Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Tracking
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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.
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SciScore for 10.1101/2021.03.17.21253847: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Another limitation of the study is that the data of days-since-symptom-onset came from limited documentation in the medical records based on patient self-reporting. These data may not always be accurate or complete. Ct values were not available from some rRT-PCR methods used in this study, and are in general not well-standardized …
SciScore for 10.1101/2021.03.17.21253847: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Another limitation of the study is that the data of days-since-symptom-onset came from limited documentation in the medical records based on patient self-reporting. These data may not always be accurate or complete. Ct values were not available from some rRT-PCR methods used in this study, and are in general not well-standardized across platforms 19. This may lead to heterogeneity in the nucleic acid data presented. We had access to a relatively small number of serial samples from immunocompromised patients. Finally, due to the poor culturability of most clinical specimens, we did not use viral culture to assess the infectivity of antigen positive samples. In future studies, the Microbubbling SARS-CoV-2 Antigen Assay can be automated and multiplexed with other infectious disease antigens, and applied to other sample types. With the convenient computer vision and ML based algorithms on smartphone for automated bubble recognition, quantitation and result classification, this assay also has the potential to be used at the point-of-care for frequent and repeated testing. Our relatively simple computer vision and ML pipeline produced accurate automatic classification results, which we expect will be further improved through training more sophisticated deep learning-based systems on larger datasets. In particular, a common failure mode in our current visual bubble detection system is when bubbles appear blurry because they are not on the focal plane, or are inconsistently lit in di...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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