Biomedical research and diagnoses depend on an understanding of molecular diversity, yet current analytical methods find it difficult to discern minute differences in the structure or makeup of biomolecules, for instance proteins. A novel analytical method created by University of Tokyo researchers aids in resolving this issue. The novel technique, known as voltage-matrix nanopore profiling, uses machine learning and multivoltage solid-state nanopore recordings to accurately classify proteins in complicated mixtures according to their inherent electrical signatures.
The study, which was published in Chemical Science, shows how “molecular individuality” may be recognized and categorized using this new framework without the need for labels or changes. The study has the potential to provide the groundwork for more sophisticated and extensive uses of molecular analysis in a number of fields, including the detection of disease.
Solid-state nanopores are microscopic tunnels that allow a protein or other molecule to pass through, propelled by an ionic current through the aperture. By adding voltage to this process, the signals created when molecules travel through the nanopores may be used to identify the molecule. While nanopore technologies have revolutionized DNA and RNA analysis, their use with proteins has been limited due to their complicated structures and signal unpredictability.
The team’s novel method captures both stable and voltage-dependent signal patterns by methodically changing voltage settings. These characteristics may be arranged into a voltage matrix to help a machine learning model identify proteins even in mixtures, expanding the use of nanopore measurements beyond sequencing to include generic molecular profiling.
It is challenging to recognize and categorize proteins in intricate biological combinations. Professor Sotaro Uemura of the University of Tokyo’s Department of Biological Sciences stated, “Traditional techniques such as mass spectrometry or enzyme-linked immunosorbent assay (ELISA) frequently fail to resolve subtle structural differences or dynamic states, especially without labeling.” Although solid-state nanopores provide a possible remedy, earlier methods’ dependence on single-voltage measurements restricted their use. Our goal was to get over these restrictions.
The researchers used mixes of two cancer-related protein indicators, carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3), to illustrate the idea. Each protein’s unique response patterns were discovered by creating a voltage matrix from data captured under six different voltage situations. When a brief synthetic DNA fragment called an aptamer was bound to CEA, the method also identified changes in molecular populations.
The researchers also tested the voltage-matrix framework on mouse serum samples to see how practical it is. By comparing sera that had or had not been centrifuged and evaluating them under various voltage circumstances, they discovered that the two types of samples could be readily discriminated within the voltage matrix. This study shows that the approach can identify and categorize tiny compositional variations in complex, biologically produced materials, indicating that it has the potential to be used in real-world bioanalytical and diagnostic situations.
We can produce a voltage-matrix that displays both strong, voltage-independent molecular characteristics and voltage-sensitive structural alterations by methodically altering voltage settings and utilizing machine learning, according to Uemura. In addition to increasing detection sensitivity, our work creates a novel method for classifying and representing molecule signals across voltages, which enables us to display molecular uniqueness and determine mixture compositions.
In the future, the group intends to expand the framework to human serum or saliva samples and create a parallelized nanopore system that can perform several tasks at once for real-time molecular profiling.
Looking ahead, the team intends to expand the framework to include human serum or saliva samples, as well as to create a parallelized nanopore system capable of performing multiple tasks simultaneously for real-time molecular profiling—a foundation that could eventually support applications ranging from biomedical diagnostics to environmental monitoring.
