Experimental Design and Analysis of Antibody Microarrays: Applying Methods from cDNA Arrays

Statistical issues in the design and planning of proteomic profiling experiments

The statistical design of a clinical proteomics experiment is a critical part of well-undertaken investigation. Standard concepts from experimental design such as randomization, replication and blocking should be applied in all experiments, and this is possible when the experimental conditions are well understood by the investigator. The large number of proteins simultaneously considered in proteomic discovery experiments means that determining the number of required replicates to perform a powerful experiment is more complicated than in simple experiments. However, by using information about the nature of an experiment and making simple assumptions this is achievable for a variety of experiments useful for biomarker discovery and initial validation.

Prediction of individual response to anticancer therapy: historical and future perspectives

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

Exploiting fluorescence for multiplex immunoassays on protein microarrays

Protein microarray technology is becoming the method of choice for identifying protein interaction partners, detecting specific proteins, carbohydrates and lipids, or for characterizing protein interactions and serum antibodies in a massively parallel manner. Availability of the well-established instrumentation of DNA arrays and development of new fluorescent detection instruments promoted the spread of this technique. Fluorescent detection has the advantage of high sensitivity, specificity, simplicity and wide dynamic range required by most measurements. Fluorescence through specifically designed probes and an increasing variety of detection modes offers an excellent tool for such microarray platforms. Measuring for example the level of antibodies, their isotypes and/or antigen specificity simultaneously can offer more complex and comprehensive information about the investigated biological phenomenon, especially if we take into consideration that hundreds of samples can be measured in a single assay. Not only body fluids, but also cell lysates, extracted cellular components, and intact living cells can be analyzed on protein arrays for monitoring functional responses to printed samples on the surface. As a rapidly evolving area, protein microarray technology offers a great bulk of information and new depth of knowledge. These are the features that endow protein arrays with wide applicability and robust sample analyzing capability. On the whole, protein arrays are emerging new tools not just in proteomics, but glycomics, lipidomics, and are also important for immunological research. In this review we attempt to summarize the technical aspects of planar fluorescent microarray technology along with the description of its main immunological applications.

High-throughput proteomics using antibody microarrays: an update

Antibody-based microarrays are a rapidly emerging technology that has advanced from the first proof-of-concept studies to demanding serum protein profiling applications during recent years, displaying great promise within disease proteomics. Miniaturized micro- and nanoarrays can be fabricated with an almost infinite number of antibodies carrying the desired specificities. While consuming only minute amounts of reagents, multiplexed and ultrasensitive assays can be performed targeting high- as well as low-abundance analytes in complex nonfractionated proteomes. The microarray images generated can then be converted into protein expression profiles or protein atlases, revealing a detailed composition of the sample. The technology will provide unique opportunities for fields such as disease diagnostics, biomarker discovery, patient stratification, predicting disease recurrence and drug target discovery. This review describes an update of high-throughput proteomics, using antibody-based microarrays, focusing on key technological advances and novel applications that have emerged over the last 3 years.

Experimental designs for array comparative genomic hybridization technology

Array comparative genomic hybridization (aCGH) technology is commonly used to estimate genome-wide copy-number variation and to evaluate associations between copy number and disease. Although aCGH technology is well developed and there are numerous algorithms available for estimating copy number, little attention has been paid to the important issue of the statistical experimental design. Herein, we review classical statistical experimental designs and discuss their relevance to aCGH technology as well as their importance for downstream statistical analyses. Furthermore, we provide experimental design guidance for various study objectives.

Data Analysis Strategies for Protein Microarrays

Microarrays constitute a new platform which allows the discovery and characterization of proteins. According to different features, such as content, surface or detection system, there are many types of protein microarrays which can be applied for the identification of disease biomarkers and the characterization of protein expression patterns. However, the analysis and interpretation of the amount of information generated by microarrays remain a challenge. Further data analysis strategies are essential to obtain representative and reproducible results. Therefore, the experimental design is key, since the number of samples and dyes, among others aspects, would define the appropriate analysis method to be used. In this sense, several algorithms have been proposed so far to overcome analytical difficulties derived from fluorescence overlapping and/or background noise. Each kind of microarray is developed to fulfill a specific purpose. Therefore, the selection of appropriate analytical and data analysis strategies is crucial to achieve successful biological conclusions. In the present review, we focus on current algorithms and main strategies for data interpretation.

Biomarker discovery and clinical proteomics

New biomarkers are urgently needed to accelerate efforts in developing new drugs and treatments of known diseases. New clinical and translational proteomics studies emerge almost every day. However, discovery of new diagnostic biomarkers lags behind because of variability at every step in proteomics studies (e.g., assembly of a cohort of patients, sample preparation and the nature of body fluids, selection of a profiling method and uniform protocols for data analysis).Quite often, the validation step that follows the discovery phase does not reach desired levels of sensitivity and specificity or reproducibility between laboratories. Mass spectrometry and gel-based methods do not provide enough throughput for screening thousands of clinical samples. Further development of protein arrays may address this issue.Despite many obstacles, proteomics delivers vast amounts of information useful for understanding the molecular mechanisms underlying diseases.

Sensing the insulin signaling pathway with an antibody array

The development of insulin resistance and type 2 diabetes is determined by various factors, including defects within the insulin signaling pathway. Mediators of insulin resistance operate through activation of various protein kinase C isoforms, IκB kinase β (IKKβ), and/or c-Jun N-terminal kinase, and subsequent inhibition of the proximal insulin signaling pathway via the insulin receptor substrate 1 and Akt. These mechanisms are still largely unresolved because of the complexity of the molecular events. In this study, an expression and activation state profiling of multiple known key signaling biomolecules involved in insulin metabolic and mitogenic signaling pathways was evaluated using a phosphospecific antibody array platform. The results of the arrayed antibodies were verified by the multiplexed bead array assay and conventional Western blot analysis, and confirmed the well-known inhibitory effects of phorbol esters on insulin signaling pathway activation. Of interest, the increase in protein kinase C signaling responses with phorbol esters was associated with activation of the lipid phosphatase PTEN and a 27 kDa HSP. Thus, this insulin signaling antibody array provides a powerful and effective way to investigate the mechanism of insulin resistance and likely assist the development of innovative therapeutic drugs for type 2 diabetes.

Proteomics studies reveal important information on small molecule therapeutics: a case study on plasma proteins

The most abundant proteins in serum, such as albumin and IgG, act as molecular sponges that bind and transport low molecular weight proteins/peptides and drugs. In the near future, pharmacoproteomics, the use of proteomic technologies in the field of drug discovery and development, and interactomics, the branch of proteomics which is concerned with identifying interactions between proteins, will allow researchers to (i) know the specific protein changes that occur in biological compartments in response to drug administration; (ii) design small novel therapeutic molecules that can have extended half-lives if carried by plasma protein in the blood stream. Advances in these fields will open new avenues of tailor-made molecular therapy, reducing present limitations on treatment arising from toxicity and inefficiency.

In this short review we report and discuss the most recent developments arising from the use of proteomic tools in blood plasma protein research, looking at the identification of proteins found in plasma as well as their interactions with small molecules such as drugs, peptides, organic chemicals and metals. We believe this research demonstrates that proteomic technologies, and in particular pharmacoproteomics, interactomics and post-translational modification analysis, could be instrumental in the design of new tailor-made drugs leading to substantial improvements in molecular therapy.

Upregulation of beta-catenin levels in superior frontal cortex of chronic alcoholics

BACKGROUND

Chronic and excessive alcohol misuse results in neuroadaptive changes in the brain. The complex nature of behavioral, psychological, emotional, and neuropathological characteristics associated with alcoholism is likely a reflection of the network of proteins that are affected by alcohol-induced gene expression patterns in specific brain regions. At the molecular level, however, knowledge remains limited regarding alterations in protein expression levels affected by chronic alcohol abuse. Thus, novel techniques that allow a comprehensive assessment of this complexity will enable the simultaneous assessment of changes across a group of proteins in the relevant neural circuitry.

METHODS

A proteomics analysis was performed using antibody microarrays to determine differential protein levels in superior frontal cortices between chronic alcoholics and age- and gender-matched control subjects. Seventeen proteins related to the catenin signaling pathway were analyzed, including alpha-, beta-, and delta-catenins, their upstream activators cadherin-3 (type I cadherin) and cadherin-5 (type II cadherin), and 5 cytoplasmic regulators c-Src, CK1 epsilon, GSK-3beta, PP2A-C alpha, and APC, as well as the nuclear complex partner of beta-catenin CBP and 2 downstream genes Myc and cyclin D1. ILK, G(alpha1), G(beta1), and G(beta2), which are activity regulators of GSK-3beta, were also analyzed.

RESULTS

Both alpha- and beta-catenin showed significantly increased levels, while delta-catenin did not change significantly, in chronic alcoholics. In addition, the level of the beta-catenin downstream gene product Myc was significantly increased. Average levels of the catenin regulators c-Src, CK1 epsilon, and APC were also increased in chronic alcoholics, but the changes were not statistically significant.

CONCLUSION

Chronic and excessive alcohol consumption leads to an upregulation of alpha- and beta-catenin levels, which in turn increase downstream gene expressions such as Myc that is controlled by beta-catenin signaling. This study showed that the beta-catenin signal transduction pathway was upregulated by chronic alcohol abuse, and prompts further investigation of mechanisms underlying the upregulation of alpha- and beta-catenins in alcoholism, which may have considerable pathogenic and therapeutic relevance.

Multiplexed cell signaling analysis of human breast cancer applications for personalized therapy

Phosphoprotein driven cellular signaling events represent most of the new molecular targets for cancer treatment. Application of reverse-phase protein microarray technology for the study of ongoing signaling activity within breast tumor specimens holds great potential for elucidating and profiling signaling activity in real-time for patient-tailored therapy. Analysis of laser capture microdissection primary human breast tumors and metastatic lesions reveals pathway specific profiles and a new way to classify cancer based on functional signaling portraits. Moreover, the data demonstrate the requirement of laser capture microdissection for analysis and reveal the metastasis-specific changes that occur within a new microenvironment. Analysis of biopsy material from clinical trials for targeted therapeutics demonstrates the feasibility and utility of comprehensive signal pathway activation profiling for molecular analysis.

Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA

Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.

High-throughput antibody microarrays for quantitative proteomic analysis

The antibody microarray is an intrinsically robust and quantitative system that delivers high-throughput and parallel measurements on particular sets of known proteins. It has become an important proteomics research tool, complementary to the conventional unbiased separation-based and mass spectrometry-based approaches. This review summarizes the technical aspects of production and the application for quantitative proteomic analysis with an emphasis on disease proteomics, especially the identification of biomarkers. Quality control, data analysis methods and the challenges for quantitative assays are also discussed.

Experimental approach for assessing the outcome accuracy of antibody microarray experiments

An experimental strategy for quality control of antibody microarray analyses is proposed. The method utilizes proteins that are prepared for regular antibody microarray experiments. There is no need to use exogenous positive or negative reference markers and no need to determine the absolute concentration of each individual protein in the sample. Validation experiments support the basic principle of the proposed approach. This method can be a useful tool for assessing the outcome accuracy of microarray experiments.

Proteomic analysis of waldenstrom macroglobulinemia

To better understand the molecular changes that occur in Waldenstrom macroglobulinemia (WM), we employed antibody-based protein microarrays to compare patterns of protein expression between untreated WM and normal bone marrow controls. Protein expression was defined as a >2-fold or 1.3-fold change in at least 67% of the tumor samples. Proteins up-regulated by >2-fold included Ras family proteins, such as Rab-4 and p62DOK, and Rho family proteins, such as CDC42GAP and ROKalpha. Other proteins up-regulated by >1.3-fold included cyclin-dependent kinases, apoptosis regulators, and histone deacetylases (HDAC). We then compared the samples of patients with symptomatic and asymptomatic WM and showed similar protein expression signatures, indicating that the dysregulation of signaling pathways occurs early in the disease course. Three proteins were different by >2-fold in symptomatic versus asymptomatic, including the heat shock protein HSP90. Elevated protein expression was confirmed by immunohistochemistry and immunoblotting. Functional significance was validated by the induction of apoptosis and inhibition of proliferation using specific HDAC and HSP90 inhibitors. This study, therefore, identifies, for the first time, multiple novel proteins that are dysregulated in WM, which both enhance our understanding of disease pathogenesis and represent targets of novel therapeutics.

Software package for automatic microarray image analysis (MAIA)

Although various software solutions are currently available for microarray image analysis, one would still expect to develop algorithms ensuring higher level of intelligence and robustness. We present a fully functional software package for automatic processing of the two-color microarray images including spot localization, quantification and quality control. The developed algorithms aim at making ratio estimates more resistant to array contamination and offer automatic tools to evaluate spot quality.

AVAILABILITY

A demo version of the software can be downloaded from http://bioinfo.curie.fr/projects/maia. A full version is freely available to non-commercial users upon request from the authors.

Technology insight: pharmacoproteomics for cancer--promises of patient-tailored medicine using protein microarrays

Patient-tailored medicine can be defined as the selection of specific therapeutics to treat disease in a particular individual based on genetic, genomic or proteomic information. While individualized treatments have been used in medicine for years, advances in cancer treatment have now generated a need to more precisely define and identify those patients who will derive the most benefit from new-targeted agents. Cellular signaling pathways are a protein-based network, and the intended drug effect is to disrupt aberrant protein phosphorylation-based enzymatic activity and epigenetic phenomena. Pharmacoproteomics, or the tailoring of therapy based on proteomic knowledge, will begin to take a central role in this process. A new type of protein array platform, the reverse-phase protein microarray, shows potential for providing detailed information about the state of the cellular 'circuitry' from small samples such as patient biopsy specimens. Measurements of hundreds of specific phosphorylated proteins that span large classes of important signaling pathways can be obtained at once from only a few thousand cells. Clinical implementation of these new proteomic tools to aid the clinical, medical and surgical oncologist in making decisions about patient care will now require thoughtful communication between practicing clinicians and research scientists.

Signal stability of Cy3 and Cy5 on antibody microarrays

Background

The antibody microarray technique is a newly emerging proteomics tool for differential protein expression analyses that uses fluorescent dyes Cy 3 and Cy 5. Environmental factors, such as light exposure, can affect the signal intensity of fluorescent dyes on microarray slides thus, it is logical to scan microarray slides immediately after the final wash and drying processes. However, no research data are available concerning time-dependent changes of fluorescent signals on antibody microarray slides to this date. In the present study, microarray slides were preserved at -20°C after regular microarray experiments and were rescanned at day 10, 20 and 30 to evaluate change in signal intensity.

Results

Fluorescent intensities of microarray spots were detected using a confocal laser scanner after the experiment at day 0, and re-examined at day 10, 20 and 30, respectively. Fluorescent intensities of rescanned microarray spots did not show significant changes when compared with those scanned immediately after standard microarray experiments.

Conclusion

Microarray slides can be preserved and rescanned multiple times using a confocal laser scanner over a period of days or weeks.

A new two-color Fab labeling method for autoantigen protein microarrays

Antigen microarrays hold great promise for profiling the humoral immune response in the settings of autoimmunity, allergy and cancer. This approach involves immobilizing antigens on a slide surface and then exposing the array to biological fluids containing immunoglobulins. Although these arrays have proven extremely useful as research tools, they suffer from several sources of variability. To address these issues, we have developed a new two-color Fab labeling method that allows two samples to be applied simultaneously to the same array. This straightforward labeling approach improves reproducibility and reliably detects changes in autoantibody concentrations. Using this technique we profiled serum from a mouse model of systemic lupus erythematosus (SLE) and detected both expected and previously unrecognized reactivities. The improved labeling and detection method described here overcomes several problems that have hindered antigen microarrays and should facilitate translation to the clinical setting.

Antibody Arrays: Technical Considerations and Clinical Applications in Cancer

Antibody arrays represent one of the high-throughput techniques that are able to detect multiple proteins simultaneously. One of the main advantages of this technology over other proteomic approaches is that the identities of the measured proteins are known or can be readily characterized, allowing a biological interpretation of the results. Features such as lower sample volume and antibody concentration requirements, higher format versatility, and reproducibility support the increasing use of antibody arrays in cancer research. Clinical applications include disease marker discovery for diagnosis, prognosis, and drug response, characterization of signaling and protein pathways, and modifications associated with disease development and progression. This report presents an overview of technical issues of the main antibody array formats and various applications in cancer research. Antibody arrays are high-throughput tools that improve the functional characterization of molecular bases for disease. Furthermore, identification and validation of protein expression patterns, characteristic of cancer progression, and tumor subtypes may intervene and improve tailored therapies in the clinical management of cancer patients.

Profiling cancer stem cells using protein array technology

Since cancer cells and somatic stem cells share the biological characteristics of self-renewal and proliferation, it has been suggested that the principles of stem cell biology can be applied to improve our understanding of cancer biology. Recent studies have shown that the majority of cancers appear to originate from a small subset of cells that have the ability of self-renewal and to proliferate, namely 'cancer stem cells'. The isolation of cancer stem cells has been demonstrated using cell surface markers in haematopoietic and non-haematopoietic malignancies. Advances in protein array technologies have enabled the use of minuscule amounts of biological materials to profile these cells at the molecular level. Using a combination of protein arrays and cancer stem cell isolation techniques, a higher resolution molecular profiling can be performed, which might improve therapies targeting the cancer stem cells.

Recent advances of protein microarrays

Technological innovations and novel applications have greatly advanced the field of protein microarrays. Over the past two years, different types of protein microarrays have been used for serum profiling, protein abundance determinations, and identification of proteins that bind DNA or small compounds. However, considerable development is still required to ensure common quality standards and to establish large content repertoires. Here, we summarize applications available to date and discuss recent technological achievements and efforts on standardization.