Tracing fast nanopore-translocating analytes requires a high-frequency measurement system that warrants a temporal resolution better than 1 µs. This constraint may practically shift the challenge from increasing the sampling bandwidth to dealing with the rapidly growing noise with frequencies typically above 10 kHz, potentially making it still uncertain if all translocation events are unambiguously captured. Here, a numerical simulation model is presented as an alternative to discern translocation events with different experimental settings including pore dimension, bias voltage, the charge state of the analyte, salt concentration, and electrolyte viscosity. The model allows for simultaneous analysis of forces exerting on a large analyte cohort along their individual trajectories; these forces are responsible for the analyte movement leading eventually to the nanopore translocation. Through tracing the analyte trajectories, the Brownian force is found to dominate the analyte movement in electrolytes until the last moment at which the electroosmotic force determines the final translocation act. The mean dwell time of analytes mimicking streptavidin decreases from ≈6 to ≈1 µs with increasing the bias voltage from ±100 to ±500 mV. The simulated translocation events qualitatively agree with the experimental data with streptavidin. The simulation model is also helpful for the design of new solid-state nanopore sensors.

Nanopore technology is widely used for sequencing DNA, RNA, and peptides with single-molecule resolution, for fingerprinting single proteins, and for detecting metabolites. However, the molecular driving forces controlling the analyte capture, its residence time, and its escape have remained incompletely understood. The recently developed Nanopore Electro-Osmotic trap (NEOtrap) is well fit to study these basic physical processes in nanopore sensing, as it reveals previously missed events. Here, we use the NEOtrap to quantitate the electro-osmotic and electrophoretic forces that act on proteins inside the nanopore. We establish a physical model to describe the capture and escape processes, including the trapping energy potential. We verified the model with experimental data on CRISPR dCas9-RNA-DNA complexes, where we systematically screened crucial modeling parameters such as the size and net charge of the complex. Tuning the balance between electrophoretic and electro-osmotic forces in this way, we compare the trends in the kinetic parameters with our theoretical models. The result is a comprehensive picture of the major physical processes in nanopore trapping, which helps to guide the experiment design and signal interpretation in nanopore experiments.

Nanopores are versatile single-molecule sensors offering a simple label-free readout with great sensitivity. We recently introduced the nanopore electro-osmotic trap (NEOtrap) which can trap and sense single unmodified proteins for long times. The trapping is achieved by the electro-osmotic flow (EOF) generated from a DNA-origami sphere docked onto the pore, but thermal fluctuations of the origami limited the trapping of small proteins. Here, we use site-specific cholesterol functionalization of the origami sphere to firmly link it to the lipid-coated nanopore. We can lock the origami in either a vertical or horizontal orientation which strongly modulates the EOF. The optimized EOF greatly enhances the trapping capacity, yielding reduced noise, reduced measurement heterogeneity, an increased capture rate, and 100-fold extended observation times. We demonstrate the trapping of a variety of single proteins, including small ones down to 14 kDa. The cholesterol functionalization significantly expands the application range of the NEOtrap technology.

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recognition. Here, we propose a generalized machine-learning based method, named pulse detection transformer (PETR), for pulse detection. PETR determines the start and end time points of individual pulses, thereby singling out pulse segments in a time-sequential trace. It is objective without needing to specify any threshold. It provides a generalized interface for downstream algorithms for specific application scenarios. PETR is validated using both simulated and experimental nanopore translocation data. It returns a competitive performance in detecting pulses through assessing them with several standard metrics. Finally, the generalization nature of the PETR output is demonstrated using two representative algorithms for feature extraction.