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 O 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 (Δ/), and RPS peak dwell time (). 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.