A Rapid and Affordable Point-of-care Test for Detection of SARS-Cov-2-Specific Antibodies Based on Hemagglutination and Artificial Intelligence-Based Image Interpretation

doi:10.21203/rs.3.rs-712902/v1

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Research Article

A Rapid and Affordable Point-of-care Test for Detection of SARS-Cov-2-Specific Antibodies Based on Hemagglutination and Artificial Intelligence-Based Image Interpretation

https://doi.org/10.21203/rs.3.rs-712902/v1

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Diagnostic tests that detect antibodies (AB) against SARS-CoV-2 for evaluation of seroprevalence and guidance of health care measures are important tools for managing the COVID-19 pandemic. Current tests have certain limitations with regard to turnaround time, costs and availability, particularly in point-of-care (POC) settings. We established a hemagglutination-based AB test (HAT) that is based on bi-specific proteins which contain a dromedary-derived antibody (nanobody) binding red blood cells (RBD) and a SARS-CoV-2-derived antigen, such as the receptor-binding domain of the Spike protein (Spike-RBD). While the nanobody mediates swift binding to RBC, the antigen moiety directs instantaneous, visually apparent hemagglutination in the presence of SARS-CoV-2-specific AB generated in COVID-19 patients or vaccinated individuals. Method comparison studies with assays cleared by emergency use authorization (EUA) demonstrate high specificity and sensitivity. To further increase objectivity of test interpretation, we developed an image analysis tool based on digital image acquisition (via a cell phone) and a machine learning algorithm based on defined sample-training and –validation datasets. Preliminary data, including a small clinical study, provides proof of principle for test performance in a POC setting. Together, the data support the interpretation that this AB test format, which we refer to as ‘NanoSpot.ai’, is suitable for POC testing, can be manufactured at very low costs and, based on its generic mode of action, can likely be adapted to a variety of other pathogens.

Scientific Communication

General Microbiology

Immunology

antibodies

SARS-CoV-2

COVID-19 pandemic

red blood cells (RBD)

emergency use authorization (EUA)

Competing interest reported. VR and HH serve as inventors on a patent application by the University of Utah (related to the constructs). BC, BW, RS, RZ, and SS are shareholders in Techcyte. KT, ETL, ESCPW, VP, AMS, LH, ME, PS and MA have no conflict of interests.

SupplementaryFigures.pdf
Supplementary Figure 1 Lack of agglutinating function of NanoSpike and NanoNuc in the absence of SARS-CoV-2-specfic AB. (a,b) Hemagglutination assay of whole EDTA blood from a COVD19-negative donor that was treated with NanoSpike (a) or NanoNuc (b) at indicated concentrations. Blood treated with NanoControl or a combination of NanoSpike and NanoLink is shown for comparison. Supplementary Figure 2 Stability of recombinant proteins used in the NanoSpot assay. (a,b) Hemagglutination assay based on serum samples (undiluted or 1:5-diluted) from a convalescent COVID-19- patient. The recombinant proteins were stored at the indicated temperatures for four weeks (a) or 16 weeks (b). Proteins stored at -20ºC (a) or freshly prepared proteins (b) served for comparison. Supplementary Figure 3 Example of low-level spontaneous hemagglutination. Hemagglutination assay of blood from a COVD19-negative donor that was treated with NanoControl (Negative Control), NanoSpike (Test Result) and NanoLink (Positive Control) for one minute. Note the subtle, but clearly visible, agglutination in Negative Control and Test Result samples. Supplementary Figure 4 NanoSpot.ai tests excluded from study due to technical issues One test with insufficient image quality (blurred focus, Nano_03) and two samples that failed the confidence score of 0.5 (Nano_39, Nano_44) were excluded from test interpretation. Please note that these issues can likely be addressed by improvement of the mobile app (image focusing) and additional training of AI (recognizing artifacts, such as insufficient sample dilution (Nano_39) and air bubbles (Nano_44)). Supplementary Figure 5 S-Flow assay using samples from clinical study HEK293T cells, which were transfected with a control vector or a SARS-CoV2 Spike expression vector, were incubated with serum from indicated patients, and the presence of αSpike AB was detected by flow cytometry using secondary AB against IgM and IgG, as indicated. Live cells as identified by forward and side scatter are shown. Supplementary Figure 6 Correlation of ELISA and NanoSpot.ai Results from the clinical study obtained by ELISA (arbitrary units) and NanoSpot.ai (AI bins) were correlated and a trendline was added in Excel. The resulting Pearson correlation index (r), as determined by Excel, is indicated. AI bins obtained by NanoControl were subtracted from AI bins obtained by NanoSpike to eliminate background agglutination.
SITable1.xlsx
Supplementary Table 1 Count of data labels by agglutination level and workflow usage. Shown are the number of samples for each agglutination level used in training, validation and holdout test.
SITable2.xlsx
Supplementary Table 2 Full set of results from clinical study to evaluate performance of NanoSpot.ai.
SIVideo2.mp4
Supplementary movie 1 The procedure of NanoSpot.ai. Test cards with pre-spotted proteins were used.

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Editorial decision: Major revision
06 Oct, 2021
Reviews received at journal
04 Oct, 2021
Reviewers agreed at journal
01 Sep, 2021
Reviews received at journal
10 Aug, 2021
Reviewers agreed at journal
30 Jul, 2021
Reviewers agreed at journal
22 Jul, 2021
Reviewers invited by journal
19 Jul, 2021
Editor assigned by journal
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Editor invited by journal
19 Jul, 2021
Submission checks completed at journal
19 Jul, 2021
First submitted to journal
13 Jul, 2021

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A Rapid and Affordable Point-of-care Test for Detection of SARS-Cov-2-Specific Antibodies Based on Hemagglutination and Artificial Intelligence-Based Image Interpretation

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