Unbiased Data Analysis for the Parameterization of Fast Translocation Events through Nanopores

Millisecond Label-Free Single Peptide Detection and Identification Using Nanoscale Electrochromatography and Resistive Pulse Sensing

We are developing a unique protein identification method that consists of generating peptides proteolytically from a single protein molecule (i.e., peptide fingerprints) with peptide detection and identification carried out using nanoscale electrochromatography and label-free resistive pulse sensing (RPS). As a step in realizing this technology, we report herein the nanoscale electrochromatography of model peptides using thermoplastic columns with surfaces engineered to identify peptides via their molecularly dependent mobility (i.e., time-of-flight, ToF). ToFs were elucidated using a dual in-plane nanopore sensor, which consisted of two in-plane nanopores placed on either end of the nanoelectrochromatography column. The surface of the nanocolumn, which consisted of poly(methyl methacrylate) (PMMA), was activated with an O2 plasma, creating surface carboxylic acid groups (-COOH) inducing a surface charge on the column wall as well as affecting its hydrophilicity. To understand scaling effects, we carried out microchip and nanochannel electrochromatography of the peptides labeled with an ATTO 532 reporter to allow for single-molecule tracking. Our results indicated that the apparent mobilities of the model peptides did not allow for their separation in a microchannel, but when performed in a nanocolumn, clear differences in their apparent mobilities could be observed especially when operated at high electric field strengths. We next performed label-free detection of peptides using the dual in-plane nanopore sensor with the two pores separated by a 5 μm (length) column with a 50 nm width and depth. When a single peptide molecule passed through an in-plane nanopore, the sensor read a pair of resistive pulses with a time difference equivalent to ToF. We identified the peptides by evaluating their ToF, normalized RPS current transient amplitude (ΔI/I0), and RPS peak dwell time (td). We could identify the model peptides with nearly 100% classification accuracy at the single-molecule level using machine learning with a single molecule measurement requiring <10 ms.

Crowding induced switching of polymer translocation by the amalgamation of entropy and osmotic pressure

The translocation of polymers is omnipresent in inherently crowded biological systems. We investigate the dynamics of polymer translocation through a pore in free and crowded environments using Langevin dynamics simulation. We observed a location-dependent translocation rate of monomers showcasing counterintuitive behavior in stark contrast to the bead velocity along the polymer backbone. The free energy calculation of asymmetrically placed polymers indicates a critical number of segments to direct receiver-side translocation. For one-sided crowding, we have identified a critical crowding size revealing a nonzero probability of translocation toward the crowded-side. Moreover, we have observed that shifting the polymer toward the crowded-side compensates for one-sided crowding, yielding an equal probability akin to a crowder-free system. In two-sided crowding, a slight variation in crowder size and packing fraction induces a polymer to switch its translocation direction. These conspicuous yet counter-intuitive phenomena are rationalized by minimalistic theoretical arguments based on osmotic pressure and radial entropic forces.

Evidence of Cytolysin A nanopore incorporation in mammalian cells assessed by a graphical user interface

Technologies capable of assessing cellular metabolites with high precision and temporal resolution are currently limited. Recent developments in the field of nanopore sensors allow the non-stochastic quantification of metabolites, where a nanopore is acting as an electrical transducer for selective substrate binding proteins (SBPs). Here we show that incorporation of the pore-forming toxin Cytolysin A (ClyA) into the plasma membrane of Chinese hamster ovary cells (CHO-K1) results in the appearance of single-channel conductance amenable to multiplexed automated patch-clamp (APC) electrophysiology. In CHO-K1 cells, SBPs modify the ionic current flowing though ClyA nanopores, thus demonstrating its potential for metabolite sensing of living cells. Moreover, we developed a graphical user interface for the analysis of the complex signals resulting from multiplexed APC recordings. This system lays the foundation to bridge the gap between recent advances in the nanopore field (e.g., proteomic and transcriptomic) and potential cellular applications.

A Generalized Transformer-Based Pulse Detection Algorithm

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.