PMA: Protein Microarray Analyser, a user-friendly tool for data processing and normalization

BAPCP: A comprehensive and user-friendly web tool for identifying biomarkers from protein microarray technologies

BACKGROUND AND OBJECTIVE

Proteome microarrays are one of the popular high-throughput screening methods for large-scale investigation of protein interactions in cells. These interactions can be measured on protein chips when coupled with fluorescence-labeled probes, helping indicate potential biomarkers or discover drugs. Several computational tools were developed to help analyze the protein chip results. However, existing tools fail to provide a user-friendly interface for biologists and present only one or two data analysis methods suitable for limited experimental designs, restricting the use cases.

METHODS

In order to facilitate the biomarker examination using protein chips, we implemented a user-friendly and comprehensive web tool called BAPCP (Biomarker Analysis tool for Protein Chip Platforms) in this research to deal with diverse chip data distributions.

RESULTS

BAPCP is well integrated with standard chip result files and includes 7 data normalization methods and 7 custom-designed quality control/differential analysis filters for biomarker extraction among experiment groups. Moreover, it can handle cost-efficient chip designs that repeat several blocks/samples within one single slide. Using experiments of the human coronavirus (HCoV) protein microarray and the E. coli proteome chip that helps study the immune response of Kawasaki disease as examples, we demonstrated that BAPCP can accelerate the time-consuming week-long manual biomarker identification process to merely 3 min.

CONCLUSIONS

The developed BAPCP tool provides substantial analysis support for protein interaction studies and conforms to the necessity of expanding computer usage and exchanging information in bioscience and medicine. The web service of BAPCP is available at https://cosbi.ee.ncku.edu.tw/BAPCP/.

Humoral immunoprofiling identifies novel biomarkers and an immune suppressive autoantibody phenotype at the site of disease in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous cancer, with minimal response to therapeutic intervention and with 85% of cases diagnosed at an advanced stage due to lack of early symptoms, highlighting the importance of understanding PDAC immunology in greater detail. Here, we applied an immunoproteomic approach to investigate autoantibody responses against cancer-testis and tumor-associated antigens in PDAC using a high-throughput multiplexed protein microarray platform, comparing humoral immune responses in serum and at the site of disease in order to shed new light on immune responses in the tumor microenvironment. We simultaneously quantified serum or tissue IgG and IgA antibody isotypes and subclasses in a cohort of PDAC, disease control and healthy patients, observing inter alia that subclass utilization in tumor tissue samples was predominantly immune suppressive IgG4 and inflammatory IgA2, contrasting with predominant IgG3 and IgA1 subclass utilization in matched sera and implying local autoantibody production at the site of disease in an immune-tolerant environment. By comparison, serum autoantibody subclass profiling for the disease controls identified IgG4, IgG1, and IgA1 as the abundant subclasses. Combinatorial analysis of serum autoantibody responses identified panels of candidate biomarkers. The top IgG panel included ACVR2B, GAGE1, LEMD1, MAGEB1 and PAGE1 (sensitivity, specificity and AUC values of 0.933, 0.767 and 0.906). Conversely, the top IgA panel included AURKA, GAGE1, MAGEA10, PLEKHA5 and XAGE3aV1 (sensitivity, specificity, and AUC values of 1.000, 0.800, and 0.954). Assessment of antigen-specific serum autoantibody glycoforms revealed abundant sialylation on IgA in PDAC, consistent with an immune suppressive IgA response to disease.

Protein array processing software for automated semiquantitative analysis of serum antibody repertoires

Effective immunotherapies activate natural antitumor immune responses in patients undergoing treatment. The ability to monitor immune activation in response to immunotherapy is critical in measuring treatment efficacy over time and across patient cohorts. Protein arrays are systematically arranged, large collections of annotated proteins on planar surfaces, which can be used for the characterization of disease-specific and treatment-induced antibody repertoires in individuals undergoing immunotherapy. However, the absence of appropriate image analysis and data processing software presents a substantial hurdle, limiting the uptake of this approach in immunotherapy research. We developed a first, automated semiquantitative open-source software package for the analysis of widely used protein macroarrays. The software allows accurate single array and inter-array comparative studies through the tackling of intra-array inconsistencies arising from experimental disparities. The innovative and automated image analysis process includes adaptive positioning, background identification and subtraction, removal of null signals, robust statistical analysis, and protein pair validation. The normalized values allow a convenient semiquantitative data analysis of different samples or timepoints. Enabling accurate characterization of sample series to identify disease-specific immune profiles or their relative changes in response to treatment may serve as a diagnostic or predictive tool of disease.

Auto-immunoproteomics analysis of COVID-19 ICU patients revealed increased levels of autoantibodies related to the male reproductive system

Background: Coronavirus disease (COVID-19) manifests many clinical symptoms, including an exacerbated immune response and cytokine storm. Autoantibodies in COVID-19 may have severe prodromal effects that are poorly understood. The interaction between these autoantibodies and self-antigens can result in systemic inflammation and organ dysfunction. However, the role of autoantibodies in COVID-19 complications has yet to be fully understood. Methods: The current investigation screened two independent cohorts of 97 COVID-19 patients [discovery (Disc) cohort from Qatar (case = 49 vs. control = 48) and replication (Rep) cohort from New York (case = 48 vs. control = 28)] utilizing high-throughput KoRectly Expressed (KREX) Immunome protein-array technology. Total IgG autoantibody responses were evaluated against 1,318 correctly folded and full-length human proteins. Samples were randomly applied on the precoated microarray slides for 2 h. Cy3-labeled secondary antibodies were used to detect IgG autoantibody response. Slides were scanned at a fixed gain setting using the Agilent fluorescence microarray scanner, generating a 16-bit TIFF file. Group comparisons were performed using a linear model and Fisher's exact test. Differentially expressed proteins were used for KEGG and WIKIpathway annotation to determine pathways in which the proteins of interest were significantly over-represented. Results and conclusion: Autoantibody responses to 57 proteins were significantly altered in the COVID-19 Disc cohort compared to healthy controls (p ≤ 0.05). The Rep cohort had altered autoantibody responses against 26 proteins compared to non-COVID-19 ICU patients who served as controls. Both cohorts showed substantial similarities (r 2 = 0.73) and exhibited higher autoantibody responses to numerous transcription factors, immunomodulatory proteins, and human disease markers. Analysis of the combined cohorts revealed elevated autoantibody responses against SPANXN4, STK25, ATF4, PRKD2, and CHMP3 proteins in COVID-19 patients. The sequences for SPANXN4 and STK25 were cross-validated using sequence alignment tools. ELISA and Western blot further verified the autoantigen-autoantibody response of SPANXN4. SPANXN4 is essential for spermiogenesis and male fertility, which may predict a potential role for this protein in COVID-19-associated male reproductive tract complications, and warrants further research.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

Pro-MAP: a robust pipeline for the pre-processing of single channel protein microarray data

BACKGROUND

The central role of proteins in diseases has made them increasingly attractive as therapeutic targets and indicators of cellular processes. Protein microarrays are emerging as an important means of characterising protein activity. Their accurate downstream analysis to produce biologically significant conclusions is largely dependent on proper pre-processing of extracted signal intensities. However, existing computational tools are not specifically tailored to the nature of these data and lack unanimity.

RESULTS

Here, we present the single-channel Protein Microarray Analysis Pipeline, a tailored computational tool for analysis of single-channel protein microarrays enabling biomarker identification, implemented in R, and as an interactive web application. We compared four existing background correction and normalization methods as well as three array filtering techniques, applied to four real datasets with two microarray designs, extracted using two software programs. The normexp, cyclic loess, and array weighting methods were most effective for background correction, normalization, and filtering respectively.

CONCLUSIONS

Thus, here we provided a versatile and effective pre-processing and differential analysis workflow for single-channel protein microarray data in form of an R script and web application ( https://metaomics.uct.ac.za/shinyapps/Pro-MAP/ .) for those not well versed in the R programming language.

Heterosubtypic, cross-reactive immunity to human Cytomegalovirus glycoprotein B

Cytomegalovirus (CMV) genome is highly variable and heterosubtypic immunity should be considered in vaccine development since it can enhance protection in a cross-reactive manner. Here, we developed a protein array to evaluate heterosubtypic immunity to CMV glycoprotein B (gB) in natural infection and vaccination. DNA sequences of four antigenic domains (AD1, AD2, AD4/5, and AD5) of gB were amplified from six reference and 12 clinical CMV strains, and the most divergent genotypes were determined by phylogenetic analysis. Assigned genotypes were in vitro translated and immobilized on protein array. Then, we tested immune response of variable serum groups (primarily infected patients, reactivated CMV infections and healthy individuals with latent CMV infection, as well gB-vaccinated rabbits) with protein in situ array (PISA). Serum antibodies of all patient cohorts and gB-vaccinated rabbits recognized many genetic variants of ADs on protein array, including but not limited to the subtype of infecting strain. High-grade cross-reactivity was observed. In several patients, we observed none or neglectable immune response to AD1 and AD2, while the same patients showed high antibody response to AD4/5 and AD5. Among the primary infected patients, AD5 was the predominant AD, in antibody response. The most successful CMV vaccine to date contains gB and demonstrates only 50% efficacy. In this study, we showed that heterosubtypic and cross-reactive immunity to CMV gB is extensive. Therefore, the failure of CMV gB vaccines cannot be explained by a highly, strain-specific immunity. Our observations suggest that other CMV antigens should be addressed in vaccine design.

protGear: A protein microarray data pre-processing suite

Protein microarrays are versatile tools for high throughput study of the human proteome, but systematic and non-systematic sources of bias constrain optimal interpretation and the ultimate utility of the data. Published guidelines to limit technical variability whilst maintaining important biological variation favour DNA-based microarrays that often differ fundamentally in their experimental design. Rigorous tools to guide background correction, the quantification of within-sample variation, normalisation, and batch correction specifically for protein microarrays are limited, require extensive investigation and are not centrally accessible.

Here, we develop a generic one-stop-shop pre-processing suite for protein microarrays that is compatible with data from the major protein microarray scanners. Our graphical and tabular interfaces facilitate a detailed inspection of data and are coupled with supporting guidelines that enable users to select the most appropriate algorithms to systematically address bias arising in customized experiments. The localization and distribution of background signal intensities determine the optimal correction strategy. A novel function overcomes the limitations in the interpretation of the coefficient of variation when signal intensities are at the lower end of the detection threshold. We demonstrate essential considerations in the experimental design and their impact on a range of algorithms for normalization and minimization of batch effects.

Our user-friendly interactive web-based platform eliminates the need for prowess in programming. The open-source R interface includes illustrative examples, generates an auditable record, enables reproducibility, and can incorporate additional custom scripts through its online repository. This versatility will enhance its broad uptake in the infectious disease and vaccine development community.

Ropporin-1 and 1B Are Widely Expressed in Human Melanoma and Evoke Strong Humoral Immune Responses

Antibodies that block immune regulatory checkpoints (programmed cell death 1, PD-1 and cytotoxic T-lymphocyte-associated antigen 4, CTLA-4) to mobilise immunity have shown unprecedented clinical efficacy against cancer, demonstrating the importance of antigen-specific tumour recognition. Despite this, many patients still fail to benefit from these treatments and additional approaches are being sought. These include mechanisms that boost antigen-specific immunity either by vaccination or adoptive transfer of effector cells. Other than neoantigens, epigenetically regulated and shared antigens such as NY-ESO-1 are attractive targets; however, tissue expression is often heterogeneous and weak. Therefore, peptide-specific therapies combining multiple antigens rationally selected to give additive anti-cancer benefits are necessary to achieve optimal outcomes. Here, we show that Ropporin-1 (ROPN1) and 1B (ROPN1B), cancer restricted antigens, are highly expressed and immunogenic, inducing humoral immunity in patients with advanced metastatic melanoma. By multispectral immunohistochemistry, 88.5% of melanoma patients tested (n = 54/61) showed ROPN1B expression in at least 1 of 2/3 tumour cores in tissue microarrays. Antibody responses against ROPN1A and ROPN1B were detected in 71.2% of melanoma patients tested (n = 74/104), with increased reactivity seen with more advanced disease stages. Thus, ROPN1A and ROPN1B may indeed be viable targets for cancer immunotherapy, alone or in combination with other cancer antigens, and could be combined with additional therapies such as immune checkpoint blockade.

Dual Antiangiogenesis Agents Bevacizumab Plus Trebananib, without Chemotherapy, in First-line Treatment of Metastatic Colorectal Cancer: Results of a Phase II Study

PURPOSE

To assess the efficacy and safety of dual antiangiogenesis agents, bevacizumab plus trebananib, without chemotherapy, in first-line treatment of metastatic colorectal cancer (mCRC).

PATIENTS AND METHODS

This open-label phase II study enrolled patients with unresectable mCRC with no prior systemic treatment. All patients received bevacizumab 7.5 mg/kg 3-weekly and trebananib 15 mg/kg weekly. The primary endpoint was disease control [stable disease, partial response (PR), or complete response (CR)] at 6 months (DC6m). Secondary endpoints included toxicity, overall response rate (ORR), progression-free survival (PFS), and overall survival (OS). Exploratory biomarkers in plasma angiogenesis-related proteins, tumor gene expression, and plasma antibodies to tumor antigens were examined.

RESULTS

Forty-five patients were enrolled from four Australian sites. DC6m was 63% [95% confidence interval (CI), 47-77]. ORR was 17% (95% CI, 7-32), comprising of seven PRs. Median duration of response was 20 months (range, 10-48 months). Median PFS was 8.4 months and median OS 31.4 months. Grade 1-2 peripheral edema and joint-related symptoms were common. Overall incidence of grade 3-4 adverse events (AE) of any type was 33% (n = 15). Expected AEs of bevacizumab treatment did not appear to be increased by the addition of trebananib.

CONCLUSIONS

In a first-line mCRC population, the dual antiangiogenic combination, bevacizumab plus trebananib, without chemotherapy, was efficacious with durable responses. The toxicity profile of the combination was manageable and did not exceed that expected with bevacizumab +/- chemotherapy. Exploratory biomarker results raise the hypothesis that the antiangiogenic combination may enable the antitumor immune response in immunotolerant colorectal cancer.

Antibody Printing Technologies

Antibody microarrays are routinely employed in the lab and in the clinic for studying protein expression, protein-protein, and protein-drug interactions. The microarray format reduces the size scale at which biological and biochemical interactions occur, leading to large reductions in reagent consumption and handling times while increasing overall experimental throughput. Specifically, antibody microarrays, as a platform, offer a number of different advantages over traditional techniques in the areas of drug discovery and diagnostics. While a number of different techniques and approaches have been developed for creating micro and nanoscale antibody arrays, issues relating to sensitivity, cost, and reproducibility persist. The aim of this review is to highlight current state-of the-art techniques and approaches for creating antibody arrays by providing latest accounts of the field while discussing potential future directions.

PAWER: protein array web exploreR

Background

Protein microarray is a well-established approach for characterizing activity levels of thousands of proteins in a parallel manner. Analysis of protein microarray data is complex and time-consuming, while existing solutions are either outdated or challenging to use without programming skills. The typical data analysis pipeline consists of a data preprocessing step, followed by differential expression analysis, which is then put into context via functional enrichment. Normally, biologists would need to assemble their own workflow by combining a set of unrelated tools to analyze experimental data. Provided that most of these tools are developed independently by various bioinformatics groups, making them work together could be a real challenge.

Results

Here we present PAWER, the online web tool dedicated solely to protein microarray analysis. PAWER enables biologists to carry out all the necessary analysis steps in one go. PAWER provides access to state-of-the-art computational methods through the user-friendly interface, resulting in publication-ready illustrations. We also provide an R package for more advanced use cases, such as bespoke analysis workflows.

Conclusions

PAWER is freely available at https://biit.cs.ut.ee/pawer.

Gain-Scanning for Protein Microarray Assays

Protein microarrays consist of known proteins spotted onto solid substrates and are used to perform highly multivariate assessments of protein-binding interactions. Human protein arrays are routinely applied to pathogen detection, immune response biomarker profiling, and antibody specificity profiling. Here, we describe and demonstrate a new data processing procedure, gain-scan, in which data were acquired under multiple photomultiplier tube (PMT) settings, followed by data fitting with a power function model to estimate the incident light signals of the array spots. Data acquisition under multiple PMT settings solves the difficulty of determining the single optimal PMT gain setting and allows us to maximize the detection of low-intensity signals while avoiding the saturation of high-intensity ones at the same time. The gain-scan data acquisition and fitting also significantly lower the variances over the detectable range of signals and improve the linear data normalization. The performance of the proposed procedure was verified by analyzing the profiling data of both the human polyclonal serum samples and the monoclonal antibody samples with both technical replicates and biological replicates. We showed that the multigain power function was an appropriate model for describing data acquired under multiple PMT settings. The gain-scan fitting alone or in combination with the linear normalization could effectively reduce the technical variability of the array data and lead to better sample separability and more sensitive differential analysis.

Immunoprofiling of Breast Cancer Antigens Using Antibodies Derived from Local Lymph Nodes

Tumor antigens are responsible for initiating an immune response in cancer patients, and their identification may provide new biomarkers for cancer diagnosis and targets for immunotherapy. The general use of serum antibodies to identify tumor antigens has several drawbacks, including dilution, complex formation, and background reactivity. In this study, antibodies were generated from antibody-secreting cells (ASC) present in tumor-draining lymph nodes of 20 breast cancer patients (ASC-probes) and were used to screen breast cancer cell lines and protein microarrays. Half of the ASC-probes reacted strongly against extracts of the MCF-7 breast cancer cell line, but each with a distinct antigen recognition profile. Three of the positive ASC-probes reacted differentially with recombinant antigens on a microarray containing cancer-related proteins. The results of this study show that lymph node-derived ASC-probes provide a highly specific source of tumor-specific antibodies. Each breast cancer patient reacts with a different antibody profile which indicates that targeted immunotherapies may need to be personalized for individual patients. Focused microarrays in combination with ASC-probes may be useful in providing immune profiles and identifying tumor antigens of individual cancer patients.

A decade of Nucleic Acid Programmable Protein Arrays (NAPPA) availability: News, actors, progress, prospects and access

Understanding the dynamic of the proteome is a critical challenge because it requires high sensitive methodologies in high-throughput formats in order to decipher its modifications and complexity. While molecular biology provides relevant information about cell physiology that may be reflected in post-translational changes, High-Throughput (HT) experimental proteomic techniques are essential to provide valuable functional information of the proteins, peptides and the interconnections between them. Hence, many methodological developments and innovations have been reported during the last decade. To study more dynamic protein networks and fine interactions, Nucleic Acid Programmable Protein Arrays (NAPPA) was introduced a decade ago. The tool is rapidly maturing and serving as a gateway to characterize biological systems and diseases thanks primarily to its accuracy, reproducibility, throughput and flexibility. Currently, NAPPA technology has proved successful in several research areas adding valuable information towards innovative diagnostic and therapeutic applications. Here, the basic and latest advances within this modern technology in basic, translational research are reviewed, in addition to presenting its exciting new directions. Our final goal is to encourage more scientists/researchers to incorporate this method, which can help to remove bottlenecks in their particular research or biomedical projects. SIGNIFICANCE: Nucleic Acid Programmable Protein Arrays (NAPPA) is becoming an essential tool for functional proteomics and protein-protein interaction studies. The technology impacts decisively on projects aiming massive screenings and the latest innovations like the multiplexing capability or printing consistency make this a promising method to be integrated in novel and combinatorial proteomic approaches.