Coupling capillary-driven microfluidics with lateral flow immunoassay for signal enhancement

Abstract

Microfluidics has emerged as a versatile technology that is applied to enhance the performance of analytical techniques, among others. Pursuing this, we present a capillary-driven microfluidic device that improves the sensitivity of lateral flow immunoassay rapid tests thanks to offering an automated washing step. A novel multilevel microfluidic chip was 3D-printed with a photocurable black resin, sealed by an optically clear pressure-sensitive adhesive, and linked to the lateral flow strip. To depict the efficacy of microfluidics and the washing step, cortisol was measured quantitatively within the proposed device. Measuring cortisol levels is a way to capture physiological stress responses. Among biofluids, saliva is less infectious and easier to sample than others. However, higher sensitivity is demanded because the salivary cortisol concentrations are much lower than in blood. We carried out a competitive lateral flow immunoassay protocol with the difference that the microfluidic device applies an automated washing step after the sample is drained downstream. It washes the trapped quantum-dot-labeled antibodies out from nitrocellulose, diminishing background noise as these are bonded to cortisols and not to the immobilized receptors. Fluorescence spectroscopy, as a high-precision analysis, was successfully applied to determine clinically relevant salivary cortisol concentrations within a buffer quantitatively. The microfluidic design relied on a 3D valve that avoids reagent cross-contamination. This cross-contamination could make the washing buffer impure and undesirably dilute the sample. The proposed device is cost-effective, self-powered, robust, and ideal for non-expert users.

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 813863 (BORGES). This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 953426 (PANACEA). This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 801342 (Tecniospring INDUSTRY). Also, the work is funded in part by the Ministerio de Ciencia y Innovación (PID2020-114070RB-I00), Agencia Estatal de Investigación (CPP2021-009021), and AGAUR (2021PROD00064).

Peer Reviewed

Postprint (published version)