Nanofluidic Platforms for Single-Molecule Protein Characterization and Peptide Fingerprinting of Proteins

Abstract

Studying proteins at the single-molecule level provides direct access to molecularbehavior that is masked in ensemble measurements. This approach enables the resolution of mechanistic details hidden in bulk experiments, captures the stochastic nature of biochemical processes, and reveals heterogeneity among molecules of identical sequence. These capabilities are essential for understanding structure-function relationships and for developing sensitive protein-based diagnostic technologies. Conventional protein analysis techniques often involve complex workflows and sophisticated instrumentation with modest limits of detection. Although fluorescence-based methods can interrogate single molecules, they typically require labeling, lasers, and advanced microscopy. Here, we report a label-free strategy for analyzing intact proteins using resistive pulse sensing (RPS) with a dual in-plane nanopore (DNP) sensor fabricated in thermoplastic via nano-injection molding. Critical features as small as 30 nm were produced using UV-resin mold inserts replicated from silicon masters, providing a scalable and reproducible fabrication route. Subsequent thermal fusion bonding reduced the effective pore size to ~10 nm. The device architecture consists of two in-plane nanopores flanking a confined nanochannel (50 × 50 nm cross-section; 5–10 µm length), enabling measurement of apparent electrophoretic mobility through a molecular-dependent time-of-flight (ToF), defined as the time difference between two consecutive RPS events (a peak pair). In addition to ToF, peak amplitude (ΔI/I₀) and dwell time (TD) were extracted from each pore, yielding five independent features for single-molecule analysis. Using four model proteins, we demonstrated successful detection and identification. Furthermore, analysis of the temporal RPS waveforms showed that event 3 shape encodes structural information, with distinct peak profiles observed for spherical and non- spherical proteins, reflecting their rotational dynamics within the nanopore. Unlike conventional biological nanopores, which suffer from membrane instability, or solid-state out-of-plane nanopores that require serial FIB or TEM drilling and exhibit limited sampling efficiency, our in-plane architecture is embedded within a nanofluidic network. This configuration improves analyte capture efficiency through geometric confinement, enables scalable manufacturing without serial drilling, and allows seamless integration with upstream processing modules. Using the same platform developed for protein analysis, we then evaluated nanoscale electrochromatography of peptides in thermoplastic nanochannels under high electric field strengths. Time-of-flight (ToF) measurements showed improved discrimination of apparent electrophoretic mobility under nanoscale confinement. Multivariate feature analysis combined with supervised machine learning (neural network) enabled near-100% classification accuracy of model peptides at the single-molecule level, with each measurement completed in less than 10 ms. Finally, we developed an integrated nanofluidic system that couples an immobilized nanoscale enzymatic reactor (INER) with the DNP-ToF sensor, termed the Proteomic Time-of- Flight (P-ToF) platform. Proteins are electrokinetically transported through a trypsin- functionalized nanoreactor, where digestion occurs during transit, and the resulting peptide fragments are immediately analyzed downstream by the DNP-ToF reader. Using Amyloid-β (1– 40) as a model protein, we demonstrated real-time on-chip digestion and peptide identification. Multivariate feature extraction combined with supervised Random Forest classification enabled 4 construction of peptide fingerprints with 100% training and test accuracy, which were subsequently used for accurate identification of digestion fragments. Building on these advances, our nanofluidic platforms provide a practical pathway toward clinical diagnostics in settings where sample quantity is severely limited, such as cerebrospinal fluid for neurodegenerative diseases or minimally invasive liquid biopsies for cancer. Because the system operates at the single-molecule level and does not require labeling or bulk amplification, it is inherently suited for analyzing low-abundance proteins and heterogeneous biomarker populations that are often masked in ensemble measurements. The demonstrated analysis of Amyloid-β highlights its relevance to Alzheimer’s disease, while the modular P-ToF architecture can be readily adapted to other disease-associated proteins and proteolytic signatures. By integrating enzymatic processing, separation, and multi-parameter machine-learning–based identification within a single thermoplastic device, this platform offers a scalable and high-sensitivity alternative to conventional mass spectrometry workflows, particularly for mass-limited clinical samples. Collectively, these capabilities position our nanofluidic technology as a strong candidate for next-generation precision diagnostics in oncology and neurodegenerative disease.