We developed a generalized technique to characterize polymer-nanopore interactions via single channel ionic current measurements. Physical interactions between analytes, such as DNA, proteins or synthetic polymers, and a nanopore cause multiple discrete states in the ionic current. We modeled the transitions of the ionic current to individual states with an equivalent electrical circuit of the nanopore system, which allowed us to describe the system response. This enables the estimation of short-lived states in single-molecule nanopore data that are presently not characterized by existing analysis techniques. Our approach considerably improves the range and resolution of single-molecule characterization with nanopores. For example, we characterized the residence times of molecules in the nanopore that are three times shorter than those estimated with existing algorithms. Because the molecule’s residence time follows an exponential distribution, we recover nearly 20-fold more events per unit time that can be used for analysis. Furthermore, the measurement range was extended from 11 monomers to as few as 8. Finally, we apply this technique to recover a known sequence of single stranded DNA from previously published ion channel recordings, identifying discrete current states with sub-picoampere resolution.

Biological and solid-state nanometer-scale pores are the basis for numerous emerging analytical technologies for use in precision medicine. We developed Modular Single-Molecule Analysis Interface (MOSAIC), an open source analysis software that improves the accuracy and throughput of nanopore-based measurements. Two key algorithms are implemented: ADEPT, which uses a physical model of the nanopore system to characterize short-lived events that do not reach their steady-state current, and CUSUM+, a version of the cumulative sum statistical method optimized for longer events that do. We show that ADEPT detects previously unreported conductance states that occur as double-stranded DNA translocates through a 2.4 nm solid-state nanopore and reveals new interactions between short single-stranded DNA and the vestibule of a biological pore. These findings demonstrate the utility of MOSAIC and the ADEPT algorithm, and offer a new tool that can improve the analysis of nanopore-based measurements.

Electron spin resonance (ESR) spectroscopy's affinity for detecting paramagnetic free radicals, or spins, has been increasingly employed to examine a large variety of biochemical interactions. Such paramagnetic species are broadly found in nature and can be intrinsic (defects in solid-state materials systems, electron/hole pairs, stable radicals in proteins) or, more often, purposefully introduced into the material of interest (doping/attachment of paramagnetic spin labels to biomolecules of interest). Using ESR to trace the reactionary path of paramagnetic spins or spin-active proxy molecules provides detailed information about the reaction's transient species and the label's local environment. For many biochemical systems, like those involving membrane proteins, synthesizing the necessary quantity of spin-labeled biomolecules (typically 50 pmol to 100 pmol) is quite challenging and often limits the possible biochemical reactions available for investigation. Quite simply, ESR is too insensitive. Here, we demonstrate an innovative approach that greatly enhances ESR's sensitivity (>20000× improvement) by developing a near-field, nonresonant, X-band ESR spectrometric method. Sensitivity improvement is confirmed via measurement of 140 amol of the most common nitroxide spin label in a ≈593 fL liquid cell at ambient temperature and pressure. This experimental approach eliminates many of the typical ESR sample restrictions imposed by conventional resonator-based ESR detection and renders the technique feasible for spatially resolved measurements on a wider variety of biochemical samples. Thus, our approach broadens the pool of possible biochemical and structural biology studies, as well as greatly enhances the analytical power of existing ESR applications.