Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Dynamics Tracking

Abstract

Background: Little is known about the dynamics of SARS-CoV-2 antigen burden in respiratory samples in different patient populations at different stages of infection. Current rapid antigen tests cannot quantitate and track antigen dynamics with high sensitivity and specificity in respiratory samples.

Methods: We developed and validated an ultra-sensitive SARS-CoV-2 antigen assay with smartphone readout using the Microbubbling Digital Assay previously developed by our group, which is a platform that enables highly sensitive detection and quantitation of protein biomarkers. A computer vision-based algorithm was developed for microbubble smartphone image recognition and quantitation. A machine learning-based classifier was developed to classify the smartphone images based on detected microbubbles. Using this assay, we tracked antigen dynamics in serial swab samples from COVID patients hospitalized in ICU and immunocompromised COVID patients.

Results: The limit of detection (LOD) of the Microbubbling SARS-CoV-2 Antigen Assay was 0.5 pg/mL (10.6 fM) recombinant nucleocapsid (N) antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs, comparable to many rRT-PCR methods. The assay had high analytical specificity towards SARS-CoV-2. Compared to EUA-approved rRT-PCR methods, the Microbubbling Antigen Assay demonstrated a positive percent agreement (PPA) of 97% (95% confidence interval (CI), 92-99%) in symptomatic individuals within 7 days of symptom onset and positive SARS-CoV-2 nucleic acid results, and a negative percent agreement (NPA) of 97% (95% CI, 94-100%) in symptomatic and asymptomatic individuals with negative nucleic acid results. Antigen positivity rate in NP swabs gradually decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity of the same samples. The computer vision and machine learning-based automatic microbubble image classifier could accurately identify positives and negatives, based on microbubble counts and sizes. Total microbubble volume, a potential marker of antigen burden, correlated inversely with Ct values and days-after-symptom-onset. Antigen was detected for longer periods of time in immunocompromised patients with hematologic malignancies, compared to immunocompetent individuals. Simultaneous detectable antigens and nucleic acids may indicate the presence of replicating viruses in patients with persistent infections.

Conclusions: The Microbubbling SARS-CoV-2 Antigen Assay enables sensitive and specific detection of acute infections, and quantitation and tracking of antigen dynamics in different patient populations at various stages of infection. With smartphone compatibility and automated image processing, the assay is well-positioned to be adapted for point-of-care diagnosis and to explore the clinical implications of antigen dynamics in future studies.

Figure 1.

Schematic diagram of the procedures of the Microbubbling SARS-CoV-2 Antigen Assay. Nasopharyngeal (NP) swab eluant is first treated with lysis buffer to release N proteins from SARS-CoV-2 viruses. N protein is then detected by the smartphone-based microbubbling digital assay. The microbubble images are quantitated and classified as positive or negative by computer vision and ML algorithms.

Figure 2.

Analytical performance of the Microbubbling SARS-CoV-2 Antigen Assay. A. Dose response curve for spiked recombinant N protein in buffer (PBS, 1% BSA (Bovine serum albumin), pH7.4). Mean ± standard deviation; n=10 at 0, n=3 for other datapoints. B. Specificity of the Microbubbling SARS-CoV-2 Antigen Assay. Inactivated SARS-CoV-2 viruses were tested at 1×10⁵ copies/mL (1.4×10⁴ pfu/mL). CoV-229e, CoV-NL63 and CoV-OC43 were tested at 1×10⁵ pfu/mL. Mean ± standard deviation; n=3. C. Dose response curve for spiked inactivated SARS-CoV-2 viruses in rRT-PCR-negative NP swab pool. Mean ± standard deviation; n=10 at 0, n=3 for other datapoints. D. Determining limit of detection (LOD) of spiked inactivated SARS-CoV-2 viruses in PCR-negative NP swab pool, following the FDA antigen template guideline. Assay signal (number of microbubbles) comparison between rRT-PCR-negative NP swab pool (n=10), 2000 copies/mL (n=21) and 4000 copies/mL (n=21) SARS-CoV-2 spiked into negative NP swab pool. Each diamond represents one independent experiment.

Figure 3.

Automatic microbubble recognition, quantitation and classification using computer vision and ML algorithms. A. Example images from clinical NP swab samples, and microbubble detection using the computer vision algorithm. Green circles show microbubble detections from the computer vision system overlaid on the original images. B. Upper panel: Log transformed total bubble volume correlated inversely with Ct. Pearson linear correlation (dotted line) r= −0.56. Lower panel: Total bubble volume decreased with days-after-symptom-onset. P= 0.04 between day1 and 2, p= 0.02 between day1 and day 3 using Student’s t test. C. ROC curve comparing classification performance of the ML algorithm against the total bubble volume thresholding. D. Decision boundaries from the ML algorithm that learns linear boundaries to accurately classify images as negatives or positives in the validation data set (n=168), based on the number of automatically detected small and large microbubbles in each image. Bubbles were categorized as small if their radius was less than a heuristically set threshold of 8 pixels (in 450 × 450 pixel images, corresponding to about 50 microns), and as large if otherwise. An inset within the plot shows the enlarged area near the decision boundary.

Figure 4.

Tracking antigen dynamics using the Microbubbling SARS-CoV-2 Antigen Assay in serial swab samples in A. COVID inpatients in ICU and B. immunocompromised COVID patients with either hematological malignancies or transplants. A red + indicates a positive antigen result, whereas a blue - indicates a negative antigen result. Color intensities of the green squares in A. indicate the quantitative results of N1 gene copy number using RT-qPCR. Colors of the squares in B. indicate the qualitative nucleic acid results using FDA EUA-approved rRT-PCR methods.