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. 2020 Nov 30:10:553837.
doi: 10.3389/fcimb.2020.553837. eCollection 2020.

Foundation and Clinical Evaluation of a New Method for Detecting SARS-CoV-2 Antigen by Fluorescent Microsphere Immunochromatography

Chunyan Zhang  1 , Lei Zhou  2   3 , Kang Du  4 , Ying Zhang  5 , Jing Wang  6 , Lijuan Chen  7 , Yanning Lyu  7 , Jun Li  5 , Hao Liu  2   3 , Junli Huo  8   9 , Fei Li  8   9 , Jiayi Wang  10   11 , Peipei Sang  1 , Si Lin  12 , Yi Xiao  12 , Kan Zhang  10   11 , Kunlun He  13
Affiliations

Foundation and Clinical Evaluation of a New Method for Detecting SARS-CoV-2 Antigen by Fluorescent Microsphere Immunochromatography

Chunyan Zhang et al. Front Cell Infect Microbiol. .

Abstract

Purpose: To develop a rapid detection reagent for SARS-CoV-2 antigen for the auxiliary diagnosis of new coronary pneumonia (COVID-19), and perform the methodological evaluation and clinical evaluation of the reagent.

Method: SARS-CoV-2 N-protein test strip was created by combining fluorescent microsphere labeling technology and immunochromatographic technology, based on the principle of double antibody sandwich. Then we evaluated the analytical capability and clinical application of the strips.

Result: The limit of detection of the strips for recombinant N protein was 100 ng/ml and for activated SARS -CoV-2 virus was 1 × 103 TCID50/ml. The strips also have high analytical specificity and anti-interference capability. According to the predetermined cut-off value, the specificity of the test strip in healthy controls and patients with other respiratory disease was 100.00 and 97.29%, the sensitivity in COVID-19 cases at progress stage and cured stage was 67.15 and 7.02%. The positive percentage agreement and negative percentage agreement of antigen strip to RNA test were 83.16 and 94.45%.

Conclusion: SARS-CoV-2 fluorescence immunochromatographic test strip can achieve fast, sensitive and accurate detection, which can meet the clinical requirements for rapid detection of viruses on the spot.

Keywords: COVID-19; SARS-CoV-2; antigen detection; clinical evaluation; immunochromatographic.

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Conflict of interest statement

SL and YX were employed by the company Beijing Savant Biotechnology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
SARS-CoV-2 N-protein specific antibody selection and cross reaction (A). SARS-CoV-2 N-protein was chose for antigen immunoassay (B). The sequence comparison between SARS-CoV-2 N-protein and that of the other 6 kinds of coronaviruses. From the top to the bottom of the list: 1, HCoV-229E (AOG74787.1_N); 2, SARS-CoV-2 (YP009724397.2_N); 3, HCoV-HKU1(AGW27885.1_N); 4,MERS-CoV (AHC74105.1_N); 5, HCoV-NL63 (AFV53152.1_N); 6, HCoV-OC43 (QDH43730.1_N); 7, SARS-CoV ShanghaiQXC1(AAR86795.1_N); 8,consensus sequence, the black area (C). The expression of SARS-CoV-2 N-protein (D). Affinity determination between monoclonal antibody and SARS-CoV-2 N-protein. 6A8, 6A6, 7e4, 1c7, 14b9, 7a7, and 1B12 represent seven monoclonal antibodies selected from antibody library of Beijing Biosynthesis Biotechnology Co. Ltd (E). Cross-reaction result. Anti-SARS-CoV-2 N-protein monoclonal antibody-1 and antibody-2 were selected as immune target.
Figure 2
Figure 2
Reagent preparation and detection process (A). Preparation of antibody detection reagent strip (B). Oropharyngeal swab sampling (C). The method of double-antibody sandwiched antigens (D). Read the result using UV-LED (E). Analyzed by the detector.
Figure 3
Figure 3
Clinical application and evaluation of antigen strips (A). ROC curve for the cutoff determine of antigen strips (B). The results of antigen strips in COVID-19 cases (progress stage, ●; cured stage, ○), patients with other respiratory diseases (■), and healthy controls (□). ***p < 0.001; ns, nonsense (C). The antigen detection rate at different time of development of COVID-19. Antigen test (●), RNA test (○) (D). Diagnosis value of antigen strips, RNA detection and combined evaluation.
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