Understanding molecular diversity is a cornerstone of biomedical research and diagnostics. However, existing analytical tools often fall short when it comes to distinguishing subtle variations in the structure or composition of biomolecules, such as proteins. In a groundbreaking development, researchers at the University of Tokyo have unveiled a novel analytical approach that addresses this challenge. This method, known as voltage-matrix nanopore profiling, leverages multivoltage solid-state nanopore recordings combined with machine learning to accurately classify proteins in complex mixtures based on their intrinsic electrical signatures.
The study, published in the journal Chemical Science, demonstrates the potential of this new framework to identify and classify “molecular individuality” without the need for labels or modifications. This advancement promises to lay the groundwork for more sophisticated applications of molecular analysis across various fields, including disease diagnosis.
Revolutionizing Protein Analysis with Nanopores
Solid-state nanopores are minuscule tunnels through which a protein or other molecule can pass, driven by an ionic current. By applying voltage during this process, the signals generated as molecules traverse the nanopores can be used to identify them. While nanopore technologies have significantly advanced DNA and RNA analysis, their application to proteins has been limited due to the complex structures of proteins and the variability in signal behavior.
The University of Tokyo team’s innovative approach systematically varies voltage conditions to capture both stable and voltage-dependent signal patterns. By organizing these features into a voltage matrix, a machine learning model can distinguish proteins even within mixtures, extending the use of nanopore measurements beyond sequencing to general molecular profiling.
Expert Insights on the New Approach
Professor Sotaro Uemura from the Department of Biological Sciences at the University of Tokyo explained the significance of the research:
“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.”
He added,
“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.”
Practical Applications and Future Prospects
To illustrate the 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 approach also detected shifts in molecular populations when an aptamer, a short synthetic DNA segment, was bound to CEA.
Further demonstrating the practicality of their approach, the researchers applied the voltage-matrix framework to mouse serum samples. By comparing sera that had or had not undergone centrifugation and analyzing them under multiple voltage conditions, they found that the two types of samples could be clearly distinguished within the voltage matrix. This result indicates the method’s potential to detect and classify subtle compositional differences in complex, biologically derived samples, supporting its applicability to real-world bioanalytical and diagnostic contexts.
Looking Ahead: Expanding the Framework
Professor Uemura highlighted the broader implications of their work:
“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. 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.”
Looking to the future, the research team plans to extend the framework to human serum or saliva samples and develop a parallelized nanopore system capable of performing multiple tasks simultaneously for real-time molecular profiling. This foundation could ultimately support applications ranging from biomedical diagnostics to monitoring environmental changes.
This development follows a growing trend in the integration of machine learning with molecular analysis, promising to enhance the precision and scope of diagnostic tools. As the technology matures, it could revolutionize how we approach complex biological systems, offering new insights and capabilities in the fight against diseases and in the understanding of molecular diversity.
