were correctly classified, 15/24 lung tumor patient sera were correctly classified.ConclusionOur results suggest that a distinctive serum protein profile involving abundant proteins may be observed in lung cancer patients relative to healthy subjects or patients with chronic disease and may have utility as part of strategies for detecting lung cancer.

Antibody Microarray Profiling Reveals Individual and Combined Serum Proteins Associated with Pancreatic Cancer

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.

Two-color, rolling-circle amplification on antibody microarrays for sensitive, multiplexed serum-protein measurements

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.

Molecular profiling of human hepatocellular carcinoma defines mutually exclusive interferon regulation and insulin-like growth factor II overexpression.

Molecular subtyping of human hepatocellular carcinoma (HCC) with potential mechanistic and therapeutic impact has not been achieved thus far. We have analyzed the mRNA expression patterns of 43 different human HCC samples and 3 HCC cell lines in comparison with normal adult liver using high-density cDNA microarrays. Two main groups of HCC, designated group A (65%) and group B (35%), were distinguished based on clustering of the most highly varying genes. Group A HCCs were characterized by induction of a number of interferon (IFN)-regulated genes, whereas group B was characterized mainly by down-regulation of several apoptosis-relevant and IFN-regulated genes. The number of apoptotic tumor cells and tumor-infiltrating lymphocytes was significantly higher in tumors of group A as compared with those of group B. Based on the expression pattern, group B was further subdivided into two subgroups, designated subgroup B1 (6 of 43 tumors, 14%) and subgroup B2 (9 of 43 tumors, 21%). A prominent characteristic of subgroup B1 was high overexpression of insulin-like growth factor (IGF)-II. All tested HCC cell lines expressed equally high concentrations of IGF-II transcripts and co-segregated with group B1 in clustering. IGF-II overexpression and induction of IFN-related genes were mutually exclusive, even when analysis was extended to other cancer expression profile studies. Moreover, IFN-γ treatment substantially reduced IGF-II expression in HCC cells. In conclusion, cDNA microarray analyses provided subtyping of HCCs that is related to intratumor inflammation and tumor cell apoptosis. This profiling may be of mechanistic and therapeutic impact because IGF-II overexpression has been linked to reduced apoptosis and increased proliferation and may be accessible to therapeutic intervention.

Optimized Normalization for Antibody Microarrays and Application to Serum-Protein Profiling

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.