Optimizing Nucleic Acid Biomarker Detection in a Solid-State Nanopore Through Probabilistic Modeling

Abstract

We have established a solid-state (SS-) nanopore assay with high specificity for nucleic acid targets, enabling applications like epigenetic screening, microRNA analysis, and pathogen detection . Despite its proven specificity and sensitivity, translational biomarker detection at clinically relevant concentrations is hindered by very low event rates and long acquisition times. It has been noted that most small-molecule translocations are not detectable due to low signal to noise ratio or fast timescales. Here, we describe the probability that a given translocation will produce a detectable event by considering tunable system parameters, and use it to show that these key experimental variables can influence the measured event rate in a predictable fashion. This model elucidates the dependencies of nanopore/analyte interactions and suggests methods by which measurement sensitivity can be optimized. Event probability can be determined experimentally by comparing expected (Rexp) to observed (Robs) event rates for dsDNA constructs of various lengths bound to monovalent streptavidin. Rexp can be derived analytically from the Nernst-Planck equation and Knudsen diffusion coefficient, and Robs measured as a function of fundamental experimental parameters, including the driving potential, target concentration, nanopore radius, and data acquisition rate. Using these data to generate probability distributions, we show that Robs can be predicted for arbitrary experimental conditions from first principles. With this approach, we are able to better understand and even influence the sensitivity of our SS-nanopore assay. The explicit use of the probability distribution provides insight into the mechanisms that govern event generation, and our approach may be expanded to take into account any fundamental system parameter. Tuning system parameters according to the underlying probability distribution will enable optimized detection of low-abundance or challenging nucleic acid biomarker targets.