Affinity Exploration of SARS-CoV-2 RBD Variants to mAb-Functionalized Plasmonic Metasurfaces for Label-Free Immunoassay Boosting.
ACS Nano
; 17(4): 3383-3393, 2023 02 28.
Article
in English
| MEDLINE | ID: covidwho-2185519
ABSTRACT
Plasmonic metasurfaces (PMs) functionalized with the monoclonal antibody (mAb) are promising biophotonic sensors for biomolecular interaction analysis and convenient immunoassay of various biomarkers, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Previous PM biosensing suffers from the slow affinity detection rate and lack of sufficient immunoassay studies on various SARS-CoV-2 variants. Here, we develop a high-efficiency affinity testing method based on label-free PM sensors with mAbs and demonstrate their binding characteristics to 12 spike receptor binding domain (RBD) variants of SARS-CoV-2. In addition to the research of plasmonic near-field influence on surface biomolecule sensing, we provide a comprehensive report about the Langmuir binding equilibrium of molecular kinetics between 12 SARS-CoV-2 RBD variants and mAb-functionalized PMs, which plays a crucial role in label-free immunosensing. A high-affinity mAb can be combined with the highly sensitive propagating plasmonic mode to boost the detection of SARS-CoV-2 variants. Owing to a better understanding of molecular dynamics on PMs, we develop an ultrasensitive biosensor of the SARS-CoV-2 Omicron variant. The experiments show great distinguishment of P < 0.0001 from respiratory diseases induced by other viruses, and the limit of detection is 2 orders smaller than the commercial colloidal gold immunoassay. Our study shows the label-free biosensing by low-cost wafer-scale PMs, which will provide essential information on biomolecular interaction and facilitate high-precision point-of-care testing for emerging SARS-CoV-2 variants in the future.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
Topics:
Variants
Limits:
Humans
Language:
English
Journal:
ACS Nano
Year:
2023
Document Type:
Article
Affiliation country:
Acsnano.2c08153
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