New Molecular Analysis Tool to Improve Disease Diagnosis

Traditional approaches like ELISA or mass spectrometry require labeling or complex processing and can miss minute molecular differences. Now, researchers have developed a label-free analytical method called voltage-matrix nanopore profiling, which can classify biomolecules based on their intrinsic electrical signatures.

The breakthrough by researchers from The University of Tokyo (Tokyo, Japan) combines multivoltage solid-state nanopore recordings with machine learning to create a multidimensional map of molecular signals. Their research, published in Chemical Science, demonstrate how this technique can identify and classify proteins in complex mixtures without modification, paving the way for next-generation molecular diagnostics.

Solid-state nanopores act as nanoscale tunnels through which individual molecules pass, driven by ionic current. By systematically varying the voltage, the team recorded how protein molecules interacted with the nanopores under different electrical conditions. These measurements formed a voltage matrix, which captured both stable and voltage-sensitive features of each molecule. Machine learning algorithms then analyzed these features to accurately classify and distinguish proteins, even when they were mixed together.

To validate their approach, the researchers analyzed mixtures containing two cancer-related biomarkers: carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3). Using six voltage conditions, they constructed distinct signal patterns that reliably differentiated each protein. The technique also detected molecular changes when CEA bound with an aptamer—a short, synthetic DNA sequence—demonstrating its sensitivity to subtle structural alterations.

In another test using mouse serum samples, the voltage-matrix framework successfully distinguished between centrifuged and non-centrifuged sera, confirming its potential for real-world diagnostic use. The method revealed compositional differences invisible to conventional single-voltage nanopore measurements, underscoring its ability to uncover molecular diversity in complex biological fluids.

By integrating physics, nanotechnology, and artificial intelligence, this new analytical framework offers a powerful tool for understanding molecular individuality—the unique electrical signature of each molecule. It holds promise for biomedical applications ranging from disease diagnosis to environmental monitoring, providing a foundation for high-resolution, real-time molecular profiling.

“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,” said Professor Sotaro Uemura. “Our study is not simply about improving detection sensitivity — it establishes a new way to represent and classify molecular signals across voltages, allowing us to visualize molecular individuality and estimate compositions within mixtures.”

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