Beating the reaction limits of biosensor sensitivity with dynamic tracking of single binding events

Abstract

The clinical need for ultrasensitive molecular analysis has motivated the development of several endpoint-assay technologies capable of single-molecule readout. These endpoint assays are now primarily limited by the affinity and specificity of the molecular-recognition agents for the analyte of interest. In contrast, a kinetic assay with single-molecule readout could distinguish between low-abundance, high-affinity (specific analyte) and high-abundance, low-affinity (nonspecific background) binding by measuring the duration of individual binding events at equilibrium. Here, we describe such a kinetic assay, in which individual binding events are detected and monitored during sample incubation. This method uses plasmonic gold nanorods and interferometric reflectance imaging to detect thousands of individual binding events across a multiplex solid-phase sensor with a large area approaching that of leading bead-based endpoint-assay technologies. A dynamic tracking procedure is used to measure the duration of each event. From this, the total rates of binding and debinding as well as the distribution of binding-event durations are determined. We observe a limit of detection of 19 fM for a proof-of-concept synthetic DNA analyte in a 12-plex assay format.

Keywords: biosensor; molecular recognition; nanophotonics; nanotechnology; single-molecule detection.

Fig. 1.

Dynamic measurements of single binding events across a large microarray. (A) Rendered image of IRIS chip perfusion chamber for dynamic measurements of molecular interactions. A DNA or antibody microarray is printed on the IRIS chip. Then, the chamber is formed by layering a patterned adhesive gasket and antireflection-coated coverglass viewing window. The IRIS chip has two through-holes for sample perfusion. The entire disposable costs about $5 USD. (B) Nucleic acid assay with IRIS. DNA-conjugated gold nanorods are preincubated with the sample solution and hybridized with complementary nucleic acids. The mixture is flowed over the chip. Complementary nucleic acid strands tether nanorods to the cDNA microarray spot. (C) Schematic of dynamic detection of single nanorods with IRIS. Images are simulated. Nanorods on the chip surface are observed as diffraction-limited spots and automatically detected using purpose-built software. (D) Plots of total nanoparticle binding to six complementary (red) and six noncomplementary (green) DNA spots over time, as measured with dynamic tracking, for one representative experiment where the analyte concentration is 316 fM.

Fig. 2.

(A) Dynamic tracking improves sensitivity when the debinding rate is nonzero. Assuming first-order reaction kinetics, equilibrium is reached when the rate of new analyte binding is balanced by the rate of debinding from the surface. Naïvely counting the instantaneous number of bound analyte provides no additional information once equilibrium has been reached. Dynamic tracking distinguishes the binding of new particles from the debinding of old ones. The cumulative number of binding events continues to increase, even at equilibrium. (B–D) Diagram of the multistep dynamic tracking algorithm, described in the text.

Fig. 3.

Experimental evaluation of kinetic IRIS measurements with dynamic tracking. (A) Instantaneous number of nanorods binding to complementary and noncomplementary control spots over time. The analyte concentration is 316 fM, and the nanorod concentration is 14 pM. The sensor reaches equilibrium within 90 min. (B) Total number of nanorod binding and debinding for the complementary spot in A, as measured with dynamic tracking. The rate of total nanorod binding is constant throughout, and equilibrium is reached when the rates of binding and debinding are equal. (C) Histogram of binding-event durations across all experiments, with a biexponential fit. This biexponential distribution is thought to be caused by differences in affinity between side-immobilized and end-immobilized nanorods.

Fig. 4.

Rate of particle binding versus analyte concentration for the same experiments analyzed using dynamic tracking (A) or naïve counting (B). A modified first-order kinetic model was fit to the data and used to determine the LOD and critical concentration (c*). Insets show the same data and model on linear axes. Dynamic tracking improved the LOD by 36-fold compared with naïve counting. Notably, the dynamic tracking LOD (19 fM) is 3.6-fold lower than the critical concentration (68 fM), at which just one molecule is bound to the sensor on average.