Implementation of microfluidic sandwich ELISA for superior detection of plant pathogens

Abstract

Rapid and economical screening of plant pathogens is a high-priority need in the seed industry. Crop quality control and disease surveillance demand early and accurate detection in addition to robustness, scalability, and cost efficiency typically required for selective breeding and certification programs. Compared to conventional bench-top detection techniques routinely employed, a microfluidic-based approach offers unique benefits to address these needs simultaneously. To our knowledge, this work reports the first attempt to perform microfluidic sandwich ELISA for Acidovorax citrulli (Ac), watermelon silver mottle virus (WSMoV), and melon yellow spot virus (MYSV) screening. The immunoassay occurs on the surface of a reaction chamber represented by a microfluidic channel. The capillary force within the microchannel draws a reagent into the reaction chamber as well as facilitates assay incubation. Because the underlying pad automatically absorbs excess fluid, the only operation required is sequential loading of buffers/reagents. Buffer selection, antibody concentrations, and sample loading scheme were optimized for each pathogen. Assay optimization reveals that the 20-folds lower sample volume demanded by the microchannel structure outweighs the 2- to 4-folds higher antibody concentrations required, resulting in overall 5-10 folds of reagent savings. In addition to cutting the assay time by more than 50%, the new platform offers 65% cost savings from less reagent consumption and labor cost. Our study also shows 12.5-, 2-, and 4-fold improvement in assay sensitivity for Ac, WSMoV, and MYSV, respectively. Practical feasibility is demonstrated using 19 real plant samples. Given a standard 96-well plate format, the developed assay is compatible with commercial fluorescent plate readers and readily amendable to robotic liquid handling systems for completely hand-free assay automation.

Figure 1. Schematic workflow depicting sequential molecular binding events of the sandwich ELISA.

Within each reaction chamber, the capture antibody is adsorbed on the reactive surface followed by surface passivation by a blocking buffer. Upon target binding to the capture antibody, alkaline phosphates (AP)-tagged detection antibody specific to the antigen is added. Addition of fluorescent substrate (PNPP or p-nitrophenyl phosphate for the traditional well format, and Attophos for the micofluidic format) activated by AP generates detectable fluorescent signal, indicating successful binding events.

Figure 2. Coating and blocking buffer selection.

A. Different coating buffers (Optibind A-L) were tested for maximum surface binding of 11E5, 2D6, and 5E7 (n = 3). B. Blocking buffers (2% BSA, 3% skim milk, 1% casein, and Optiblock solution) were tested for Ac, WSMoV, and MYSV detection (n = 3). An ideal blocking buffer resulted in highest S/N ratios. Error bars indicate ± standard deviations.

Figure 3. Optimization of antibody concentration.

Nine different conditions for each disease panel were tested on the microfluidic platform using combinations of three concentrations of capture Ab (11E5, 2D6, and 5E7) and three concentrations of detection Ab (MPC-AP, MYSV6-AP). Panel A–C show results for Ac, WSMoV, and MYSV detection, respectively (n = 4). The S/N ratios were plotted for each of the conditions tested. Error bars indicate ± standard deviations.

Figure 4. Specificity determination.

Specificity of the 11E5/MPC, 2D6/MYSV6, and 5E7/MYSV6 antibody pairs was tested for Ac, WSMoV, and MYSV, respectively. The antibody pairs were tested against bacteria (Ac, SQB, Pf, and DAc) and viral (TYLCV) protein standards (n = 3). The data shows averaged S/N values with error bars representing standard deviations. The dotted horizontal line indicates the threshold (or the cutoff value) of S/N = 2 (twice of values obtained from negative controls).

Figure 5. Study of repetitive sample loading.

Effect of repetitive sample loading on assay dynamic range was investigated, using Ac as a model. The plot indicates that multiple loading helps increase assay sensitivity (n = 3). Error bars indicate ± standard deviations.

Figure 6. Sensitivity determination of the microfluidic platform.

Comparison of assay dynamic range for Ac (A), WSMoV (B), and MYSV (C) detection between protein standards and spiked plant extracts (n = 3) by the microfluidic platform. Error bars indicate ± standard deviations.