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

Abstract

Background: High-sensitivity severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen assays are desirable to mitigate false negative results. Limited data are available to quantify and track SARS-CoV-2 antigen burden in respiratory samples from different populations.

Methods: We developed the Microbubbling SARS-CoV-2 Antigen Assay (MSAA) with smartphone readout, with a limit of detection of 0.5 pg/mL (10.6 fmol/L) nucleocapsid antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs. We developed a computer vision and machine learning-based automatic microbubble image classifier to accurately identify positives and negatives and quantified and tracked antigen dynamics in intensive care unit coronavirus disease 2019 (COVID-19) inpatients and immunocompromised COVID-19 patients.

Results: Compared to qualitative reverse transcription-polymerase chain reaction methods, the MSAA demonstrated a positive percentage agreement of 97% (95% CI 92%-99%) and a negative percentage agreement of 97% (95% CI 94%-100%) in a clinical validation study with 372 residual clinical NP swabs. In immunocompetent individuals, the antigen positivity rate in swabs decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity. Antigen was detected for longer and variable periods of time in immunocompromised patients with hematologic malignancies. Total microbubble volume, a quantitative marker of antigen burden, correlated inversely with cycle threshold values and days-after-symptom-onset. Viral sequence variations were detected in patients with long duration of high antigen burden.

Conclusions: The MSAA enables sensitive and specific detection of acute infections and quantification and tracking of antigen burden and may serve as a screening method in longitudinal studies to identify patients who are likely experiencing active rounds of ongoing replication and warrant close viral sequence monitoring.

Keywords: SARS-CoV-2; longitudinal NP swab samples; microbubbling assay; viral antigen.

Fig. 1.

Schematic of the Microbubbling SARS-CoV-2 Antigen Assay. 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 quantified and classified as positive or negative by computer vision and ML algorithms. Scale bar, 500 μm.

Fig. 2.

Performance of the Microbubbling SARS-CoV-2 Antigen Assay. (A) Dose–response curve of recombinant N protein in buffer. Mean ± 3 SD; n = 10 at 0, n = 3 at other points. (B) Specificity test. Mean ± 3 SD; n = 3. (C) Dose–response curve of inactivated SARS-CoV-2 viruses in rRT-PCR–negative NP swab pool. Mean ± 3 SD; n = 10 at 0, n = 3 at other points. (D) LOD following the FDA antigen template guideline.

Fig. 3.

(A) Examples from clinical NP swab samples. (B) Upper: log-transformed total bubble volume correlated inversely with Ct by Pearson linear correlation. Lower: total bubble volume decreased with days-after-symptom-onset. (C) ROC curve comparing classification performance of 2 approaches. (D) Linear decision boundaries from the ML algorithm that learns to accurately classify images as negatives or positives in the validation data set.

Fig. 4.

Tracking antigen burden using Microbubbling SARS-CoV-2 Antigen Assay in (A) inpatients in intensive care unit and (B) immunocompromised patients (rRT-PCR persistently positive) with either hematological malignancies or transplants. Numbers in boxes in (A) indicate N antigen burden quantitated by MSAA (inactivated virus equivalent, ×1000 copies/mL). Heatmap colors in (A) indicate N1 gene copy number. Heatmap colors in (B) indicate N antigen burden quantitated by MSAA.