Single-Molecule Bioelectronics

Discrete amplitude levels in ordered, time-domain data often represent different underlying latent states of the system that is being interrogated. Analysis and feature extraction from these data sets generally requires considering the order of each individual point; this approach cannot take advantage of contemporary general-purpose graphics processing units (gpGPU) and single-instruction multiple-data (SIMD) instruction set architectures. Two sources of such data from single-molecule biological measurements are nanopores and single- molecule field effect transistor (smFET) nanotube devices; both generate streams of time-ordered current or voltage data, typically sampled near 1 MS/s, with run times of minutes, yielding terabyte-scale datasets. Here, we present three gpGPU-based algorithms to overcome limitations associated with serial event detection in time series data, resulting in a 250× improvement in the rate with which we can detect salient features in nanopore and smFET datasets. The code is freely available.

Small molecules such as neurotransmitters are critical for biochemical functions in living systems. While conventional ultraviolet–visible spectroscopy and mass spectrometry lack portability and are unsuitable for time-resolved measurements in situ, techniques such as amperometry and traditional field-effect detection require a large ensemble of molecules to reach detectable signal levels. Here we demonstrate the potential of carbon-nanotube-based single-molecule field-effect transistors (smFETs), which can detect the charge on a single molecule, as a new platform for recognizing and assaying small molecules. smFETs are formed by the covalent attachment of a probe molecule, in our case a DNA aptamer, to a carbon nanotube. Conformation changes on binding are manifest as discrete changes in the nanotube electrical conductance. By monitoring the kinetics of conformational changes in a binding aptamer, we show that smFETs can detect and quantify serotonin at the single-molecule level, providing unique insights into the dynamics of the aptamer–ligand system. In particular, we show the involvement of G-quadruplex formation and the disruption of the native hairpin structure in the conformational changes of the serotonin–aptamer complex. The smFET is a label-free approach to analysing molecular interactions at the single-molecule level with high temporal resolution, providing additional insights into complex biological processes.

We have developed and used single-molecule field-effect transistors (smFETs) to characterize the conformational free-energy landscape of RNA stem-loops. Stem-loops are one of the most common RNA structural motifs and serve as building blocks for the formation of complex RNA structures. Given their prevalence and integral role in RNA folding, the kinetics of stem-loop (un)folding has been extensively characterized using both experimental and computational approaches. Interestingly, these studies have reported vastly disparate timescales of (un)folding, which has been interpreted as evidence that (un)folding of even simple stem-loops occurs on a highly rugged conformational energy landscape. Because smFETs do not rely on fluorophore reporters of conformation or mechanical (un)folding forces, they provide a unique approach that has allowed us to directly monitor tens of thousands of (un)folding events of individual stem-loops at a 200 μs time resolution. Our results show that under our experimental conditions, stem-loops (un)fold over a 1–200 ms timescale during which they transition between ensembles of unfolded and folded conformations, the latter of which is composed of at least two sub-populations. The 1–200 ms timescale of (un)folding we observe here indicates that smFETs report on complete (un)folding trajectories in which unfolded conformations of the RNA spend long periods of time wandering the free-energy landscape before sampling one of several misfolded conformations or the natively folded conformation. Our findings highlight the extremely rugged landscape on which even the simplest RNA structural elements fold and demonstrate that smFETs are a unique and powerful approach for characterizing the conformational free-energy of RNA.

Observation of biomolecular interactions at the single-molecule level reveals kinetic information crucial for understanding biophysical processes and provide the basis for a new class of molecular diagnostics based on time-domain analysis of molecular interactions. Point-functionalized carbon nanotubes, otherwise known as single-molecule field-effect transistors (smFETs), have shown significant advantage over fluorescence-based approaches for such single-molecule applications due to their high measurement bandwidths, virtually unlimited observation times, low cost, and capabilities for integration with CMOS in large arrays. Here, we demonstrate the capabilities of single-molecule field-effect transistors to measure DNA hybridization kinetics and to selectively detect small molecules through conformational changes of a single-stranded DNA aptamer. In both cases, kinetics can also be modified electrostatically by changes in the bias between the smFET and the surrounding electrolyte.

Accurate and low-cost analysis of biomolecules is important for many applications. This work seeks to further improve the measurement bandwidths achievable with solid-state nanopores, which have emerged as an important platform for this analysis. We report single-stranded DNA translocation recordings at a bandwidth of 10 MHz copolymers of 80 (C₂₀A₂₀C₂₀A₂₀), 90 (C₃₀A₃₀C₃₀), and 200 (C₅₀A₅₀C₅₀A₅₀) nucleotides through Si nanopores with effective diameters of 1.4–2.1 nm and effective membrane thicknesses 0.5–8.9 nm. By optimizing glass chips with thin nanopores and by integrating them with custom-designed amplifiers based on complementary metal-oxide-semiconductor technology, this work demonstrates detection of translocation events as brief as 100 ns with a signal-to-noise ratio exceeding seven at a measurement bandwidth of 10 MHz. We also report data robustness and variability across 13 pores of similar size and thickness, yielding a current blockade between 30 and 60% with a mean ionic current blockade (ΔI) of ∼3–9 nA and a characteristic dwell time of ∼2–21 ns per nucleotide. These measurements show that characteristic translocation rates are at least 10 times faster than previously recorded. We detect transient intraevent fluctuations, multiple current levels within translocation events, and variability of DNA translocation event signatures and durations.