Understanding molecular diversity is a cornerstone of biomedical research and diagnostics. However, existing analytical tools often fall short in distinguishing subtle variations in the structure or composition of biomolecules, such as proteins. Researchers at the University of Tokyo have unveiled a groundbreaking analytical approach that addresses this challenge. Known as voltage-matrix nanopore profiling, this method combines multivoltage solid-state nanopore recordings with machine learning to accurately classify proteins in complex mixtures based on their intrinsic electrical signatures.
The study, published in the journal Chemical Science, showcases how this innovative framework can identify and classify “molecular individuality” without the need for labels or modifications. This advancement holds the potential to lay the groundwork for more sophisticated applications of molecular analysis, particularly in disease diagnosis.
Revolutionizing Protein Analysis
Solid-state nanopores are minuscule tunnels through which a protein or other molecule can pass, 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 significantly advanced DNA and RNA analysis, their application to proteins has been limited due to the complex structures and variable signal behaviors of proteins. The University of Tokyo’s approach systematically varies voltage conditions, capturing both stable and voltage-dependent signal patterns. Organizing these features into a voltage matrix allows a machine learning model to distinguish proteins even within mixtures, expanding 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 in 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.”
Practical Applications and Demonstrations
To demonstrate the efficacy of 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 approach 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 ability to detect and classify subtle compositional differences in complex, biologically derived samples, reinforcing its potential applicability in 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.”
Future Prospects and Implications
Looking ahead, the research team plans to extend the framework to human serum or saliva samples and to develop a parallelized nanopore system capable of performing multiple tasks simultaneously for real-time molecular profiling. Such advancements could ultimately support a wide range of applications, from biomedical diagnostics to environmental monitoring.
This development follows a growing trend in the integration of machine learning with molecular analysis techniques, promising to enhance the precision and scope of diagnostic tools. The potential to revolutionize how diseases are diagnosed and monitored is substantial, offering a glimpse into a future where molecular analysis is more accurate and accessible than ever before.
As the field of molecular diagnostics continues to evolve, innovations like voltage-matrix nanopore profiling represent a significant leap forward. By providing a more nuanced understanding of molecular diversity, this approach could pave the way for breakthroughs in personalized medicine and beyond.
