Revolutionizing ELISA: A one-step blocking approach using lipid bilayer coatings
Source abstract
Abstract
Enzyme-linked immunosorbent assay (ELISA) is an essential technique for biomolecule detection in diagnostics and research, but traditional protocols are time-consuming and variable. Conventional blocking agents like bovine serum albumin (BSA) pose ethical issues and potential assay interference. This study presents a one-step lipid-blocking approach to simplify the ELISA procedure. Compared to conventional blockers, the Lipid Universal Coating Assembly (LUCA) antifouling blocker showed comparable sensitivity while significantly reducing processing time and steps. Furthermore, it showed improved co-blocking efficacy and signal intensity in conventional protocols when combined with minimal BSA concentrations. Stability tests under accelerated aging confirmed the robustness of the LUCA antifouling blocker. Additionally, it effectively facilitated antibody detection for COVID-19 antigens in direct ELISA and human IL-6 antigen in Sandwich ELISA, suggesting its versatile applicability. Taken together, our findings reveal that one-step lipid blocking not only streamlines the ELISA process but also maintains high sensitivity and specificity, providing an efficient, user-friendly, and environmentally friendly alternative to traditional ELISA blockers and a robust platform for rapid diagnostics. ; Agency for Science, Technology and Research (A*STAR) ; This work was supported by National Research Foundation of Korea (NRF) grants funded by the Ministry of Science and ICT (2021K1A4A7A0209781012) and National Institute of Health (NIH) grants funded by the Korea Disease Control and Prevention Agency (KDCA) (2022ER240600). This work was also supported by a sponsored research agreement from LUCA AICell Inc. (RCA-LUCA AICell REQ0239282). H.T. was supported by a SINGA graduate scholarship from the A*STAR Graduate Academy, Singapore.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Biosensing Techniques and Applications · Biosensors and Analytical Detection · Advanced biosensing and bioanalysis techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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