Nano-needle technology from Prof. Amit Meller's lab enables rapid, digital identification of individual proteins in near real-time, paving the way for early diagnosis of cancer and other diseases through simple blood tests.
Opinion article Published in the journal Nature in 2023, it reviewed the seven most important technologies worth following, and the first on the list: single-molecule protein sequencing. The article highlighted the central contribution of Prof. Amit Meller's research group from the Technion in improving the relevant technologies towards the commercialization of protein sequencing systems and a real revolution in biological and medical research. Now, in an article in the prestigious journal Nature Nanotechnology, Prof. Meller presents a breakthrough in this area. Since proteins are the building blocks of the body, their rapid and accurate identification is essential for understanding most physiological processes in the human body, for early diagnosis of diseases, and for the correct adjustment of medical treatment.
Prof. Meller, who led the study with postdoctoral student Dr. Niraj Soni, is a faculty member in the Faculty of Biomedical Engineering and the Faculty of Biology and a member of the Russell Berry Institute for Nanotechnology at the Technion. Researchers from the University of Illinois and Rice University participated in the study.
The technology presented in the article inNature Nanotechnology The unique approach developed by Prof. Meller – mapping biological molecules using synthetic nanometer nozzles manufactured using advanced nanotechnology – is being applied to identify the “fingerprint” of entire proteins. Methods for identifying proteins by passing them through nano-nozzles already exist, but to achieve identification at the individual protein level, researchers must use antibodies or complicated molecular motors to slow down the movement of the protein through the nozzle – processes that complicate and make the process more expensive.
The technology developed by Prof. Meller simplifies the aforementioned process and also produces controlled movement of the protein within the nozzle – a stick-slip movement, i.e. sticking and sliding. In this process, the system measures the flow of particles (ions) in the nozzle, thus providing the unique fingerprint for specific proteins. Using a machine learning (ML) model allows the identification of the protein from the flow signature at a speed that exceeds other techniques by several orders of magnitude. A single passage of a protein takes a fraction of a second, and the identification occurs almost in real time. The system allows for digital counting of the proteins in the sample and their cataloging.
The research focused on an amino acid called cysteine, which is essential for many physiological functions in the cell and the entire body. Since about 97% of proteins in the human body contain cysteine or cysteine residues, the approach is applicable to almost the entire proteome (all proteins). In addition, the technology is not limited to a specific amino acid, and the lab is already working on expanding it to a large number of amino acids, including those that have undergone post-translational modifications (PTMs).
The new method has many and diverse clinical applications, including cancer diagnosis and precise treatment adaptation based on simple tests such as blood tests. From a scientific perspective, it is expected to advance the field of protein research, while expanding its applicability to a wide range of proteins. According to Prof. Meller, the future idea is to develop an integrated platform that will be used for rapid protein diagnosis in hospitals, laboratories and clinical research.
The research was supported by the European Research Commission (ADG-ERC under the Horizon 2020 program).
for the article inNature Nanotechnology
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