Single-tier point-of-care serodiagnosis of Lyme disease

Abstract

Point-of-care (POC) serological testing provides actionable information for several difficult to diagnose illnesses, empowering distributed health systems. Accessible and adaptable diagnostic platforms that can assay the repertoire of antibodies formed against pathogens are essential to drive early detection and improve patient outcomes. Here, we report a POC serologic test for Lyme disease (LD), leveraging synthetic peptides tuned to be highly specific to the LD antibody repertoire across patients and compatible with a paper-based platform for rapid, reliable, and cost-effective diagnosis. A subset of antigenic epitopes conserved across Borrelia burgdorferi genospecies and targeted by IgG and IgM antibodies, were selected based on their seroreactivity to develop a multiplexed panel for a single-step measurement of combined IgM and IgG antibodies from LD patient sera. Multiple peptide epitopes, when combined synergistically using a machine learning-based diagnostic model, yielded a high sensitivity without any loss in specificity. We blindly tested the platform with samples from the U.S. Centers for Disease Control & Prevention (CDC) LD repository and achieved a sensitivity and specificity matching the lab-based two-tier results with a single POC test, correctly discriminating cross-reactive look-alike diseases. This computational LD diagnostic test can potentially replace the cumbersome two-tier testing paradigm, improving diagnosis and enabling earlier effective treatment of LD patients while also facilitating immune monitoring and surveillance of the disease in the community.

Figure 1.

Overview of the paper-based multiplexed vertical flow assay (xVFA) and point-of-care diagnosis of Lyme disease (LD). a Transmission of Borrelia burgdorferi through the bite of Ixodeous ticks and the presentation of various antigens generating an immune response from the host. b Comparison of incidence of LD cases in the northeastern US from 2000 and 2019 indicating an increase in the incidence of cases due to the growing population of ticks. Worldwide incidence of LD in 2019. Legend indicates the number of cases per 100,000 people. c Centralized laboratory-based two-tier serology of LD uses relatively expensive instruments and trained personnel, resulting in high turnaround time and cost per test. d Point-of-care xVFA assay using low-cost paper layers and a smartphone reader that provides results for a multiplexed LD assay in <20 minutes. e The xVFA contains a selected peptide panel immobilized on a nitrocellulose membrane that reacts with IgM and IgG antibodies from LD patient serum. f Stability of modVlsE-FlaB peptide and VlsE recombinant protein indicating a loss in performance of a protein immobilized assay by more than 50% over a 90-day period. Standard deviation indicates three replicates. g Combining IgM and IgG detection in a single xVFA assay enhances the sensitivity of an individual immunoreaction spot. h Smartphone-based portable reader and automated image processing of the signals from the peptide panel before and after the assay, yielding normalized signal intensities. The individual peptide spots are analyzed using a multiplexed model to classify samples as either LD positive or negative.

Figure 2.

B. burgdorferi antigenic peptide library screening and selection of prevalent LD-specific epitopes. a Overview of the screening of epitopes to select the most relevant peptide antigens that function in the paper-based xVFA platform. b Heatmap representing the reactivity of peptides screened against LD positive (+) and negative (−) patient samples to develop the paper-based multi-antigen xVFA platform. c Variance of the peptide signal intensity obtained using xVFA plotted in decreasing order of reactivity to identify the most active antigens. d Heatmap representing the correlation of peptides against each other. Inset shows examples of highly correlated peptides (Rec164 and OspC1) and non-correlated peptides (modVlsE-FlaB and OppA4) which yield additional information for a diagnostic panel.

Figure 3.

Training of the multi-antigen xVFA panel using early-stage LD patient samples obtained from the Lyme Disease Biobank. a The multi-antigen coated sensing membrane and map of each antigen location spotted in duplicates along with the positive and negative control reaction spots. b Heatmap representing the average signal intensity obtained from the multi-antigen panel for the individual peptides and the positive and negative control spots. The color scale on the top represents the average %CV in measurement for all peptides per patient sample. c Receiver operator characteristics (ROC) resulting from the neural network-based multiplexed diagnostic model comparing the model's performance when different numbers of peptides are used to train the network. The inset shows the confusion matrix for a 3-peptide model. d Bar plot showing the area under the curve (AUC) for the different input features used to train the diagnostic model. A 3-peptide model including single immunoreaction spots from modVlsE-FlaB, Var2FlaB, and OppA4 yields the highest AUC of 0.94. e The final prediction outcome from the neural network-based diagnostic model, indicated as values from 0 to 1, where each dot represents a test that was perform on the xVFA using a patient sample (50 patient samples tested in triplicate). The red dotted line at 0.5 indicates the threshold for positivity of the diagnostic model.

Figure 4.

Blinded validation of the multi-antigen xVFA and the neural network-based diagnostic model using patient samples obtained from the CDC. a Prediction outcome from the neural network-based diagnostic model using the trained network indicated as values from 0 to 1, where each dot represents a test that was performed on the xVFA using patient samples that had different stages of LD, healthy control samples from regions where LD is endemic and non-endemic, cross-reactive samples from look-alike diseases such as fibromyalgia, rheumatoid arthritis, multiple sclerosis, mononucleosis, syphilis, and severe periodontitis. The red dotted line at 0.5 indicates the threshold for positivity of the diagnostic model. Three xVFA tests were run per sample. b Bar plot comparing the sensitivity (with and without the inclusion of early acute stage samples) and specificity of the rapid point-of-care, multiplexed vertical flow assay (xVFA) with centralized lab based Standard Two-tier Testing (STTT) and Modified Two-tier Testing (MTTT), gold standard diagnostic algorithms commercially used for LD diagnosis. One sample t-test was performed to compare the performance of the xVFA and the two-tier gold standard algorithms. The xVFA sensitivity and specificity was not statistically significantly different (p>0.05) from the two-tiered centralized lab assays with the exception of MTTT algorithm (*, p<0.05) when early-stage disease samples were also included.