Understanding molecular diversity is crucial to advancements in biomedical research and diagnostics. Yet, existing analytical tools often fall short in distinguishing subtle variations among biomolecules, such as proteins. In a groundbreaking development, researchers at the University of Tokyo have introduced an innovative analytical method that addresses this challenge. Known as voltage-matrix nanopore profiling, this approach combines multivoltage solid-state nanopore recordings with machine learning to accurately classify proteins based on their intrinsic electrical signatures.
The study, recently published in Chemical Science, showcases how this framework can identify and classify “molecular individuality” without requiring labels or modifications. This advancement holds the potential to significantly broaden the applications of molecular analysis, particularly in disease diagnosis.
The Science Behind Voltage-Matrix Nanopore Profiling
Solid-state nanopores, essentially tiny tunnels through which proteins or other molecules pass, are driven by ionic currents. By applying voltage during this process, the signals generated as molecules traverse the nanopores can be used to identify them. While nanopore technologies have been transformative in DNA and RNA analysis, their application to proteins has been limited due to the proteins’ complex structures and variable signal behaviors.
The University of Tokyo team has addressed these challenges by systematically varying voltage conditions, capturing both stable and voltage-dependent signal patterns. These features are organized into a voltage matrix, enabling a machine learning model to distinguish proteins even within complex mixtures. This approach extends the use of nanopore measurements beyond sequencing to general molecular profiling.
“Identifying and classifying proteins within complex biological mixtures is difficult. Traditional methods like enzyme-linked immunosorbent assay (ELISA) or mass spectrometry often struggle to resolve subtle structural differences or dynamic states, especially without labeling,” said Professor Sotaro Uemura from the Department of Biological Sciences at the University of Tokyo. “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.”
Real-World Applications and Testing
To validate their concept, the researchers analyzed mixtures containing two cancer-related protein biomarkers: carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3). By constructing a voltage matrix from signals recorded under six different voltage conditions, they identified distinct response patterns characteristic of each protein. The method also detected shifts in molecular populations when an aptamer, a short synthetic DNA segment, was bound to CEA.
Further testing involved applying the voltage-matrix framework to mouse serum samples. By comparing sera that had undergone centrifugation with those that had not, and analyzing them under multiple voltage conditions, the researchers found that the two types of samples could be clearly distinguished within the voltage matrix. This result underscores the method’s potential applicability to real-world bioanalytical and diagnostic contexts.
“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,” Uemura explained. “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.”
Implications for Future Research and Applications
Looking forward, the research team plans to extend their framework to human serum or saliva samples. They also aim to develop a parallelized nanopore system capable of performing multiple tasks simultaneously for real-time molecular profiling. This foundation could ultimately support a wide range of applications, from biomedical diagnostics to environmental monitoring.
The announcement comes amidst a growing interest in leveraging AI and machine learning for more precise and efficient molecular analysis. As researchers continue to explore the potential of these technologies, the implications for healthcare and environmental sciences are profound. This development not only represents a significant step forward in protein analysis but also opens new avenues for research and application in various scientific fields.
