Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics

Fundamental Uses of Peptides as a New Model in Both Treatment and Diagnosis

An amino acid short chain is known as a peptide. Peptide bonds are the connections that hold the amino acids of a peptide together in a particular order. Characteristically, the shorter length of peptides helps to identify them from proteins. Different ways are used to classify peptides, including chain length, source of peptides, or their biological functions. The fact that peptides serve several purposes suggests that there is a foundation for improvement in peptide production and structure to enhance action. In addition, many patents on peptides for therapeutic and diagnostic approaches have been obtained. This review aims to give an overview of peptides used recently in treatment and diagnosis.

Pushing the detection limits: strategies towards highly sensitive optical-based protein detection

Proteins are one of the main constituents of living cells. Studying the quantities of proteins under physiological and pathological conditions can give valuable insights into health status, since proteins are the functional molecules of life. To be able to detect and quantify low-abundance proteins in biofluids for applications such as early disease diagnostics, sensitive analytical techniques are desired. An example of this application is using proteins as biomarkers for detecting cancer or neurological diseases, which can provide early, lifesaving diagnoses. However, conventional methods for protein detection such as ELISA, mass spectrometry, and western blotting cannot offer enough sensitivity for certain applications. Recent advances in optical-based micro- and nano-biosensors have demonstrated promising results to detect proteins at low quantities down to the single-molecule level, shining lights on their capacities for ultrasensitive disease diagnosis and rare protein detection. However, to date, there is a lack of review articles synthesizing and comparing various optical micro- and nano-sensing methods of enhancing the limits of detections of the antibody-based protein assays. The purpose of this article is to critically review different strategies of improving assay sensitivity using miniaturized biosensors, such as assay miniaturization, improving antibody binding capacity, sample purification, and signal amplification. The pros and cons of different methods are compared, and the future perspectives of this research field are discussed.

Spatial Bias in Antibody Microarrays May Be an Underappreciated Source of Variability

Antibody microarrays enable multiplexed protein detection with minimal reagent consumption, but they continue to be plagued by lack of reproducibility. Chemically functionalized glass slides are used as substrates, yet antibody binding spatial inhomogeneity across the slide has not been analyzed in antibody microarrays. Here, we characterize spatial bias across five commercial slides patterned with nine overlapping dense arrays (by combining three buffers and three different antibodies), and we measure signal variation for both antibody immobilization and the assay signal, generating 270 heatmaps. Spatial bias varied across models, and the coefficient of variation ranged from 4.6 to 50%, which was unexpectedly large. Next, we evaluated three layouts of spot replicates-local, random, and structured random-for their capacity to predict assay variation. Local replicates are widely used but systematically underestimate the whole-slide variation by up to seven times; structured random replicates gave the most accurate estimation. Our results highlight the risk and consequences of using local replicates: the underappreciation of spatial bias as a source of variability, poor assay reproducibility, and possible overconfidence in assay results. We recommend the detailed characterization of spatial bias for antibody microarrays and the description and use of distributed positive replicates for research and clinical applications.

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.

Studies in lung cancer cytokine proteomics: a review

INTRODUCTION

Proteins are molecules that have role in the progression of the diseases. Proteomics is a tool that can play an effective role in identifying diagnostic and therapeutic biomarkers for lung cancer. Cytokines are proteins that play a decisive role in activating body's immune system in lung cancer. They can increase the growth of the tumor (oncogenic cytokines) or limit tumor growth (anti-tumor cytokines) by regulating related signaling pathways such as proliferation, growth, metastasis, and apoptosis.

AREAS COVERED

In the present study, a total of 223 papers including 196 research papers and 27 review papers, extracted from PubMed and Scopus and published from 1997 to present, are reviewed. The most important involved-cytokines in lung cancer including TNF-α, IFN- γ, TGF-β, VEGF and interleukins such as IL-6, IL-17, IL-8, IL-10, IL-22, IL-1β and IL-18 are introduced. Also, the pathological and biological role of such cytokines in cancer signaling pathways is explained.

EXPERT OPINION

In lung cancer, the cytokine expression changes under the physiological conditions of the immune system, and inflammatory cytokines are associated with the progression of lung cancer. Therefore, the cytokine expression profile can be used in the diagnosis, prognosis, prediction of therapeutic responses, and survival of patients with lung cancer.

Protein Microarrays for Ocular Diseases

The eye is a multifaceted organ organized in several compartments with particular properties that reflect their diverse functions. The prevalence of ocular diseases is increasing, mainly because of its relationship with aging and of generalized lifestyle changes. However, the pathogenic molecular mechanisms of many common eye pathologies remain poorly understood. Considering the unquestionable importance of proteins in cellular processes and disease progression, proteomic techniques, such as protein microarrays, represent a valuable approach to analyze pathophysiological protein changes in the ocular environment. This technology enables to perform multiplex high-throughput protein expression profiling with minimal sample volume requirements broadening our knowledge of ocular proteome network in eye diseases.In this review, we present a brief summary of the main types of protein microarrays (antibody microarrays, reverse-phase protein microarrays, and protein microarrays) and their application for protein change detection in chronic ocular diseases such as dry eye, age-related macular degeneration, diabetic retinopathy, and glaucoma. The validation of these specific protein changes in eye pathologies may lead to the identification of new biomarkers, depiction of ocular disease pathways, and assistance in the diagnosis, prognosis, and development of new therapeutic options for eye pathologies.

Proteomic Data Analysis for Differential Profiling of the Autoimmune Diseases SLE, RA, SS, and ANCA-Associated Vasculitis

Early and correct diagnosis of inflammatory rheumatic diseases (IRD) poses a clinical challenge due to the multifaceted nature of symptoms, which also may change over time. The aim of this study was to perform protein expression profiling of four systemic IRDs, systemic lupus erythematosus (SLE), ANCA-associated systemic vasculitis (SV), rheumatoid arthritis (RA), and Sjögren’s syndrome (SS), and healthy controls to identify candidate biomarker signatures for differential classification. A total of 316 serum samples collected from patients with SLE, RA, SS, or SV and from healthy controls were analyzed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was examined using Wilcoxon signed rank test, and condensed biomarker panels were identified using advanced bioinformatics and state-of-the art classification algorithms to pinpoint signatures reflecting each disease (raw data set available at https://figshare.com/s/3bd3848a28ef6e7ae9a9.). In this study, we were able to classify the included individual IRDs with high accuracy, as demonstrated by the ROC area under the curve (ROC AUC) values ranging between 0.96 and 0.80. In addition, the groups of IRDs could be separated from healthy controls at an ROC AUC value of 0.94. Disease-specific candidate biomarker signatures and general autoimmune signature were identified, including several deregulated analytes. This study supports the rationale of using multiplexed affinity-based technologies to reflect the biological complexity of autoimmune diseases. A multiplexed approach for decoding multifactorial complex diseases, such as autoimmune diseases, will play a significant role for future diagnostic purposes, essential to prevent severe organ- and tissue-related damage.

Immune-related protein signature in serum stratify relapsed mantle cell lymphoma patients based on risk

Background

Response to modern treatment strategies, which combine cytotoxic compounds with immune stimulatory agents and targeted treatment is highly variable among MCL patients. Thus, providing prognostic and predictive markers for risk adapted therapy is warranted and molecular information that can help in patient stratification is a necessity. In relapsed MCL, biopsies are rarely available and molecular information from tumor tissue is often lacking. Today, the main tool to access risk is the MCL international prognostic index (MIPI), which does not include detailed biological information of relevance for different treatment options. To enable continuous monitoring of patients, non-invasive companion diagnostic tools are needed which can further reduce cost and patient distress and enable efficient measurements of biological markers.

Methods

We have assessed if serum-based protein profiling can identify immune related proteins that stratify relapsed MCL patients based on risk. Overall, 371 scFv targeting 158 proteins were assessed using an antibody microarray platform. We profiled patients (n = 44) who had been treated within the MCL6-Philemon trial combining targeted and immune-modulatory treatment.

Results

The downstream processing led to the identification of the relapsed immune signature (RIS) consisting of 11 proteins with potential to stratify patients with long and short overall survival (OS). Moreover, in this population, MIPI alone failed to separate high, intermediate and low risk patients, but a combined index based on MIPI together with RIS, MIPI_ris, showed improved performance and significantly stratified all three risk groups based on OS.

Conclusions

Our results show that addition of biological parameters to previous prognostic indices improves patient stratification among patients treated with BTK inhibitor triplet combination, particularly, in the identification of an extreme high risk group.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07678-4.

Antibody Micropatterned Lubricant-Infused Biosensors Enable Sub-Picogram Immunofluorescence Detection of Interleukin 6 in Human Whole Plasma

Recent studies have shown a correlation between elevated interleukin 6 (IL-6) concentrations and the risk of respiratory failure in COVID-19 patients. Therefore, detection of IL-6 at low concentrations permits early diagnosis of worst-case outcome in viral respiratory infections. Here, a versatile biointerface is presented that eliminates nonspecific adhesion and thus enables immunofluorescence detection of IL-6 in whole human plasma or whole human blood during coagulation, down to a limit of detection of 0.5 pg mL^-1 . The sensitivity of the developed lubricant-infused biosensor for immunofluorescence assays in detecting low molecular weight proteins such as IL-6 is facilitated by i) producing a bioink in which the capture antibody is functionalized by an epoxy-based silane for covalent linkage to the fluorosilanized surface and ii) suppressing nonspecific adhesion by patterning the developed bioink into a lubricant-infused coating. The developed biosensor addresses one of the major challenges for biosensing in complex fluids, namely nonspecific adhesion, therefore paving the way for highly sensitive biosensing in complex fluids.

An array of 60,000 antibodies for proteome-scale antibody generation and target discovery

Antibodies are essential for elucidating gene function. However, affordable technology for proteome-scale antibody generation does not exist. To address this, we developed Proteome Epitope Tag Antibody Library (PETAL) and its array. PETAL consists of 62,208 monoclonal antibodies (mAbs) against 15,199 peptides from diverse proteomes. PETAL harbors binders for a great multitude of proteins in nature due to antibody multispecificity, an intrinsic antibody feature. Distinctive combinations of 10,000 to 20,000 mAbs were found to target specific proteomes by array screening. Phenotype-specific mAb-protein pairs were found for maize and zebrafish samples. Immunofluorescence and flow cytometry mAbs for membrane proteins and chromatin immunoprecipitation-sequencing mAbs for transcription factors were identified from respective proteome-binding PETAL mAbs. Differential screening of cell surface proteomes of tumor and normal tissues identified internalizing tumor antigens for antibody-drug conjugates. By finding high-affinity mAbs at a fraction of current time and cost, PETAL enables proteome-scale antibody generation and target discovery.

Protein Microarrays: Valuable Tools for Ocular Diseases Research

The eye is a complex organ comprised of several compartments with exclusive and specialized properties that reflect their diverse functions. Although the prevalence of eye pathologies is increasing, mainly because of its correlation with aging and of generalized lifestyle changes, the pathogenic molecular mechanisms of many common ocular diseases remain poorly understood. Therefore, there is an unmet need to delve into the pathogenesis, diagnosis, and treatment of eye diseases to preserve ocular health and reduce the incidence of visual impairment or blindness. Proteomics analysis stands as a valuable tool for deciphering protein profiles related to specific ocular conditions. In turn, such profiles can lead to real breakthroughs in the fields of ocular science and ophthalmology. Among proteomics techniques, protein microarray technology stands out by providing expanded information using very small volumes of samples. In this review, we present a brief summary of the main types of protein microarrays and their application for the identification of protein changes in chronic ocular diseases such as dry eye, glaucoma, age-related macular degeneration, or diabetic retinopathy. The validation of these specific protein alterations could provide new biomarkers, disclose eye diseases pathways, and help in the diagnosis and development of novel therapies for eye pathologies.

The role of proteomics in the age of immunotherapies

The antigenic landscape of the adaptive immune response is determined by the peptides presented by immune cells. In recent years, a number of immune-based cancer therapies have been shown to induce remarkable clinical responses through the activation of the patient's immune system. As a result, there is a need to identify immune biomarkers capable of predicting clinical response. Recent advances in proteomics have led to considerable developments in the more comprehensive profiling of the immune response. "Immunoproteomics" utilises a rapidly increasing collection of technologies in order to identify and quantify antigenic peptides or proteins. This includes gel-based, array-based, mass spectrometry (MS), DNA-based, or computer-based (in silico) approaches. Immunoproteomics is yielding an understanding of disease and disease progression, vaccine candidates, and biomarkers to a depth not before understood. This review gives an overview of the emerging role of proteomics in improving personalisation of immunotherapy treatment.

The effects of antigen size, binding site valency, and flexibility on fab-antigen binding near solid surfaces

Next generation antibody microarray devices have the potential to outperform current molecular detection methods and realize new applications in medicine, scientific research, and national defense. However, antibody microarrays, or arrays of antibody fragments ("fabs"), continue to evade mainstream use in part due to persistent reliability problems despite improvements to substrate design and protein immobilization strategies. Other factors could be disrupting microarray performance, including effects resulting from antigen characteristics. Target molecules embody a wide range of sizes, shapes, number of epitopes, epitope accessibility, and other physical and chemical properties. As a result, it may not be ideal for microarray designs to utilize the same substrate or immobilization strategy for all of the capture molecules. This study investigates how three antigen properties, such as size, binding site valency, and molecular flexibility, affect fab binding. The work uses an advanced, experimentally validated, coarse-grain model and umbrella sampling to calculate the free energy of ligand binding and how this energy landscape is different on the surface compared to in the bulk. The results confirm that large antigens interact differently with immobilized fabs compared to smaller antigens. Analysis of the results shows that despite these differences, tethering fabs in an upright orientation on hydrophilic surfaces is the best configuration for antibody microarrays.

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

High-throughput serum proteomics for the identification of protein biomarkers of mortality in older men

The biological perturbations associated with incident mortality are not well elucidated, and there are limited biomarkers for the prediction of mortality. We used a novel high‐throughput proteomics approach to identify serum peptides and proteins associated with 5‐year mortality in community‐dwelling men age ≥65 years who participated in a longitudinal observational study of musculoskeletal aging (Osteoporotic Fractures in Men: MrOS). In a discovery phase, serum specimens collected at baseline in 2473 men were analyzed using liquid chromatography–ion mobility–mass spectrometry, and incident mortality in the subsequent 5 years was ascertained by tri‐annual questionnaire. Rigorous statistical methods were utilized to identify 56 peptides (31 proteins) that were associated with 5‐year mortality. In an independent replication phase, selected reaction monitoring was used to examine 21 of those peptides in baseline serum from 750 additional men; 81% of those peptides remained significantly associated with mortality. Mortality‐associated proteins included a variety involved in inflammation or complement activation; several have been previously linked to mortality (e.g., C‐reactive protein, alpha 1‐antichymotrypsin) and others are not previously known to be associated with mortality. Other novel proteins of interest included pregnancy‐associated plasma protein, VE‐cadherin, leucine‐rich α‐2 glycoprotein 1, vinculin, vitronectin, mast/stem cell growth factor receptor, and Saa4. A panel of peptides improved the predictive value of a commonly used clinical predictor of mortality. Overall, these results suggest that complex inflammatory pathways, and proteins in other pathways, are linked to 5‐year mortality risk. This work may serve to identify novel biomarkers for near‐term mortality.

Advances in the development of human protein microarrays

INTRODUCTION

High-content protein microarrays in principle enable the functional interrogation of the human proteome in a broad range of applications, including biomarker discovery, profiling of immune responses, identification of enzyme substrates, and quantifying protein-small molecule, protein-protein and protein-DNA/RNA interactions. As with other microarrays, the underlying proteomic platforms are under active technological development and a range of different protein microarrays are now commercially available. However, deciphering the differences between these platforms to identify the most suitable protein microarray for the specific research question is not always straightforward. Areas covered: This review provides an overview of the technological basis, applications and limitations of some of the most commonly used full-length, recombinant protein and protein fragment microarray platforms, including ProtoArray Human Protein Microarrays, HuProt Human Proteome Microarrays, Human Protein Atlas Protein Fragment Arrays, Nucleic Acid Programmable Arrays and Immunome Protein Arrays. Expert commentary: The choice of appropriate protein microarray platform depends on the specific biological application in hand, with both more focused, lower density and higher density arrays having distinct advantages. Full-length protein arrays offer advantages in biomarker discovery profiling applications, although care is required in ensuring that the protein production and array fabrication methodology is compatible with the required downstream functionality.

Current applications of antibody microarrays

The concept of antibody microarrays is one of the most versatile approaches within multiplexed immunoassay technologies. These types of arrays have increasingly become an attractive tool for the exploratory detection and study of protein abundance, function, pathways, and potential drug targets. Due to the properties of the antibody microarrays and their potential use in basic research and clinical analytics, various types of antibody microarrays have already been developed. In spite of the growing number of studies utilizing this technique, few reviews about antibody microarray technology have been presented to reflect the quality and future uses of the generated data. In this review, we provide a summary of the recent applications of antibody microarray techniques in basic biology and clinical studies, providing insights into the current trends and future of protein analysis.

Early Detection of Pancreatic Cancer: The Role of Industry in the Development of Biomarkers

A diagnosis of pancreatic cancer is devastating owing to its poor prognosis, with a 5-year survival rate of only 9%. Currently, most individuals are diagnosed at a late stage when treatment options are limited. Early detection of pancreatic cancer provides the greatest hope for making substantial improvements in survival. The Kenner Family Research Fund in partnership with the American Pancreatic Association has sponsored a series of fora to stimulate discussion and collaboration on early detection of pancreatic cancer. At the first forum in 2014, "Early Detection of Sporadic Pancreatic Cancer Summit Conference," a strategic plan was set forth by an international group of interdisciplinary scientific representatives and subsequently The Strategic Map for Innovation was generated. The current conference report is the third forum in the series, "Early Detection of Pancreatic Cancer: The Role of Industry in the Development of Biomarkers," which was held in Boston, Massachusetts, on October 27, 2016. This report provides an overview of examples of innovative initiatives by industry and confirms the critical need for collaboration among industry, government, research institutions, and advocacy groups in order to make pancreatic cancer more easily detectable in its earlier stages, when it is more treatable.

Tumor tissue protein signatures reflect histological grade of breast cancer

Histological grade is one of the most commonly used prognostic factors for patients diagnosed with breast cancer. However, conventional grading has proven technically challenging, and up to 60% of the tumors are classified as histological grade 2, which represents a heterogeneous cohort less informative for clinical decision making. In an attempt to study and extend the molecular puzzle of histologically graded breast cancer, we have in this pilot project searched for additional protein biomarkers in a new space of the proteome. To this end, we have for the first time performed protein expression profiling of breast cancer tumor tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics). Uniquely, the results showed that several biologically relevant proteins reflecting histological grade could be delineated. In more detail, the tentative biomarker panels could be used to i) build a candidate model classifying grade 1 vs. grade 3 tumors, ii) demonstrate the molecular heterogeneity among grade 2 tumors, and iii) potentially re-classify several of the grade 2 tumors to more like grade 1 or grade 3 tumors. This could, in the long-term run, lead to improved prognosis, by which the patients could benefit from improved tailored care.

Affinity chromatography: A versatile technique for antibody purification

Antibodies continue to be extremely utilized entities in myriad applications including basic research, imaging, targeted delivery, chromatography, diagnostics, and therapeutics. At production stage, antibodies are generally present in complex matrices and most of their intended applications necessitate purification. Antibody purification has always been a major bottleneck in downstream processing of antibodies, due to the need of high quality products and associated high costs. Over the years, extensive research has focused on finding better purification methodologies to overcome this holdup. Among a plethora of different techniques, affinity chromatography is one of the most selective, rapid and easy method for antibody purification. This review aims to provide a detailed overview on affinity chromatography and the components involved in purification. An array of support matrices along with various classes of affinity ligands detailing their underlying working principles, together with the advantages and limitations of each system in purifying different types of antibodies, accompanying recent developments and important practical methodological considerations to optimize purification procedure are discussed.