Microarray-based evaluation of selected recombinant timothy grass allergens expressed in E. Coli and N. Benthamiana.

BACKGROUND: Timothy grass (Phleum pratense) is a significant source of allergens, and recombinant allergens are increasingly used for diagnostic purposes. However, the performance of different recombinant allergen production systems in diagnostic assays needs further investigation to optimize their use in clinical settings. OBJECTIVE: The main objective of this study was to analyze and compare the diagnostic performance of recombinant timothy grass allergens produced in E. coli and N. benthamiana using a custom-made microarray chip. METHODS: Recombinant timothy grass allergens Phl p 1, Phl p 2, Phl p 5, Phl p 6, Phl p 11, and Phl p 12 were produced in E. coli and/or N. benthamiana. A total of 113 patient serum samples were tested to evaluate the diagnostic sensitivity, specificity, inter-assay variability, and correlation of allergen-specific IgE detection compared to commercial multiplex tests (ALEX and ISAC). Additionally, the prevalence of sIgE to these allergens was assessed. RESULTS: Phl p 1, Phl p 2, Phl p 5, Phl p 6 and Phl p 11 showed high or very high positive correlation in immunoreactivity with other commercial multiplex tests. Notably, Phl p 11 fused with maltose-binding protein (MBP) demonstrated high diagnostic specificity and sensitivity, with a 0.3 arbitrary cut-off value. However, a high intra-assay variation was observed. The study also assessed specific IgE prevalence to timothy grass allergens within the tested patient cohort. CONCLUSIONS: Recombinant allergens from both E. coli and N. benthamiana demonstrated strong diagnostic potential on the microarray platform, with Phl p 11 (MBP-fused) showing particularly high performance. High intra-assay variation highlights the need for further optimization in allergen formulation and microarray storage conditions. These results highlight the potential of recombinant allergens for diagnostic applications, despite challenges with allergen stability in microarray formats. Specific IgE prevalence to timothy allergens revealed a sensitization profile consistent with findings from multiple studies.

Protein Microarrays for High Throughput Hydrogen/Deuterium Exchange Monitored by FTIR Imaging.

Proteins form the fastest-growing therapeutic class. Due to their intrinsic instability, loss of native structure is common. Structure alteration must be carefully evaluated as structural changes may jeopardize the efficiency and safety of the protein-based drugs. Hydrogen deuterium exchange (HDX) has long been used to evaluate protein structure and dynamics. The rate of exchange constitutes a sensitive marker of the conformational state of the protein and of its stability. It is often monitored by mass spectrometry. Fourier transform infrared (FTIR) spectroscopy is another method with very promising capabilities. Combining protein microarrays with FTIR imaging resulted in high throughput HDX FTIR measurements. BaF2 slides bearing the protein microarrays were covered by another slide separated by a spacer, allowing us to flush the cell continuously with a flow of N2 gas saturated with 2H2O. Exchange occurred simultaneously for all proteins and single images covering ca. 96 spots of proteins that could be recorded on-line at selected time points. Each protein spot contained ca. 5 ng protein, and the entire array covered 2.5 × 2.5 mm2. Furthermore, HDX could be monitored in real time, and the experiment was therefore not subject to back-exchange problems. Analysis of HDX curves by inverse Laplace transform and by fitting exponential curves indicated that quantitative comparison of the samples is feasible. The paper also demonstrates how the whole process of analysis can be automatized to yield fast analyses.

High-throughput peptide array analysis and computational techniques for serological profiling of flavivirus infections: Implications for diagnostics and vaccine development.

Arthropod-borne viruses, such as dengue virus (DENV), pose significant global health threats, with DENV alone infecting around 400 million people annually and causing outbreaks beyond endemic regions. This study aimed to enhance serological diagnosis and discover new drugs by identifying immunogenic protein regions of DENV. Utilizing a comprehensive approach, the study focused on peptides capable of distinguishing DENV from other flavivirus infections through serological analyses. Over 200 patients with confirmed arbovirus infection were profiled using high-density pan flavivirus peptide arrays comprising 6253 peptides and the computational method matrix of local coupling energy (MLCE). Twenty-four peptides from nonstructural and structural viral proteins were identified as specifically recognized by individuals with DENV infection. Six peptides were confirmed to distinguish DENV from Zika virus (ZIKV), West Nile virus (WNV), Yellow Fever virus (YFV), Usutu virus (USUV), and Chikungunya virus (CHIKV) infections, as well as healthy controls. Moreover, the combination of two immunogenic peptides emerged as a potential serum biomarker for DENV infection. These peptides, mapping to highly accessible regions on protein structures, show promise for diagnostic and prophylactic strategies against flavivirus infections. The described methodology holds broader applicability in the serodiagnosis of infectious diseases.

Identification of autoantibodies as potential non-invasive biomarkers for intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) presents a challenging diagnosis due to its nonspecific early clinical manifestations, often resulting in late-stage detection and high mortality. Diagnosing iCCA is further complicated by its limited accuracy, often necessitating multiple invasive procedures for precise identification. Despite carbohydrate antigen 19-9 (CA19-9) having been investigated and employed for iCCA diagnosis, it demonstrates modest diagnostic performance. Consequently, the identification of novel biomarkers with improved sensitivity and specificity remains an imperative yet formidable task. Autoantibodies, as early indicators of the immune response against cancer, offer a promising avenue for enhancing diagnostic accuracy. Our study aimed to identify non-invasive blood-based autoantibody biomarkers capable of distinguishing iCCA patients from healthy individuals (CTRs). We profiled autoantibodies in 26 serum samples (16 iCCAs and 10 CTRs) using protein microarrays containing 1622 functional proteins. Leveraging machine learning techniques, we identified a signature composed of three autoantibody biomarkers (NDE1, PYCR1, and VIM) in conjunction with CA19-9 for iCCA detection. This combined signature demonstrated superior diagnostic performance with an AUC of 96.9%, outperforming CA19-9 alone (AUC: 83.8%). These results suggest the potential of autoantibody biomarkers to develop a complementary non-invasive diagnostic utility for routine iCCA screening.

A Peptide Microarray Platform Approach for Discovery of Immunodominant Antibody Epitopes.

Immunodominant epitope discovery platforms play an important role in identifying novel biomarkers for effective immunotherapies and diagnostics. Methods to analyze the B-cell repertoire have been improved both experimentally and computationally. We developed an enhanced peptide microarray platform to discover and subsequently screen immunodominant epitopes. We utilized SARS-Cov-2 IgG positive and negative samples as a proof-of-concept to demonstrate the power of these improved peptide microarrays. The method identified significantly discriminant epitopes that classify positive and negative samples with good performance both as single peptides and in combination. We provide the assay conditions and parameters that justify the use of peptide microarrays in the selection of high-affinity epitopes, and we directly compare peptide performance against proteins. The results suggest that this platform can be used to confidently identify immunodominant antiviral epitopes while also serving as a useful tool for high-volume screening.

Identification of Schistosoma haematobium and Schistosoma mansoni linear B-cell epitopes with diagnostic potential using in silico immunoinformatic tools and peptide microarray technology.

INTRODUCTION: Immunoinformatic tools can be used to predict schistosome-specific B-cell epitopes with little sequence identity to human proteins and antigens other than the target. This study reports an approach for identifying schistosome peptides mimicking linear B-cell epitopes using in-silico tools and peptide microarray immunoassay validation. METHOD: Firstly, a comprehensive literature search was conducted to obtain published schistosome-specific peptides and recombinant proteins with the best overall diagnostic performances. For novel peptides, linear B-cell epitopes were predicted from target recombinant proteins using ABCpred, Bcepred and BepiPred 2.0 in-silico tools. Together with the published peptides, predicted peptides with the highest probability of being B-cell epitopes and the lowest sequence identity with proteins from human and other pathogens were selected. Antibodies against the peptides were measured in sera, using peptide microarray immunoassays. Area under the ROC curve was calculated to assess the overall diagnostic performances of the peptides. RESULTS: Peptide AA81008-19-30 had excellent and acceptable diagnostic performances for discriminating S. mansoni and S. haematobium positives from healthy controls, with AUC values of 0.8043 and 0.7326 respectively for IgG. Peptides MS3_10186-123-131, MS3_10385-339-354, SmSPI-177-193, SmSPI-379-388, MS3-10186-40-49 and SmS-197-214 had acceptable diagnostic performances for discriminating S. mansoni positives from healthy controls with AUC values ranging from 0.7098 to 0.7763 for IgG. Peptides SmSPI-359-372, Smp126160-438-452 and MS3 10186-25-41 had acceptable diagnostic performances for discriminating S. mansoni positives from S. mansoni negatives with AUC values of 0.7124, 0.7156 and 0.7115 respectively for IgG. Peptide MS3-10186-40-49 had an acceptable diagnostic performance for discriminating S. mansoni positives from healthy controls, with an AUC value of 0.7413 for IgM. CONCLUSION: One peptide with a good diagnostic performance and nine peptides with acceptable diagnostic performances were identified using the immunoinformatic approach and peptide microarray validation. There is need for evaluation of the peptides with true negatives and a good standard positive reference.

The emergence of cell-based protein arrays to test for polyspecific off-target binding of antibody therapeutics.

Specificity profiling is a requirement for monoclonal antibodies (mAbs) and antibody-directed biotherapeutics such as CAR-T cells prior to initiating human trials. However, traditional approaches to assess the specificity of mAbs, primarily tissue cross-reactivity studies, have been unreliable, leading to off-target binding going undetected. Here, we review the emergence of cell-based protein arrays as an alternative and improved assessment of mAb specificity. Cell-based protein arrays assess binding across the full human membrane proteome, ~6,000 membrane proteins each individually expressed in their native structural configuration within live or unfixed cells. Our own profiling indicates a surprisingly high off-target rate across the industry, with 33% of lead candidates displaying off-target binding. Moreover, about 20% of therapeutic mAbs in clinical development and currently on the market display off-target binding. Case studies and off-target rates at different phases of biotherapeutic drug approval suggest that off-target binding is likely a major cause of adverse events and drug attrition.

Whole genome and reverse protein phase array landscapes of patient derived osteosarcoma xenograft models.

Osteosarcoma is the most common primary bone malignancy in children and young adults, and it has few treatment options. As a result, there has been little improvement in survival outcomes in the past few decades. The need for models to test novel therapies is especially great in this disease since it is both rare and does not respond to most therapies. To address this, an NCI-funded consortium has characterized and utilized a panel of patient-derived xenograft models of osteosarcoma for drug testing. The exomes, transcriptomes, and copy number landscapes of these models have been presented previously. This study now adds whole genome sequencing and reverse-phase protein array profiling data, which can be correlated with drug testing results. In addition, four additional osteosarcoma models are described for use in the research community.

Simultaneous detection of five mycotoxins in traditional Chinese medicines (TCMs) by visual protein microarray.

The pollution of mycotoxins to crops such as traditional Chinese medicines (TCMs) is an established problem throughout the world. Thus, mycotoxin determination in TCMs during production and processing is significantly necessary, which means rapid, sensitive and accurate analytical methods are needed. In this work, a new method of visual protein microarray based on a 96-well microtiter plate was proposed. Combined with a colorimetric method, five mycotoxins (ochratoxin A, zearalenone, deoxynivalenol, aflatoxin B1 and fumonisin B1) in 90 samples (TCMs) could be detected simultaneously within 30 minutes. The detection limits for the five mycotoxins are 0.25 µg/kg, 0.33 µg/kg, 11.84 µg/kg, 0.06 µg/kg, and 3.58 µg/kg, which can satisfy specified requirements of mycotoxins in the Chinese Pharmacopoeia (2020 edition) adequately. Under repeated conditions, experiments were carried out on actual samples to verify the feasibility of the method. The results showed that the recoveries of all analytes were between 70 % and 120 %, and the relative standard deviations were less than 15 %. In comparison to LC-MS/MS, this method significantly reduces the required time, and the colorimetric technique offers more direct results compared to fluorescence-based screening assays. This method exhibits substantial potential for the rapid and sensitive on-site detection of TCMs for quality control.

Reverse Phase Proteomic Array Profiling of Asparagine Synthetase Expression in Newly Diagnosed Acute Myeloid Leukemia.

Asparaginase-based therapy is a cornerstone in acute lymphoblastic leukemia (ALL) treatment, capitalizing on the methylation status of the asparagine synthetase (ASNS) gene, which renders ALL cells reliant on extracellular asparagine. Contrastingly, ASNS expression in acute myeloid leukemia (AML) has not been thoroughly investigated, despite studies suggesting that AML with chromosome 7/7q deletions might have reduced ASNS levels. Here, we leverage reverse phase protein arrays to measure ASNS expression in 810 AML patients and assess its impact on outcomes. We find that AML with inv(16) has the lowest overall ASNS expression. While AML with deletion 7/7q had ASNS levels slightly lower than those of AML without deletion 7/7q, this observation was not significant. Low ASNS expression correlated with improved overall survival (46 versus 54 weeks, respectively, p = 0.011), whereas higher ASNS levels were associated with better response to venetoclax-based therapy. Protein correlation analysis demonstrated association between ASNS and proteins involved in methylation and DNA repair. In conclusion, while ASNS expression was not lower in patients with deletion 7/7q as initially predicted, ASNS levels were highly variable across AML patients. Further studies are needed to assess whether patients with low ASNS expression are susceptible to asparaginase-based therapy due to their inability to augment compensatory ASNS expression upon asparagine depletion.

Peptide libraries: from epitope mapping to in-depth high-throughput analysis.

Peptide arrays are a valuable instrument in the characterization of protein-protein interactions (PPIs) and immunogenic regions. New methods were developed to exploit the high-throughput potential of peptide arrays to obtain more in-depth information, replacing traditional resource-intensive experiments. Here, we discuss the recent advances in peptide-array-based technologies and the remaining challenges.

Quantitative Detection of Multiple Cardiovascular Biomarkers by an Antibody Microarray-Based Metal-Enhanced Fluorescence Assay.

Accurate detection of multiple cardiovascular biomarkers is crucial for the timely screening of acute coronary syndrome (ACS) and differential diagnosis from acute aortic syndrome (AAS). Herein, an antibody microarray-based metal-enhanced fluorescence assay (AMMEFA) has been developed to quantitatively detect 7 cardiovascular biomarkers through the formation of a sandwich immunoassay on the poly(glycidyl methacrylate-co-2-hydroxyethyl methacrylate)-decorated GNR-modified slide (GNR@P(GMA-HEMA) slide). The AMMEFA exhibits high specificity and sensitivity, the linear ranges span 5 orders of magnitude, and the limits of detection (LODs) of cardiac troponin I (cTnI), heart-type fatty acid binding protein (H-FABP), C-reactive protein (CRP), copeptin, myoglobin, D-Dimer, and N-terminal pro-brain natriuretic peptide (NT-proBNP) reach 0.07, 0.2, 65.7, 0.6, 0.2, 8.3, and 0.3 pg mL-1, respectively. To demonstrate its practicability, the AMMEFA has been applied to quantitatively analyze 7 cardiovascular biomarkers in 140 clinical plasma samples. In addition, the expression levels of cardiovascular biomarkers were analyzed by the least absolute shrinkage and selector operator (LASSO) regression, and the area under receiver operator characteristic curves (AUCs) of healthy donors (HDs), ACS patients, and AAS patients are 0.99, 0.98, and 0.97, respectively.

Label-free OIRD detection of protein microarrays on high dielectric constant substrate with enhanced intrinsic sensitivity.

Oblique-incidence reflectivity difference (OIRD) is a dielectric constant-sensitive technique and exhibits intriguing applications in label-free and high-throughput detection of protein microarrays. With the outstanding advantage of being compatible with arbitrary substrates, however, the effect of the substrate, particularly its dielectric constant on the OIRD sensitivity has not been fully disclosed. In this paper, for the first time we investigated the dependence of OIRD sensitivity on the dielectric constant of the substrate under top-incident OIRD configuration by combining theoretical modeling and experimental evaluation. Optical modeling suggested that the higher dielectric constant substrate exhibits a higher intrinsic sensitivity. Experimentally, three substrates including glass, fluorine-doped tin oxide (FTO) and silicon (Si) with different dielectric constants were selected as microarray substrates and their detection performances were evaluated. In good agreement with the modeling, high dielectric constant Si-based microarray exhibited the highest sensitivity among three chips, reaching a detection limit of as low as 5 ng mL-1 with streptavidin as the model target. Quantification of captured targets on three chips with on-chip enzyme-linked immunosorbent assay (ELISA) further confirmed that the enhanced performance originates from the high dielectric constant enhanced intrinsic OIRD sensitivity. This work thus provides a new way to OIRD-based label-free microarrays with improved sensitivity.

Changes in serum immunoglobulin G glycosylation patterns for antiphospholipid syndrome patients with lectin microarray.

Antiphospholipid syndrome is a rare autoimmune disease characterized by persistent antiphospholipid antibodies. Immunoglobulin G plays a vital role in disease progression, with its structure and function affected by glycosylation. We aimed to investigate the changes in the serum immunoglobulin G glycosylation pattern in antiphospholipid syndrome patients. We applied lectin microarray on samples from 178 antiphospholipid syndrome patients, 135 disease controls (including Takayasu arteritis, rheumatoid arthritis and cardiovascular disease) and 100 healthy controls. Lectin blots were performed for validation of significant differences. Here, we show an increased immunoglobulin G-binding level of soybean agglutinin (p = 0.047, preferring N-acetylgalactosamine) in antiphospholipid syndrome patients compared with healthy and disease controls. Additionally, the immunoglobulin G from antiphospholipid syndrome patients diagnosed with pregnancy events had lower levels of fucosylation (p = 0.001, recognized by Lotus tetragonolobus) and sialylation (p = 0.030, recognized by Sambucus nigra I) than those with simple thrombotic events. These results suggest the unique serum immunoglobulin G glycosylation profile of antiphospholipid syndrome patients, which may inform future studies to design biomarkers for more accurate diagnosis of antiphospholipid syndrome and even for the prediction of clinical symptoms in patients.

Definition of the binding specificity of the T7 bacteriophage primase by analysis of a protein binding microarray using a thermodynamic model.

Protein binding microarrays (PBM), SELEX, RNAcompete and chromatin-immunoprecipitation have been intensively used to determine the specificity of nucleic acid binding proteins. While the specificity of proteins with pronounced sequence specificity is straightforward, the determination of the sequence specificity of proteins of modest sequence specificity is more difficult. In this work, an explorative data analysis workflow for nucleic acid binding data was developed that can be used by scientists that want to analyse their binding data. The workflow is based on a regressor realized in scikit-learn, the major machine learning module for the scripting language Python. The regressor is built on a thermodynamic model of nucleic acid binding and describes the sequence specificity with base- and position-specific energies. The regressor was used to determine the binding specificity of the T7 primase. For this, we reanalysed the binding data of the T7 primase obtained with a custom PBM. The binding specificity of the T7 primase agrees with the priming specificity (5'-GTC) and the template (5'-GGGTC) for the preferentially synthesized tetraribonucleotide primer (5'-pppACCC) but is more relaxed. The dominant contribution of two positions in the motif can be explained by the involvement of the initiating and elongating nucleotides for template binding.

Revisiting the antigen markers of vector-borne parasitic diseases identified by immunomics: identification and application to disease control.

INTRODUCTION: Protein microarray is a promising immunomic approach for identifying biomarkers. Based on our previous study that reviewed parasite antigens and recent parasitic omics research, this article expands to include information on vector-borne parasitic diseases (VBPDs), namely, malaria, schistosomiasis, leishmaniasis, babesiosis, trypanosomiasis, lymphatic filariasis, and onchocerciasis. AREAS COVERED: We revisit and systematically summarize antigen markers of vector-borne parasites identified by the immunomic approach and discuss the latest advances in identifying antigens for the rational development of diagnostics and vaccines. The applications and challenges of this approach for VBPD control are also discussed. EXPERT OPINION: The immunomic approach has enabled the identification and/or validation of antigen markers for vaccine development, diagnosis, disease surveillance, and treatment. However, this approach presents several challenges, including limited sample size, variability in antigen expression, false-positive results, complexity of omics data, validation and reproducibility, and heterogeneity of diseases. In addition, antigen involvement in host immune evasion and antigen sensitivity/specificity are major issues in its application. Despite these limitations, this approach remains promising for controlling VBPD. Advances in technology and data analysis methods should continue to improve candidate antigen identification, as well as the use of a multiantigen approach in diagnostic and vaccine development for VBPD control.

Harnessing the potential of reverse-phase protein array technology: Advancing precision oncology strategies.

The last few decades have seen remarkable strides in the field of cancer therapy. Precision oncology coupled with comprehensive genomic profiling has become routine clinical practice for solid tumors, the advent of immune checkpoint inhibitors has transformed the landscape of oncology treatment, and the number of cancer drug approvals has continued to increase. Nevertheless, the application of genomics-driven precision oncology has thus far benefited only 10%-20% of cancer patients, leaving the majority without matched treatment options. This limitation underscores the need to explore alternative avenues with regard to selecting patients for targeted therapies. In contrast with genomics-based approaches, proteomics-based strategies offer a more precise understanding of the intricate biological processes driving cancer pathogenesis. This perspective underscores the importance of integrating complementary proteomic analyses into the next phase of precision oncology to establish robust biomarker-drug associations and surmount challenges related to drug resistance. One promising technology in this regard is the reverse-phase protein array (RPPA), which excels in quantitatively detecting protein modifications, even with limited amounts of sample. Its cost-effectiveness and rapid turnaround time further bolster its appeal for application in clinical settings. Here, we review the current status of genomics-driven precision oncology, as well as its limitations, with an emphasis on drug resistance. Subsequently, we explore the application of RPPA technology as a catalyst for advancing precision oncology. Through illustrative examples drawn from clinical trials, we demonstrate its utility for unraveling the molecular mechanisms underlying drug responses and resistance.

Antibody array-based proteome approach reveals proteins involved in grape seed development.

Grape (Vitis vinifera) is one of the most widely cultivated fruits globally, primarily used for processing and fresh consumption. Seedless grapes are favored by consumers for their convenience, making the study of seedlessness a subject of great interest to scientists. To identify regulators involved in this process in grape, a monoclonal antibody (mAb)-array-based proteomics approach, which contains 21,120 mAbs, was employed for screening proteins/antigens differentially accumulated in grape during development. Differences in antigen signals were detected between seeded and seedless grapes revealing the differential accumulation of 2,587 proteins. After immunoblotting validation, 71 antigens were further immunoprecipitated and identified by mass spectrometry (MS). An in planta protein-protein interaction (PPI) network of those differentially accumulated proteins was established using mAb antibody by immunoprecipitation (IP)-MS, which reveals the alteration of pathways related to carbon metabolism and glycolysis. To validate our result, a seedless-related protein, DUF642 domain-containing protein (VvDUF642), which is functionally uncharacterized in grapes, was ectopically overexpressed in tomato (Solanum lycopersicum "MicroTom") and led to a reduction in seed production. PPI network indicated that VvDUF642 interacts with pectin acetylesterase (VvPAE) in grapes, which was validated by BiFC and Co-IP. As anticipated, overexpression of VvPAE substantially reduced seed production in tomato. Moreover, S. lycopersicum colourless non-ripening expression was altered in VvDUF642- and VvPAE-overexpressing plants. Taken together, we provided a high-throughput method for the identification of proteins involved in the seed formation process. Among those, VvDUF642 and VvPAE are potential targets for breeding seedless grapes and other important fruits in the future.

Antibody reliability influences observed mRNA-protein correlations in tumour samples.

Reverse phase protein arrays (RPPA) have been used to quantify the abundance of hundreds of proteins across thousands of tumour samples in the Cancer Genome Atlas. By number of samples, this is the largest tumour proteomic dataset available and it provides an opportunity to systematically assess the correlation between mRNA and protein abundances. However, the RPPA approach is highly dependent on antibody reliability and approximately one-quarter of the antibodies used in the the Cancer Genome Atlas are deemed to be somewhat less reliable. Here, we assess the impact of antibody reliability on observed mRNA-protein correlations. We find that, in general, proteins measured with less reliable antibodies have lower observed mRNA-protein correlations. This is not true of the same proteins when measured using mass spectrometry. Furthermore, in cell lines, we find that when the same protein is quantified by both mass spectrometry and RPPA, the overall correlation between the two measurements is lower for proteins measured with less reliable antibodies. Overall our results reinforce the need for caution in using RPPA measurements from less reliable antibodies.

Low-Cost Peptide Microarrays for Mapping Continuous Antibody Epitopes.

Understanding antibody specificity and defining response profiles to antigens continue to be essential to both vaccine research and therapeutic antibody development. Peptide scanning assays enable mapping of continuous epitopes in order to delineate antibody-antigen interactions beyond traditional immunoassay formats. We have developed a relatively low-cost method to generate peptide microarray slides for antibody binding studies that allow for interrogation of up to 1536 overlapping peptides derived from the target antigens on a single microslide. Using an IntavisAG MultiPep RS peptide synthesizer and a Digilab MicroGrid II 600 microarray printer robot, each peptide is tagged with a polyethylene glycol aminooxy terminus to improve peptide solubility, orientation, and conjugation efficiency to the slide surface. Interrogation of the surface can then be performed using polyclonal immune sera or monoclonal antibodies, and sensitive detection using an InnoScan 1100 AL scanner with fluorescent-conjugated secondary reagents maximizes conservation of reagents.