Voltage-Matrix Nanopore Profiling Enables Protein Classification in Complex Samples

Differentiating subtle structural and compositional differences among biomolecules like proteins is a critical challenge in biomedical research and diagnostics. Researchers at the University of Tokyo have developed a novel method called voltage-matrix nanopore profiling that addresses this challenge by combining multivoltage solid-state nanopore recordings with machine learning to classify proteins accurately in complex mixtures based on their intrinsic electrical signatures. 

Solid-state nanopores are nanoscale tunnels that allow biomolecules to pass through, disrupting an ionic current and creating electrical signals characteristic of the molecules. While nanopore technology revolutionized DNA and RNA sequencing, its use in protein analysis faces complexities due to proteins’ diverse and dynamic structures causing variable signal patterns. The University of Tokyo team overcame limitations of single-voltage nanopore measurements by systematically varying voltages during molecule translocation, generating a “voltage matrix” of signal responses. This matrix captures both voltage-independent and voltage-dependent molecular behaviors, allowing machine learning algorithms to distinguish proteins in mixtures and enabling molecular profiling beyond sequencing.

The approach, published in Chemical Science, can identify “molecular individuality” without requiring labels or modifications. Senior author Professor Sotaro Uemura explained that traditional methods, such as ELISA or mass spectrometry, struggle with subtle protein differences. “Solid-state nanopores provide a promising solution, but previous approaches were limited by their reliance on single-voltage measurements. Our work set out to overcome these limitations.”

This method was validated by analyzing cancer-related biomarkers carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3) individually and in mixtures. By recording signals under six voltage conditions, the researchers identified unique voltage-dependent response patterns for each protein. They also detected changes when an aptamer bound selectively to CEA, demonstrating sensitivity to molecular interactions. Further testing on mouse serum samples, comparing centrifuged and raw samples, showed the voltage matrix could differentiate subtle compositional differences in complex, biologically derived samples.

Uemura noted, “By systematically varying voltage conditions and applying machine learning, we can create a voltage-matrix that reveals both robust, voltage-independent molecular features and voltage-sensitive structural changes … allowing us to visualize molecular individuality and estimate compositions within mixtures.” Future plans include applying this framework to human serum and saliva and developing parallel nanopore systems for real-time molecular profiling that could support diverse diagnostics and environmental monitoring applications.