The detection of freely circulating disease biomarkers in bodily fluids, also known as liquid biopsy, has taken important strides toward the implementation of truly personalized medicine. However, it still suffers from low sensitivity and high costs, which render its clinical implementation often not practical or affordable. In particular, the identification and quantification of oligonucleotide biomarkers is hampered by the need of technologies that are expensive, require highly trained personnel, and are prone to error. Nonetheless, the recent clinical breakthroughs demonstrating the importance of detecting cancerous or viral biomarker to susceptibility, onset, and aggressiveness of the disease, motivate the need for further research that could render their detection simpler, cheaper, and thus more widely available. Therefore, developing a low-cost, easy to implement, robust and reliable sensing method for the detection of genetic biomarkers of disease would render diagnosis and disease progression monitoring more affordable and thus accessible to all, regardless of their socioeconomic status and geographic location. Similarly, such a method would decrease the testing burden on clinics and hospital, thus allowing to diagnose and follow up on more patients more often and monitor disease spread within the population, which would be particularly important in case of a future pandemic.
Based on these needs and opportunities, ANFIBIO seeks to implement a breakthrough concept of DNA and RNA identification that takes inspiration from sequencing technologies and leverages direct surface enhanced Raman spectroscopy (SERS)-based sensing and machine learning approaches. ANFIBIO will deliver a sensitive, accurate, and low-cost platform for the detection of biomarkers of disease of clinical relevance. Its main concept takes inspiration from the fundamental advantages of both short- and long-read sequencing technologies and aims to overcome their limitations to deliver a clinically relevant diagnostic technology that can be available to all.
Based on the above considerations, the specific and measurable project objectives of ANFIBIO are:
OBJECTIVE 1. To DESIGN and OPTIMIZE bespoke star-shaped gold nanoparticles (i.e. nanostars) to provide SERS signal enhancement and detect key disease biomarkers even at very low concentration.
OBJECTIVE 2. To AMPLIFY the SERS signal of DNA and RNA targets that directly interact with the metallic surface of the nanostars by understanding the key features driving DNA and RNA adsorption and stabilization onto the metallic surface and identifying the key conditions to obtain well-resolved and intense spectra with high reproducibility, so that a machine learning algorithm can be devised to discriminate even seemingly identical spectra. Ideally, spectra obtained from specimens that differ only by one point mutation should be discernible.
OBJECTIVE 3. To DETERMINE the identity and relative position of constituent nucleobases in specific target oligonucleotide biomarkers by employing machine learning algorithms, starting with short reads and then moving on to medium-size reads. Machine learning is new in materials science and chemistry; it has never been employed in SERS as proposed here.
OBJECTIVE 4. To VALIDATE the method and DELIVER a technology for identification and quantification of biomarkers of prostate cancer (PCa) and Influenza A virus (IAV) in bodily fluids of patients.
